WEBVTT 00:00:00.000 --> 00:00:00.610 align:middle line:90% 00:00:00.610 --> 00:00:02.440 align:middle line:84% The following content is provided 00:00:02.440 --> 00:00:06.340 align:middle line:84% by MIT OpenCourseWare under a Creative Commons license. 00:00:06.340 --> 00:00:08.470 align:middle line:84% Additional information about our license 00:00:08.470 --> 00:00:10.870 align:middle line:84% and MIT OpenCourseWare in general 00:00:10.870 --> 00:00:12.730 align:middle line:90% is available at ocw.mit.edu. 00:00:12.730 --> 00:00:17.110 align:middle line:90% 00:00:17.110 --> 00:00:18.220 align:middle line:90% GEORGE CHURCH: OK. 00:00:18.220 --> 00:00:23.410 align:middle line:90% Welcome to the second proteins. 00:00:23.410 --> 00:00:27.160 align:middle line:84% Just as with the RNA analyses, we started with, in that case, 00:00:27.160 --> 00:00:28.930 align:middle line:84% a brief discussion of RNA structure, 00:00:28.930 --> 00:00:31.450 align:middle line:84% and then moved on to RNA quantitation. 00:00:31.450 --> 00:00:36.400 align:middle line:84% This case, we spent a bit more time on protein structure, 00:00:36.400 --> 00:00:37.900 align:middle line:84% and we'll spend a little less time 00:00:37.900 --> 00:00:41.830 align:middle line:84% on protein quantitation, which will be the main topic today. 00:00:41.830 --> 00:00:45.282 align:middle line:84% But partly because the RNA and protein quantitation 00:00:45.282 --> 00:00:47.740 align:middle line:84% have many themes in common, so we've covered some of those. 00:00:47.740 --> 00:00:53.290 align:middle line:84% And we'll talk about how one can integrate protein quantitation 00:00:53.290 --> 00:00:59.110 align:middle line:84% with RNA quantitation and with metabolism. 00:00:59.110 --> 00:01:02.380 align:middle line:84% So last time we talked about mainly 00:01:02.380 --> 00:01:04.989 align:middle line:84% with structure and interaction of proteins 00:01:04.989 --> 00:01:07.210 align:middle line:84% with small molecules at the structural level. 00:01:07.210 --> 00:01:09.520 align:middle line:84% And this time we'll be building up 00:01:09.520 --> 00:01:13.450 align:middle line:84% towards the quantitating proteins and their interactions 00:01:13.450 --> 00:01:17.440 align:middle line:84% with other proteins of small molecules 00:01:17.440 --> 00:01:22.060 align:middle line:84% so that we can address that as the first step 00:01:22.060 --> 00:01:24.160 align:middle line:84% towards the network analysis that 00:01:24.160 --> 00:01:27.590 align:middle line:84% will be the subject of the last three lectures of the course. 00:01:27.590 --> 00:01:31.015 align:middle line:84% So we'll get a hint of that at the end of today's talk. 00:01:31.015 --> 00:01:36.520 align:middle line:90% 00:01:36.520 --> 00:01:39.620 align:middle line:84% So at the beginning of the course, 00:01:39.620 --> 00:01:41.320 align:middle line:84% I had one slide talking about how 00:01:41.320 --> 00:01:46.700 align:middle line:84% purification was a revolution in many different fields-- 00:01:46.700 --> 00:01:49.210 align:middle line:84% one of them resulting in recombinant DNA and the Genome 00:01:49.210 --> 00:01:50.002 align:middle line:90% Project, and so on. 00:01:50.002 --> 00:01:52.043 align:middle line:84% Today we're going to talk more about purification 00:01:52.043 --> 00:01:54.280 align:middle line:84% from the standpoint from what does it tell us 00:01:54.280 --> 00:01:55.870 align:middle line:84% about the properties of the molecule 00:01:55.870 --> 00:01:59.860 align:middle line:84% itself as opposed to just purifying for its own sake. 00:01:59.860 --> 00:02:03.550 align:middle line:84% How can we go in and data mine and get the maximum value out 00:02:03.550 --> 00:02:05.140 align:middle line:90% of purification? 00:02:05.140 --> 00:02:08.710 align:middle line:84% The things that we expect from purification 00:02:08.710 --> 00:02:11.200 align:middle line:84% is to reduce some source of noise. 00:02:11.200 --> 00:02:13.900 align:middle line:84% That is to say, in the process of identification 00:02:13.900 --> 00:02:19.540 align:middle line:84% or quantitation of proteins or their components, 00:02:19.540 --> 00:02:23.410 align:middle line:90% we want to remove sources-- 00:02:23.410 --> 00:02:27.040 align:middle line:84% the major source of noise would be environmental contamination, 00:02:27.040 --> 00:02:31.000 align:middle line:84% or more often contamination with other bona fide members 00:02:31.000 --> 00:02:34.330 align:middle line:84% of a mixture, other proteins that may be very abundant 00:02:34.330 --> 00:02:36.760 align:middle line:84% or breakdown products or related products. 00:02:36.760 --> 00:02:40.400 align:middle line:84% And we want to separate it into enough different components 00:02:40.400 --> 00:02:42.790 align:middle line:84% so that the major ones are off in their own little bin 00:02:42.790 --> 00:02:45.040 align:middle line:90% and don't interfere too much. 00:02:45.040 --> 00:02:49.450 align:middle line:84% So the second reason why we might want to purify 00:02:49.450 --> 00:02:52.030 align:middle line:84% is to prepare materials for in vitro experiments. 00:02:52.030 --> 00:02:54.430 align:middle line:84% While we're studying networks in the next three classes, 00:02:54.430 --> 00:02:56.215 align:middle line:84% we'll find that the real problem is 00:02:56.215 --> 00:02:58.090 align:middle line:84% that they're complicated enough that you need 00:02:58.090 --> 00:03:03.940 align:middle line:84% some way of isolating one network component from all 00:03:03.940 --> 00:03:05.660 align:middle line:90% the possible interactions. 00:03:05.660 --> 00:03:08.235 align:middle line:84% And one way to do that is to purify that network component, 00:03:08.235 --> 00:03:09.610 align:middle line:84% or a number of network components 00:03:09.610 --> 00:03:11.920 align:middle line:84% out and make a subnetwork completely artificially 00:03:11.920 --> 00:03:12.770 align:middle line:90% in vitro. 00:03:12.770 --> 00:03:15.010 align:middle line:84% So that's another use of purification. 00:03:15.010 --> 00:03:16.863 align:middle line:84% And finally-- and this is the main theme 00:03:16.863 --> 00:03:18.280 align:middle line:84% for the next couple of slides-- is 00:03:18.280 --> 00:03:19.930 align:middle line:84% to discover biochemical properties 00:03:19.930 --> 00:03:21.830 align:middle line:90% about that component itself. 00:03:21.830 --> 00:03:25.090 align:middle line:84% So what can you glean from the purification process? 00:03:25.090 --> 00:03:27.970 align:middle line:84% And this requires careful use of purification. 00:03:27.970 --> 00:03:30.670 align:middle line:84% Now, many of these methods were developed 00:03:30.670 --> 00:03:33.460 align:middle line:84% because they work well, but now we go back 00:03:33.460 --> 00:03:35.020 align:middle line:84% and we look at which ones can give us 00:03:35.020 --> 00:03:37.510 align:middle line:84% information about the biology and the chemistry 00:03:37.510 --> 00:03:38.930 align:middle line:90% of the system. 00:03:38.930 --> 00:03:43.840 align:middle line:84% And so we have the charge of a molecule, which 00:03:43.840 --> 00:03:45.460 align:middle line:84% we talked about last time as being 00:03:45.460 --> 00:03:49.660 align:middle line:84% a very important relatively long range interaction 00:03:49.660 --> 00:03:51.620 align:middle line:90% 1/r interaction. 00:03:51.620 --> 00:03:54.850 align:middle line:84% And here are two methods I mentioned, one involving 00:03:54.850 --> 00:03:59.350 align:middle line:84% electric field, isoelectric focusing, which determines 00:03:59.350 --> 00:04:01.360 align:middle line:84% the pH at which the charge is neutral 00:04:01.360 --> 00:04:03.130 align:middle line:90% and there's no net movement. 00:04:03.130 --> 00:04:04.930 align:middle line:84% In ion exchange chromatography, we 00:04:04.930 --> 00:04:07.330 align:middle line:84% have a mobile phase and a solid phase. 00:04:07.330 --> 00:04:11.290 align:middle line:84% And charge enters prominently into that. 00:04:11.290 --> 00:04:16.930 align:middle line:84% Size is something that will be a recurring theme tonight, where 00:04:16.930 --> 00:04:20.920 align:middle line:84% we'll be talking about the mass of protein complexes, 00:04:20.920 --> 00:04:23.510 align:middle line:84% the mass of individual protein subunits, 00:04:23.510 --> 00:04:27.817 align:middle line:84% and the mass of peptides cleaved out of those basic subunits. 00:04:27.817 --> 00:04:30.400 align:middle line:84% And you can see there are quite a number of different methods. 00:04:30.400 --> 00:04:34.940 align:middle line:84% Sedimentation velocity will be one that we will use-- 00:04:34.940 --> 00:04:38.950 align:middle line:90% electrophoresis, and so on. 00:04:38.950 --> 00:04:41.350 align:middle line:84% Solubility and hydrophobicity I'll 00:04:41.350 --> 00:04:44.050 align:middle line:84% lump together here as properties, 00:04:44.050 --> 00:04:47.110 align:middle line:84% kind of bulk properties of the amino acid composition 00:04:47.110 --> 00:04:48.580 align:middle line:90% of peptides and proteins. 00:04:48.580 --> 00:04:54.880 align:middle line:84% And they refer to their affinity for hydrophobic solid phases 00:04:54.880 --> 00:05:02.050 align:middle line:84% or hydrophobic mobile phases, solvents, and so on. 00:05:02.050 --> 00:05:05.560 align:middle line:84% The biological significance of hydrophobicity 00:05:05.560 --> 00:05:12.280 align:middle line:84% might be the affinity for lipid bilayers 00:05:12.280 --> 00:05:14.200 align:middle line:84% or affinity for other hydrophobic patches 00:05:14.200 --> 00:05:16.370 align:middle line:90% of other proteins. 00:05:16.370 --> 00:05:19.150 align:middle line:84% Now, we have-- the size tells us something 00:05:19.150 --> 00:05:22.900 align:middle line:84% about the stoichiometry of protein-protein interactions 00:05:22.900 --> 00:05:28.540 align:middle line:84% when we're looking at native measures of size of complexes. 00:05:28.540 --> 00:05:31.450 align:middle line:84% And other indications of specific binding, 00:05:31.450 --> 00:05:35.650 align:middle line:84% whether it's between proteins or a protein in a small molecule 00:05:35.650 --> 00:05:37.960 align:middle line:84% can be detected by affinity chromatography 00:05:37.960 --> 00:05:44.890 align:middle line:84% or another related method as [INAUDIBLE] precipitation, 00:05:44.890 --> 00:05:48.700 align:middle line:84% where you'll have one ligand, like an antibody, which 00:05:48.700 --> 00:05:51.040 align:middle line:84% is specific for a particular protein epitope 00:05:51.040 --> 00:05:53.110 align:middle line:84% and will pull down all the proteins that 00:05:53.110 --> 00:06:00.760 align:middle line:84% are associated with that epitope, that surface property. 00:06:00.760 --> 00:06:02.990 align:middle line:84% And another sedimentation method-- 00:06:02.990 --> 00:06:05.410 align:middle line:84% now, the first sedimentation method 00:06:05.410 --> 00:06:07.900 align:middle line:84% was a velocity method, a kinetic method, 00:06:07.900 --> 00:06:12.520 align:middle line:84% where the largest particles, once you 00:06:12.520 --> 00:06:15.100 align:middle line:84% take into account buoyant density, all other things being 00:06:15.100 --> 00:06:18.460 align:middle line:84% equal, the largest particles will sediment most quickly. 00:06:18.460 --> 00:06:22.090 align:middle line:84% If you set it up so that things are nearly equally buoyant 00:06:22.090 --> 00:06:25.660 align:middle line:84% and you build up a density gradient by centrifugal field, 00:06:25.660 --> 00:06:28.360 align:middle line:84% then you get particles separating 00:06:28.360 --> 00:06:32.110 align:middle line:84% by their properties, which can include 00:06:32.110 --> 00:06:36.930 align:middle line:84% the binding of metal ions, which greatly affect 00:06:36.930 --> 00:06:39.210 align:middle line:84% the density of nucleic acids, for example, 00:06:39.210 --> 00:06:43.890 align:middle line:84% and hence, nucleic acid protein complexes. 00:06:43.890 --> 00:06:48.270 align:middle line:84% Now, this is a particularly awesome pair. 00:06:48.270 --> 00:06:52.290 align:middle line:84% And historically, it figures prominently into proteomics. 00:06:52.290 --> 00:06:56.550 align:middle line:90% It's still quite viable. 00:06:56.550 --> 00:06:59.190 align:middle line:84% And they illustrate some important points. 00:06:59.190 --> 00:07:05.130 align:middle line:84% When we talk about the mass of a native protein 00:07:05.130 --> 00:07:08.730 align:middle line:84% or of a protein that's been denatured by a detergent 00:07:08.730 --> 00:07:14.220 align:middle line:84% micelle, such as Sodium Dodecyl Sulfate, SDS, 00:07:14.220 --> 00:07:19.410 align:middle line:84% its mass can be resolved by fairly potent 00:07:19.410 --> 00:07:21.210 align:middle line:84% polyacrylamide gel electrophoresis, where 00:07:21.210 --> 00:07:24.330 align:middle line:84% you get sieving, where the gel causes sieving. 00:07:24.330 --> 00:07:28.740 align:middle line:84% And this micelle, this detergent micelle size itself, 00:07:28.740 --> 00:07:32.010 align:middle line:84% or the micelle is dependent upon the size of the unfolded 00:07:32.010 --> 00:07:34.890 align:middle line:84% or partially unfolded protein chain. 00:07:34.890 --> 00:07:44.970 align:middle line:84% And so you actually get a fairly good calibratable plot 00:07:44.970 --> 00:07:48.990 align:middle line:84% of mass of the protein versus the mobility 00:07:48.990 --> 00:07:52.230 align:middle line:84% in an electric field when the protein embeds itself 00:07:52.230 --> 00:07:58.740 align:middle line:90% in this detergent micelle. 00:07:58.740 --> 00:08:06.060 align:middle line:84% So this observation of the potentability of this detergent 00:08:06.060 --> 00:08:08.760 align:middle line:84% to denature proteins, and then to resolve based 00:08:08.760 --> 00:08:11.580 align:middle line:84% on mass with the small exceptions of very 00:08:11.580 --> 00:08:14.910 align:middle line:84% hydrophobic proteins or very carbohydrate-rich proteins-- 00:08:14.910 --> 00:08:19.200 align:middle line:84% most other proteins will have a very nice calibratable 00:08:19.200 --> 00:08:20.520 align:middle line:90% relationship. 00:08:20.520 --> 00:08:23.370 align:middle line:84% Similarly, the charge on a protein 00:08:23.370 --> 00:08:26.880 align:middle line:84% approaches 0 as the pH gets to the point 00:08:26.880 --> 00:08:29.490 align:middle line:84% where it's titrating out all the type titratable groups. 00:08:29.490 --> 00:08:31.020 align:middle line:90% And this can be calculated. 00:08:31.020 --> 00:08:34.659 align:middle line:84% And the resolution of this is about 1 part in 100 or better. 00:08:34.659 --> 00:08:38.190 align:middle line:84% So both of these together are two very 00:08:38.190 --> 00:08:39.270 align:middle line:90% high resolution methods. 00:08:39.270 --> 00:08:41.280 align:middle line:84% And you can think of trying to divide-- you 00:08:41.280 --> 00:08:43.559 align:middle line:90% have a complex protein mixture. 00:08:43.559 --> 00:08:46.110 align:middle line:84% You want to divide it up into a lot of separate bins, maybe 00:08:46.110 --> 00:08:47.400 align:middle line:90% 100 of each. 00:08:47.400 --> 00:08:49.360 align:middle line:84% And if you combine two dimensions, 00:08:49.360 --> 00:08:51.360 align:middle line:84% as you often do here, where you'll 00:08:51.360 --> 00:08:56.280 align:middle line:84% run first isoelectric, and then second the SDS dimension, 00:08:56.280 --> 00:08:58.020 align:middle line:84% now you got 100 bins in one dimension, 00:08:58.020 --> 00:09:00.060 align:middle line:84% and each of those bins turns into 100 bins 00:09:00.060 --> 00:09:01.120 align:middle line:90% in the second dimension. 00:09:01.120 --> 00:09:03.078 align:middle line:84% So theoretically, you've got on the order of 10 00:09:03.078 --> 00:09:04.080 align:middle line:90% to the fourth bins. 00:09:04.080 --> 00:09:07.680 align:middle line:84% And if some of the things that you want to-- 00:09:07.680 --> 00:09:10.230 align:middle line:84% some of the rare proteins you want to analyze 00:09:10.230 --> 00:09:11.730 align:middle line:84% are in one of those bins, you might 00:09:11.730 --> 00:09:15.090 align:middle line:84% have gotten as much as a 10 to the fourth-fold enrichment 00:09:15.090 --> 00:09:17.710 align:middle line:84% for those rare proteins away from the more common ones, 00:09:17.710 --> 00:09:21.630 align:middle line:84% which are all too easy to stumble upon. 00:09:21.630 --> 00:09:25.140 align:middle line:84% Now, before we get to an actual example 00:09:25.140 --> 00:09:29.640 align:middle line:84% over two-dimensional gel, I want to motivate this 00:09:29.640 --> 00:09:32.700 align:middle line:84% by the computational components to it 00:09:32.700 --> 00:09:35.400 align:middle line:84% and the computational biology that you 00:09:35.400 --> 00:09:39.870 align:middle line:84% can get from these multi-dimensional separations. 00:09:39.870 --> 00:09:44.010 align:middle line:84% And this comes from our recurring interest 00:09:44.010 --> 00:09:46.470 align:middle line:84% in comparing whenever we can calculate 00:09:46.470 --> 00:09:50.730 align:middle line:84% a property of a system, or in this case of a protein, 00:09:50.730 --> 00:09:54.100 align:middle line:84% we do so, and we compare it with the observations. 00:09:54.100 --> 00:09:59.430 align:middle line:84% And some of the properties here are the localization 00:09:59.430 --> 00:10:00.930 align:middle line:84% in the cell, which essentially is 00:10:00.930 --> 00:10:04.350 align:middle line:84% an association of the protein as it's made, 00:10:04.350 --> 00:10:06.930 align:middle line:84% the post-synthetic modifications to the proteins, 00:10:06.930 --> 00:10:09.330 align:middle line:84% such as proteolysis and phosphorylation, 00:10:09.330 --> 00:10:14.520 align:middle line:84% and so on, its charge, and its mass. 00:10:14.520 --> 00:10:18.840 align:middle line:84% And here is shown a plot of calculated charge, 00:10:18.840 --> 00:10:22.770 align:middle line:84% or isoelectric point in pH units. 00:10:22.770 --> 00:10:25.950 align:middle line:84% P being the negative logarithm of the hydrogen ion 00:10:25.950 --> 00:10:26.972 align:middle line:90% concentration. 00:10:26.972 --> 00:10:28.680 align:middle line:84% So it's calculated on the horizontal axis 00:10:28.680 --> 00:10:30.670 align:middle line:84% and observed on the vertical axis. 00:10:30.670 --> 00:10:37.530 align:middle line:84% And what we're seeing here is if there were a perfect x equal y 00:10:37.530 --> 00:10:39.870 align:middle line:84% relationship here, where calculated and observed 00:10:39.870 --> 00:10:42.900 align:middle line:84% were the same, then all the dots would lie on this line, 00:10:42.900 --> 00:10:45.600 align:middle line:84% and though the outliers, initially they 00:10:45.600 --> 00:10:49.680 align:middle line:84% were such things as frame shifts of the DNA sequence, 00:10:49.680 --> 00:10:52.050 align:middle line:84% producing a wrong calculated protein. 00:10:52.050 --> 00:10:54.780 align:middle line:84% Once those were corrected, then there 00:10:54.780 --> 00:10:57.120 align:middle line:84% were observed proteolytic cleavages, which 00:10:57.120 --> 00:10:59.460 align:middle line:84% could be mapped down through the exact amino acid, 00:10:59.460 --> 00:11:02.430 align:middle line:84% and then those corrected a few more on to the line. 00:11:02.430 --> 00:11:05.250 align:middle line:84% And the remainders were other post-synthetic modifications, 00:11:05.250 --> 00:11:07.210 align:middle line:90% such as phosphorylation. 00:11:07.210 --> 00:11:10.200 align:middle line:84% So here's a reason to embrace your outliers. 00:11:10.200 --> 00:11:12.930 align:middle line:84% Each one of these things is an exciting story, 00:11:12.930 --> 00:11:17.340 align:middle line:84% where you either have a correction to a previous data 00:11:17.340 --> 00:11:24.450 align:middle line:84% type or a new discovery of a post-synthetic modification. 00:11:24.450 --> 00:11:29.220 align:middle line:84% Now, how do we actually calculate all these facts 00:11:29.220 --> 00:11:31.590 align:middle line:90% about protein properties? 00:11:31.590 --> 00:11:34.230 align:middle line:84% Some of these have biological significance, 00:11:34.230 --> 00:11:35.940 align:middle line:90% which we've listed. 00:11:35.940 --> 00:11:37.710 align:middle line:84% Where it is in the cell, obviously, 00:11:37.710 --> 00:11:40.380 align:middle line:84% matters to its carrying out its function. 00:11:40.380 --> 00:11:43.030 align:middle line:84% How big it is determines what other proteins are associated, 00:11:43.030 --> 00:11:43.630 align:middle line:90% and so on. 00:11:43.630 --> 00:11:44.820 align:middle line:90% So how do we calculate this? 00:11:44.820 --> 00:11:47.370 align:middle line:84% The protein charge which was in the previous slide 00:11:47.370 --> 00:11:50.040 align:middle line:84% is a simple linear function where 00:11:50.040 --> 00:11:53.680 align:middle line:84% you sum up the pKas of each of the individual amino acids. 00:11:53.680 --> 00:11:56.280 align:middle line:84% Now, pKa, again, p means negative logarithm, 00:11:56.280 --> 00:11:59.820 align:middle line:84% and the Ka means the equilibrium association 00:11:59.820 --> 00:12:07.500 align:middle line:84% constant of the proton with each of these chargeable residues. 00:12:07.500 --> 00:12:11.370 align:middle line:84% So depending on the pH, there are a wide variety 00:12:11.370 --> 00:12:13.350 align:middle line:84% of nearly physiological pHs where 00:12:13.350 --> 00:12:15.950 align:middle line:84% [INAUDIBLE] and histidines will be positively charged, 00:12:15.950 --> 00:12:21.210 align:middle line:84% the blues, and the red ones can be tyrosine and cysteine. 00:12:21.210 --> 00:12:24.630 align:middle line:84% Especially aspartate and glutamate 00:12:24.630 --> 00:12:28.050 align:middle line:84% can be negatively charged in the range of pHs 00:12:28.050 --> 00:12:30.790 align:middle line:84% that we saw in the previous slide. 00:12:30.790 --> 00:12:34.170 align:middle line:84% And so this is calculated as that simple sum. 00:12:34.170 --> 00:12:42.810 align:middle line:84% And protein mass is calibratable with knowns. 00:12:42.810 --> 00:12:45.720 align:middle line:84% Even if you have a very complex empirical relationship, 00:12:45.720 --> 00:12:49.620 align:middle line:84% such as that detergent binding FBS gel electrophoresis. 00:12:49.620 --> 00:12:52.440 align:middle line:84% It sounds very-- too many moving parts 00:12:52.440 --> 00:12:53.760 align:middle line:90% to be completely theoretical. 00:12:53.760 --> 00:12:57.750 align:middle line:84% But if you calibrate it with good known proteins, or protein 00:12:57.750 --> 00:13:01.050 align:middle line:84% complexes if you're doing a native electrophoresis 00:13:01.050 --> 00:13:05.190 align:middle line:84% or native sedimentation velocity, 00:13:05.190 --> 00:13:08.190 align:middle line:84% then you can get a curve where you can interpolate and find 00:13:08.190 --> 00:13:11.565 align:middle line:84% masses quite accurately, or at least around about 2%. 00:13:11.565 --> 00:13:14.340 align:middle line:90% 00:13:14.340 --> 00:13:16.860 align:middle line:84% But mass spectrometry is commonly 00:13:16.860 --> 00:13:20.160 align:middle line:84% applied to peptide masses, sometimes whole protein masses. 00:13:20.160 --> 00:13:23.250 align:middle line:84% And here, assuming the mass spectrometer 00:13:23.250 --> 00:13:25.350 align:middle line:84% is properly calibrated and so forth, this 00:13:25.350 --> 00:13:26.940 align:middle line:90% is a simple isotope sum. 00:13:26.940 --> 00:13:31.050 align:middle line:84% And this can be carried out sometimes to four or six 00:13:31.050 --> 00:13:33.780 align:middle line:90% significant figures. 00:13:33.780 --> 00:13:41.550 align:middle line:84% And it really is a simple sum of the isotopes measured 00:13:41.550 --> 00:13:42.390 align:middle line:90% by physics. 00:13:42.390 --> 00:13:44.802 align:middle line:84% And this can include post-synthetic modifications. 00:13:44.802 --> 00:13:46.260 align:middle line:84% As you're getting down to peptides, 00:13:46.260 --> 00:13:48.570 align:middle line:84% the post-synthetic modification becomes a much larger 00:13:48.570 --> 00:13:53.745 align:middle line:84% fractional effect on the measures that you're making. 00:13:53.745 --> 00:13:56.670 align:middle line:90% 00:13:56.670 --> 00:13:58.800 align:middle line:84% Not only the mass, but the liquid chromatography 00:13:58.800 --> 00:14:02.730 align:middle line:84% properties of either a protein or a peptide can be calculated. 00:14:02.730 --> 00:14:04.930 align:middle line:84% Here you take amino acid composition 00:14:04.930 --> 00:14:07.980 align:middle line:84% and do linear regression on a calibration set. 00:14:07.980 --> 00:14:14.010 align:middle line:84% And you can get precision on the order of 5% or better. 00:14:14.010 --> 00:14:17.460 align:middle line:84% A subset of localization, we have motifs. 00:14:17.460 --> 00:14:19.380 align:middle line:84% Sometimes hydrophobicity is a part 00:14:19.380 --> 00:14:22.530 align:middle line:84% of those motifs in their description. 00:14:22.530 --> 00:14:24.660 align:middle line:84% Expression would not be something-- 00:14:24.660 --> 00:14:28.050 align:middle line:84% we've seen the motifs that are involved in regulation 00:14:28.050 --> 00:14:29.710 align:middle line:90% of transcription and so forth. 00:14:29.710 --> 00:14:33.660 align:middle line:84% But kind of the shortcut that might take you directly there 00:14:33.660 --> 00:14:36.910 align:middle line:84% in certain cases is the codon adaptation index. 00:14:36.910 --> 00:14:40.350 align:middle line:84% This is something where the hypothesis is 00:14:40.350 --> 00:14:43.470 align:middle line:84% that you can go directly from the nucleotide sequence 00:14:43.470 --> 00:14:45.780 align:middle line:84% of a protein coding region, and if it 00:14:45.780 --> 00:14:49.080 align:middle line:84% uses codons that are very abundant, 00:14:49.080 --> 00:14:51.630 align:middle line:84% that correspond to very abundant transfer RNAs, 00:14:51.630 --> 00:14:52.890 align:middle line:90% then that protein-- 00:14:52.890 --> 00:14:55.830 align:middle line:84% then that's saying that the evolutionary pressures 00:14:55.830 --> 00:14:59.310 align:middle line:84% producing that particular choice of codons 00:14:59.310 --> 00:15:02.520 align:middle line:84% is revealing that that protein is going to be high abundance. 00:15:02.520 --> 00:15:06.930 align:middle line:84% So in a way, this is a way of going directly without-- 00:15:06.930 --> 00:15:15.060 align:middle line:84% due to this observation and somewhat logical expectation. 00:15:15.060 --> 00:15:15.780 align:middle line:90% so 00:15:15.780 --> 00:15:19.080 align:middle line:84% Now we have all sorts of separation methods 00:15:19.080 --> 00:15:21.330 align:middle line:84% and motivation for studying them, more than just 00:15:21.330 --> 00:15:22.350 align:middle line:90% using them. 00:15:22.350 --> 00:15:26.040 align:middle line:84% But now we want to look at a particular case of separation. 00:15:26.040 --> 00:15:30.300 align:middle line:84% We're talking about complexes and protein localization. 00:15:30.300 --> 00:15:42.120 align:middle line:84% So this here is another example of a two-dimensional gel, where 00:15:42.120 --> 00:15:49.110 align:middle line:84% the isoelectric point is on the x-axis here 00:15:49.110 --> 00:15:53.070 align:middle line:84% and the vertical axis is this estimate of molecular mass, 00:15:53.070 --> 00:15:56.160 align:middle line:84% provided by the association of the protein with the SDS 00:15:56.160 --> 00:15:59.940 align:middle line:84% micelles and the effect on gel electrophoresis. 00:15:59.940 --> 00:16:04.890 align:middle line:84% And this is a 2D gel, a small section of it, not the full pH 00:16:04.890 --> 00:16:06.900 align:middle line:84% range nor the full molecular weight range, 00:16:06.900 --> 00:16:13.210 align:middle line:84% but this has a good fraction of the proteins that are secreted. 00:16:13.210 --> 00:16:15.720 align:middle line:84% Now, to what extent can we calculate this? 00:16:15.720 --> 00:16:18.190 align:middle line:84% We've shown a number of calculations and observations 00:16:18.190 --> 00:16:18.900 align:middle line:90% so far. 00:16:18.900 --> 00:16:24.600 align:middle line:84% And we pointed out in last class that to some extent 00:16:24.600 --> 00:16:27.730 align:middle line:84% the transmembrane regions of a protein could-- 00:16:27.730 --> 00:16:31.440 align:middle line:84% this is one of the better algorithms in protein sequence 00:16:31.440 --> 00:16:32.600 align:middle line:90% gazing. 00:16:32.600 --> 00:16:34.890 align:middle line:84% And taking that one step further, 00:16:34.890 --> 00:16:36.800 align:middle line:84% you can actually say, OK, we know 00:16:36.800 --> 00:16:41.330 align:middle line:84% that this might have a motif or two that interact 00:16:41.330 --> 00:16:44.840 align:middle line:84% with membranes, or somehow are part 00:16:44.840 --> 00:16:49.010 align:middle line:84% of the process by which proteins are targeted 00:16:49.010 --> 00:16:52.130 align:middle line:84% and move across membranes so that you 00:16:52.130 --> 00:16:54.830 align:middle line:84% can divide it into several different subcellular 00:16:54.830 --> 00:16:56.360 align:middle line:90% localizations-- 00:16:56.360 --> 00:16:58.670 align:middle line:84% in eukaryotes, the mitochondrial localization, 00:16:58.670 --> 00:17:03.293 align:middle line:84% and chloroplasts in plants, secreted 00:17:03.293 --> 00:17:05.210 align:middle line:84% proteins that go all the way across the plasma 00:17:05.210 --> 00:17:08.819 align:middle line:84% membrane, and other locations, such as within the membrane. 00:17:08.819 --> 00:17:13.220 align:middle line:84% And this can have an 85% success rate, 00:17:13.220 --> 00:17:15.630 align:middle line:90% meaning a 15% false negative. 00:17:15.630 --> 00:17:18.770 align:middle line:84% And you can see modest false positives here, 00:17:18.770 --> 00:17:25.150 align:middle line:84% too, over predictions in 295 transmembranes. 00:17:25.150 --> 00:17:28.700 align:middle line:90% 00:17:28.700 --> 00:17:31.900 align:middle line:84% So let's return to mass, and this time 00:17:31.900 --> 00:17:33.550 align:middle line:84% in the context of mass spectrometry. 00:17:33.550 --> 00:17:34.510 align:middle line:90% What do we have-- 00:17:34.510 --> 00:17:39.510 align:middle line:90% 00:17:39.510 --> 00:17:40.710 align:middle line:90% what's our starting point? 00:17:40.710 --> 00:17:43.310 align:middle line:84% Well, if you look on the far left side of slide 11, 00:17:43.310 --> 00:17:47.660 align:middle line:84% you'll see the simplest of the atoms, hydrogen, 00:17:47.660 --> 00:17:51.350 align:middle line:84% and you would expect this to have an atomic mass of 1. 00:17:51.350 --> 00:17:56.940 align:middle line:84% Well, since carbon-12 is assumed to be precisely 12, 00:17:56.940 --> 00:17:59.930 align:middle line:84% it turns out that hydrogen is not precisely 1 when 00:17:59.930 --> 00:18:01.760 align:middle line:90% you actually measure it. 00:18:01.760 --> 00:18:05.990 align:middle line:84% And it's not even good enough for government standards. 00:18:05.990 --> 00:18:12.270 align:middle line:84% When you actually, say, add a CH2, it adds up to 14. 00:18:12.270 --> 00:18:14.480 align:middle line:84% And that is discriminatable from a 00:18:14.480 --> 00:18:18.110 align:middle line:84% nitrogen-14 with a good enough mass spectrometer. 00:18:18.110 --> 00:18:22.640 align:middle line:84% As it turns out, for most biochemical-- 00:18:22.640 --> 00:18:25.280 align:middle line:84% protein analyses, you don't depend 00:18:25.280 --> 00:18:29.810 align:middle line:90% on the sixth decimal point. 00:18:29.810 --> 00:18:32.930 align:middle line:90% You can get away, really, with-- 00:18:32.930 --> 00:18:35.930 align:middle line:84% or certainly don't depend on having 00:18:35.930 --> 00:18:40.280 align:middle line:84% the 10 to the minus three atomic mass units as your resolution. 00:18:40.280 --> 00:18:44.540 align:middle line:84% But you do depend upon in a much-- in a kilodalton 00:18:44.540 --> 00:18:49.970 align:middle line:84% size peptide being able to get 1 part in 10 00:18:49.970 --> 00:18:53.420 align:middle line:84% to the fourth, 1 atomic mass unit. 00:18:53.420 --> 00:18:55.490 align:middle line:84% Another big consideration here is 00:18:55.490 --> 00:18:58.460 align:middle line:84% that not only are these things not exactly integers, 00:18:58.460 --> 00:19:01.160 align:middle line:84% but in natural abundance, they're 00:19:01.160 --> 00:19:04.910 align:middle line:84% a mixture of the major isotope on the far left 00:19:04.910 --> 00:19:07.700 align:middle line:84% and the second and sometimes third 00:19:07.700 --> 00:19:11.180 align:middle line:84% and fourth stable isotopes, usually non-radioactive 00:19:11.180 --> 00:19:14.360 align:middle line:84% isotopes, which are present in nature. 00:19:14.360 --> 00:19:19.310 align:middle line:84% And the most abundant in this particular list is C13. 00:19:19.310 --> 00:19:21.620 align:middle line:84% And it's most abundant in two senses. 00:19:21.620 --> 00:19:24.890 align:middle line:84% One is it's the highest fractional abundance 00:19:24.890 --> 00:19:28.160 align:middle line:90% of any of these elements. 00:19:28.160 --> 00:19:32.270 align:middle line:84% And secondly, carbon itself is very common in peptides. 00:19:32.270 --> 00:19:34.820 align:middle line:84% If you cleave your protein up with trypsin, 00:19:34.820 --> 00:19:38.960 align:middle line:84% which cleaves c-terminal to lysines and arginines, 00:19:38.960 --> 00:19:43.130 align:middle line:84% you'll get on the order of 10 or 20 00:19:43.130 --> 00:19:45.410 align:middle line:90% of these peptides per protein. 00:19:45.410 --> 00:19:47.420 align:middle line:84% And they might be 10 amino acids long. 00:19:47.420 --> 00:19:51.770 align:middle line:84% And so they might have on the order of 40 carbons in them. 00:19:51.770 --> 00:19:56.150 align:middle line:84% So now the fractional abundance of C13 with 40 carbons 00:19:56.150 --> 00:19:57.965 align:middle line:90% is getting close to unity. 00:19:57.965 --> 00:20:01.460 align:middle line:90% 00:20:01.460 --> 00:20:04.400 align:middle line:84% And we'll see an example of exactly how this plays out 00:20:04.400 --> 00:20:08.240 align:middle line:84% in terms of the multiple combinations of isotopes 00:20:08.240 --> 00:20:09.950 align:middle line:90% that can occur. 00:20:09.950 --> 00:20:12.500 align:middle line:84% Sulfur, on the other hand, has more stable isotopes. 00:20:12.500 --> 00:20:14.510 align:middle line:84% It has four different stable isotopes, 00:20:14.510 --> 00:20:17.030 align:middle line:84% but each one is a fairly small fraction. 00:20:17.030 --> 00:20:19.820 align:middle line:84% And the probability of having a sulfur in a given peptide 00:20:19.820 --> 00:20:20.593 align:middle line:90% is low. 00:20:20.593 --> 00:20:22.760 align:middle line:84% I mean, that's the probability of having one sulfur. 00:20:22.760 --> 00:20:26.150 align:middle line:84% The probability of having 40 sulfurs is vanishingly small. 00:20:26.150 --> 00:20:28.340 align:middle line:84% So we've gone through calculating 00:20:28.340 --> 00:20:31.250 align:middle line:90% all this charged mass. 00:20:31.250 --> 00:20:33.680 align:middle line:84% Now we're going to do liquid chromatography, 00:20:33.680 --> 00:20:38.030 align:middle line:84% in particular hydrophobic measures. 00:20:38.030 --> 00:20:40.280 align:middle line:84% And this all falls under the heading 00:20:40.280 --> 00:20:44.120 align:middle line:84% of high-performance liquid chromatography, which 00:20:44.120 --> 00:20:47.240 align:middle line:84% is achieved under high pressure typically 00:20:47.240 --> 00:20:50.810 align:middle line:90% to get it to go rapidly. 00:20:50.810 --> 00:20:52.850 align:middle line:84% And so you'll have these little-- 00:20:52.850 --> 00:20:54.770 align:middle line:84% you'll digest your protein with trypsin. 00:20:54.770 --> 00:20:56.330 align:middle line:84% You get a series of these peptides, 00:20:56.330 --> 00:21:01.250 align:middle line:90% say, 10 amino acids or so. 00:21:01.250 --> 00:21:04.070 align:middle line:84% And then they're injected in a liquid phase. 00:21:04.070 --> 00:21:08.420 align:middle line:84% They bind to the solid phase by the hydrophobic properties. 00:21:08.420 --> 00:21:10.360 align:middle line:90% You might have it. 00:21:10.360 --> 00:21:13.640 align:middle line:84% And then you'll have a readout, where, as a function of time, 00:21:13.640 --> 00:21:16.700 align:middle line:84% you get abundance where the peaks can be measured by mass 00:21:16.700 --> 00:21:20.160 align:middle line:90% or ion counting and so on. 00:21:20.160 --> 00:21:22.760 align:middle line:84% And these can be collected or simply run 00:21:22.760 --> 00:21:25.513 align:middle line:90% into a mass spectrometer. 00:21:25.513 --> 00:21:26.930 align:middle line:84% There are going to be two phases-- 00:21:26.930 --> 00:21:28.760 align:middle line:84% the mobile phase and solid phase. 00:21:28.760 --> 00:21:35.000 align:middle line:84% Talking about the mobile phase first, 00:21:35.000 --> 00:21:39.200 align:middle line:84% the hydrophobic tendency of any given peptide 00:21:39.200 --> 00:21:41.210 align:middle line:84% is going to be some kind of related 00:21:41.210 --> 00:21:45.230 align:middle line:84% to the sum of the individual amino acid components. 00:21:45.230 --> 00:21:47.330 align:middle line:84% And you can either have a isocratic elution, 00:21:47.330 --> 00:21:50.300 align:middle line:84% where you have basically constant migration speed, 00:21:50.300 --> 00:21:54.290 align:middle line:84% no change in the content of the mobile phase, 00:21:54.290 --> 00:21:58.130 align:middle line:84% or you can have the mobile phase change its composition, 00:21:58.130 --> 00:22:01.340 align:middle line:84% say, for something that's almost entirely water at the beginning 00:22:01.340 --> 00:22:06.380 align:middle line:84% to something that has a high organic content, up to 40% 00:22:06.380 --> 00:22:11.450 align:middle line:84% or so acetonitrile, and so that at the end of the gradient, 00:22:11.450 --> 00:22:13.410 align:middle line:84% even the most hydrophobic peptides 00:22:13.410 --> 00:22:15.740 align:middle line:84% now have just as much affinity for the mobile phase 00:22:15.740 --> 00:22:18.785 align:middle line:84% as the solid phase, and they come flowing off. 00:22:18.785 --> 00:22:22.380 align:middle line:84% The solid phase has a number of different options. 00:22:22.380 --> 00:22:25.370 align:middle line:84% The main one we've been talking about, implicitly at least, 00:22:25.370 --> 00:22:28.490 align:middle line:84% is the reverse phase, where you have 00:22:28.490 --> 00:22:34.520 align:middle line:84% the hydrophobic carbon-18 alkyl chains immobilized 00:22:34.520 --> 00:22:41.880 align:middle line:84% to a highly porous media in a column that 00:22:41.880 --> 00:22:44.850 align:middle line:90% can withstand high pressure. 00:22:44.850 --> 00:22:49.980 align:middle line:84% Or it can have differing polarities, size exclusion, 00:22:49.980 --> 00:22:53.760 align:middle line:84% the same sort of things that we were talking 00:22:53.760 --> 00:22:57.165 align:middle line:84% about with electrophoresis, except now the force is 00:22:57.165 --> 00:22:59.640 align:middle line:84% a non-electric field, but it's simply 00:22:59.640 --> 00:23:02.940 align:middle line:84% the pressure differentiable between the injection 00:23:02.940 --> 00:23:03.960 align:middle line:90% port and the output. 00:23:03.960 --> 00:23:08.330 align:middle line:90% 00:23:08.330 --> 00:23:11.390 align:middle line:84% So here is a specific case worked out 00:23:11.390 --> 00:23:16.670 align:middle line:84% example of how you calculate the affinity of a peptide 00:23:16.670 --> 00:23:20.210 align:middle line:90% to a hydrophobic column. 00:23:20.210 --> 00:23:22.430 align:middle line:84% This is a C18 column, referring to the number 00:23:22.430 --> 00:23:24.350 align:middle line:90% of carbons in the alkyl chain. 00:23:24.350 --> 00:23:27.748 align:middle line:84% So you can see that this is like a kind of a lipid type phase. 00:23:27.748 --> 00:23:30.290 align:middle line:84% It's the sort of thing you might see in the middle of a lipid 00:23:30.290 --> 00:23:31.820 align:middle line:90% bilayer membrane. 00:23:31.820 --> 00:23:37.310 align:middle line:84% And in this plot, you have relative retention time 00:23:37.310 --> 00:23:39.260 align:middle line:90% along the vertical axis. 00:23:39.260 --> 00:23:45.080 align:middle line:84% And the residue number refers to having short peptides 00:23:45.080 --> 00:23:48.620 align:middle line:84% cleaved, sort of walking along the protein. 00:23:48.620 --> 00:23:51.770 align:middle line:84% These have been synthesized so they march along the proteins, 00:23:51.770 --> 00:23:56.660 align:middle line:84% very analogous to how in an earlier class 00:23:56.660 --> 00:24:00.350 align:middle line:84% Rosetta made synthetic oligonucleotides marching along 00:24:00.350 --> 00:24:02.030 align:middle line:90% the human genome. 00:24:02.030 --> 00:24:04.520 align:middle line:84% And this was done in order to calibrate 00:24:04.520 --> 00:24:07.640 align:middle line:84% to see how the composition, the amino acid composition 00:24:07.640 --> 00:24:10.490 align:middle line:84% of a peptide might affect its mobility 00:24:10.490 --> 00:24:12.060 align:middle line:90% and allow you to calculate it. 00:24:12.060 --> 00:24:19.940 align:middle line:84% And so what you end up with here is a somewhat intuitive way 00:24:19.940 --> 00:24:23.400 align:middle line:84% of summing the contributions of each of these amino acids. 00:24:23.400 --> 00:24:28.760 align:middle line:84% Now, remember, this is done under fairly acidic conditions. 00:24:28.760 --> 00:24:32.210 align:middle line:84% So the normally charged acid groups 00:24:32.210 --> 00:24:34.770 align:middle line:84% will now be protonated and be neutral. 00:24:34.770 --> 00:24:38.930 align:middle line:84% And so what we'll find is mainly a spectrum from the very 00:24:38.930 --> 00:24:40.350 align:middle line:90% slowest to be eluted. 00:24:40.350 --> 00:24:42.950 align:middle line:84% They require the most organic, and hence the most-- 00:24:42.950 --> 00:24:46.400 align:middle line:84% the longest retention time would be the highly hydrophobic 00:24:46.400 --> 00:24:49.010 align:middle line:84% aromatics, tryptophan and phenylalanine, 00:24:49.010 --> 00:24:53.477 align:middle line:84% have a component which you obtain by linear regression 00:24:53.477 --> 00:24:54.560 align:middle line:90% of each of these peptides. 00:24:54.560 --> 00:24:56.227 align:middle line:84% You know the sequence of these peptides. 00:24:56.227 --> 00:24:58.910 align:middle line:84% You calculate their composition vector. 00:24:58.910 --> 00:25:01.070 align:middle line:84% And then you relate it to the retention time 00:25:01.070 --> 00:25:03.800 align:middle line:84% that's observed, so that the lower plot is the observed 00:25:03.800 --> 00:25:04.430 align:middle line:90% ones. 00:25:04.430 --> 00:25:05.990 align:middle line:84% And then after you do the regression, 00:25:05.990 --> 00:25:07.490 align:middle line:84% you get a set of these coefficients. 00:25:07.490 --> 00:25:09.950 align:middle line:84% You can now plug them in an additive sense 00:25:09.950 --> 00:25:11.677 align:middle line:90% to make this calculated plot. 00:25:11.677 --> 00:25:13.760 align:middle line:84% And you can see that there's very good correlation 00:25:13.760 --> 00:25:15.477 align:middle line:90% up and down. 00:25:15.477 --> 00:25:17.810 align:middle line:84% It might have been better if these authors had done this 00:25:17.810 --> 00:25:21.080 align:middle line:84% as a plot of calculated versus observed, 00:25:21.080 --> 00:25:23.600 align:middle line:84% as we did in the previous one, and then showed the scatter 00:25:23.600 --> 00:25:25.250 align:middle line:90% and did a regression curve. 00:25:25.250 --> 00:25:26.580 align:middle line:90% But you get the idea. 00:25:26.580 --> 00:25:29.600 align:middle line:84% And so the most hydrophobic ones are at the top, 00:25:29.600 --> 00:25:31.700 align:middle line:84% and the ones that are acid conditions 00:25:31.700 --> 00:25:32.870 align:middle line:90% are the most highly charged. 00:25:32.870 --> 00:25:34.520 align:middle line:84% There's the positive charge ones, the lysine, 00:25:34.520 --> 00:25:36.062 align:middle line:84% the histidine, and arginine down here 00:25:36.062 --> 00:25:37.970 align:middle line:84% at the bottom, and the acidic ones, 00:25:37.970 --> 00:25:42.410 align:middle line:84% since they're close to neutral or near 0 here. 00:25:42.410 --> 00:25:45.980 align:middle line:84% And there's a slight effect as to whether the amino acid is 00:25:45.980 --> 00:25:47.240 align:middle line:90% at the N-terminus or not. 00:25:47.240 --> 00:25:51.680 align:middle line:90% 00:25:51.680 --> 00:25:55.850 align:middle line:84% So now we've calculated the reverse phase behavior here 00:25:55.850 --> 00:26:00.260 align:middle line:84% on the far left-hand side of slide 17. 00:26:00.260 --> 00:26:02.330 align:middle line:84% And this is separated by hydrophobicity, 00:26:02.330 --> 00:26:05.450 align:middle line:84% and then we have mass, which we've outlined 00:26:05.450 --> 00:26:07.280 align:middle line:90% how you can calculate the mass. 00:26:07.280 --> 00:26:09.890 align:middle line:84% So now you can calculate both its hydrophobicity 00:26:09.890 --> 00:26:12.350 align:middle line:84% on the vertical axis, and mass on the horizontal. 00:26:12.350 --> 00:26:15.170 align:middle line:84% And you can make this, in the computer, 00:26:15.170 --> 00:26:18.920 align:middle line:90% this two-dimensional plot. 00:26:18.920 --> 00:26:21.060 align:middle line:84% In this case, this is actually observed data. 00:26:21.060 --> 00:26:24.500 align:middle line:84% But you could also superimpose on it the calculated ones. 00:26:24.500 --> 00:26:30.200 align:middle line:84% These are slightly streakier in the reverse phase retention 00:26:30.200 --> 00:26:34.820 align:middle line:84% time, rt, just because of the scale that one uses, 00:26:34.820 --> 00:26:39.235 align:middle line:84% or the properties of the separation method. 00:26:39.235 --> 00:26:40.610 align:middle line:84% Now, if you blow up-- if you take 00:26:40.610 --> 00:26:43.220 align:middle line:84% one of these little two-dimensional spots 00:26:43.220 --> 00:26:47.810 align:middle line:84% and zoom in on it, and look at it in greater detail, 00:26:47.810 --> 00:26:52.040 align:middle line:84% you see that it actually is a complex set of peaks 00:26:52.040 --> 00:26:56.030 align:middle line:84% in the mass direction, and fairly simple 00:26:56.030 --> 00:26:57.188 align:middle line:90% in the retention time. 00:26:57.188 --> 00:26:58.730 align:middle line:84% Now, you could say, well, maybe these 00:26:58.730 --> 00:27:00.410 align:middle line:90% are all different peptides. 00:27:00.410 --> 00:27:02.960 align:middle line:84% But in fact-- and they are, in a certain sense. 00:27:02.960 --> 00:27:07.640 align:middle line:84% But they're trivial relatives of this, where you need to think-- 00:27:07.640 --> 00:27:11.370 align:middle line:84% where you can data mine and get additional information. 00:27:11.370 --> 00:27:18.740 align:middle line:84% So each of these are separate isotope peaks, meaning, 00:27:18.740 --> 00:27:21.680 align:middle line:84% remember, this might have 40 carbons in this peptide. 00:27:21.680 --> 00:27:24.860 align:middle line:84% And so it's going to be a binomial distribution, 00:27:24.860 --> 00:27:30.410 align:middle line:84% where this is the case where you have zero carbon-13's. 00:27:30.410 --> 00:27:32.270 align:middle line:84% In other words, it's all carbon-12's. 00:27:32.270 --> 00:27:35.570 align:middle line:84% And then the next peak over to the right 00:27:35.570 --> 00:27:37.250 align:middle line:90% is going to have one carbon-13. 00:27:37.250 --> 00:27:39.860 align:middle line:84% The next peak over is going to have two carbon-13's. 00:27:39.860 --> 00:27:43.070 align:middle line:84% And n minus 2, where n is the total number of carbons 00:27:43.070 --> 00:27:44.930 align:middle line:84% in that peptide, which would be carbon-12, 00:27:44.930 --> 00:27:46.580 align:middle line:84% the most abundant one, and so on. 00:27:46.580 --> 00:27:48.620 align:middle line:84% And you'd get every possible combination 00:27:48.620 --> 00:27:52.090 align:middle line:84% in a binomial distribution just as you would expect. 00:27:52.090 --> 00:27:53.090 align:middle line:90% Now, what does this say? 00:27:53.090 --> 00:27:54.465 align:middle line:84% It tells you at least two things. 00:27:54.465 --> 00:27:57.800 align:middle line:84% One is the distance between these, you can see here, 00:27:57.800 --> 00:28:00.350 align:middle line:90% is a half an atomic mass unit. 00:28:00.350 --> 00:28:02.630 align:middle line:84% Well, how do we get a half of an atomic mass unit? 00:28:02.630 --> 00:28:04.345 align:middle line:90% I mean, they're supposed to be-- 00:28:04.345 --> 00:28:06.220 align:middle line:84% I mean, we know they're not perfect integers, 00:28:06.220 --> 00:28:07.400 align:middle line:90% but this is way off. 00:28:07.400 --> 00:28:10.780 align:middle line:84% And the reason is because what's actually measured 00:28:10.780 --> 00:28:12.278 align:middle line:90% is the mass over charge. 00:28:12.278 --> 00:28:14.320 align:middle line:84% You're not measuring the mass-- mass over charge. 00:28:14.320 --> 00:28:17.470 align:middle line:84% And so this is saying this particular peptide has a +2 00:28:17.470 --> 00:28:18.190 align:middle line:90% charge state. 00:28:18.190 --> 00:28:19.910 align:middle line:90% That's an important fact. 00:28:19.910 --> 00:28:22.290 align:middle line:84% It's going to be hard to interpret its mass 00:28:22.290 --> 00:28:24.040 align:middle line:84% if you don't know its charge, because it's 00:28:24.040 --> 00:28:27.063 align:middle line:90% m/z that's measured. 00:28:27.063 --> 00:28:28.480 align:middle line:84% The other thing that's measured is 00:28:28.480 --> 00:28:30.503 align:middle line:84% from the exact binomial distribution 00:28:30.503 --> 00:28:32.920 align:middle line:84% you can get an estimate of the number of carbons in there, 00:28:32.920 --> 00:28:35.290 align:middle line:84% because if they're a huge number of carbons, 00:28:35.290 --> 00:28:39.430 align:middle line:84% then it will turn out that one of the secondary peaks here, 00:28:39.430 --> 00:28:43.000 align:middle line:84% one of the rightward peaks, will actually 00:28:43.000 --> 00:28:44.140 align:middle line:90% be the most abundant one. 00:28:44.140 --> 00:28:45.760 align:middle line:84% If it has a small number of carbons, 00:28:45.760 --> 00:28:50.200 align:middle line:84% then the zero carbon-13 peak will be the all around winner. 00:28:50.200 --> 00:28:54.135 align:middle line:84% And so from the relative heights of 0, 1, and 2, 00:28:54.135 --> 00:28:55.760 align:middle line:84% you can estimate the number of carbons. 00:28:55.760 --> 00:28:58.690 align:middle line:84% So two facts you can get from this high resolution 00:28:58.690 --> 00:29:02.650 align:middle line:90% view of that peak. 00:29:02.650 --> 00:29:11.440 align:middle line:84% So now we've got these two phases, these two dimensions 00:29:11.440 --> 00:29:13.270 align:middle line:90% pretty well in hand-- 00:29:13.270 --> 00:29:14.860 align:middle line:90% the reverse phase and the mass. 00:29:14.860 --> 00:29:16.750 align:middle line:84% Now we're going to add another dimension-- 00:29:16.750 --> 00:29:18.260 align:middle line:84% actually, a couple more dimensions. 00:29:18.260 --> 00:29:19.660 align:middle line:84% One is another peptide dimension, 00:29:19.660 --> 00:29:24.280 align:middle line:84% which is a Strong Cation Exchange, SCX. 00:29:24.280 --> 00:29:26.170 align:middle line:84% What this means is the peptides will 00:29:26.170 --> 00:29:28.505 align:middle line:84% have different cationic properties, different charges, 00:29:28.505 --> 00:29:31.060 align:middle line:84% and they will bind to different extents to this. 00:29:31.060 --> 00:29:34.990 align:middle line:84% And you can put these in tandem, either physically 00:29:34.990 --> 00:29:36.100 align:middle line:90% or conceptually. 00:29:36.100 --> 00:29:38.008 align:middle line:84% You'll run one, take a bunch of fractions, 00:29:38.008 --> 00:29:39.550 align:middle line:84% and then put it on the reverse phase. 00:29:39.550 --> 00:29:42.460 align:middle line:84% The first phase literally is physically connected 00:29:42.460 --> 00:29:45.040 align:middle line:90% to the mass spectrometer. 00:29:45.040 --> 00:29:47.690 align:middle line:84% And then we'll talk about some upstream. 00:29:47.690 --> 00:29:50.290 align:middle line:84% Now, upstream separation methods, 00:29:50.290 --> 00:29:53.400 align:middle line:84% before you fragment it into peptides, you can separate. 00:29:53.400 --> 00:29:56.170 align:middle line:84% It can have dimensions on the proteins, which can tell you 00:29:56.170 --> 00:29:58.120 align:middle line:90% about the complexes. 00:29:58.120 --> 00:30:01.310 align:middle line:84% Let's go through a specific example of complexes. 00:30:01.310 --> 00:30:03.860 align:middle line:90% 00:30:03.860 --> 00:30:07.000 align:middle line:84% So if you take the entire yeast proteome-- 00:30:07.000 --> 00:30:12.940 align:middle line:84% grind up yeast, throw it on a first separation, 00:30:12.940 --> 00:30:18.400 align:middle line:84% which is the sedimentation velocity-- remember, 00:30:18.400 --> 00:30:20.050 align:middle line:84% there's equilibrium and velocity. 00:30:20.050 --> 00:30:24.055 align:middle line:84% Velocity is mainly responsive to the size of the complex. 00:30:24.055 --> 00:30:25.180 align:middle line:90% This is a native dimension. 00:30:25.180 --> 00:30:27.820 align:middle line:84% We're not denaturing with SDS native. 00:30:27.820 --> 00:30:29.380 align:middle line:84% And so you'll see the things that 00:30:29.380 --> 00:30:32.260 align:middle line:84% go absolutely the fastest in the cell 00:30:32.260 --> 00:30:36.640 align:middle line:84% and among the most abundant are the ribosomes 00:30:36.640 --> 00:30:38.020 align:middle line:90% and the ribosomal subunits. 00:30:38.020 --> 00:30:40.180 align:middle line:84% This is done under conditions where you just 00:30:40.180 --> 00:30:44.950 align:middle line:84% tease apart the two ribosomal subunits so that they're 00:30:44.950 --> 00:30:45.670 align:middle line:90% separated. 00:30:45.670 --> 00:30:48.490 align:middle line:84% And the bigger of the two is called 60S. 00:30:48.490 --> 00:30:50.980 align:middle line:84% The S refers to Svedberg, who was 00:30:50.980 --> 00:30:55.870 align:middle line:84% one of the pioneers of sedimentation velocity. 00:30:55.870 --> 00:30:59.890 align:middle line:84% And this can be correlated. the Rate at which it goes down, 00:30:59.890 --> 00:31:04.690 align:middle line:84% this stabilized gradient centrifugal field is 00:31:04.690 --> 00:31:07.030 align:middle line:90% related to the complex. 00:31:07.030 --> 00:31:09.040 align:middle line:84% And then so that's the first dimension 00:31:09.040 --> 00:31:10.990 align:middle line:90% is horizontally, sedimentation. 00:31:10.990 --> 00:31:15.940 align:middle line:84% The very top axis going horizontally is sedimentation. 00:31:15.940 --> 00:31:22.030 align:middle line:84% And then going down is SDS gel electrophoresis. 00:31:22.030 --> 00:31:24.070 align:middle line:84% So now you're taking these native complexes 00:31:24.070 --> 00:31:27.190 align:middle line:84% and breaking them up into their component proteins, 00:31:27.190 --> 00:31:29.020 align:middle line:84% where high molecular weight is up, 00:31:29.020 --> 00:31:30.520 align:middle line:84% and low molecular weight is down. 00:31:30.520 --> 00:31:32.895 align:middle line:84% And you can see that there are quite a number of proteins 00:31:32.895 --> 00:31:35.320 align:middle line:84% in each of these two subunits, the 60S and the 40S. 00:31:35.320 --> 00:31:38.080 align:middle line:84% Now, each of these proteins-- so that's the second dimension. 00:31:38.080 --> 00:31:40.780 align:middle line:84% Now, you cleave it into peptides, 00:31:40.780 --> 00:31:45.250 align:middle line:84% and you analyze the peptides by retention time in all the three 00:31:45.250 --> 00:31:46.690 align:middle line:90% dimensions-- 00:31:46.690 --> 00:31:49.210 align:middle line:84% strong cation exchange, retention time, and mass. 00:31:49.210 --> 00:31:50.710 align:middle line:84% And then you add a fourth one, which 00:31:50.710 --> 00:31:53.230 align:middle line:84% we'll develop a little later, where you can actually 00:31:53.230 --> 00:31:55.280 align:middle line:84% break up the peptides into little pieces. 00:31:55.280 --> 00:32:00.560 align:middle line:84% So you identify which protein each of the peptides came from. 00:32:00.560 --> 00:32:03.580 align:middle line:84% Now, unfortunately, not all peptides 00:32:03.580 --> 00:32:06.440 align:middle line:84% are equal in their ionization potential. 00:32:06.440 --> 00:32:09.160 align:middle line:84% And so if it ionizes poorly, you won't 00:32:09.160 --> 00:32:12.310 align:middle line:84% detect a particular peptide from a protein. 00:32:12.310 --> 00:32:14.710 align:middle line:84% If you detect a large number of peptides from a protein, 00:32:14.710 --> 00:32:17.050 align:middle line:84% that probably means that it's abundant in your sample 00:32:17.050 --> 00:32:19.630 align:middle line:84% or your fraction, and it also means 00:32:19.630 --> 00:32:21.880 align:middle line:84% that you can believe that the computer 00:32:21.880 --> 00:32:25.520 align:middle line:84% identification of that protein is probably pretty solid. 00:32:25.520 --> 00:32:28.210 align:middle line:84% So if you get five or more peptides from a particular 00:32:28.210 --> 00:32:30.402 align:middle line:84% protein in your database search-- 00:32:30.402 --> 00:32:32.860 align:middle line:84% and we'll talk just a moment about how you do that database 00:32:32.860 --> 00:32:34.060 align:middle line:90% search-- 00:32:34.060 --> 00:32:36.130 align:middle line:84% then you believe it, and it's very solid. 00:32:36.130 --> 00:32:39.160 align:middle line:84% And so if you look at the-- you can see that all these-- as you 00:32:39.160 --> 00:32:44.260 align:middle line:84% look at essentially the protein fingerprint for each of the 60S 00:32:44.260 --> 00:32:47.122 align:middle line:84% fractions in the sedimentation, they look pretty similar. 00:32:47.122 --> 00:32:48.580 align:middle line:84% And when you run out the mass spec, 00:32:48.580 --> 00:32:53.110 align:middle line:84% you get similar sets of peptide signatures. 00:32:53.110 --> 00:32:55.990 align:middle line:90% And they correspond to-- 00:32:55.990 --> 00:32:58.540 align:middle line:84% most of them, especially the most abundant ones, 00:32:58.540 --> 00:33:02.410 align:middle line:84% correspond to the known 60S proteins. 00:33:02.410 --> 00:33:07.530 align:middle line:84% If you go a little bit slower, less mass, 00:33:07.530 --> 00:33:11.190 align:middle line:84% the 40S subunits and you analyze the subsequences 00:33:11.190 --> 00:33:14.640 align:middle line:84% of the mass spec signatures for those peptides, 00:33:14.640 --> 00:33:16.860 align:middle line:90% then they mainly turn up 40S. 00:33:16.860 --> 00:33:18.570 align:middle line:90% There's some exceptions in both. 00:33:18.570 --> 00:33:22.020 align:middle line:84% There are some other categories, which may be interesting. 00:33:22.020 --> 00:33:26.500 align:middle line:84% The most interesting in this particular study, 00:33:26.500 --> 00:33:32.130 align:middle line:84% the authors highlighted was [? Weimer ?] 116p, which, 00:33:32.130 --> 00:33:33.780 align:middle line:84% remember, this is a very mature field. 00:33:33.780 --> 00:33:35.340 align:middle line:90% This is a very recent study. 00:33:35.340 --> 00:33:37.770 align:middle line:84% And ribosomes were very well characterized. 00:33:37.770 --> 00:33:39.900 align:middle line:84% And I think we had the conceit that at least 00:33:39.900 --> 00:33:42.040 align:middle line:84% in microbial systems, such as E. coli and yeast, 00:33:42.040 --> 00:33:44.700 align:middle line:84% we really understood all the proteins that were required 00:33:44.700 --> 00:33:46.980 align:middle line:90% to make a ribosome hum. 00:33:46.980 --> 00:33:49.650 align:middle line:84% But here was a new ribosomal protein, 00:33:49.650 --> 00:33:56.460 align:middle line:84% which has since been confirmed that this is a bona fide part 00:33:56.460 --> 00:33:57.510 align:middle line:90% of the 40S subunit. 00:33:57.510 --> 00:34:00.160 align:middle line:84% You can see here it had many peptide hits. 00:34:00.160 --> 00:34:05.580 align:middle line:84% So it was equal in abundance to the other 40S subunit proteins. 00:34:05.580 --> 00:34:10.489 align:middle line:90% 00:34:10.489 --> 00:34:12.889 align:middle line:84% So here we had in a certain sense 00:34:12.889 --> 00:34:16.010 align:middle line:84% five or so dimensions, the sedimentation, 00:34:16.010 --> 00:34:18.949 align:middle line:84% which is the native complex size, the denatured protein 00:34:18.949 --> 00:34:23.525 align:middle line:84% subunit size, the peptide ionic-- 00:34:23.525 --> 00:34:29.100 align:middle line:90% 00:34:29.100 --> 00:34:33.270 align:middle line:84% the ion exchange, the peptide mass, 00:34:33.270 --> 00:34:36.480 align:middle line:90% and the peptide fragmentation. 00:34:36.480 --> 00:34:38.400 align:middle line:84% So when we talk about fragmentation, 00:34:38.400 --> 00:34:40.320 align:middle line:84% that's what MS/MS sometimes means. 00:34:40.320 --> 00:34:43.530 align:middle line:84% It means that you're doing first mass separation of the peptide. 00:34:43.530 --> 00:34:45.870 align:middle line:84% You break it up, and you do another mass separation 00:34:45.870 --> 00:34:47.070 align:middle line:90% of the component parts. 00:34:47.070 --> 00:34:48.810 align:middle line:84% And that allows us to do database 00:34:48.810 --> 00:34:53.170 align:middle line:84% searching and sequencing of the peptides. 00:34:53.170 --> 00:34:54.159 align:middle line:90% Now, how this works-- 00:34:54.159 --> 00:34:55.365 align:middle line:84% this is a blow up of something that 00:34:55.365 --> 00:34:57.840 align:middle line:84% was in an earlier slide, where you can really see the region 00:34:57.840 --> 00:34:58.800 align:middle line:90% where you've got electrospray. 00:34:58.800 --> 00:35:01.420 align:middle line:84% We'll have an even closer blow up with this in a moment. 00:35:01.420 --> 00:35:05.340 align:middle line:84% But basically, your reverse phase liquid chromatography 00:35:05.340 --> 00:35:07.350 align:middle line:84% is going directly into this vacuum 00:35:07.350 --> 00:35:13.560 align:middle line:84% here, generating little spray at 4,000 volts, 00:35:13.560 --> 00:35:18.060 align:middle line:84% and then these molecular ions will go through the vacuum 00:35:18.060 --> 00:35:22.260 align:middle line:84% through a series of magnetic octopoles 00:35:22.260 --> 00:35:24.840 align:middle line:84% until it hits an ion trap, which helps 00:35:24.840 --> 00:35:29.258 align:middle line:84% you determine the m/z, and finally a detector. 00:35:29.258 --> 00:35:31.800 align:middle line:84% You can see there's a variety of different pressures in here, 00:35:31.800 --> 00:35:34.770 align:middle line:84% sort of increasing pressure from the point where you have 00:35:34.770 --> 00:35:37.410 align:middle line:84% the aqueous solvent going in all the way 00:35:37.410 --> 00:35:43.303 align:middle line:84% to the detector, which is the highest vacuum at around 10 00:35:43.303 --> 00:35:47.490 align:middle line:90% to the minus fifth torr. 00:35:47.490 --> 00:35:49.710 align:middle line:84% Now, here's where the [INAUDIBLE] mass spectrometry, 00:35:49.710 --> 00:35:57.510 align:middle line:84% or MS/MS, or collision-induced dissociation, you have-- 00:35:57.510 --> 00:36:00.045 align:middle line:84% here's your ion beam of molecular peptide ions. 00:36:00.045 --> 00:36:03.930 align:middle line:90% You've now turned peptides in-- 00:36:03.930 --> 00:36:06.900 align:middle line:84% they're each on their own little space. 00:36:06.900 --> 00:36:09.360 align:middle line:84% And in the middle of this quadrupole, 00:36:09.360 --> 00:36:13.800 align:middle line:84% this magnetic environment here, you 00:36:13.800 --> 00:36:17.760 align:middle line:84% bring in an inert gas, like argon, 00:36:17.760 --> 00:36:22.290 align:middle line:84% to collide with these rapidly moving ions, 00:36:22.290 --> 00:36:24.990 align:middle line:84% and they will break the chain, basically, 00:36:24.990 --> 00:36:26.370 align:middle line:90% at any covalent bond. 00:36:26.370 --> 00:36:31.290 align:middle line:84% And if you think of the peptide at a chain backbone as having 00:36:31.290 --> 00:36:34.470 align:middle line:84% three different covalent bonds, there's the peptide bond 00:36:34.470 --> 00:36:37.500 align:middle line:84% itself, and then there's a carbon-nitrogen bond, 00:36:37.500 --> 00:36:41.610 align:middle line:90% and a carbonyl nitrogen bond-- 00:36:41.610 --> 00:36:43.650 align:middle line:84% sorry-- and a carbon-carbon bond. 00:36:43.650 --> 00:36:45.870 align:middle line:84% It can break at any of those three positions, 00:36:45.870 --> 00:36:49.260 align:middle line:84% and then you'll generate a set of fragments coming in 00:36:49.260 --> 00:36:54.390 align:middle line:84% from the N-terminus with three possible C-terminal fragments. 00:36:54.390 --> 00:36:57.815 align:middle line:84% And coming in, if a C-terminus is cleaving at the same point, 00:36:57.815 --> 00:36:59.440 align:middle line:84% the whole series is coming in that way. 00:36:59.440 --> 00:37:02.370 align:middle line:84% And as you're coming in from the N-terminus, 00:37:02.370 --> 00:37:04.800 align:middle line:84% you give it A, B, and C, depending 00:37:04.800 --> 00:37:07.620 align:middle line:84% on whether it breaks at the C carbon-carbon 00:37:07.620 --> 00:37:10.560 align:middle line:84% bond, the carbon-nitrogen bond, or the nitrogen-carbon bond, A, 00:37:10.560 --> 00:37:11.940 align:middle line:90% B, and C respectively. 00:37:11.940 --> 00:37:14.820 align:middle line:84% And the same ones coming in from the C-terminus 00:37:14.820 --> 00:37:20.680 align:middle line:84% are called Z, Y, and X. And so, as it turns out, 00:37:20.680 --> 00:37:24.630 align:middle line:84% just empirically, if you sort through all the chemistry, 00:37:24.630 --> 00:37:30.240 align:middle line:84% in most cases, the peptide bond is the one 00:37:30.240 --> 00:37:32.640 align:middle line:90% that's most actively cleaved. 00:37:32.640 --> 00:37:36.720 align:middle line:84% And so the B ions and their complementary Y ions 00:37:36.720 --> 00:37:37.960 align:middle line:90% dominate the picture. 00:37:37.960 --> 00:37:39.460 align:middle line:84% Now, the other ones will be present. 00:37:39.460 --> 00:37:42.120 align:middle line:84% Especially if they come from a very abundant peptide, 00:37:42.120 --> 00:37:46.170 align:middle line:84% they can swap out the B and Y ions 00:37:46.170 --> 00:37:47.640 align:middle line:90% for a less abundant peptide. 00:37:47.640 --> 00:37:50.400 align:middle line:84% But all other things being equal, B and Y will dominate, 00:37:50.400 --> 00:37:53.025 align:middle line:84% and most of the rest of the discussion will be about those. 00:37:53.025 --> 00:37:56.640 align:middle line:90% 00:37:56.640 --> 00:37:58.770 align:middle line:84% This is the closest picture we'll 00:37:58.770 --> 00:38:00.360 align:middle line:90% show of the ionization step. 00:38:00.360 --> 00:38:06.480 align:middle line:84% This is a step, which has not been thoroughly enough studied, 00:38:06.480 --> 00:38:10.110 align:middle line:84% in the sense that this ionization step, where 00:38:10.110 --> 00:38:14.670 align:middle line:84% the droplets of aqueous and organic 00:38:14.670 --> 00:38:18.090 align:middle line:84% solvent coming out of the separation column 00:38:18.090 --> 00:38:21.210 align:middle line:90% is subjected to the vacuum. 00:38:21.210 --> 00:38:24.240 align:middle line:84% The water starts being released from the droplet. 00:38:24.240 --> 00:38:27.420 align:middle line:84% The protons-- remember, this acidic media associates 00:38:27.420 --> 00:38:28.740 align:middle line:90% with a molecular ion. 00:38:28.740 --> 00:38:30.630 align:middle line:84% They kind of explode because there's 00:38:30.630 --> 00:38:33.480 align:middle line:84% too much positive charge in a small space, 00:38:33.480 --> 00:38:36.660 align:middle line:84% until you finally have a molecular ion associated 00:38:36.660 --> 00:38:40.103 align:middle line:84% with one or two net positive charges. 00:38:40.103 --> 00:38:42.270 align:middle line:84% Remember, we had an example just a little while ago. 00:38:42.270 --> 00:38:43.728 align:middle line:84% We had a net positive charge of +2. 00:38:43.728 --> 00:38:46.560 align:middle line:90% 00:38:46.560 --> 00:38:49.860 align:middle line:84% You don't need to have neutrality in this situation. 00:38:49.860 --> 00:38:52.230 align:middle line:84% Anyway, this is poorly understood in the sense 00:38:52.230 --> 00:38:56.040 align:middle line:84% that some peptides ionize much better than others. 00:38:56.040 --> 00:38:58.710 align:middle line:84% And we'll come back to this when we talk about quantification. 00:38:58.710 --> 00:39:02.880 align:middle line:84% But for right now, what we want to do is ask how do we analyze 00:39:02.880 --> 00:39:05.910 align:middle line:84% the complex spectra that comes out when you fragment-- 00:39:05.910 --> 00:39:07.440 align:middle line:90% when you take-- 00:39:07.440 --> 00:39:09.310 align:middle line:84% first, you get a fairly simple spectra, 00:39:09.310 --> 00:39:13.832 align:middle line:84% which is just a list of all the masses of all of the peptides. 00:39:13.832 --> 00:39:15.540 align:middle line:84% And remember, some will be weak, and some 00:39:15.540 --> 00:39:22.560 align:middle line:84% will be strong because of this voodoo ionization. 00:39:22.560 --> 00:39:23.808 align:middle line:90% But then we break them. 00:39:23.808 --> 00:39:26.100 align:middle line:84% However, whatever the intensity of the original peptide 00:39:26.100 --> 00:39:28.170 align:middle line:84% was, it will make a bunch of daughter ions, which 00:39:28.170 --> 00:39:30.410 align:middle line:84% will be the B ions coming in from the N-terminus, 00:39:30.410 --> 00:39:32.170 align:middle line:90% the Y ions from the C-terminus. 00:39:32.170 --> 00:39:34.800 align:middle line:84% And you'll have this big mixture, a nested set, 00:39:34.800 --> 00:39:37.895 align:middle line:84% that get increasingly large as you get further 00:39:37.895 --> 00:39:39.270 align:middle line:84% from the N-terminus and the B ion 00:39:39.270 --> 00:39:41.460 align:middle line:90% series and their complements. 00:39:41.460 --> 00:39:44.640 align:middle line:84% And the sum of the B ion with its complementary Y ion 00:39:44.640 --> 00:39:47.880 align:middle line:84% has to be the original molecular mass corrected 00:39:47.880 --> 00:39:51.670 align:middle line:84% for the chemistry that occurs right at the cleavage. 00:39:51.670 --> 00:39:53.380 align:middle line:90% And so here's a real example. 00:39:53.380 --> 00:39:55.740 align:middle line:84% We're going to work through it so that you 00:39:55.740 --> 00:39:59.460 align:middle line:84% can see what happens to a typical peptide 00:39:59.460 --> 00:40:02.470 align:middle line:84% here in the upper right-hand corner. 00:40:02.470 --> 00:40:05.300 align:middle line:84% And this is tandem mass spectrometry. 00:40:05.300 --> 00:40:07.050 align:middle line:84% Remember, there is a-- if you think of it, 00:40:07.050 --> 00:40:13.860 align:middle line:84% there was a single mass peptide that was then broken 00:40:13.860 --> 00:40:15.450 align:middle line:90% into all these little pieces. 00:40:15.450 --> 00:40:22.260 align:middle line:84% And the almost intact peptide will 00:40:22.260 --> 00:40:26.400 align:middle line:84% be on the far end of the horizontal axis, which 00:40:26.400 --> 00:40:30.760 align:middle line:84% is the mass axis, close to 1,200 atomic mass units. 00:40:30.760 --> 00:40:36.150 align:middle line:84% And then a relative abundance is the vertical axis, 00:40:36.150 --> 00:40:39.870 align:middle line:84% as it's just related to the ion counts. 00:40:39.870 --> 00:40:43.200 align:middle line:84% And you can see there's some variation here. 00:40:43.200 --> 00:40:45.130 align:middle line:90% This is not due to ionization. 00:40:45.130 --> 00:40:47.910 align:middle line:84% This is due to the cleavage efficiency, cleaving 00:40:47.910 --> 00:40:52.560 align:middle line:84% at each of the bonds for the Y series, which is in blue here. 00:40:52.560 --> 00:40:55.230 align:middle line:84% And it tends to be the higher peaks, and the B 00:40:55.230 --> 00:40:57.840 align:middle line:84% series in red, which tends to be slightly lower peaks. 00:40:57.840 --> 00:40:59.760 align:middle line:84% And then you've got these little arrows, 00:40:59.760 --> 00:41:02.700 align:middle line:84% the darker arrows indicate the Y ion series that 00:41:02.700 --> 00:41:04.500 align:middle line:90% separate two adjacent peaks. 00:41:04.500 --> 00:41:07.080 align:middle line:84% Because what's the difference between those two peaks 00:41:07.080 --> 00:41:09.610 align:middle line:84% is the addition of one amino acid. 00:41:09.610 --> 00:41:15.012 align:middle line:84% And so the focusing on the blue series, the Y 00:41:15.012 --> 00:41:16.470 align:middle line:84% ions coming in from the C-terminus, 00:41:16.470 --> 00:41:20.280 align:middle line:84% the shortest Y, the Y1, would be just the C-terminal amino acid 00:41:20.280 --> 00:41:22.230 align:middle line:90% itself, which would be arginine. 00:41:22.230 --> 00:41:23.910 align:middle line:84% And its distance from the origin would 00:41:23.910 --> 00:41:28.020 align:middle line:84% be about the mass of the arginine itself. 00:41:28.020 --> 00:41:29.620 align:middle line:84% And then you add a glycine to it, 00:41:29.620 --> 00:41:32.500 align:middle line:84% which is a small delta, and then an alanine, 00:41:32.500 --> 00:41:35.250 align:middle line:84% and an isoleucine, and serine, and so forth. 00:41:35.250 --> 00:41:38.940 align:middle line:84% And you can see here very clearly 00:41:38.940 --> 00:41:43.920 align:middle line:84% the leucine and the asparagine and the valine, so that Y ion 00:41:43.920 --> 00:41:45.660 align:middle line:90% series all the way down. 00:41:45.660 --> 00:41:47.460 align:middle line:84% The G is the last one documented. 00:41:47.460 --> 00:41:50.730 align:middle line:84% The N and the S at the highest molecular weight 00:41:50.730 --> 00:41:52.140 align:middle line:90% are not visible. 00:41:52.140 --> 00:41:53.940 align:middle line:84% And actually, many of these things 00:41:53.940 --> 00:41:56.070 align:middle line:90% you'll have very weak peaks. 00:41:56.070 --> 00:41:58.740 align:middle line:84% You'll essentially have missing peaks 00:41:58.740 --> 00:42:02.560 align:middle line:84% that are corresponding to one of the delta amino acids. 00:42:02.560 --> 00:42:05.190 align:middle line:84% So in that case, the distance between the two prominent peaks 00:42:05.190 --> 00:42:06.710 align:middle line:90% will be two amino acids. 00:42:06.710 --> 00:42:09.210 align:middle line:84% You can see this starting to get to be a challenging pattern 00:42:09.210 --> 00:42:11.760 align:middle line:84% recognition problem, because you've got all the B ions mixed 00:42:11.760 --> 00:42:12.602 align:middle line:90% in with the Y ions. 00:42:12.602 --> 00:42:14.310 align:middle line:84% And this is summarized in the next slide. 00:42:14.310 --> 00:42:17.190 align:middle line:90% 00:42:17.190 --> 00:42:20.680 align:middle line:84% The B ions are mixed in with the Y ions. 00:42:20.680 --> 00:42:23.580 align:middle line:84% And some of the ions are missing. 00:42:23.580 --> 00:42:26.145 align:middle line:84% Each ion has multiple isotopic forms. 00:42:26.145 --> 00:42:28.020 align:middle line:84% That wasn't so evident in the previous slide. 00:42:28.020 --> 00:42:30.660 align:middle line:84% But that blow up I showed a while back, 00:42:30.660 --> 00:42:33.810 align:middle line:84% you had that binomial distribution. 00:42:33.810 --> 00:42:41.040 align:middle line:84% There is the lingering presence of A, C, X, 00:42:41.040 --> 00:42:44.070 align:middle line:84% and Z type ions, where you've got cleavage 00:42:44.070 --> 00:42:45.990 align:middle line:90% of some of the other bonds. 00:42:45.990 --> 00:42:48.580 align:middle line:84% Ions can lose a water or an ammonia. 00:42:48.580 --> 00:42:50.820 align:middle line:84% You've got noise from other peptides 00:42:50.820 --> 00:42:53.370 align:middle line:84% and from contaminants in the system. 00:42:53.370 --> 00:42:56.730 align:middle line:84% And you've got amino acid modifications, which is not 00:42:56.730 --> 00:42:58.090 align:middle line:90% a contaminant or a bad thing. 00:42:58.090 --> 00:42:58.530 align:middle line:90% It's a good thing. 00:42:58.530 --> 00:42:59.863 align:middle line:90% This is what you're looking for. 00:42:59.863 --> 00:43:02.370 align:middle line:84% But these can be in trace amounts in this system. 00:43:02.370 --> 00:43:07.990 align:middle line:90% 00:43:07.990 --> 00:43:10.060 align:middle line:84% Now, there are two ways to approach 00:43:10.060 --> 00:43:14.862 align:middle line:84% the awesome amount of data that you can get out of these. 00:43:14.862 --> 00:43:16.820 align:middle line:84% Remember, you've got all these multi dimensions 00:43:16.820 --> 00:43:21.460 align:middle line:84% finally ending in this forest of B ions and Y ions 00:43:21.460 --> 00:43:23.382 align:middle line:90% and all the rest of it in there. 00:43:23.382 --> 00:43:24.590 align:middle line:90% And there are two approaches. 00:43:24.590 --> 00:43:28.450 align:middle line:84% One is we'll call it de novo peptide sequencing, which 00:43:28.450 --> 00:43:32.200 align:middle line:84% would be analogous to the de novo DNA sequencing 00:43:32.200 --> 00:43:33.010 align:middle line:90% that we were doing 00:43:33.010 --> 00:43:36.940 align:middle line:84% and the other is if you tell me the sequence then 00:43:36.940 --> 00:43:41.710 align:middle line:84% I'll find it in my data kind of game OK it's doing a database 00:43:41.710 --> 00:43:45.130 align:middle line:84% search where you're limited to proteins that are very, very-- 00:43:45.130 --> 00:43:46.870 align:middle line:84% to finding peptides that you already 00:43:46.870 --> 00:43:50.870 align:middle line:84% know about, or can hypothesize from a genome sequence. 00:43:50.870 --> 00:43:52.180 align:middle line:90% So this is the first category. 00:43:52.180 --> 00:43:53.770 align:middle line:90% This is de novo sequencing. 00:43:53.770 --> 00:43:55.660 align:middle line:84% And it takes on all the challenges 00:43:55.660 --> 00:43:56.950 align:middle line:90% in the previous slide. 00:43:56.950 --> 00:44:00.160 align:middle line:84% It takes on the possibility of missing data 00:44:00.160 --> 00:44:02.800 align:middle line:84% for a particular ion species that you think should be there, 00:44:02.800 --> 00:44:06.520 align:middle line:84% but for some reason are not efficiently cleaved 00:44:06.520 --> 00:44:10.540 align:middle line:84% by the argon in the collision-induced association. 00:44:10.540 --> 00:44:11.980 align:middle line:84% And it takes into account that you 00:44:11.980 --> 00:44:15.790 align:middle line:84% have to have one set of B ions, a nested set of masses 00:44:15.790 --> 00:44:18.070 align:middle line:84% from the N-terminus that have to be 00:44:18.070 --> 00:44:19.840 align:middle line:84% complementary to this nested set you 00:44:19.840 --> 00:44:21.590 align:middle line:90% get coming in from C-terminus. 00:44:21.590 --> 00:44:23.475 align:middle line:90% So this is dynamic programming. 00:44:23.475 --> 00:44:25.600 align:middle line:84% And you can probably count how many different times 00:44:25.600 --> 00:44:28.550 align:middle line:84% we've done a dynamic programming algorithm in this class. 00:44:28.550 --> 00:44:31.210 align:middle line:84% And so hopefully you're happy that you did at least one 00:44:31.210 --> 00:44:32.260 align:middle line:90% of them by hand. 00:44:32.260 --> 00:44:36.460 align:middle line:84% And this one we won't belabor, but here you 00:44:36.460 --> 00:44:38.410 align:middle line:84% can see how it kind of conceptually 00:44:38.410 --> 00:44:40.475 align:middle line:84% maps to the simplest one that we talked 00:44:40.475 --> 00:44:42.850 align:middle line:84% about at the beginning, which is comparing two amino acid 00:44:42.850 --> 00:44:43.570 align:middle line:90% sequences. 00:44:43.570 --> 00:44:47.470 align:middle line:84% There, the indels were caused by evolutionary change. 00:44:47.470 --> 00:44:49.570 align:middle line:84% Here the insertions and deletions 00:44:49.570 --> 00:44:53.290 align:middle line:84% are due to a missing ion due to inefficient cleavage in the gas 00:44:53.290 --> 00:44:55.540 align:middle line:90% phase. 00:44:55.540 --> 00:44:58.330 align:middle line:84% This is further complicated by this necessity 00:44:58.330 --> 00:45:01.240 align:middle line:84% of essentially sequencing in both from the B and the Y 00:45:01.240 --> 00:45:03.250 align:middle line:84% simultaneously and making sure that you 00:45:03.250 --> 00:45:08.890 align:middle line:84% have the best combination of B and Y assignments. 00:45:08.890 --> 00:45:10.330 align:middle line:90% So that's de novo sequencing. 00:45:10.330 --> 00:45:14.450 align:middle line:84% Now, in slide 29, we have the alternative, 00:45:14.450 --> 00:45:18.220 align:middle line:84% which is by far more commonly applied, 00:45:18.220 --> 00:45:20.920 align:middle line:84% an example of the alternative, which 00:45:20.920 --> 00:45:22.700 align:middle line:90% is you tell me a sequence. 00:45:22.700 --> 00:45:25.300 align:middle line:84% I'll find it in my data, [INAUDIBLE].. 00:45:25.300 --> 00:45:28.330 align:middle line:84% And where you're basically calculating the spectrum 00:45:28.330 --> 00:45:30.610 align:middle line:84% that you might expect for each peptide 00:45:30.610 --> 00:45:32.550 align:middle line:84% that you might expect in a genome. 00:45:32.550 --> 00:45:34.300 align:middle line:84% So you basically use the genome to predict 00:45:34.300 --> 00:45:35.920 align:middle line:90% the protein coding regions. 00:45:35.920 --> 00:45:39.430 align:middle line:84% Use those to do in silico digestion 00:45:39.430 --> 00:45:41.710 align:middle line:84% with trypsin, basically cleave after 00:45:41.710 --> 00:45:46.250 align:middle line:84% every lysine and arginine, and maybe after some other ones 00:45:46.250 --> 00:45:49.180 align:middle line:84% are complicated rules where trypsin doesn't always cleave 00:45:49.180 --> 00:45:50.467 align:middle line:90% after lysines and arginines. 00:45:50.467 --> 00:45:52.550 align:middle line:84% And sometimes there'll be other proteases present. 00:45:52.550 --> 00:45:54.217 align:middle line:84% And you have to take those into account. 00:45:54.217 --> 00:45:56.800 align:middle line:84% In any case, you generate a virtual set of peptides. 00:45:56.800 --> 00:45:59.500 align:middle line:84% You generate a virtual set of mass peaks. 00:45:59.500 --> 00:46:02.170 align:middle line:84% Now, since we don't know the rules that 00:46:02.170 --> 00:46:04.240 align:middle line:84% determine the height of those mass peaks, 00:46:04.240 --> 00:46:07.960 align:middle line:84% we wish we did, but we don't, so we just set it to a unit, 00:46:07.960 --> 00:46:10.580 align:middle line:84% to some arbitrary to make them all the same. 00:46:10.580 --> 00:46:13.300 align:middle line:84% So you're not going to be getting a great correlation 00:46:13.300 --> 00:46:15.220 align:middle line:84% coefficient based on the heights, 00:46:15.220 --> 00:46:18.080 align:middle line:84% but merely whether it's there or not. 00:46:18.080 --> 00:46:20.530 align:middle line:84% And that's what you do is every time you have 00:46:20.530 --> 00:46:25.525 align:middle line:84% a hit between your predicted spectrum and the other one, 00:46:25.525 --> 00:46:27.400 align:middle line:84% no matter what the intensity of the other one 00:46:27.400 --> 00:46:30.190 align:middle line:84% is, so you waited on the observed intensity, 00:46:30.190 --> 00:46:33.880 align:middle line:84% but you have no real calculated intensity. 00:46:33.880 --> 00:46:37.120 align:middle line:84% And this correlation coefficient serves as a way 00:46:37.120 --> 00:46:39.610 align:middle line:90% of prioritizing your scores. 00:46:39.610 --> 00:46:41.230 align:middle line:90% And if you have a-- 00:46:41.230 --> 00:46:47.380 align:middle line:84% very often the best score will be the database hit 00:46:47.380 --> 00:46:49.030 align:middle line:90% for the peptide that you want. 00:46:49.030 --> 00:46:53.320 align:middle line:84% Now, if you're expecting post-synthetic modifications, 00:46:53.320 --> 00:46:56.540 align:middle line:84% you need to tell the algorithm to add the appropriate mass 00:46:56.540 --> 00:46:57.920 align:middle line:90% to the appropriate amino acid. 00:46:57.920 --> 00:47:00.580 align:middle line:84% So for example, if you expect a phosphoserine, 00:47:00.580 --> 00:47:03.610 align:middle line:84% you have to put the phosphate mass into the program 00:47:03.610 --> 00:47:05.120 align:middle line:90% and associate it with a serine. 00:47:05.120 --> 00:47:07.330 align:middle line:84% So the serine can be either a regular serine 00:47:07.330 --> 00:47:08.140 align:middle line:90% or a phosphoserine. 00:47:08.140 --> 00:47:12.100 align:middle line:84% So that's another complexity there. 00:47:12.100 --> 00:47:16.030 align:middle line:84% So now we've gone through the richness of the separation 00:47:16.030 --> 00:47:16.720 align:middle line:90% methods. 00:47:16.720 --> 00:47:19.090 align:middle line:84% Separation is intimately connected 00:47:19.090 --> 00:47:22.120 align:middle line:84% with getting us to a mass spectrum which is clean enough 00:47:22.120 --> 00:47:25.150 align:middle line:84% to do either de novo sequencing or database searching. 00:47:25.150 --> 00:47:27.130 align:middle line:84% Now that we've got it identified, 00:47:27.130 --> 00:47:28.840 align:middle line:90% let's try to quantitate it. 00:47:28.840 --> 00:47:31.840 align:middle line:84% We can quantitate it one of two ways, just the same 00:47:31.840 --> 00:47:34.135 align:middle line:84% as with the RNAs, either on an absolute scale 00:47:34.135 --> 00:47:36.970 align:middle line:90% or on a relative scale. 00:47:36.970 --> 00:47:38.005 align:middle line:90% What is involved? 00:47:38.005 --> 00:47:40.660 align:middle line:90% 00:47:40.660 --> 00:47:45.640 align:middle line:84% We'll make an analogy to the RNA quantification methods, 00:47:45.640 --> 00:47:47.590 align:middle line:84% which I believe we've had something 00:47:47.590 --> 00:47:50.200 align:middle line:84% very similar to the left-hand side of slide 31 00:47:50.200 --> 00:47:52.253 align:middle line:84% here, when we talked about RNAs, all the ways we 00:47:52.253 --> 00:47:53.170 align:middle line:90% could quantitate them. 00:47:53.170 --> 00:47:58.060 align:middle line:84% A subset of these have an analog in the protein 00:47:58.060 --> 00:48:01.190 align:middle line:84% domain on the right-hand side of this slide. 00:48:01.190 --> 00:48:05.260 align:middle line:84% So for example, one of our favorite methods that we used 00:48:05.260 --> 00:48:06.370 align:middle line:90% was microarrays. 00:48:06.370 --> 00:48:08.690 align:middle line:90% That's the top line of the RNA. 00:48:08.690 --> 00:48:10.580 align:middle line:84% This is where you would have the gene 00:48:10.580 --> 00:48:12.260 align:middle line:84% segments, either oligonucleotides 00:48:12.260 --> 00:48:15.050 align:middle line:84% or [INAUDIBLE],, immobilized on a microarray. 00:48:15.050 --> 00:48:19.370 align:middle line:84% Fluorescently label your RNA and quantitate. 00:48:19.370 --> 00:48:22.430 align:middle line:84% For proteins, the equivalent would 00:48:22.430 --> 00:48:26.420 align:middle line:84% be an antibody array aimed at unique features 00:48:26.420 --> 00:48:27.740 align:middle line:90% of each protein. 00:48:27.740 --> 00:48:29.570 align:middle line:84% This is in very early days, because we 00:48:29.570 --> 00:48:30.500 align:middle line:90% are antibody limited. 00:48:30.500 --> 00:48:34.340 align:middle line:84% We do not have antibodies against every protein surface 00:48:34.340 --> 00:48:35.090 align:middle line:90% epitope. 00:48:35.090 --> 00:48:37.050 align:middle line:84% And they are not specific enough. 00:48:37.050 --> 00:48:41.090 align:middle line:90% There's a lot of crosstalk. 00:48:41.090 --> 00:48:45.650 align:middle line:84% We mentioned in the second line that microarrays could not 00:48:45.650 --> 00:48:49.400 align:middle line:84% measure the composition of alternative slices, 00:48:49.400 --> 00:48:51.180 align:middle line:84% or the size of the messenger RNA. 00:48:51.180 --> 00:48:53.030 align:middle line:84% So that was best done by a Northern blot, 00:48:53.030 --> 00:48:55.220 align:middle line:84% which actually measured the size, 00:48:55.220 --> 00:48:57.890 align:middle line:90% but was not high throughput. 00:48:57.890 --> 00:49:00.500 align:middle line:84% The equivalent for that for proteins is called a Western. 00:49:00.500 --> 00:49:04.670 align:middle line:84% These are all puns on Ed Southern's name. 00:49:04.670 --> 00:49:08.210 align:middle line:84% The Westerns will allow you to measure 00:49:08.210 --> 00:49:11.210 align:middle line:84% the size of native or denaturing proteins, 00:49:11.210 --> 00:49:12.800 align:middle line:84% and then detect them with antibodies. 00:49:12.800 --> 00:49:14.540 align:middle line:90% Again, antibody limited. 00:49:14.540 --> 00:49:18.080 align:middle line:84% If we had a technology breakthrough that would give us 00:49:18.080 --> 00:49:20.930 align:middle line:84% all the antibodies we need, just like we had, 00:49:20.930 --> 00:49:24.620 align:middle line:84% it's easy to dial up any nucleic acid you want just 00:49:24.620 --> 00:49:26.430 align:middle line:90% by synthesis. 00:49:26.430 --> 00:49:29.180 align:middle line:90% 00:49:29.180 --> 00:49:32.750 align:middle line:84% There's no real equivalent to PCR for a protein world. 00:49:32.750 --> 00:49:35.000 align:middle line:84% You can tag proteins with nucleic acids 00:49:35.000 --> 00:49:36.770 align:middle line:90% and do PCR on the nucleic acids. 00:49:36.770 --> 00:49:41.480 align:middle line:84% But there's no real direct amplication on proteins. 00:49:41.480 --> 00:49:44.090 align:middle line:84% Reporter constructs basically work the same for each. 00:49:44.090 --> 00:49:47.180 align:middle line:84% You have something that is highly specific because you 00:49:47.180 --> 00:49:52.100 align:middle line:84% constructed it in vivo, but it is 00:49:52.100 --> 00:50:00.020 align:middle line:84% a sum of all the RNA and protein expression steps that 00:50:00.020 --> 00:50:01.265 align:middle line:90% give you the reporter. 00:50:01.265 --> 00:50:04.010 align:middle line:90% 00:50:04.010 --> 00:50:06.440 align:middle line:84% Fluorescent in situ hybridization 00:50:06.440 --> 00:50:08.030 align:middle line:84% in the case of RNA or fluorescent 00:50:08.030 --> 00:50:10.220 align:middle line:84% in situ antibodies in case of proteins 00:50:10.220 --> 00:50:14.300 align:middle line:84% is a great way of correlating quasi quantitative information 00:50:14.300 --> 00:50:18.350 align:middle line:84% with a subcellular or suborganismal localization. 00:50:18.350 --> 00:50:20.750 align:middle line:84% Tag counting, there's nothing equivalent for proteins, 00:50:20.750 --> 00:50:22.205 align:middle line:84% and mass spectrometry can be used 00:50:22.205 --> 00:50:23.330 align:middle line:90% for differentiable display. 00:50:23.330 --> 00:50:27.440 align:middle line:90% 00:50:27.440 --> 00:50:32.090 align:middle line:84% What are the sort of numbers of molecules we have? 00:50:32.090 --> 00:50:35.990 align:middle line:84% A ballpark when we're dealing with quantitation-- slide 32, 00:50:35.990 --> 00:50:37.620 align:middle line:90% it depends on the organism. 00:50:37.620 --> 00:50:40.610 align:middle line:84% Some of the simplest ones, like you find in yeast, 00:50:40.610 --> 00:50:42.890 align:middle line:84% we mentioned the messenger RNA molecules 00:50:42.890 --> 00:50:48.920 align:middle line:84% might be less than one per cell, just stochastic fluctuations. 00:50:48.920 --> 00:50:56.270 align:middle line:84% And in a human cell, it's probably a fairly good 00:50:56.270 --> 00:50:59.630 align:middle line:84% approximation or assumption that almost every nucleotide 00:50:59.630 --> 00:51:03.950 align:middle line:84% in the human genome can be transcribed. 00:51:03.950 --> 00:51:08.060 align:middle line:84% Maybe there's some leakiness where 00:51:08.060 --> 00:51:10.550 align:middle line:84% on the order of 1 in 10 to the fourth cells 00:51:10.550 --> 00:51:14.115 align:middle line:84% will have a little leakiness at any particular nucleotide. 00:51:14.115 --> 00:51:15.740 align:middle line:84% So that's kind of the background level. 00:51:15.740 --> 00:51:17.330 align:middle line:84% It's 10 to the minus fourth per cell. 00:51:17.330 --> 00:51:19.670 align:middle line:84% And it's really only achievable detection 00:51:19.670 --> 00:51:22.490 align:middle line:90% with reverse transcriptase PCR. 00:51:22.490 --> 00:51:27.275 align:middle line:84% The entire transcriptome of a human cell 00:51:27.275 --> 00:51:31.400 align:middle line:84% is on the order of half a million transcripts messenger 00:51:31.400 --> 00:51:32.360 align:middle line:90% RNAs. 00:51:32.360 --> 00:51:35.690 align:middle line:84% And so if any particular messenger RNA got up to 10 00:51:35.690 --> 00:51:37.520 align:middle line:90% to the fifth, it would dominate. 00:51:37.520 --> 00:51:40.160 align:middle line:84% And this happens in some cases, like reticulocytes. 00:51:40.160 --> 00:51:44.180 align:middle line:84% Maybe 90% of the messenger RNA might be globin. 00:51:44.180 --> 00:51:46.390 align:middle line:84% Now, for proteins, you'll typically 00:51:46.390 --> 00:51:47.390 align:middle line:90% have bursts of proteins. 00:51:47.390 --> 00:51:48.515 align:middle line:90% You have one messenger RNA. 00:51:48.515 --> 00:51:51.050 align:middle line:84% You might get 10 to 1,000 proteins made, 00:51:51.050 --> 00:51:52.920 align:middle line:90% depending on the organism. 00:51:52.920 --> 00:51:58.160 align:middle line:84% And so you typically have a corresponding amplification 00:51:58.160 --> 00:51:59.120 align:middle line:90% in the last line. 00:51:59.120 --> 00:52:04.790 align:middle line:90% 00:52:04.790 --> 00:52:12.710 align:middle line:84% Now, when people assess casually whether a particular method is 00:52:12.710 --> 00:52:18.980 align:middle line:84% quantitative or not, they can be easily intimidated. 00:52:18.980 --> 00:52:21.140 align:middle line:90% So they might say-- 00:52:21.140 --> 00:52:25.670 align:middle line:84% I've commented that the ionization of-- 00:52:25.670 --> 00:52:27.695 align:middle line:84% so ESI stands for Electrospray Ionization 00:52:27.695 --> 00:52:31.430 align:middle line:90% in mass spectrometry. 00:52:31.430 --> 00:52:37.580 align:middle line:84% If you take a protein and cleave it with trypsin, 00:52:37.580 --> 00:52:41.900 align:middle line:84% in principle, every tryptic fragment, 00:52:41.900 --> 00:52:45.770 align:middle line:84% since trypsin cleaves pretty close to completely fairly 00:52:45.770 --> 00:52:49.550 align:middle line:84% easily, every tryptic fragment, every peptide 00:52:49.550 --> 00:52:51.140 align:middle line:90% should be equimolar. 00:52:51.140 --> 00:52:55.610 align:middle line:84% If you now inject that into the HPLC, into the mass spec, 00:52:55.610 --> 00:53:00.740 align:middle line:84% every peak integrated intensity should be equal, 00:53:00.740 --> 00:53:02.090 align:middle line:90% because they're all equimolar. 00:53:02.090 --> 00:53:08.660 align:middle line:84% And then when you find that, no, they vary over two orders 00:53:08.660 --> 00:53:12.410 align:middle line:84% of magnitude-- that is to say some are 100 times more intense 00:53:12.410 --> 00:53:13.738 align:middle line:90% peaks than others-- 00:53:13.738 --> 00:53:15.530 align:middle line:84% then you might get discouraged and say, oh, 00:53:15.530 --> 00:53:19.700 align:middle line:84% isn't a quantitative science at all. 00:53:19.700 --> 00:53:24.530 align:middle line:84% I can't deal with a factor of 100 difference. 00:53:24.530 --> 00:53:27.800 align:middle line:84% But I think that you need to reassess that when 00:53:27.800 --> 00:53:29.750 align:middle line:84% people say that mass spectrometry is not 00:53:29.750 --> 00:53:31.280 align:middle line:90% quantitative. 00:53:31.280 --> 00:53:35.630 align:middle line:84% The two requirements for quantitation 00:53:35.630 --> 00:53:38.330 align:middle line:84% is that you have reproducibility, 00:53:38.330 --> 00:53:45.320 align:middle line:84% and that you have a way of calibrating or calculating. 00:53:45.320 --> 00:53:47.317 align:middle line:84% If you can calculate from first principles, 00:53:47.317 --> 00:53:48.650 align:middle line:90% then you don't need calibration. 00:53:48.650 --> 00:53:51.480 align:middle line:84% If it's too empirical, then you need calibration. 00:53:51.480 --> 00:53:57.620 align:middle line:84% But you do not need that every disparate object behaves 00:53:57.620 --> 00:53:59.570 align:middle line:90% exactly the same way. 00:53:59.570 --> 00:54:03.650 align:middle line:84% Not every peptide has to give the same quantitative answer 00:54:03.650 --> 00:54:06.980 align:middle line:84% simply has to be reproducible and calibrate able 00:54:06.980 --> 00:54:11.240 align:middle line:84% with that same peptide and so here's an example of establish 00:54:11.240 --> 00:54:13.580 align:middle line:84% two examples in a row of establishing 00:54:13.580 --> 00:54:19.880 align:middle line:84% the reproducibility here this is from that ribosomal protein 00:54:19.880 --> 00:54:23.570 align:middle line:84% experiment that I showed earlier with the complexes 00:54:23.570 --> 00:54:27.570 align:middle line:84% of sedimentation velocity and the multi dimensions. 00:54:27.570 --> 00:54:32.540 align:middle line:84% And this is just you do a measurement on day one, 00:54:32.540 --> 00:54:34.670 align:middle line:84% and you do the whole experiment over on day two, 00:54:34.670 --> 00:54:42.830 align:middle line:84% and then you compare the intensity of the peaks. 00:54:42.830 --> 00:54:46.430 align:middle line:84% And you get what is a fairly good straight line 00:54:46.430 --> 00:54:52.610 align:middle line:84% relationship on a log-log curve over about a little 00:54:52.610 --> 00:54:54.335 align:middle line:90% over 3 logs. 00:54:54.335 --> 00:54:57.350 align:middle line:90% 00:54:57.350 --> 00:54:59.420 align:middle line:84% There were many moving parts in that experiment. 00:54:59.420 --> 00:55:01.520 align:middle line:84% There were all those different dimensions. 00:55:01.520 --> 00:55:04.280 align:middle line:84% And the whole experiment was not designed to be quantitative. 00:55:04.280 --> 00:55:07.130 align:middle line:84% There are no internal controls, and so forth, or so on. 00:55:07.130 --> 00:55:10.520 align:middle line:84% Nevertheless, this is a good starting point 00:55:10.520 --> 00:55:14.300 align:middle line:84% for convincing yourself or determining whether something 00:55:14.300 --> 00:55:17.840 align:middle line:84% is reproducible enough that you can make it quantitative. 00:55:17.840 --> 00:55:22.020 align:middle line:84% Here's another way of measuring the reproducibility. 00:55:22.020 --> 00:55:23.645 align:middle line:84% That one was a correlation coefficient, 00:55:23.645 --> 00:55:26.300 align:middle line:84% a linear correlation coefficient on a log-log plot. 00:55:26.300 --> 00:55:28.130 align:middle line:84% Here is the coefficient of variation. 00:55:28.130 --> 00:55:30.420 align:middle line:84% I think we may have mentioned this before. 00:55:30.420 --> 00:55:32.090 align:middle line:84% This is just the standard deviation 00:55:32.090 --> 00:55:33.650 align:middle line:90% normalized by the mean. 00:55:33.650 --> 00:55:36.000 align:middle line:84% In the upper left-hand part of this slide, 00:55:36.000 --> 00:55:39.170 align:middle line:84% you can see that the CV, or Coefficient of Variation, 00:55:39.170 --> 00:55:41.490 align:middle line:84% is just the standard deviation divided by the mean. 00:55:41.490 --> 00:55:45.420 align:middle line:84% So you can report it in terms of percentages of variation. 00:55:45.420 --> 00:55:49.370 align:middle line:84% So here with a calibration standards of peptides, 00:55:49.370 --> 00:55:53.690 align:middle line:84% you get somewhere between 2% and 28% coefficient of variation. 00:55:53.690 --> 00:55:57.860 align:middle line:84% That means you can trust these things to be within 2% to 28% 00:55:57.860 --> 00:56:02.400 align:middle line:84% of their absolute amounts when you calibrate them. 00:56:02.400 --> 00:56:06.350 align:middle line:84% So these two are just two examples 00:56:06.350 --> 00:56:09.080 align:middle line:84% that should reassure you that there is reproducibility 00:56:09.080 --> 00:56:10.760 align:middle line:90% and you can calibrate. 00:56:10.760 --> 00:56:17.210 align:middle line:90% 00:56:17.210 --> 00:56:23.330 align:middle line:84% Calibration can be an expensive proposition. 00:56:23.330 --> 00:56:25.820 align:middle line:84% But there are various motivations 00:56:25.820 --> 00:56:30.200 align:middle line:84% for measuring, quantitating both proteins and nucleic acids 00:56:30.200 --> 00:56:31.130 align:middle line:90% on an absolute scale. 00:56:31.130 --> 00:56:36.290 align:middle line:84% And for example, you might want to compare them to each other. 00:56:36.290 --> 00:56:40.430 align:middle line:84% You might want to say, to what extent 00:56:40.430 --> 00:56:43.580 align:middle line:84% is it the case that the most abundant proteins result 00:56:43.580 --> 00:56:45.710 align:middle line:84% from the most abundant messenger RNAs? 00:56:45.710 --> 00:56:48.560 align:middle line:84% Or you could imagine a world where 00:56:48.560 --> 00:56:51.140 align:middle line:84% these are completely independent, since one 00:56:51.140 --> 00:56:52.220 align:middle line:90% is transcription factors. 00:56:52.220 --> 00:56:54.650 align:middle line:84% The other is translation factors. 00:56:54.650 --> 00:56:57.080 align:middle line:84% There's no reason that they necessarily are synced up. 00:56:57.080 --> 00:56:59.690 align:middle line:84% Or you could imagine a hypothesis 00:56:59.690 --> 00:57:02.150 align:middle line:84% where it's a lot of work to make a lot of protein, 00:57:02.150 --> 00:57:04.190 align:middle line:84% and so everything has to be working right 00:57:04.190 --> 00:57:05.840 align:middle line:90% for the most abundant ones. 00:57:05.840 --> 00:57:07.340 align:middle line:84% And for the least abundant ones, you 00:57:07.340 --> 00:57:09.620 align:middle line:90% can have a little more slop. 00:57:09.620 --> 00:57:14.340 align:middle line:84% So this analysis critiqued a little bit 00:57:14.340 --> 00:57:16.220 align:middle line:84% in the subsequent paper that we'll 00:57:16.220 --> 00:57:23.180 align:middle line:84% talk about after the break can be interpreted 00:57:23.180 --> 00:57:24.080 align:middle line:90% as being consistent. 00:57:24.080 --> 00:57:25.400 align:middle line:84% When you include all the proteins, 00:57:25.400 --> 00:57:27.275 align:middle line:84% you have a very good correlation coefficient. 00:57:27.275 --> 00:57:29.870 align:middle line:84% This is a linear Pearson correlation coefficient. 00:57:29.870 --> 00:57:32.960 align:middle line:90% 00:57:32.960 --> 00:57:37.310 align:middle line:84% But as you restrict yourself to the lowest abundance proteins, 00:57:37.310 --> 00:57:38.840 align:middle line:90% it falls apart. 00:57:38.840 --> 00:57:43.520 align:middle line:84% You have less significant Pearson correlation 00:57:43.520 --> 00:57:44.832 align:middle line:90% coefficients. 00:57:44.832 --> 00:57:46.040 align:middle line:90% So let's take a little break. 00:57:46.040 --> 00:57:49.440 align:middle line:84% And we'll talk about critiquing this a little bit, 00:57:49.440 --> 00:57:52.430 align:middle line:84% and improving, and asking other motivations 00:57:52.430 --> 00:57:56.950 align:middle line:84% for putting protein on absolute scale, and doing ratios. 00:57:56.950 --> 00:57:58.000 align:middle line:90%