1 00:00:00,500 --> 00:00:03,270 The following content is provided under a Creative 2 00:00:03,270 --> 00:00:04,630 Commons license. 3 00:00:04,630 --> 00:00:07,140 Your support will help MIT OpenCourseWare 4 00:00:07,140 --> 00:00:11,470 continue to offer high-quality educational resources for free. 5 00:00:11,470 --> 00:00:14,100 To make a donation or view additional materials 6 00:00:14,100 --> 00:00:18,050 from hundreds of MIT courses, visit MIT OpenCourseWare 7 00:00:18,050 --> 00:00:19,000 at ocw.mit.edu. 8 00:00:24,079 --> 00:00:26,380 ELIZABETH NOLAN: ...going to do today is some kind 9 00:00:26,380 --> 00:00:31,070 of superficial overview and focusing on things that are 10 00:00:31,070 --> 00:00:35,270 important to consider when either designing experiments 11 00:00:35,270 --> 00:00:39,140 that probe binding, or also when reading about experiments done 12 00:00:39,140 --> 00:00:44,990 by others, and thinking about their data and how the data was 13 00:00:44,990 --> 00:00:46,694 fit. 14 00:00:46,694 --> 00:00:50,210 So the readings this week were some excerpts 15 00:00:50,210 --> 00:00:52,680 from two different types of review articles-- 16 00:00:52,680 --> 00:00:54,650 so the Wedd paper and the Giedroc paper. 17 00:00:54,650 --> 00:00:57,860 And I guess, just to start, what did you 18 00:00:57,860 --> 00:01:02,099 think about these readings and the reviews? 19 00:01:02,099 --> 00:01:03,140 What kind of impressions? 20 00:01:03,140 --> 00:01:05,480 Did you like them or not like them? 21 00:01:05,480 --> 00:01:07,440 How are they different, and all that? 22 00:01:07,440 --> 00:01:12,595 AUDIENCE: I liked when they went into certain considerations 23 00:01:12,595 --> 00:01:13,890 you need to keep in mind. 24 00:01:13,890 --> 00:01:14,936 That was kind of helpful with the longer one. 25 00:01:14,936 --> 00:01:16,360 ELIZABETH NOLAN: All right, in the Wedd. 26 00:01:16,360 --> 00:01:17,401 AUDIENCE: The longer one? 27 00:01:17,401 --> 00:01:19,975 ELIZABETH NOLAN: So in the Wedd one. 28 00:01:19,975 --> 00:01:22,450 AUDIENCE: It was like there was a 15-page one. 29 00:01:22,450 --> 00:01:23,326 Yeah, yeah, this one. 30 00:01:23,326 --> 00:01:24,658 ELIZABETH NOLAN: Yeah, this one. 31 00:01:24,658 --> 00:01:25,292 AUDIENCE: Yeah. 32 00:01:25,292 --> 00:01:26,250 ELIZABETH NOLAN: Great. 33 00:01:26,250 --> 00:01:28,120 Challenges of different-- determining 34 00:01:28,120 --> 00:01:30,400 metal-protein affinities. 35 00:01:30,400 --> 00:01:32,280 AUDIENCE: Mhm. 36 00:01:32,280 --> 00:01:34,928 Understanding, like, pH effects, which 37 00:01:34,928 --> 00:01:37,344 wasn't something I'd thought about it in a while, I guess. 38 00:01:37,344 --> 00:01:38,300 ELIZABETH NOLAN: Mhm. 39 00:01:38,300 --> 00:01:39,256 Mhm. 40 00:01:39,256 --> 00:01:42,740 AUDIENCE: I really enjoyed the manganese review 41 00:01:42,740 --> 00:01:46,930 because I haven't been introduced much to metals 42 00:01:46,930 --> 00:01:47,550 in biology. 43 00:01:47,550 --> 00:01:49,260 That's kind of like where I want to go, 44 00:01:49,260 --> 00:01:50,760 so I really liked this review. 45 00:01:50,760 --> 00:01:52,260 ELIZABETH NOLAN: OK, so that's good, 46 00:01:52,260 --> 00:01:55,690 a good introduction to one aspect of the field of metal 47 00:01:55,690 --> 00:01:57,760 homeostasis. 48 00:01:57,760 --> 00:01:58,859 Any other thoughts? 49 00:02:02,142 --> 00:02:07,370 OK, so what I would say in terms of why we selected excerpts 50 00:02:07,370 --> 00:02:13,760 from these two papers, one, as Alex mentioned, this review 51 00:02:13,760 --> 00:02:16,490 by Wedd, it's extremely comprehensive. 52 00:02:16,490 --> 00:02:19,190 And the introductory parts give some really good, 53 00:02:19,190 --> 00:02:24,080 just brief and clear summary about considerations 54 00:02:24,080 --> 00:02:27,770 and pitfalls that happen when people are studying 55 00:02:27,770 --> 00:02:30,230 metal-protein interactions. 56 00:02:30,230 --> 00:02:32,780 And so right off the bat, there's 57 00:02:32,780 --> 00:02:35,270 an emphasis on some important things 58 00:02:35,270 --> 00:02:37,640 to think about when either designing your own experiment 59 00:02:37,640 --> 00:02:41,190 or reading about experiments done by others. 60 00:02:41,190 --> 00:02:44,130 And then we didn't assign this whole paper, 61 00:02:44,130 --> 00:02:47,240 but one of the great things about this review article 62 00:02:47,240 --> 00:02:49,400 is that there's this systematic consideration 63 00:02:49,400 --> 00:02:51,620 of many different types of binding problems. 64 00:02:51,620 --> 00:02:55,490 And the considerations are applicable to more than just 65 00:02:55,490 --> 00:02:57,000 a metal-protein interaction. 66 00:02:57,000 --> 00:02:59,360 But if you think about biochemistry in broad terms, 67 00:02:59,360 --> 00:03:01,550 there's many different types of binding problems. 68 00:03:01,550 --> 00:03:05,330 So it's just something to keep in mind for a resource 69 00:03:05,330 --> 00:03:09,000 if you ever need that down the road there. 70 00:03:09,000 --> 00:03:10,640 And then the other one is very much 71 00:03:10,640 --> 00:03:14,480 looking at the biological system and competition 72 00:03:14,480 --> 00:03:17,660 between host and microbe for metal nutrients. 73 00:03:17,660 --> 00:03:20,630 And so there's a lot of questions involving 74 00:03:20,630 --> 00:03:25,580 metal-protein thermodynamics, so what are relative affinities? 75 00:03:25,580 --> 00:03:27,740 There's also questions about kinetics there that 76 00:03:27,740 --> 00:03:30,560 aren't-- they're not really addressed in this. 77 00:03:30,560 --> 00:03:32,750 But a lot of effort these days is 78 00:03:32,750 --> 00:03:34,880 going towards trying to understand 79 00:03:34,880 --> 00:03:38,060 these metal transport systems and also host 80 00:03:38,060 --> 00:03:42,200 defense factors that are involved in this tug-of-war. 81 00:03:42,200 --> 00:03:50,030 And also it relates to topics that will come up in lecture. 82 00:03:50,030 --> 00:03:53,300 Joanne will be focusing on iron homeostasis and heme, 83 00:03:53,300 --> 00:03:57,080 but many of the concepts are similar. 84 00:03:57,080 --> 00:03:59,480 And another nice thing about this Giedroc review, 85 00:03:59,480 --> 00:04:00,980 and it's something that will come up 86 00:04:00,980 --> 00:04:04,100 as we talk about binding experiments more, 87 00:04:04,100 --> 00:04:07,100 is this figure 5. 88 00:04:07,100 --> 00:04:10,010 So they're talking about a technique called 89 00:04:10,010 --> 00:04:14,390 isothermal titration calorimetry and using this method 90 00:04:14,390 --> 00:04:16,279 to determine binding affinities. 91 00:04:16,279 --> 00:04:19,370 And they've done a lot of simulations. 92 00:04:19,370 --> 00:04:23,540 And so if you ever end up thinking about binding problems 93 00:04:23,540 --> 00:04:26,180 or doing experiments to look at binding, 94 00:04:26,180 --> 00:04:29,150 you can begin to have a qualitative appreciation 95 00:04:29,150 --> 00:04:33,740 for what data mean by studying simulations like this one. 96 00:04:33,740 --> 00:04:35,420 Or in the packet I've made, there's 97 00:04:35,420 --> 00:04:38,810 one looking at, say, optical absorption spectroscopy 98 00:04:38,810 --> 00:04:40,580 and what a titration curve will look 99 00:04:40,580 --> 00:04:42,800 at for different systems that have 100 00:04:42,800 --> 00:04:47,330 different affinities between a metal and a ligand there. 101 00:04:47,330 --> 00:04:51,810 So basically, what we'll do is consider 102 00:04:51,810 --> 00:04:55,690 just a simple one-to-one bimolecular complex 103 00:04:55,690 --> 00:04:58,600 in recitation today and talk about, 104 00:04:58,600 --> 00:05:02,660 thinking about, determining a dissociation constant value, 105 00:05:02,660 --> 00:05:07,670 which is often how biochemists measure Kd, different methods, 106 00:05:07,670 --> 00:05:09,680 and a lot of the things one needs 107 00:05:09,680 --> 00:05:14,150 to consider experimentally when studying 108 00:05:14,150 --> 00:05:15,650 metal-protein equilibria. 109 00:05:15,650 --> 00:05:18,710 And again, many of these aspects apply to other types 110 00:05:18,710 --> 00:05:19,730 of binding problems. 111 00:05:19,730 --> 00:05:21,680 So it could be protein-small molecule, 112 00:05:21,680 --> 00:05:24,670 protein-protein interaction, protein-DNA. 113 00:05:24,670 --> 00:05:26,810 Some are specific to metals because they 114 00:05:26,810 --> 00:05:31,230 have their unique characteristics and behavior. 115 00:05:31,230 --> 00:05:45,060 OK, so if we think about a simple, bimolecular, one-to-one 116 00:05:45,060 --> 00:05:50,370 complex, all right, and have a metal, 117 00:05:50,370 --> 00:05:54,450 M. And often we think about a metal as being a Lewis acid. 118 00:05:54,450 --> 00:05:55,950 And then we have some ligand, and we 119 00:05:55,950 --> 00:06:01,500 can think of that as a Lewis base forming a complex, so ML. 120 00:06:04,170 --> 00:06:08,790 Often we talk about free versus bound, so free metal or free 121 00:06:08,790 --> 00:06:18,410 ligand as metal or ligand that's not complexed versus bound 122 00:06:18,410 --> 00:06:19,610 because it's in a complex. 123 00:06:28,280 --> 00:06:31,550 And today we'll think about the ligand 124 00:06:31,550 --> 00:06:34,310 as being some protein that has a site for a metal, 125 00:06:34,310 --> 00:06:39,360 but it could also be a small molecule, for instance. 126 00:06:39,360 --> 00:06:43,250 So in introductory chemistry, typically 127 00:06:43,250 --> 00:06:46,930 talk about affinity constants for equilibria. 128 00:06:46,930 --> 00:06:49,760 In biochemical experiments, people often 129 00:06:49,760 --> 00:06:54,560 report affinity as a Kd, so dissociation constant. 130 00:06:54,560 --> 00:06:58,790 So if we think about the equation for Kd, 131 00:06:58,790 --> 00:07:02,560 we have the concentration of the complex. 132 00:07:02,560 --> 00:07:03,140 I'm sorry. 133 00:07:03,140 --> 00:07:06,080 That's the Ka. 134 00:07:06,080 --> 00:07:11,680 Concentration of the metal ligand 135 00:07:11,680 --> 00:07:15,580 over the concentration of the complex, 136 00:07:15,580 --> 00:07:22,210 so the Kd also equals 1 over the Ka. 137 00:07:22,210 --> 00:07:25,070 And you can also think about the Kd 138 00:07:25,070 --> 00:07:30,740 in terms of a ratio of the rate constants for dissociation 139 00:07:30,740 --> 00:07:32,950 and association here. 140 00:07:32,950 --> 00:07:41,010 K off over K on, here, just as different ways to show this. 141 00:07:41,010 --> 00:07:44,870 So if we just look at this equation here, the units 142 00:07:44,870 --> 00:07:51,670 for a dissociation constant, our concentration, so units, 143 00:07:51,670 --> 00:07:55,840 it could be anything from millimolar, micromolar, 144 00:07:55,840 --> 00:08:01,040 nanomolar, et cetera, here. 145 00:08:01,040 --> 00:08:04,940 And if we think about a system having increased affinity, 146 00:08:04,940 --> 00:08:14,140 so let's say the protein affinity is high. 147 00:08:14,140 --> 00:08:16,780 That is a lower Kd. 148 00:08:16,780 --> 00:08:20,410 So a protein with a nanomolar Kd value 149 00:08:20,410 --> 00:08:23,650 for a metal, that's higher affinity than a protein, 150 00:08:23,650 --> 00:08:25,570 say, with a micromolar or millimolar 151 00:08:25,570 --> 00:08:28,090 affinity for that metal here-- 152 00:08:28,090 --> 00:08:30,070 so lower Kd, higher affinity. 153 00:08:32,630 --> 00:08:37,780 So what is the common data fitting 154 00:08:37,780 --> 00:08:42,059 that we might see in a textbook or in some experiments? 155 00:08:42,059 --> 00:08:43,900 We think about it as very similar 156 00:08:43,900 --> 00:08:46,300 to thinking about steady-state kinetics in terms 157 00:08:46,300 --> 00:08:58,140 of the plot and the equations. 158 00:08:58,140 --> 00:09:04,590 So imagine we have some protein, so we have our ligand. 159 00:09:04,590 --> 00:09:10,530 And we titrate in some metal, let's say plus 2. 160 00:09:10,530 --> 00:09:15,960 And we have some measure of response 161 00:09:15,960 --> 00:09:17,760 to see formation of that complex. 162 00:09:17,760 --> 00:09:20,420 So maybe it has a color, like it's 163 00:09:20,420 --> 00:09:24,210 a protein that binds cobalt. And cobalt gives some 164 00:09:24,210 --> 00:09:27,070 new d-to-d transitions that we can monitor, 165 00:09:27,070 --> 00:09:30,610 or maybe it's some other method here. 166 00:09:30,610 --> 00:09:35,340 OK, so we can have a response that tells us 167 00:09:35,340 --> 00:09:41,810 about formation of the complex versus the concentration 168 00:09:41,810 --> 00:09:45,810 of free metal, here. 169 00:09:45,810 --> 00:09:53,260 And say we get something that looks like this. 170 00:09:53,260 --> 00:09:58,630 What we can say is that the response 171 00:09:58,630 --> 00:10:04,060 equals a constant times the concentration 172 00:10:04,060 --> 00:10:14,710 of free metal over the Kd plus the concentration of free metal 173 00:10:14,710 --> 00:10:16,540 here. 174 00:10:16,540 --> 00:10:23,380 So effectively, we get the Kd. 175 00:10:23,380 --> 00:10:26,620 So similar to thinking about KM in steady-state 176 00:10:26,620 --> 00:10:29,050 kinetics, but keep in mind the Kd and KM are two 177 00:10:29,050 --> 00:10:32,260 different things here for that. 178 00:10:35,529 --> 00:10:42,110 So if we think about this type of plot 179 00:10:42,110 --> 00:10:44,990 and we think about setting up an experiment, 180 00:10:44,990 --> 00:10:47,710 so say we have a protein and we want to determine 181 00:10:47,710 --> 00:10:50,200 its affinity for some metal. 182 00:10:50,200 --> 00:10:51,630 What do we need to know? 183 00:10:59,470 --> 00:11:01,430 AUDIENCE: A different concentration 184 00:11:01,430 --> 00:11:02,614 for what you're putting in. 185 00:11:02,614 --> 00:11:04,780 ELIZABETH NOLAN: Yeah, well, that's for sure, right? 186 00:11:04,780 --> 00:11:08,710 So you need to know the concentration, one, 187 00:11:08,710 --> 00:11:12,250 of the protein in your cuvette, or in whatever sample 188 00:11:12,250 --> 00:11:16,340 hold you're using, and then the concentration of the metal 189 00:11:16,340 --> 00:11:18,460 you're titrating in. 190 00:11:18,460 --> 00:11:21,610 But beyond that, based on this equation, what 191 00:11:21,610 --> 00:11:22,660 do we need to know? 192 00:11:30,628 --> 00:11:32,620 AUDIENCE: You're trying to determine Kd. 193 00:11:32,620 --> 00:11:33,536 ELIZABETH NOLAN: Yeah. 194 00:11:33,536 --> 00:11:35,495 AUDIENCE: Then we need to know M-free. 195 00:11:35,495 --> 00:11:36,904 I'm not sure what B is. 196 00:11:36,904 --> 00:11:38,570 ELIZABETH NOLAN: Yeah, so this is just-- 197 00:11:38,570 --> 00:11:41,760 I mean, think back to steady-state kinetics, 198 00:11:41,760 --> 00:11:44,920 right there. 199 00:11:44,920 --> 00:11:50,690 So I'm just putting it in as that because we don't 200 00:11:50,690 --> 00:11:52,670 know what this response is. 201 00:11:52,670 --> 00:11:55,430 But imagine you normalize the data to 1 202 00:11:55,430 --> 00:11:57,700 such that your maximum response is 1. 203 00:11:57,700 --> 00:12:00,250 B would be 1. 204 00:12:00,250 --> 00:12:06,080 Yeah, so metal-free, so you just mentioned the concentration 205 00:12:06,080 --> 00:12:08,450 of metal you're adding in. 206 00:12:08,450 --> 00:12:16,070 So let's say you add in 1 micromolar of a metal, 207 00:12:16,070 --> 00:12:19,070 and you have 10 micromolar of protein. 208 00:12:19,070 --> 00:12:21,260 What is your free metal concentration? 209 00:12:26,996 --> 00:12:30,392 AUDIENCE: But you subtract the balance from the total metal. 210 00:12:30,392 --> 00:12:31,600 ELIZABETH NOLAN: Yeah, right. 211 00:12:31,600 --> 00:12:33,530 So the total is the metal going in, 212 00:12:33,530 --> 00:12:36,660 and then you have free and bound. 213 00:12:36,660 --> 00:12:41,925 Is it easy, always, to know what this is? 214 00:12:41,925 --> 00:12:43,310 AUDIENCE: Probably not. 215 00:12:43,310 --> 00:12:45,710 ELIZABETH NOLAN: Yeah, not always, right? 216 00:12:45,710 --> 00:12:49,804 And is there always free metal available, right? 217 00:12:49,804 --> 00:12:51,470 So this is something we're going to talk 218 00:12:51,470 --> 00:12:55,460 about a little bit moving forward. 219 00:12:55,460 --> 00:13:01,850 And so what we'll see is that this equation's great. 220 00:13:01,850 --> 00:13:05,630 In many instances, it can't be used 221 00:13:05,630 --> 00:13:10,460 because we don't know what the free metal concentration is, 222 00:13:10,460 --> 00:13:14,330 or we're in a regime where we don't have any free metal 223 00:13:14,330 --> 00:13:16,140 concentration. 224 00:13:16,140 --> 00:13:16,925 OK? 225 00:13:16,925 --> 00:13:19,300 AUDIENCE: Is this response [INAUDIBLE]?? 226 00:13:19,300 --> 00:13:20,340 ELIZABETH NOLAN: No. 227 00:13:20,340 --> 00:13:23,300 No, these are thermodynamic measurements, all right? 228 00:13:23,300 --> 00:13:34,670 So this-- let's say, for instance, 229 00:13:34,670 --> 00:13:39,170 that there is a system where, in the absence of metal, 230 00:13:39,170 --> 00:13:40,980 it's colorless. 231 00:13:40,980 --> 00:13:43,790 And one of the wonderful things about many transition metal 232 00:13:43,790 --> 00:13:46,100 ions is that they give us color. 233 00:13:46,100 --> 00:13:49,160 So imagine you add in a metal and you end up 234 00:13:49,160 --> 00:13:50,570 getting some transition. 235 00:13:54,760 --> 00:14:03,160 So perhaps this response is Amax at each addition of metal. 236 00:14:03,160 --> 00:14:05,770 So some sort of colorimetric titration. 237 00:14:05,770 --> 00:14:07,940 That's one example. 238 00:14:07,940 --> 00:14:11,380 You could also imagine using some sort of spectroscopy. 239 00:14:11,380 --> 00:14:14,200 And say there's some specific signal 240 00:14:14,200 --> 00:14:17,170 for your metal-bound protein that 241 00:14:17,170 --> 00:14:19,717 differs from the free metal there, 242 00:14:19,717 --> 00:14:21,550 and then you could use that and quantify it. 243 00:14:21,550 --> 00:14:28,000 So for instance, EPR NMR, any method like that, MCD, here. 244 00:14:28,000 --> 00:14:32,410 So no, this is not a rate here. 245 00:14:32,410 --> 00:14:35,650 This a response versus the concentration of free metal. 246 00:14:35,650 --> 00:14:39,250 And you see as the concentration of free metal increases, 247 00:14:39,250 --> 00:14:42,400 we're seeing an increase in whatever this observable is 248 00:14:42,400 --> 00:14:43,750 about the system here. 249 00:14:48,970 --> 00:14:51,160 So let's just consider a case. 250 00:14:51,160 --> 00:14:53,220 Let's just say we have something like this-- 251 00:14:53,220 --> 00:14:54,600 some UV this titration. 252 00:15:02,360 --> 00:15:03,540 And we have our ligand. 253 00:15:06,370 --> 00:15:09,180 And we titrate in some metal. 254 00:15:09,180 --> 00:15:12,190 And how do we often plot this? 255 00:15:12,190 --> 00:15:19,040 Let's say we have the ratio of metal over the ligand. 256 00:15:19,040 --> 00:15:22,620 And here, let's say we have some change 257 00:15:22,620 --> 00:15:25,800 in absorbance at some wavelength, 258 00:15:25,800 --> 00:15:28,360 like what we have here. 259 00:15:28,360 --> 00:15:31,170 And we'll take one extreme case. 260 00:15:31,170 --> 00:15:38,245 So here, let's say you get data that looks like this. 261 00:16:01,570 --> 00:16:02,070 OK. 262 00:16:02,070 --> 00:16:04,200 So you've done some titration. 263 00:16:04,200 --> 00:16:07,350 You've added some aliquot of metal. 264 00:16:07,350 --> 00:16:09,800 You let the solution equilibrate. 265 00:16:09,800 --> 00:16:13,940 And then you read the optical absorption spectrum. 266 00:16:13,940 --> 00:16:21,375 And so what do we learn from something like this? 267 00:16:29,240 --> 00:16:31,460 So what do we see in these data? 268 00:16:35,364 --> 00:16:37,384 AUDIENCE: There's a point of saturation. 269 00:16:37,384 --> 00:16:38,300 ELIZABETH NOLAN: Yeah. 270 00:16:38,300 --> 00:16:40,670 So something's happening here, right? 271 00:16:40,670 --> 00:16:44,210 So what we see is that over this regime-- 272 00:16:44,210 --> 00:16:49,190 which as I've drawn this, we have a ratio of metal 273 00:16:49,190 --> 00:16:51,110 to ligand of one. 274 00:16:51,110 --> 00:16:58,280 We see that this change in absorption occurs. 275 00:16:58,280 --> 00:16:59,750 It's quite linear. 276 00:16:59,750 --> 00:17:03,620 And then once we had a ratio of one to one, 277 00:17:03,620 --> 00:17:09,040 we see that there's no more increase in absorption 278 00:17:09,040 --> 00:17:10,290 observance at that wavelength. 279 00:17:10,290 --> 00:17:11,569 It plateaus. 280 00:17:11,569 --> 00:17:13,940 So what does that tell us about the interaction 281 00:17:13,940 --> 00:17:17,480 between this protein and the metal? 282 00:17:21,747 --> 00:17:23,455 AUDIENCE: Probably that one binds to one. 283 00:17:23,455 --> 00:17:24,663 ELIZABETH NOLAN: Yeah, right? 284 00:17:24,663 --> 00:17:28,099 This tells us something about stoichiometry, first of all-- 285 00:17:28,099 --> 00:17:31,190 that for whatever is causing this particular change 286 00:17:31,190 --> 00:17:33,260 in the spectrum, we see that change 287 00:17:33,260 --> 00:17:35,960 happens to one equivalent of metal and stops, 288 00:17:35,960 --> 00:17:39,565 which gives indication of a one to one stoichiometry here. 289 00:17:42,290 --> 00:17:44,240 What else does this tell us? 290 00:17:44,240 --> 00:17:46,250 So if you see something like this. 291 00:17:49,200 --> 00:17:51,350 What's happening in terms of the free metal 292 00:17:51,350 --> 00:17:54,180 concentration over this regime? 293 00:17:54,180 --> 00:17:59,060 So when there's less than one equivalent of metal added, 294 00:17:59,060 --> 00:18:02,168 where is that metal? 295 00:18:02,168 --> 00:18:04,032 AUDIENCE: It's probably with the protein. 296 00:18:04,032 --> 00:18:05,240 ELIZABETH NOLAN: Yeah, right? 297 00:18:05,240 --> 00:18:07,130 It's with the protein. 298 00:18:07,130 --> 00:18:09,650 So it's bound effectively. 299 00:18:09,650 --> 00:18:13,400 This is evidence for some sort of high-affinity complex, 300 00:18:13,400 --> 00:18:17,540 because what you see is that the absorbent change occurs up 301 00:18:17,540 --> 00:18:20,190 to one equivalent, and then it stops. 302 00:18:20,190 --> 00:18:20,810 Right? 303 00:18:20,810 --> 00:18:22,940 So we can contrast that to something 304 00:18:22,940 --> 00:18:30,440 like a case where it's more of a curve, 305 00:18:30,440 --> 00:18:32,000 like what we see up here-- 306 00:18:32,000 --> 00:18:34,640 where it takes more than one equivalent of metal 307 00:18:34,640 --> 00:18:38,720 to saturate that site. 308 00:18:38,720 --> 00:18:42,260 In this case, maybe it's one to one stoichiometry. 309 00:18:42,260 --> 00:18:43,730 Maybe it's something else. 310 00:18:43,730 --> 00:18:45,575 You need to do some more experiments to see. 311 00:18:52,970 --> 00:18:55,340 I say this is some high-affinity complex. 312 00:19:01,080 --> 00:19:10,045 So we have no or negligible concentration of free metal. 313 00:19:13,840 --> 00:19:15,970 Question one is, what does high affinity 314 00:19:15,970 --> 00:19:19,750 mean in terms of a range of Kd? 315 00:19:19,750 --> 00:19:24,640 And secondly, if there's no free metal, 316 00:19:24,640 --> 00:19:29,575 what are we going to do in terms of determining a Kd value? 317 00:19:33,140 --> 00:19:37,762 So what do we think of as high-affinity binding? 318 00:19:37,762 --> 00:19:38,596 AUDIENCE: Nanomolar? 319 00:19:38,596 --> 00:19:39,511 ELIZABETH NOLAN: Yeah. 320 00:19:39,511 --> 00:19:40,780 So that's pretty good, right? 321 00:19:44,260 --> 00:19:46,230 Nanomolar or lower Kd. 322 00:19:46,230 --> 00:19:51,120 So something like this, what happens if you see data 323 00:19:51,120 --> 00:19:57,450 like this is that typically, you'll say, OK, this 324 00:19:57,450 --> 00:20:01,370 indicates we have a one to one complex. 325 00:20:01,370 --> 00:20:03,720 And the dissociation constant has 326 00:20:03,720 --> 00:20:08,580 an upper limit that's typically in the regime of 10 nanomolars. 327 00:20:08,580 --> 00:20:10,130 So that sets the upper limit, right? 328 00:20:10,130 --> 00:20:12,900 It could be orders of magnitude lower, 329 00:20:12,900 --> 00:20:17,870 but we can't see that in these data here. 330 00:20:17,870 --> 00:20:19,560 And so that's something to watch out 331 00:20:19,560 --> 00:20:23,550 for when looking at how people analyze binding 332 00:20:23,550 --> 00:20:29,370 data, because sometimes, a Kd is reported as an absolute value 333 00:20:29,370 --> 00:20:31,180 from a direct titration. 334 00:20:31,180 --> 00:20:39,980 So this is what I would call a direct titration, meaning 335 00:20:39,980 --> 00:20:42,110 that we only have the ligand here, 336 00:20:42,110 --> 00:20:44,390 and the metal is titrated in, or whatever 337 00:20:44,390 --> 00:20:46,550 the binding partner is. 338 00:20:46,550 --> 00:20:49,670 OK, but if you're in a regime where you're just 339 00:20:49,670 --> 00:20:53,450 getting an upper limit, that value is just an upper limit. 340 00:20:53,450 --> 00:20:56,750 And it could be one nanomolar. 341 00:20:56,750 --> 00:20:58,130 It could be 10 picomolar. 342 00:20:58,130 --> 00:20:59,280 It could be femtomolar. 343 00:20:59,280 --> 00:21:00,710 There's some more experiments that 344 00:21:00,710 --> 00:21:05,370 need to be done to sort that out here. 345 00:21:05,370 --> 00:21:20,120 So let's just say we have a case where this Kd is one nanomolar. 346 00:21:20,120 --> 00:21:26,360 Thinking about this and what we know from steady state 347 00:21:26,360 --> 00:21:28,800 discussions earlier in this course-- and again, 348 00:21:28,800 --> 00:21:32,770 this isn't the same thing, but some of the same ideas apply. 349 00:21:32,770 --> 00:21:35,840 What concentration regime would you 350 00:21:35,840 --> 00:21:37,910 want to set up the experiment? 351 00:21:37,910 --> 00:21:41,380 So say you think your protein has a Kd up 352 00:21:41,380 --> 00:21:44,030 for a metal of one nanomolar. 353 00:21:44,030 --> 00:21:46,280 What concentration of protein do you 354 00:21:46,280 --> 00:21:49,884 want to use in the titration? 355 00:21:49,884 --> 00:21:51,750 AUDIENCE: Maybe high picomolar? 356 00:21:51,750 --> 00:21:53,099 ELIZABETH NOLAN: High picomolar. 357 00:21:53,099 --> 00:21:54,640 So why would you want high picomolar, 358 00:21:54,640 --> 00:21:56,140 and what does high picomolar mean? 359 00:21:59,190 --> 00:22:00,690 AUDIENCE: Because I think otherwise, 360 00:22:00,690 --> 00:22:07,730 you wouldn't be able to resolve the dissociation? 361 00:22:07,730 --> 00:22:14,460 Like, it'll basically-- if you're above 362 00:22:14,460 --> 00:22:17,768 that, it's just going to continue to look linear. 363 00:22:17,768 --> 00:22:20,744 There's going to be no curvature for you to observe 364 00:22:20,744 --> 00:22:24,230 what the dissociation would be. 365 00:22:24,230 --> 00:22:28,700 ELIZABETH NOLAN: So typically, you want to be around your Kd. 366 00:22:28,700 --> 00:22:31,070 So if the Kd is one nanomolar, you 367 00:22:31,070 --> 00:22:33,290 want to be a bit below or a bit above. 368 00:22:33,290 --> 00:22:35,480 And if you're really being rigorous, 369 00:22:35,480 --> 00:22:37,577 try a few different concentrations. 370 00:22:37,577 --> 00:22:39,410 Because at the end of the day, this response 371 00:22:39,410 --> 00:22:43,070 should be independent of that within a range of error. 372 00:22:43,070 --> 00:22:44,750 So what's the issue? 373 00:22:44,750 --> 00:22:48,110 Let's say your Kd is one nanomolar, 374 00:22:48,110 --> 00:22:50,330 or for that matter, one picomolar. 375 00:22:50,330 --> 00:22:52,400 And you'd like to set up an experiment. 376 00:22:52,400 --> 00:22:55,350 And you need an observable for this response. 377 00:22:55,350 --> 00:22:58,040 So this gets back to some of what JoAnne talked about 378 00:22:58,040 --> 00:23:02,330 in recitations two and three, and needing a detectable signal 379 00:23:02,330 --> 00:23:05,210 in the pre-steady state kinetic experiments, 380 00:23:05,210 --> 00:23:08,030 that you have to work with a high concentration of protein 381 00:23:08,030 --> 00:23:09,410 to see something. 382 00:23:09,410 --> 00:23:12,590 And so that becomes the same issue here. 383 00:23:12,590 --> 00:23:16,460 If your system would allow you to work at one nanomolar or one 384 00:23:16,460 --> 00:23:21,950 picomolar to have an observable, you would be in a range 385 00:23:21,950 --> 00:23:25,340 where you can see something other than this. 386 00:23:25,340 --> 00:23:27,930 But often, whatever we're observing, 387 00:23:27,930 --> 00:23:30,830 we need to work at a high-protein concentration, 388 00:23:30,830 --> 00:23:33,300 because the extinction coefficient is weak, 389 00:23:33,300 --> 00:23:36,260 or we just need a high concentration for whatever 390 00:23:36,260 --> 00:23:40,205 that type of signal is, which is what can put us in this regime 391 00:23:40,205 --> 00:23:41,100 here. 392 00:23:41,100 --> 00:23:44,470 So that's something to think about. 393 00:23:44,470 --> 00:23:54,500 So what can be done in order to get more information 394 00:23:54,500 --> 00:23:59,940 than what's shown here for a high-affinity site? 395 00:23:59,940 --> 00:24:04,250 So let's say you're not able to work at a concentration that's 396 00:24:04,250 --> 00:24:08,930 appropriate, based on the Kd of this high-affinity site, 397 00:24:08,930 --> 00:24:12,950 that you need to work at a higher concentration. 398 00:24:12,950 --> 00:24:14,640 What can be done? 399 00:24:14,640 --> 00:24:22,010 So effectively, what is often done 400 00:24:22,010 --> 00:24:23,900 is what I'll call an indirect approach. 401 00:24:33,000 --> 00:24:37,130 Another way this is described is to set up a competition 402 00:24:37,130 --> 00:24:54,300 titration, where you take your ligand or protein of interest, 403 00:24:54,300 --> 00:25:05,960 you take a competitor, and you titrate in the metal. 404 00:25:05,960 --> 00:25:10,570 OK, so what is this competitor? 405 00:25:10,570 --> 00:25:25,210 Typically, it's a small molecule with a known affinity, 406 00:25:25,210 --> 00:25:37,440 so a known Kd, for the metal of interest 407 00:25:37,440 --> 00:25:39,750 under the experimental conditions you're using. 408 00:25:53,120 --> 00:25:57,565 And so there's different flavors of using a competitor. 409 00:25:57,565 --> 00:26:00,970 And I'll just highlight a few in passing. 410 00:26:00,970 --> 00:26:06,370 So one way to use the competitor is 411 00:26:06,370 --> 00:26:09,820 to use some small molecule ligand that 412 00:26:09,820 --> 00:26:14,230 allows you to buffer the free metal concentration. 413 00:26:14,230 --> 00:26:20,230 So in these cases, it's some sort of system 414 00:26:20,230 --> 00:26:24,830 that will not affect the readout of, say, 415 00:26:24,830 --> 00:26:27,130 metal binding to your protein. 416 00:26:27,130 --> 00:26:29,110 So you can imagine, for instance, 417 00:26:29,110 --> 00:26:34,480 using EDTA, EGTA, NTA, like what's 418 00:26:34,480 --> 00:26:40,390 on the nickel NTA columns for affinity chromatography. 419 00:26:40,390 --> 00:26:44,350 And there are published affinity constants 420 00:26:44,350 --> 00:26:47,050 for these small molecules for different metals. 421 00:26:47,050 --> 00:26:50,350 And so you can set up a metal ion buffering system. 422 00:26:50,350 --> 00:26:51,850 And so the idea is that in addition 423 00:26:51,850 --> 00:26:53,050 to your normal buffer-- 424 00:26:53,050 --> 00:26:55,390 and we'll talk more about buffers in a minute-- 425 00:26:55,390 --> 00:26:59,110 you have a very high total concentration of metal 426 00:26:59,110 --> 00:27:03,010 and a high total concentration of a chelator. 427 00:27:03,010 --> 00:27:06,130 And you can make these buffers such 428 00:27:06,130 --> 00:27:10,040 that the buffer will buffer the free metal concentration. 429 00:27:10,040 --> 00:27:13,490 So you can buffer free metal, say, 430 00:27:13,490 --> 00:27:17,240 in the nanomolar or subnanomolar regime. 431 00:27:17,240 --> 00:27:18,760 So what does this mean? 432 00:27:18,760 --> 00:27:22,300 Your total metal concentration and total concentration 433 00:27:22,300 --> 00:27:25,690 of this competitor is much higher than the concentration 434 00:27:25,690 --> 00:27:27,520 of your protein. 435 00:27:27,520 --> 00:27:31,720 And so when you introduce-- 436 00:27:31,720 --> 00:27:33,700 you set up your titration, you have the protein 437 00:27:33,700 --> 00:27:35,560 in this buffer system, the protein 438 00:27:35,560 --> 00:27:37,190 will bind some of the metal. 439 00:27:37,190 --> 00:27:38,830 And then the buffer will adjust such 440 00:27:38,830 --> 00:27:41,680 that the free metal ion concentration you've set it at 441 00:27:41,680 --> 00:27:43,750 remains the same. 442 00:27:43,750 --> 00:27:49,190 So that gives you a way to get free metal concentrations. 443 00:27:49,190 --> 00:27:52,850 Another approach that's often used-- 444 00:27:52,850 --> 00:27:55,450 it's also controlling your overall metal concentration, 445 00:27:55,450 --> 00:27:57,640 but in a bit of a different way-- 446 00:27:57,640 --> 00:28:01,360 is to take a competitor that is also 447 00:28:01,360 --> 00:28:06,100 some sort of colorimetric or fluorescent indicator 448 00:28:06,100 --> 00:28:07,740 of the metal. 449 00:28:07,740 --> 00:28:12,310 And so in effect, what you do is you use the competitor 450 00:28:12,310 --> 00:28:18,350 as a readout for competition in the assay. 451 00:28:18,350 --> 00:28:21,310 And so what you can do is ask, OK, 452 00:28:21,310 --> 00:28:26,920 under these conditions, when the metal bind to the protein, 453 00:28:26,920 --> 00:28:29,420 there is no change in absorbance or fluorescence 454 00:28:29,420 --> 00:28:31,030 at some wavelengths. 455 00:28:31,030 --> 00:28:34,930 But there will be a change from the competitor 456 00:28:34,930 --> 00:28:36,740 at that wavelength. 457 00:28:36,740 --> 00:28:40,900 So if you put these together, you 458 00:28:40,900 --> 00:28:44,140 can ask, OK, as the metal is titrated in, 459 00:28:44,140 --> 00:28:45,640 where does the metal go? 460 00:28:45,640 --> 00:28:49,130 Do we see a response from the competitor or not? 461 00:28:49,130 --> 00:28:52,736 If not, it tells you that the protein won. 462 00:28:52,736 --> 00:28:55,450 If yes, and it's the same as the competitor 463 00:28:55,450 --> 00:28:58,510 in the absence of the protein, the competitor won. 464 00:28:58,510 --> 00:28:59,020 Right? 465 00:28:59,020 --> 00:29:01,360 So those are two cases of out competition 466 00:29:01,360 --> 00:29:03,940 where either the protein out-competes 467 00:29:03,940 --> 00:29:06,100 this competitor or the competitor 468 00:29:06,100 --> 00:29:07,750 out-competes the protein. 469 00:29:07,750 --> 00:29:09,370 That's not very helpful for actually 470 00:29:09,370 --> 00:29:13,060 determining an apparent dissociation constant value. 471 00:29:13,060 --> 00:29:16,280 It will give you information about limits here. 472 00:29:16,280 --> 00:29:18,200 But what you really want to have happen, 473 00:29:18,200 --> 00:29:20,380 and as this name suggests, is that you 474 00:29:20,380 --> 00:29:24,460 want the protein and this competitor to compete. 475 00:29:24,460 --> 00:29:27,310 So effectively, you see the response of the competitor 476 00:29:27,310 --> 00:29:30,550 attenuated, compared to the response 477 00:29:30,550 --> 00:29:31,960 in the absence of protein. 478 00:29:31,960 --> 00:29:34,570 So some metals here, some metals there. 479 00:29:34,570 --> 00:29:38,230 And then what you can do is a mathematical analysis 480 00:29:38,230 --> 00:29:41,080 to fit that data, based on knowing 481 00:29:41,080 --> 00:29:43,780 the affinity of the competitor for the metal, 482 00:29:43,780 --> 00:29:46,780 and knowing the concentrations of the competitor in the ligand 483 00:29:46,780 --> 00:29:48,340 here. 484 00:29:48,340 --> 00:29:51,160 So this is something that Wedd talks about quite a bit 485 00:29:51,160 --> 00:29:54,070 in the review that was assigned, in terms 486 00:29:54,070 --> 00:29:58,370 of setting up these competition titrations here. 487 00:29:58,370 --> 00:30:05,040 And so when done well, that can really be quite powerful here 488 00:30:05,040 --> 00:30:05,610 for that. 489 00:30:09,310 --> 00:30:12,760 And there's many other themes and variations 490 00:30:12,760 --> 00:30:14,390 about how to do that. 491 00:30:14,390 --> 00:30:19,390 But just to keep in mind, if your binding event is too tight 492 00:30:19,390 --> 00:30:21,520 to measure by a direct titration, 493 00:30:21,520 --> 00:30:25,180 you want to think about a way to do a competition 494 00:30:25,180 --> 00:30:28,050 titration here. 495 00:30:28,050 --> 00:30:33,130 So in the packet, I put in an excerpt 496 00:30:33,130 --> 00:30:38,470 from a paper that was published in 2003 showing some titration 497 00:30:38,470 --> 00:30:42,550 curves like what I sketched here, where there's 498 00:30:42,550 --> 00:30:46,750 some response to indicate how much is bound versus 499 00:30:46,750 --> 00:30:48,910 some concentration of metal. 500 00:30:48,910 --> 00:30:51,640 And one of the reasons I really like this plot 501 00:30:51,640 --> 00:30:58,030 is that it gives a qualitative sense for Kd values 502 00:30:58,030 --> 00:31:01,240 over a range of magnitudes and what that curve 503 00:31:01,240 --> 00:31:03,310 would look like here. 504 00:31:03,310 --> 00:31:07,540 And just having a sense of this qualitatively 505 00:31:07,540 --> 00:31:11,110 gives you a lot of leverage in terms of just looking at data 506 00:31:11,110 --> 00:31:13,870 and analyzing it, whether it's your own or someone else's 507 00:31:13,870 --> 00:31:15,760 in terms of, is this a high-affinity site? 508 00:31:15,760 --> 00:31:18,790 Is this a low-affinity site? 509 00:31:18,790 --> 00:31:22,390 Likewise in the Giedroc review with a different type of method 510 00:31:22,390 --> 00:31:24,295 called EITC here. 511 00:31:27,280 --> 00:31:30,580 So what we're going to do is talk a little 512 00:31:30,580 --> 00:31:36,220 bit about some general concepts and then 513 00:31:36,220 --> 00:31:40,960 some general considerations for, say, setting up these types 514 00:31:40,960 --> 00:31:43,720 of experiments. 515 00:31:43,720 --> 00:31:48,310 And so some of this relates to concepts in class. 516 00:31:48,310 --> 00:31:53,470 So JoAnne talked about the Irving Williams series. 517 00:31:53,470 --> 00:31:56,680 So based on that series, if you're, say, 518 00:31:56,680 --> 00:32:04,630 looking at some protein, and you're interested, 519 00:32:04,630 --> 00:32:13,210 say, in the Kd for binding of manganese versus zinc, 520 00:32:13,210 --> 00:32:16,200 what would you expect qualitatively? 521 00:32:16,200 --> 00:32:22,180 So imagine each of these metals is bound at the same site. 522 00:32:22,180 --> 00:32:25,420 And today in class, we talked about the different types 523 00:32:25,420 --> 00:32:28,240 of ligands that proteins use. 524 00:32:28,240 --> 00:32:32,310 So histidines or carboxylates, or maybe a cystine. 525 00:32:32,310 --> 00:32:35,450 We'll leave tyrosine out for the moment. 526 00:32:35,450 --> 00:32:37,030 But what would we expect? 527 00:32:37,030 --> 00:32:39,430 Which metal will bind with higher affinity based 528 00:32:39,430 --> 00:32:41,379 on Irving Williams? 529 00:32:41,379 --> 00:32:42,170 AUDIENCE: The zinc. 530 00:32:42,170 --> 00:32:43,545 ELIZABETH NOLAN: The zinc, right? 531 00:32:43,545 --> 00:32:47,080 So as we march along the first row for manganese, 532 00:32:47,080 --> 00:32:52,480 we see that the affinity increases and copper combined 533 00:32:52,480 --> 00:32:53,930 with higher affinity than zinc. 534 00:32:53,930 --> 00:32:56,020 So there's a swap at the end. 535 00:32:56,020 --> 00:32:58,910 So that's what we would expect. 536 00:32:58,910 --> 00:33:02,290 So what does that mean, just in terms of reading something 537 00:33:02,290 --> 00:33:03,770 in the literature? 538 00:33:03,770 --> 00:33:04,270 Right. 539 00:33:04,270 --> 00:33:08,900 If someone's reporting binding affinities for a protein, 540 00:33:08,900 --> 00:33:13,960 and you see that the values are of a similar order of magnitude 541 00:33:13,960 --> 00:33:16,930 for manganese and zinc, you might want to scratch your head 542 00:33:16,930 --> 00:33:19,671 a little bit and ask what's going on. 543 00:33:19,671 --> 00:33:20,170 Right? 544 00:33:20,170 --> 00:33:23,720 So is it a case where both metals are bound tightly 545 00:33:23,720 --> 00:33:27,100 and the titration didn't resolve a difference because you're 546 00:33:27,100 --> 00:33:29,160 just in an upper limit? 547 00:33:29,160 --> 00:33:33,340 Is there something unusual about this site 548 00:33:33,340 --> 00:33:35,440 that is causing the selectivity to be 549 00:33:35,440 --> 00:33:40,150 contrary to what we expect based on the Irving Williams series 550 00:33:40,150 --> 00:33:40,760 there? 551 00:33:40,760 --> 00:33:45,250 So the point is you can use those generalities as a guide. 552 00:33:45,250 --> 00:33:48,310 And there's always exceptions to the rule. 553 00:33:48,310 --> 00:33:50,290 I missed class on Wednesday. 554 00:33:50,290 --> 00:33:53,990 Did you go over hard-soft acid base? 555 00:33:53,990 --> 00:33:57,610 So have any of you heard about this hard-soft acid base 556 00:33:57,610 --> 00:33:59,680 concept. 557 00:33:59,680 --> 00:34:00,830 No. 558 00:34:00,830 --> 00:34:01,640 No. 559 00:34:01,640 --> 00:34:02,140 Yes. 560 00:34:02,140 --> 00:34:07,180 So, like, what's the hard-soft acid base theory? 561 00:34:10,138 --> 00:34:15,561 AUDIENCE: So smaller or electronegative things will 562 00:34:15,561 --> 00:34:19,505 associate those are, like, hard things--things--[INAUDIBLE] larger 563 00:34:19,505 --> 00:34:21,365 and fluffier atoms than-- 564 00:34:21,365 --> 00:34:22,990 ELIZABETH NOLAN: How is an atom fluffy? 565 00:34:26,070 --> 00:34:26,800 No. 566 00:34:26,800 --> 00:34:27,300 Right. 567 00:34:27,300 --> 00:34:30,699 So think about how polarizable it is. 568 00:34:30,699 --> 00:34:32,300 But that's along the right track. 569 00:34:32,300 --> 00:34:36,159 So basically, we can classify different metals 570 00:34:36,159 --> 00:34:39,760 and different ligands as being relatively hard or relatively 571 00:34:39,760 --> 00:34:40,679 soft. 572 00:34:40,679 --> 00:34:43,030 And then there can be the gray area in the middle, which 573 00:34:43,030 --> 00:34:45,270 is called borderline. 574 00:34:45,270 --> 00:34:50,650 So if we think about, say, a metal ion that's a hard Lewis 575 00:34:50,650 --> 00:34:53,800 acid that's something like calcium, for instance-- 576 00:34:53,800 --> 00:34:55,629 iron(III)-- 577 00:34:55,629 --> 00:34:59,150 these types of metals, like oxygen donors, 578 00:34:59,150 --> 00:35:03,460 which are hard bases, for instance-- 579 00:35:03,460 --> 00:35:05,637 often it's metal in a high oxidation state 580 00:35:05,637 --> 00:35:06,470 if that's an option. 581 00:35:06,470 --> 00:35:08,260 So iron(III) versus iron(II). 582 00:35:08,260 --> 00:35:10,560 Iron(III) is more hard. 583 00:35:10,560 --> 00:35:13,950 They're not very polarizable. 584 00:35:13,950 --> 00:35:18,850 And so, often hard metals are bound by hard acids. 585 00:35:18,850 --> 00:35:20,500 So an example like JoAnne brought up 586 00:35:20,500 --> 00:35:22,300 and Tara backed in today in class, 587 00:35:22,300 --> 00:35:23,800 and if you remember the structure 588 00:35:23,800 --> 00:35:26,860 from when we talked about siderophore biosynthesis, 589 00:35:26,860 --> 00:35:30,490 it uses six oxygen donors to bind iron(III). 590 00:35:30,490 --> 00:35:32,110 So from hard-soft acid base theory, 591 00:35:32,110 --> 00:35:34,560 that's a sensible ligand set. 592 00:35:34,560 --> 00:35:37,480 On the other extreme, what's soft? 593 00:35:37,480 --> 00:35:40,030 So that's a soft acid-- some metal 594 00:35:40,030 --> 00:35:41,990 with a large ionic radius. 595 00:35:41,990 --> 00:35:45,730 So if we think about to the right in the periodic table-- 596 00:35:45,730 --> 00:35:48,740 mercury, cadmium, copper one. 597 00:35:48,740 --> 00:35:51,750 And they like soft ligands, like cystine. 598 00:35:51,750 --> 00:35:54,220 So sulfur, that's quite polarizable. 599 00:35:54,220 --> 00:35:57,030 So soft, typically lower oxidation state. 600 00:35:57,030 --> 00:36:00,892 More to the right in the periodic table. 601 00:36:00,892 --> 00:36:02,850 And then you get metals that are in the middle, 602 00:36:02,850 --> 00:36:05,220 like zinc, iron(II), cobalt(II). 603 00:36:05,220 --> 00:36:06,400 There. 604 00:36:06,400 --> 00:36:08,910 So this gives you some indication of a guide, 605 00:36:08,910 --> 00:36:12,690 and why I bring this up is we've talked about the Irving 606 00:36:12,690 --> 00:36:16,110 Williams series, but depending on the ligand set, 607 00:36:16,110 --> 00:36:18,311 that series might not make sense. 608 00:36:18,311 --> 00:36:18,810 Right? 609 00:36:18,810 --> 00:36:22,740 So something like an EF-hand domain that binds calcium ions, 610 00:36:22,740 --> 00:36:24,730 it uses many oxygen donors. 611 00:36:24,730 --> 00:36:27,660 It's going to prefer calcium, say, over copper, 612 00:36:27,660 --> 00:36:30,060 even though calcium is in another place 613 00:36:30,060 --> 00:36:32,460 in the periodic table and also not defined 614 00:36:32,460 --> 00:36:35,220 by that-- formally defined by the Irving Williams series 615 00:36:35,220 --> 00:36:36,410 there. 616 00:36:36,410 --> 00:36:37,440 OK. 617 00:36:37,440 --> 00:36:43,500 So that's something you can keep in mind when analyzing the data 618 00:36:43,500 --> 00:36:44,880 just qualitatively, right? 619 00:36:44,880 --> 00:36:48,840 And so in the Giedroc review, if you look at those data, 620 00:36:48,840 --> 00:36:50,820 it's the case in many of the systems 621 00:36:50,820 --> 00:36:55,200 where what's currently reported or reported at that time 622 00:36:55,200 --> 00:36:58,950 are Kd values that are similar for certain metals that are 623 00:36:58,950 --> 00:37:01,860 separated along the first row. 624 00:37:01,860 --> 00:37:05,070 So then the question is, what's really going on? 625 00:37:05,070 --> 00:37:07,020 And some of it is an issue related 626 00:37:07,020 --> 00:37:10,440 to methods and experimental design, 627 00:37:10,440 --> 00:37:13,350 in terms of finding conditions that 628 00:37:13,350 --> 00:37:18,870 allow high-affinity binding to be studied here. 629 00:37:18,870 --> 00:37:23,880 So let's consider just some practical considerations 630 00:37:23,880 --> 00:37:30,430 in terms of experiments as we go forward. 631 00:37:30,430 --> 00:37:34,230 So in the beginning of this Wedd paper, 632 00:37:34,230 --> 00:37:36,660 he talks about a bunch of pitfalls 633 00:37:36,660 --> 00:37:40,110 that can come up in terms of experimental design. 634 00:37:40,110 --> 00:37:43,860 Do any of you recall what some of these problems are? 635 00:37:43,860 --> 00:37:53,060 You know, when he brings up on page two, 636 00:37:53,060 --> 00:37:56,030 "reliable evaluation and comparison of metal binding 637 00:37:56,030 --> 00:37:58,880 affinities is important for quantitative understanding 638 00:37:58,880 --> 00:38:01,670 of medal selection and speciation. " 639 00:38:01,670 --> 00:38:03,830 So that's central to everything that JoAnne 640 00:38:03,830 --> 00:38:06,150 has been talking about in terms of homeostasis 641 00:38:06,150 --> 00:38:07,980 the past few days in lecture. 642 00:38:07,980 --> 00:38:09,290 And then what does he say? 643 00:38:09,290 --> 00:38:13,520 "However, estimation of these metal binding constants 644 00:38:13,520 --> 00:38:16,550 is problematic at the moment, as disparate values have been 645 00:38:16,550 --> 00:38:18,530 reported in the literature." 646 00:38:18,530 --> 00:38:21,650 And then he highlights a few examples 647 00:38:21,650 --> 00:38:26,660 that are illustrative of this wider problem here. 648 00:38:26,660 --> 00:38:31,190 And so what's striking about some of these issues 649 00:38:31,190 --> 00:38:34,925 he shows in that page two of this review? 650 00:38:38,824 --> 00:38:41,815 Did these things concern you when reading the review? 651 00:39:06,760 --> 00:39:11,390 So what do these highlight in general? 652 00:39:11,390 --> 00:39:12,103 Yeah. 653 00:39:12,103 --> 00:39:12,728 AUDIENCE: Wait. 654 00:39:12,728 --> 00:39:13,894 What was the exact question? 655 00:39:13,894 --> 00:39:16,280 ELIZABETH NOLAN: So in terms of in Wedd's paper, 656 00:39:16,280 --> 00:39:20,390 he begins this paper by citing a number of examples 657 00:39:20,390 --> 00:39:22,490 of problems in the literature. 658 00:39:22,490 --> 00:39:27,140 And I guess I'm asking, were these problems striking to you? 659 00:39:27,140 --> 00:39:28,430 And if so, why? 660 00:39:28,430 --> 00:39:34,780 And really, what is generally the issue here? 661 00:39:34,780 --> 00:39:37,650 AUDIENCE: I feel like there's such a wide range of magnitude 662 00:39:37,650 --> 00:39:40,572 of the Kds that kind of points to an inconsistency 663 00:39:40,572 --> 00:39:43,869 in experimental set-up. 664 00:39:43,869 --> 00:39:44,849 ELIZABETH NOLAN: Yeah. 665 00:39:44,849 --> 00:39:46,224 AUDIENCE: To where maybe somebody 666 00:39:46,224 --> 00:39:47,942 could give something else-- 667 00:39:47,942 --> 00:39:48,900 ELIZABETH NOLAN: Right. 668 00:39:48,900 --> 00:39:52,380 So these values are hugely different that he's 669 00:39:52,380 --> 00:39:53,730 citing here. 670 00:39:53,730 --> 00:39:54,360 Right? 671 00:39:54,360 --> 00:39:57,960 I mean, 10 orders of magnitude different-- you know, 672 00:39:57,960 --> 00:40:02,340 reported Kds that vary by six orders of magnitude. 673 00:40:02,340 --> 00:40:03,780 These are huge differences. 674 00:40:03,780 --> 00:40:07,080 This isn't one nanomolar versus 10 nanomolar. 675 00:40:07,080 --> 00:40:10,034 This is hugely different, and depending 676 00:40:10,034 --> 00:40:11,700 on what number you come up with, there's 677 00:40:11,700 --> 00:40:15,750 huge implications for what that means in a biological system. 678 00:40:15,750 --> 00:40:19,440 So what are some of the reasons for why there 679 00:40:19,440 --> 00:40:22,420 may be so many discrepancies? 680 00:40:22,420 --> 00:40:24,720 And in each case, we don't really know, 681 00:40:24,720 --> 00:40:27,090 but what we're going to do now is just think about some 682 00:40:27,090 --> 00:40:29,370 of the aspects of experimental setup 683 00:40:29,370 --> 00:40:33,900 that might be affecting determination 684 00:40:33,900 --> 00:40:38,040 of one of these values and how to think about these things. 685 00:40:38,040 --> 00:40:41,610 So in terms of pitfalls, I'll begin 686 00:40:41,610 --> 00:40:44,220 with one, which is just fitting the data 687 00:40:44,220 --> 00:40:46,380 in an inappropriate manner. 688 00:40:46,380 --> 00:40:50,400 So there are so many programs out there that will fit data. 689 00:40:50,400 --> 00:40:52,750 But the end of the day, you need to ask, 690 00:40:52,750 --> 00:40:55,110 what does this fit mean? 691 00:40:55,110 --> 00:40:59,760 Is it meaningful for the system that's being studied? 692 00:40:59,760 --> 00:41:02,220 So did it take into account all parameters? 693 00:41:02,220 --> 00:41:04,270 Is it the correct stoichiometry? 694 00:41:04,270 --> 00:41:07,230 Do the numbers that come out make sense? 695 00:41:07,230 --> 00:41:12,190 What other experiments can be done to try to test that there? 696 00:41:12,190 --> 00:41:14,430 So that's a general issue. 697 00:41:14,430 --> 00:41:17,310 And then, as I've mentioned here in passing, 698 00:41:17,310 --> 00:41:22,110 often direct titrations are fit inappropriately because this is 699 00:41:22,110 --> 00:41:26,700 concluded to mean some absolute Kd when it doesn't. 700 00:41:26,700 --> 00:41:29,360 It just gives you a limit here. 701 00:41:29,360 --> 00:41:34,830 So let's just think about taking a protein 702 00:41:34,830 --> 00:41:37,350 and titrating it with a metal. 703 00:41:37,350 --> 00:41:39,810 That experiment will happen in a buffer. 704 00:41:39,810 --> 00:41:41,640 So do we need to think about the buffer? 705 00:41:46,327 --> 00:41:47,868 AUDIENCE: Yeah, but then it could be, 706 00:41:47,868 --> 00:41:50,494 like, a cuvette here for metal that you're interested in. 707 00:41:50,494 --> 00:41:52,410 ELIZABETH NOLAN: So that's the first question. 708 00:41:52,410 --> 00:41:57,770 Does the buffer influence metal speciation in the cuvette 709 00:41:57,770 --> 00:42:00,980 by having some affinity for the metal of interest? 710 00:42:00,980 --> 00:42:05,900 So from that perspective, what buffers could 711 00:42:05,900 --> 00:42:08,820 be classified as problematic? 712 00:42:08,820 --> 00:42:11,000 So you need to think about the chemical composition, 713 00:42:11,000 --> 00:42:13,457 the chemical structure of the buffer. 714 00:42:13,457 --> 00:42:15,567 AUDIENCE: EDTA or something? 715 00:42:15,567 --> 00:42:16,400 ELIZABETH NOLAN: OK. 716 00:42:16,400 --> 00:42:19,320 So EDTA could be in your buffer for some reason, 717 00:42:19,320 --> 00:42:20,810 but that's not your buffer, right? 718 00:42:20,810 --> 00:42:25,930 So the buffer is what's going to control the pH there. 719 00:42:25,930 --> 00:42:27,320 So Tris is an example. 720 00:42:27,320 --> 00:42:30,125 What are other examples of common buffers? 721 00:42:30,125 --> 00:42:30,916 AUDIENCE: Bis-Tris? 722 00:42:30,916 --> 00:42:33,800 ELIZABETH NOLAN: Yeah, bis-Tris. 723 00:42:33,800 --> 00:42:34,707 Others? 724 00:42:34,707 --> 00:42:35,895 AUDIENCE: PBS? 725 00:42:35,895 --> 00:42:37,020 ELIZABETH NOLAN: Yeah, PBS. 726 00:42:37,020 --> 00:42:38,370 So a phosphate buffer. 727 00:42:38,370 --> 00:42:42,720 That's often used in tissue culture experiments 728 00:42:42,720 --> 00:42:43,810 and other experiments. 729 00:42:43,810 --> 00:42:46,200 So let's start with the Tris buffer. 730 00:42:46,200 --> 00:42:49,080 Is it a good idea to do a metal binding 731 00:42:49,080 --> 00:42:55,750 titration where you want to get a Kd in Tris buffer? 732 00:42:55,750 --> 00:42:56,890 Shaking head no. 733 00:42:56,890 --> 00:42:58,035 So why? 734 00:42:58,035 --> 00:42:59,410 AUDIENCE: Because if you're going 735 00:42:59,410 --> 00:43:04,327 for metal being bound with protein, if the Tris is poured 736 00:43:04,327 --> 00:43:07,130 into the middle, then it might alter your readout. 737 00:43:07,130 --> 00:43:08,080 ELIZABETH NOLAN: Yeah. 738 00:43:08,080 --> 00:43:08,580 OK. 739 00:43:08,580 --> 00:43:11,130 So let's break that down. 740 00:43:11,130 --> 00:43:15,180 So one, Tris-- that has an affinity for certain metals. 741 00:43:15,180 --> 00:43:17,760 You have an amine-based buffer. 742 00:43:17,760 --> 00:43:19,180 So that's one issue. 743 00:43:19,180 --> 00:43:20,730 And then the other thing you need 744 00:43:20,730 --> 00:43:22,290 to think about in this are, what are 745 00:43:22,290 --> 00:43:25,170 the relative concentrations of the buffer 746 00:43:25,170 --> 00:43:27,960 to your protein of interest? 747 00:43:27,960 --> 00:43:31,380 So what's a typical Tris buffer concentration 748 00:43:31,380 --> 00:43:33,540 used, say, in protein purification 749 00:43:33,540 --> 00:43:37,551 or some type of experiment? 750 00:43:37,551 --> 00:43:38,634 AUDIENCE: Like normally? 751 00:43:38,634 --> 00:43:39,550 ELIZABETH NOLAN: Yeah. 752 00:43:39,550 --> 00:43:41,840 Typically higher than one million molar, too. 753 00:43:41,840 --> 00:43:42,340 Right? 754 00:43:42,340 --> 00:43:46,210 So maybe 20, 75 million molar. 755 00:43:46,210 --> 00:43:48,310 Maybe even higher than that. 756 00:43:48,310 --> 00:43:51,550 So you have this substantial concentration 757 00:43:51,550 --> 00:43:57,490 of your Tris buffer, compared to a protein concentration, 758 00:43:57,490 --> 00:43:59,770 which if you have a micromolar Kd 759 00:43:59,770 --> 00:44:04,440 you'd like to look at a micromolar range of protein. 760 00:44:04,440 --> 00:44:06,880 So that will influence the metal binding 761 00:44:06,880 --> 00:44:10,120 equilibria in the experiment. 762 00:44:10,120 --> 00:44:12,760 So then the question is, if you're doing that titration 763 00:44:12,760 --> 00:44:14,950 under that type of condition, are you 764 00:44:14,950 --> 00:44:18,280 taking that Tris metal interaction 765 00:44:18,280 --> 00:44:20,740 into account in the data analysis? 766 00:44:24,290 --> 00:44:28,840 Are there other buffers that are arguably more appropriate? 767 00:44:28,840 --> 00:44:29,990 And the answer is yes. 768 00:44:29,990 --> 00:44:32,110 So there's buffers like HEPES. 769 00:44:32,110 --> 00:44:34,780 These are buffers that are called good buffers-- 770 00:44:34,780 --> 00:44:36,940 zwitterionic buffers that in general 771 00:44:36,940 --> 00:44:43,780 have lower metal affinities, and are often used for titrations. 772 00:44:43,780 --> 00:44:49,060 What about, say, metal contamination from the buffer 773 00:44:49,060 --> 00:44:52,350 or from the water? 774 00:44:52,350 --> 00:44:56,660 So what's important to think about there? 775 00:44:56,660 --> 00:44:58,997 Is that an issue? 776 00:44:58,997 --> 00:45:01,492 AUDIENCE: If you're using hard water or something, 777 00:45:01,492 --> 00:45:03,912 there's calcium that would bind to your protein. 778 00:45:03,912 --> 00:45:05,120 ELIZABETH NOLAN: Yeah, right. 779 00:45:05,120 --> 00:45:08,280 So you need to think about the water. 780 00:45:08,280 --> 00:45:11,820 You know, where did this water come from? 781 00:45:11,820 --> 00:45:14,830 Where did your Tris come from, or whatever other buffer? 782 00:45:14,830 --> 00:45:18,350 Because again, if you have 100 million molar buffer, 783 00:45:18,350 --> 00:45:22,040 it's not only the molecules of, say, HEPES, 784 00:45:22,040 --> 00:45:24,290 but it's whatever other contaminants are in there. 785 00:45:24,290 --> 00:45:27,740 And there's a lot more of that than your protein, which 786 00:45:27,740 --> 00:45:33,230 gets into this issue of Irving Williams' series and zinc. 787 00:45:33,230 --> 00:45:36,740 So zinc contamination is everywhere. 788 00:45:36,740 --> 00:45:38,270 Zinc is everywhere. 789 00:45:38,270 --> 00:45:43,250 So are you getting a zinc contamination, say? 790 00:45:43,250 --> 00:45:46,370 And your metal binding protein, some portion 791 00:45:46,370 --> 00:45:49,610 of it is complexing zinc and you can't see that, because zinc 792 00:45:49,610 --> 00:45:52,400 is spectroscopically silent. 793 00:45:52,400 --> 00:45:53,960 That's going to be a problem. 794 00:45:53,960 --> 00:45:58,940 So that's something to think about and keep in mind. 795 00:45:58,940 --> 00:46:03,650 So for rigorous work, high-purity buffers 796 00:46:03,650 --> 00:46:05,170 can be used. 797 00:46:05,170 --> 00:46:08,840 Or there are tricks out there to demetalate buffers. 798 00:46:08,840 --> 00:46:12,590 Those tricks often have a few caveats as well for that. 799 00:46:12,590 --> 00:46:17,230 But I think contamination is something to keep in mind, 800 00:46:17,230 --> 00:46:18,740 and can be a bit of a nuisance. 801 00:46:18,740 --> 00:46:21,620 But you just need to know how to look for it and deal with it. 802 00:46:21,620 --> 00:46:25,640 And also, these contaminations-- 803 00:46:25,640 --> 00:46:27,560 it becomes an issue, too, in terms of what 804 00:46:27,560 --> 00:46:29,600 is your protein concentration? 805 00:46:29,600 --> 00:46:32,870 So if you have a one micromolar metal contamination, 806 00:46:32,870 --> 00:46:35,450 and you're working with one millimolar protein, 807 00:46:35,450 --> 00:46:37,730 it's probably OK. 808 00:46:37,730 --> 00:46:42,170 But if you're working with one or 10 micromolar protein, 809 00:46:42,170 --> 00:46:44,420 then there's a problem, because you're 810 00:46:44,420 --> 00:46:46,930 going to have more of that complexed there. 811 00:46:49,590 --> 00:46:53,730 So why are we using the buffer? 812 00:46:53,730 --> 00:46:56,670 We're using the buffer to control pH. 813 00:46:56,670 --> 00:46:58,770 So how do we want to think about pH 814 00:46:58,770 --> 00:47:00,990 from the standpoint of these titrations? 815 00:47:05,710 --> 00:47:08,730 AUDIENCE: You don't want to make something 816 00:47:08,730 --> 00:47:13,162 that you're trying to coordinate the metal with, so 817 00:47:13,162 --> 00:47:17,034 like the proteins [INAUDIBLE]. 818 00:47:17,034 --> 00:47:19,200 ELIZABETH NOLAN: Or even histidine that has 819 00:47:19,200 --> 00:47:24,920 a pKa that isn't in the regime. 820 00:47:24,920 --> 00:47:26,180 And cystine, right? 821 00:47:26,180 --> 00:47:27,760 That has a pKa. 822 00:47:27,760 --> 00:47:31,020 So often, we think about the pH of the buffer used 823 00:47:31,020 --> 00:47:33,900 in protein purification that will make the protein 824 00:47:33,900 --> 00:47:37,330 stay in a happy state. 825 00:47:37,330 --> 00:47:41,190 But then the question is, is that pH 826 00:47:41,190 --> 00:47:44,550 appropriate for the metal binding study? 827 00:47:44,550 --> 00:47:47,580 What is the effect of that pH on the ligands 828 00:47:47,580 --> 00:47:49,290 and the primary coordination sphere? 829 00:47:49,290 --> 00:47:52,020 So are they protonated or deprotonated 830 00:47:52,020 --> 00:47:53,580 or a mixture of the two? 831 00:47:53,580 --> 00:47:56,820 And then how does that affect the affinity itself? 832 00:47:56,820 --> 00:48:00,956 So these Kds will have a pH dependence based on pKas 833 00:48:00,956 --> 00:48:04,030 of the side chains here. 834 00:48:04,030 --> 00:48:07,800 And I mean, also, are there pH requirements for the metal? 835 00:48:07,800 --> 00:48:11,970 And is your experimental setup such 836 00:48:11,970 --> 00:48:16,260 that the pH remains constant throughout the titration? 837 00:48:16,260 --> 00:48:18,090 So an example-- iron(III). 838 00:48:18,090 --> 00:48:20,610 So JoAnne talked about iron(III) in class today, 839 00:48:20,610 --> 00:48:26,880 and this ridiculously low Ksp at pH 7 of 10 to the minus 18. 840 00:48:26,880 --> 00:48:29,700 You can't just have your iron(III) stock solution 841 00:48:29,700 --> 00:48:35,550 at pH 7 and have much of anything soluble. 842 00:48:35,550 --> 00:48:37,710 So what do people do about that? 843 00:48:37,710 --> 00:48:40,290 Often, the stock solution is stored in acid 844 00:48:40,290 --> 00:48:43,230 because it's soluble there. 845 00:48:43,230 --> 00:48:45,840 Can you titrate that acidic solution directly 846 00:48:45,840 --> 00:48:47,970 into your protein? 847 00:48:47,970 --> 00:48:52,060 These are just things to think about here. 848 00:48:52,060 --> 00:48:55,250 What else can be in the buffer? 849 00:48:55,250 --> 00:48:59,370 So thinking about anyone who has purified protein. 850 00:48:59,370 --> 00:49:02,269 So you brought up EDTA, right? 851 00:49:02,269 --> 00:49:03,810 And that certainly would be something 852 00:49:03,810 --> 00:49:06,310 that would need to be taken into account. 853 00:49:06,310 --> 00:49:08,040 Hopefully you would only have it present 854 00:49:08,040 --> 00:49:10,840 if you wanted to do something like a competition. 855 00:49:10,840 --> 00:49:17,040 Otherwise, that's going to be a major issue in terms 856 00:49:17,040 --> 00:49:19,050 of sorting things out. 857 00:49:19,050 --> 00:49:20,670 But what else might be in the buffer? 858 00:49:36,741 --> 00:49:39,732 So what if your protein, say, is a cytoplasmic protein 859 00:49:39,732 --> 00:49:40,940 and it has a lot of cystines? 860 00:49:45,110 --> 00:49:48,910 Are those cystines likely to be reduced or oxidized 861 00:49:48,910 --> 00:49:51,160 in the native form if it's a cytoplasm protein? 862 00:49:55,354 --> 00:49:56,104 AUDIENCE: Reduced. 863 00:49:56,104 --> 00:49:57,020 ELIZABETH NOLAN: Yeah. 864 00:49:57,020 --> 00:49:57,770 Reduced, right? 865 00:49:57,770 --> 00:49:59,480 Because that's a reducing environment. 866 00:49:59,480 --> 00:50:01,690 And then you go into the periplasm 867 00:50:01,690 --> 00:50:04,880 or the ER, which is where you find proteins 868 00:50:04,880 --> 00:50:07,260 that have more disulfide bonds. 869 00:50:07,260 --> 00:50:11,960 So let's say your protein likes to have a bunch of reduced 870 00:50:11,960 --> 00:50:13,130 cystines in it. 871 00:50:13,130 --> 00:50:17,090 Chances are you have a reducing agent in the buffer you 872 00:50:17,090 --> 00:50:19,370 use for protein purification. 873 00:50:19,370 --> 00:50:24,530 And maybe you need to keep that reducing agent around 874 00:50:24,530 --> 00:50:27,140 during an experiment, or maybe you 875 00:50:27,140 --> 00:50:29,810 can work in an anaerobic chamber and get rid of it. 876 00:50:29,810 --> 00:50:32,390 But let's just say the reducing agent's present. 877 00:50:32,390 --> 00:50:35,660 Is that something we need to think about from the standpoint 878 00:50:35,660 --> 00:50:39,590 of a metal-protein interaction? 879 00:50:39,590 --> 00:50:42,765 So what are examples of these reducing agents? 880 00:50:49,000 --> 00:50:50,000 AUDIENCE: TCEP. 881 00:50:50,000 --> 00:50:52,150 ELIZABETH NOLAN: TCEP's one, yeah. 882 00:50:52,150 --> 00:50:55,470 And we'll come back to that one in a minute. 883 00:50:55,470 --> 00:50:57,426 What are some others? 884 00:50:57,426 --> 00:50:58,650 AUDIENCE: [INAUDIBLE] 885 00:50:58,650 --> 00:50:59,010 ELIZABETH NOLAN: Yep. 886 00:50:59,010 --> 00:50:59,755 And what else? 887 00:51:03,850 --> 00:51:07,810 Another thiol-based reducing agent 888 00:51:07,810 --> 00:51:10,890 commonly used in protein purification. 889 00:51:10,890 --> 00:51:12,087 AUDIENCE: DDT? 890 00:51:12,087 --> 00:51:12,962 ELIZABETH NOLAN: DTT. 891 00:51:12,962 --> 00:51:13,524 Yeah. 892 00:51:13,524 --> 00:51:14,460 AUDIENCE: Oh, DTT. 893 00:51:14,460 --> 00:51:16,010 ELIZABETH NOLAN: DTT, right. 894 00:51:16,010 --> 00:51:22,250 So let's just consider, say, DTT and BME together. 895 00:51:22,250 --> 00:51:24,530 Is there something we need to consider there? 896 00:51:24,530 --> 00:51:28,850 Yes, because depending on your metal, these reducing agents 897 00:51:28,850 --> 00:51:30,470 will have some affinity. 898 00:51:30,470 --> 00:51:35,210 And often, they're in very large excess over the concentration 899 00:51:35,210 --> 00:51:35,870 of protein. 900 00:51:35,870 --> 00:51:39,350 So it's a similar issue to the Tris buffer issue, 901 00:51:39,350 --> 00:51:41,270 in terms of how are these reducing agents 902 00:51:41,270 --> 00:51:44,840 affecting metals speciation and metal binding 903 00:51:44,840 --> 00:51:47,700 equilibria in the experiment. 904 00:51:47,700 --> 00:51:48,950 So TCEP. 905 00:51:48,950 --> 00:51:52,550 This is Tris-carboxyethel phosphine. 906 00:51:52,550 --> 00:51:56,420 So not as commonly used in protein purification. 907 00:51:56,420 --> 00:51:58,700 But it is a reducing agent that you commonly 908 00:51:58,700 --> 00:52:02,300 see used in certain metal binding titration. 909 00:52:02,300 --> 00:52:05,870 And that's because it's thought to cause less interference. 910 00:52:05,870 --> 00:52:11,060 So the affinity-- that equilibrium constant 911 00:52:11,060 --> 00:52:12,650 is much weaker. 912 00:52:12,650 --> 00:52:15,500 So what is one of the pitfalls of using TCEP 913 00:52:15,500 --> 00:52:17,810 that people often run into? 914 00:52:17,810 --> 00:52:20,060 Do you know? 915 00:52:20,060 --> 00:52:24,289 So if you just have TCEP and aqueous solution 916 00:52:24,289 --> 00:52:25,580 AUDIENCE: It's going to start-- 917 00:52:25,580 --> 00:52:26,606 ELIZABETH NOLAN: What? 918 00:52:26,606 --> 00:52:28,874 AUDIENCE: Reducing, just if you leave it there. 919 00:52:28,874 --> 00:52:31,040 ELIZABETH NOLAN: Well, it needs something to reduce. 920 00:52:31,040 --> 00:52:36,530 So if you just have TCEP and water, is that neutral? 921 00:52:36,530 --> 00:52:37,190 Basic? 922 00:52:37,190 --> 00:52:37,690 Acidic? 923 00:52:43,540 --> 00:52:45,200 So it's acidic. 924 00:52:45,200 --> 00:52:46,750 And the manufacturer instructions 925 00:52:46,750 --> 00:52:48,250 say this pretty explicitly. 926 00:52:48,250 --> 00:52:51,430 But oftentimes they go unread, right? 927 00:52:51,430 --> 00:52:54,960 So if you end up working with quite a bit of TCEP 928 00:52:54,960 --> 00:52:58,100 in your experimental conditions, the first thing you need to ask 929 00:52:58,100 --> 00:53:05,740 is the buffer adequate to buffer the pH when TCEP's added. 930 00:53:05,740 --> 00:53:07,810 You don't want the TCEP acidifying your buffer 931 00:53:07,810 --> 00:53:09,370 and then you're not working at the pH 932 00:53:09,370 --> 00:53:11,200 you think you're working at. 933 00:53:11,200 --> 00:53:12,250 So what does that mean? 934 00:53:12,250 --> 00:53:17,020 You may want to pH adjust your TCEP solution before starting 935 00:53:17,020 --> 00:53:18,880 the experiment there. 936 00:53:18,880 --> 00:53:20,480 That's just something to keep in mind. 937 00:53:20,480 --> 00:53:24,825 I've seen that happen many, many times, in terms of the TCEP 938 00:53:24,825 --> 00:53:25,325 there. 939 00:53:29,920 --> 00:53:31,400 Temperature control. 940 00:53:31,400 --> 00:53:33,650 The equilibrium constant is temperature dependent. 941 00:53:33,650 --> 00:53:37,420 So what is the temperature control throughout one 942 00:53:37,420 --> 00:53:38,980 experiment, and then also if you're 943 00:53:38,980 --> 00:53:42,160 repeating this experiment over multiple days, 944 00:53:42,160 --> 00:53:46,000 because you want to get error analysis 945 00:53:46,000 --> 00:53:48,670 and show that it's reproducible, is that temperature 946 00:53:48,670 --> 00:53:50,435 good for that? 947 00:53:53,500 --> 00:53:55,030 So those are some key things. 948 00:53:55,030 --> 00:53:57,130 And then what do we need to think 949 00:53:57,130 --> 00:54:01,540 about in terms of using a competitor when setting up 950 00:54:01,540 --> 00:54:02,350 the experiment? 951 00:54:06,409 --> 00:54:11,540 So one, we need to know the Kd value of the competitor 952 00:54:11,540 --> 00:54:14,270 for the metal of interest. 953 00:54:14,270 --> 00:54:17,150 And hopefully, we know something about this system 954 00:54:17,150 --> 00:54:19,530 so we can make an appropriate choice, 955 00:54:19,530 --> 00:54:24,770 because as I said before, we want to see competition there. 956 00:54:24,770 --> 00:54:28,230 What could go wrong? 957 00:54:28,230 --> 00:54:30,590 And again, this isn't meant to be all gloom and doom. 958 00:54:30,590 --> 00:54:32,173 This is just, you know, you need to be 959 00:54:32,173 --> 00:54:35,330 aware of certain things that can happen in your experiments 960 00:54:35,330 --> 00:54:37,130 and know to look out for them so you 961 00:54:37,130 --> 00:54:41,220 can fix things as necessary. 962 00:54:41,220 --> 00:54:46,730 So here, we have the protein, we have the competitor, 963 00:54:46,730 --> 00:54:48,590 we have the metal. 964 00:54:48,590 --> 00:54:51,650 And as I've described it, we want the protein 965 00:54:51,650 --> 00:54:54,800 and the competitor to operate effectively, 966 00:54:54,800 --> 00:54:56,540 independent of one another. 967 00:54:56,540 --> 00:54:59,270 So they can both bind the metal, and somehow this metal 968 00:54:59,270 --> 00:55:02,210 is going to be distributed between the two 969 00:55:02,210 --> 00:55:04,820 based on the relative concentrations 970 00:55:04,820 --> 00:55:08,090 and the relative Kds. 971 00:55:08,090 --> 00:55:10,160 So what could muck that up? 972 00:55:10,160 --> 00:55:11,315 That's the ideal scenario. 973 00:55:15,642 --> 00:55:18,877 AUDIENCE: Could they both bind the metal? 974 00:55:18,877 --> 00:55:21,210 ELIZABETH NOLAN: Well, we definitely know they both can, 975 00:55:21,210 --> 00:55:21,620 right? 976 00:55:21,620 --> 00:55:22,260 AUDIENCE: Simultaneously. 977 00:55:22,260 --> 00:55:23,670 ELIZABETH NOLAN: Simultaneously. 978 00:55:23,670 --> 00:55:24,990 So what would that be called? 979 00:55:28,710 --> 00:55:31,620 So that this can be a major headache. 980 00:55:31,620 --> 00:55:33,210 What happens is that you get what's 981 00:55:33,210 --> 00:55:35,220 called a ternary complex. 982 00:55:35,220 --> 00:55:37,400 So you have the ligand, the competitor, 983 00:55:37,400 --> 00:55:40,390 and the metal as one. 984 00:55:40,390 --> 00:55:43,680 So imagine that your protein has a metal site that's 985 00:55:43,680 --> 00:55:46,800 not coordinatively saturated. 986 00:55:46,800 --> 00:55:50,760 And so as a result, maybe you have the metal in this site 987 00:55:50,760 --> 00:55:54,290 but then the competitor also binds. 988 00:55:54,290 --> 00:55:56,490 That's not good from the standpoint of setting up 989 00:55:56,490 --> 00:55:57,750 this competition, right? 990 00:55:57,750 --> 00:56:02,270 Because how do you parameterize for that? 991 00:56:02,270 --> 00:56:05,400 So that can be a big issue, and something 992 00:56:05,400 --> 00:56:09,870 that you need to watch out for when designing the experiments. 993 00:56:09,870 --> 00:56:13,350 Could something happen between the competitor and the protein 994 00:56:13,350 --> 00:56:15,120 itself in the absence of metal? 995 00:56:17,513 --> 00:56:19,096 AUDIENCE: Perhaps they could interact, 996 00:56:19,096 --> 00:56:21,590 and then, in their interactions, block the metal. 997 00:56:21,590 --> 00:56:22,506 ELIZABETH NOLAN: Yeah. 998 00:56:22,506 --> 00:56:25,100 It could block or perturb. 999 00:56:25,100 --> 00:56:26,510 So what might happen? 1000 00:56:26,510 --> 00:56:28,730 I mean, we can just imagine a scenario 1001 00:56:28,730 --> 00:56:32,300 where this protein has some hydrophobic patch. 1002 00:56:32,300 --> 00:56:34,560 And maybe this competitor has a fluorophore 1003 00:56:34,560 --> 00:56:37,100 for that's relatively hydrophobic. 1004 00:56:37,100 --> 00:56:39,710 Or maybe part of the ligand is hydrophobic. 1005 00:56:39,710 --> 00:56:42,620 And so you end up getting the competitor sticking 1006 00:56:42,620 --> 00:56:44,740 to the protein. 1007 00:56:44,740 --> 00:56:47,210 That doesn't necessarily mean the competitor won't 1008 00:56:47,210 --> 00:56:50,540 bind the metal, but it will perturb 1009 00:56:50,540 --> 00:56:52,590 how that competitor behaves. 1010 00:56:52,590 --> 00:56:54,530 That could perturb the optical readout. 1011 00:56:54,530 --> 00:56:58,200 It could perturb the metal affinity of the competitor. 1012 00:56:58,200 --> 00:57:03,510 So that's something to also watch out for. 1013 00:57:03,510 --> 00:57:06,920 So we talked about the buffer and contaminations 1014 00:57:06,920 --> 00:57:08,480 in the buffer. 1015 00:57:08,480 --> 00:57:11,580 What about the competitor here? 1016 00:57:11,580 --> 00:57:14,390 So typically, these small molecules 1017 00:57:14,390 --> 00:57:17,250 are coming from some commercial source. 1018 00:57:17,250 --> 00:57:18,020 Right? 1019 00:57:18,020 --> 00:57:23,420 And so you have similar issues, even though you're using 1020 00:57:23,420 --> 00:57:26,320 a much smaller concentration. 1021 00:57:26,320 --> 00:57:29,830 And so don't always assume what you're getting 1022 00:57:29,830 --> 00:57:32,420 is as pure as they tell you. 1023 00:57:32,420 --> 00:57:35,150 And that could be organic impurity, 1024 00:57:35,150 --> 00:57:37,010 or it could be a metal contamination, 1025 00:57:37,010 --> 00:57:39,740 because these competitors are ligands. 1026 00:57:39,740 --> 00:57:43,460 And they could have picked up some metal along the way. 1027 00:57:43,460 --> 00:57:47,630 So what can be problematic from the standpoint 1028 00:57:47,630 --> 00:57:50,660 of, say, organic impurity here? 1029 00:57:50,660 --> 00:57:52,520 One common example is that if you're 1030 00:57:52,520 --> 00:57:56,810 using something that's fluorescent or brightly 1031 00:57:56,810 --> 00:58:00,860 colored, to have an optical readout. 1032 00:58:00,860 --> 00:58:03,200 Maybe there's an impurity that wasn't 1033 00:58:03,200 --> 00:58:06,500 removed in the synthesis and purification that's 1034 00:58:06,500 --> 00:58:08,660 also very bright. 1035 00:58:08,660 --> 00:58:11,480 So you have something that's compromising the optical signal 1036 00:58:11,480 --> 00:58:13,850 of the probe. 1037 00:58:13,850 --> 00:58:16,320 And then there's also the possibility, 1038 00:58:16,320 --> 00:58:19,310 since these are ligands, that there's a contaminant that 1039 00:58:19,310 --> 00:58:21,710 can also bind a metal. 1040 00:58:21,710 --> 00:58:24,860 So if there was some byproduct that 1041 00:58:24,860 --> 00:58:28,190 wasn't fully removed during purification. 1042 00:58:28,190 --> 00:58:31,820 If that's the case, it will influence speciation as well 1043 00:58:31,820 --> 00:58:33,050 there. 1044 00:58:33,050 --> 00:58:38,960 So what does one do in terms of gold standard and testing? 1045 00:58:38,960 --> 00:58:41,210 You need to know what the primary literature is 1046 00:58:41,210 --> 00:58:44,270 about this competitor molecule, and then 1047 00:58:44,270 --> 00:58:48,230 effectively test your sample and make 1048 00:58:48,230 --> 00:58:53,980 sure it has the expected optical properties and the expected 1049 00:58:53,980 --> 00:58:57,590 behavior when binding the metal of interest. 1050 00:58:57,590 --> 00:59:02,270 And if that all looks good, then can move forward. 1051 00:59:02,270 --> 00:59:08,785 Also, just typical tests of purity, LCMS, HPLC. 1052 00:59:08,785 --> 00:59:10,910 Even with many of these, if they're highly colored, 1053 00:59:10,910 --> 00:59:15,870 a simple TLC will give you a lot of information there. 1054 00:59:15,870 --> 00:59:19,610 So I'll close with that, and just would reiterate broadly 1055 00:59:19,610 --> 00:59:23,720 that a lot of the topics discussed in the Wedd review 1056 00:59:23,720 --> 00:59:28,190 and in the packet, although from the perspective of metals 1057 00:59:28,190 --> 00:59:32,630 and proteins, it's more general to any type of binding problem. 1058 00:59:32,630 --> 00:59:37,700 And if you need more resources in terms of binding problems 1059 00:59:37,700 --> 00:59:40,640 related to metals, I highly recommend reviews 1060 00:59:40,640 --> 00:59:44,690 by Wilcox and Giedroc, in addition to this review 1061 00:59:44,690 --> 00:59:46,190 by Wedd there. 1062 00:59:46,190 --> 00:59:51,170 So they talk a lot about aspects of experimental design 1063 00:59:51,170 --> 00:59:54,220 and certain methodologies there.