Identical ligands, unrelated proteins, similar energies - When language collides with the facts of nature

Recognition of aromatic rings by two very different
mechanisms but through similar binding energies

Over the years chemists have come up with many different ways to talk about the structure and energetics of molecules and especially to compare these parameters between various compounds. Doing this comparison is not just an academic exercise; for example, knowing which drug molecules are ‘similar’ or ‘different’ can be the deciding factor in picking one drug over another. It is also crucial for knowing the kinds of side effects that drugs can induce by interacting with off-target proteins.

Unfortunately the application of these simple descriptions to matters of molecular description is a very good example of what happens when language collides with fuzzy, ill-defined facts in nature. ‘Similarity’ is a classic example. When you are talking about two drugs being similar for instance, are you talking about their similarity purely in terms of molecular structure (which itself can be defined in many different ways), or their similarity in terms of their effects on cancer cells, or their similarity to engage a common protein target in the body, or through similar side effects? Clearly there are many different ways to define similarity and all these ways are subjective to a large extent.

But there is a problem with applying language to chemical concepts even at a very limited and basic level. A great example of this conundrum is hinted at by a paper from Brian Shoichet’s group at UCSF that just came out in the journal ACS Chemical Biology. The paper asks a very fundamental question: Do identical small molecules or ligands bind to very different proteins? The question in fact goes deeper: How do you define similarity and differences between various proteins to begin with?

To investigate this question, the authors consider 59 ligands bound to 119 different proteins in the PDB. Many bind with high affinity, ranging from low nanomolar to mid micromolar. What the study does is to classify these protein-ligand pairs into three groups. The first group consists of pairs in which the same atoms in identical ligands bind to similar or identical residues in different proteins. The second group consists of the same ligand atoms in identical ligands binding to similar kinds of residues (hydrophobic, positively charged etc.). The third group in a sense is the most interesting since it involves identical ligands binding to completely different proteins; in these cases the binding involves neither similar ligand atoms nor similar protein environments.

The authors find that a good two thirds of the set of protein-ligand pairs involve identical ligands binding to proteins with dissimilar residues. In addition, half of these involve ligands binding to proteins with completely different environments. There is thus no ‘pattern-matching code’ for the same ligand binding to different proteins.

Why do identical ligands bind in very different protein environments? The simple reason is because chemical binding is to a large extent a non-specific process, and there are many ways to skin the protein-ligand cat. Hydrophobic groups bump into hydrophobic groups, positively charged groups interact with negative charged ones and polar atoms snuggle up against other polar atoms. As the authors say:

"A reason why there is no simple code for ligand recognition among binding sites is that proteins have found multiple, at least superficially unrelated ways to recognize most common ligand groups. Thus, cationic amines can be recognized both by anionic residues such as aspartate or glutamate, but they can also be recognized by cation-Pi interactions. Nucleotide phosphates can be recognized by cationic residues such as arginines, but recognition by main chain amide nitrogens in a P-loop is also common. Ligand aromatic groups can stack with tyrosines, phenylalanines and tryptophans, but they can also form cation-Pi interactions many other variations might be mentioned."

But sometimes hydrophobic groups can also snuggle up against polar atoms or poke out into solvent and polar groups can nestle into hydrophobic pockets to various extents, simply because the other atoms in the ligand compensate for such uneasy alliances by forming favorable interactions. This can lead to the same ligands binding to very different protein atoms. As I mentioned in a previous post, atoms end up somewhere simply because they can. The differential placement of atoms in protein pockets is reflected in the different binding affinities that the authors see in their set.

From an evolutionary viewpoint this observation is very interesting. Protein-small molecule binding was constrained during evolution by the basic chemistry and physics of binding on one hand and by the damage incurred by too much non-specific binding on the other (as an extreme case, if every small molecule bound to every protein, there would be way too much noise and biological signaling networks would be effectively impossible). Thus there had to be a balance between promiscuity and specificity. Nature achieved this balance by tuning the affinity of small molecules for proteins over a wide range and by making sure that even weak affinity could translate to significant biological effects.

Unfortunately these are precisely the affinities that we ourselves want to finely tune in a drug discovery program and as the paper shows, this is always going to be an uphill battle because of the multitude of interactions and the lack of correlation between ligand and binding pocket structure (one conclusion from the paper is that you cannot always predict new targets for known ligands simply by computationally comparing binding sites).

But on another level I think this problem also speaks to the paucity of the language that we have for describing binding affinity and molecular interactions in general. Our metric for similarity in this case is the presence of similar ligand atoms binding to similar protein atoms. But nature can use another very simple measure of similarity – similarity in binding energy. It is not unreasonable to say that a ligand binds similarly to two proteins if it exhibits a similar binding affinity to both of them. And this binding affinity need not even be very different since even a few kcal/mol difference in binding energy can translate to a thousand fold difference in actual affinity (say from micromolar to nanomolar). Thus, what we call dissimilar binding may actually be judged as quite similar by nature. Consider the picture at the top of the post for instance: an aromatic ring can interact with a protein through either a stacking interaction with an aromatic amino acid or through a cation-pi interaction with a positively charged amino acid. The two interactions look very different, and yet they involve the same binding affinity. 

All this goes back to something we mentioned before: Similarity is in the eye of the beholder, and what our eye sees as squiggly lines of ligands and protein residues on a computer screen, nature sees simply as thermodynamics, kinetics and quantum mechanics, and all of it lying on a continuum. We might be dismayed to know that the same ligand is binding to very different proteins, but this is because nature may not be regarding them as very different to begin with. To figure out protein-ligand binding then, we may have to see things from the point of view of nature rather than that of our impoverished language.

New place, new view, slow reactions and the origins of life

I have been unable to blog for the past few days because I was busy moving to Chapel Hill for a postdoc at UNC Chapel Hill. I am very excited about this move and my upcoming research which is going to involve protein design and folding. Regular blogging will resume soon. Until then, happy holidays, and I will leave you with the following interesting paper published by a group from my new institution.

One of the abiding puzzles in the origin of life is to explain how life arose in the relatively small amount of time it had to evolve on the planet. From a chemical perspective, this entails explaining how especially slow chemical reactions could have contributed to the complexity of life. In a new paper in PNAS, a group from UNC suggests part of a possible solution to the puzzle by demonstrating that slow reactions especially are accelerated by temperature much more than fast reactions. Recall from college physical chemistry that the rate of a typical reaction roughly doubles with ten degree rise in temperature. As the authors note, this bit of textbook wisdom is off the mark when it comes to many important reactions and needs to be appended.

They look at certain important reactions like the hydrolysis of phosphate monoesters and find that these reactions are accelerated not two or a few fold but many million fold with a rise in temperature. The increase in rate would have been significant especially under the hot, primordial conditions present on earth during its early days. Now this acceleration is free-energetic and basically corresponds to a favorable change in either the entropy or the enthaply of activation. The authors measure both these variables and find that the crucial change is in the enthalpy. It's interesting to note that a favorable change in the enthalpy would entail forming stronger interactions including hydrogen bonds between substrate and enzyme, and this is exactly the kind of process you would imagine happening during the optimization of biomolecular interactions during evolution. In fact, recent research suggests that this process of optimizing enthalpy is also synthetically mirrored during drug discovery. The authors end by explaining why a catalyst that impacted enthalpy rather than entropy favorably would have had a selective advantage in rate acceleration as the environment later cooled (and entropy became unfavorable).

Amusingly, the paper has come under criticism from some unexpected quarters, from none other than folks from the infamous 'Discovery' Institute which is funded and run by creationists. In the view of these esteemed 'scientists', the paper provides no evidence that the slow reactions which were accelerated were in fact ones which were important during the origin of life. The DI crowd seems to have fundamentally misjudged the nature of origins of life research; it's more speculative than many other fields but still remains scientific. More importantly, the criticism seems to have completely missed the fact that the general hypotheses proposed by the authors- that all slow reactions could have been vastly accelerated by temperature on a hot primordial planet- is independent of the exact nature of these reactions which may or may not have contributed to life's origins. As usual, miss the forest for the trees.

Stockbridge, R., Lewis, C., Yuan, Y., & Wolfenden, R. (2010). Impact of temperature on the time required for the establishment of primordial biochemistry, and for the evolution of enzymes Proceedings of the National Academy of Sciences, 107 (51), 22102-22105 DOI: 10.1073/pnas.1013647107

A graceful collapse

Vijay Pande's group at Stanford has become well-known for using the collective force of millions of CPUs around the world for simulating protein folding in the project known as Folding@home. One of the enduring challenges in simulating folding has been to sample the long timescales that are common in real-life folding events, and recent breakthroughs have made accessing such time domains realistic. We should expect long protein folding simulations to be within the reach of many non-specialists in the next few years.

In the latest issue of JACS, Pande's group provides an example of such advances by simulating the folding of a 39 residue protein called NTL9. The actual folding time is 1.5 ms so this is a substantially long MD simulation. To achieve this, Pande's group uses Graphic Processor Units (GPUs) of the kind that are found in video game modules. Over the last few years these units have made interesting biological phenomena accessible to chemists. C & EN has a nice article on the increasing use of GPUs for biomolecular simulation.

Pande's group also uses a set of statistical tools called Markov State Models (MSMs) to identify metastable folding states and the transition trajectories between them. MSMs provide a nifty strategy to bridge the results from several short trajectories (rather than running one long one).

What is endearing about the simulation is that that the correct structure doesn't form until much later and then quickly falls in place, like a lost kid suddenly remembering his place in the marching band. As can be seen in the video below, the missing piece of the puzzle is a short C-terminal part of a beta-sheet which seems to linger as part of an alpha helix while the rest of the sheet structure forms. After comfortably waltzing around as a little helical piece for a long time, it seems to suddenly remember its correct identity and snaps and collapses into place as part of the beta sheet. Very nice!



Admittedly, a 39 residue protein is minuscule compared to most typical proteins. But the results provide a neat proof of concept. Importantly, they also show that current force fields with implicit solvent models can be accurate enough for this kind of simulation. Further validation will test these force fields more stringently.

Voelz VA, Bowman GR, Beauchamp K, & Pande VS (2010). Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1-39). Journal of the American Chemical Society, 132 (5), 1526-8 PMID: 20070076

From bull horns to under the lens of Anton

In 1989, a young computer scientist named David Shaw was working at Morgan Stanley, one of the first Wall Street firms interested in using computer algorithms for trading. Shaw was an expert in parallel processing, speeding up calculations by executing them in a parallel process over multiple processors. Previously he had been a computer science professor at Columbia University had tried to sell his computer skills to a number of companies, but only Morgan Stanley was genuinely interested. As Shaw started working at the company, he began to think not just of programming strategies but of creative ways in which they could be applied to trading. In a meeting where he was supposed to talk only about his algorithms, he went one step beyond and described better methods for trading using these algorithms. Eyebrows went up in the room. Shaw was essentially seen as overstepping his bounds as a programmer. The higher-ups told him clearly that his job was simply building the computer architecture. He could leave the trading to them. Shaw quit and started his own company. Ten years later, it was one of the most successful hedge funds in the world and Shaw was a billionaire. One can only speculative how much the Morgan Stanley executives cried over the loss they had suffered when Shaw left.

But now D E Shaw is a totally different animal.

One of the most anticipated talks at the ACS meeting was by this Wall Street mover turned pure scientist. He is a remarkable and brilliant man. What other Wall Street hedge fund manager who made billions using mathematical algorithms for trading (and was known as “King Quant” at one point) basically retires from the dizzying world of finance to fully engage himself with computer simulations of proteins? Well, Shaw has done this, and is blazing his way toward some potentially revolutionary research. At the very least it is inspirational to see men with money actually care about basic scientific research.

He heads D E Shaw Research, a company totally separate from the financial powerhouse that has as its long-term goal, a fundamental transformation in the process of drug discovery. As the story goes, Shaw got somewhat bored of making millions and wanted to attack scientific problems that could benefit from the application of advanced computer algorithms. He got his old job as computer science professor at Columbia University and started looking around for the right problem. Fortunately for the field of biochemistry, Shaw started having discussions with a friend of his, the well-known physical chemist Richard Friesner at Columbia who is also the chief scientific advisor for the computational chemistry company Schrodinger. Friesner piqued Shaw’s interest and started giving him little problems in computational chemistry and biology which Shaw solved during his spare time. Finally he realized that MD simulations of proteins which had previously been typically restricted to the nanosecond time range stood a chance of being truly and very significantly useful if they could be expanded to the 10 microsecond-millisecond range, since this is the time scale on which most interesting biological motions such as large conformational changes occur.

Shaw started D E Shaw research and collected a team of highly talented chemists, biologists and computer scientists to tackle the problem. After a decade or so, these efforts have manifested themselves as Desmond, a protein MD program that has vastly accelerated computer simulations of proteins. Desmond essentially relies on many ingenious methods to simplify the calculation of forces and velocities involved in a typical MD computation. It especially calculates the non-bonded forces- the sheer number of which constitutes the bottleneck in these kinds of calculations- with unprecedented efficiency. What is even more remarkable is that Shaw’s group has designed ‘Anton’, a 512 node state-of-the-art machine, a special purpose machine explicitly designed for protein MD and named after Anton van Leeuwenhoek, the legendary 17th century Dutch scientist who trained the microscope on the microbial world and unearthed a wondrous universe teeming with life. Just like the 17th century Anton probed the events of the bacterial world, the 21st century Anton seeks to probe the molecular-level events of the protein world, The machine does only MD, and it does this using a razor sharp scalpel.

To give an idea of the kind of quantum leap Anton provides for MD simulation, Shaw gave some numbers, and I can swear I saw some people who were almost nodding off suddenly become wide awake. According to Shaw, the fastest supercomputer which does parallel processing today can crunch about 200 ns/day for a typical sized protein. Anton surpasses this number by two orders of magnitudes and spews out 17,400 ns or 17 microseconds per day. Such numbers would have been unthinkable a decade ago; until Desmond appeared on the scene, the world record for long protein MD simulations had been held by a group from the University of Illinois, with a total time of 10 microseconds.

So what’s the significance of being able to simulate in this time scale? Tremendous. It’s like the difference between nuclear weapons and the biggest conventional bombs previously used. When nukes arrived on the scene, some politicians like Winston Churchill shrugged them off by thinking that they were “just bigger bombs”. But as the old saying goes, quantity can have a quality all of its own. Nuclear weapons heralded a completely new era of warfare because of the ability of a single weapon to raze a whole city. The basic unit of destruction changed from a human being to entire cities. Desmond and Anton promise such conceptual transformations. As mentioned before, breaking the 10 microsecond barrier is a real turning point since most interesting physiological events happen on time scales of microseconds-milliseconds.

Entering the world of millisecond simulations is like unlocking the door to a rainforest with millions of exotic species that you suspected existed, but which you had no way of viewing and studying. In the last few years, Desmond has been used to study highly significant conduction events in ion channels, has been used to reconcile experimental and conceptual contradictions in the structure of GPCRs, and has been used to study very large conformational changes in kinases. All these events are very slow with respect to conventional MD. Shaw showed some spectacular examples of proteins actually folding and unfolding multiple times. In some cases his group has obtained quantitative agreement with kinetics and NMR experiments.

I think it was the end of the talk which made a few jaws drop. When you have a protein structure and want to find out a small molecule which can modulate its activity, one of the key goals is to first find out where the small molecule binds. With the kinds of time scales available, Shaw can achieve this with a devastatingly straightforward simulation. In a video that appeared a little surreal, he simply let the molecule roam all around the protein surface and find the binding pocket. Like a curious dog sniffing around for the buried bone, the little guy went in and out of crevices and gullies, lingered for some time outside the binding site, and then, with a little hesitation, finally ensconced himself firmly in his cozy home, having surmounted all the challenges of entropy and desolvation that he had to face.

This may not always be the best method to find binding sites and MD admittedly is not going to transform the process of drug discovery by itself, but what we witnessed in that room on Thursday was a different ball game. One in which the ball had been hit out of the park. More surprises should follow.

Simulations long enough to...put you to sleep

For something as widely used for as long as general anesthetics (GAs), one would think that their molecular mechanism of action would have been fairly understood. Far from it.

From Linus Pauling's theory of gases like xenon acting at high concentrations by forming clathrates to more recent theories of GA action on lipids and now on proteins, tantalizing clues have emerged, but speculation remains rife.

In a recent Acc. Chem. Res. review, a group of researchers explains some recent studies on GA action. Now there's a field that to me seems primed for computational studies. This is for two reasons.

Firstly, experimental information on GAs is hard to come by. Consider their chemical features; halogenated, apolar small molecules lacking polar hydrogen bonding and other interactions, binding to their targets with low affinity (it's interesting that halogenation seems to be a key criterion for GA action). In addition most GAs do not bind to a highly specific active site but instead influence protein action indirectly. Such features make any kind of NMR or x-ray structure determination an enormous challenge.

Secondly, molecular dynamics simulations (MDS) have come of age. With recent programs augmented by tremendous gains in hardware and software, microsecond to millisecond simulations have gradually become a reality. This particular field seems to provide a classic and worthy challenge for MDS, since GAs seem to interact indirectly and subtly with proteins by influencing their local and global dynamics rather than binding to well-defined active pockets. Such dynamic perturbations would fail to be captured during the pico to nanosecond timescales typically sampled by MDS. For instance, the most prevalent belief for GAs right now is that they interact with ligand-gated ion channels like the GABA and NMDA receptor and with potassium ion channels. One hypothesis for the mode of action of halothane is that it binds to the open conformation of a potassium ion channel. The channel stays open for milliseconds, thus thwarting experimental study. However, a millisecond transition provides a robust and respectable challenge for long time-scale MD simulations.

At the same time, caveats abound in the field. For instance it's easy to infer that a GA molecule binds to a certain site and obstructs the motion of a tyrosine residue, thus providing support to fluorescence quenching and other studies. But the results of such studies as well as the all-important site-directed mutagenesis studies are notoriously hard to interpret; indirect influences on protein motion may be construed as direct binding to particular sites. Plus, it seems to me that one can read too much into the mere, rather obvious observation that a molecule binding to a protein site inhibits the motion of some residues; whether that observation translates into a realistic phenomenon may be much harder to glean.

So yes, it seems that GA action provides a fertile field for computer simulation. Long MD simulations generally seem to me to be a solution looking for problems; after all most interesting molecular interactions in the body take place on the order of micro to milliseconds. There is a huge number of important problems waiting to be tackled with such tools. However, interpretations of the results will always have to be guided by the sure hand of experiment, with the always important caveat that when it comes to interpretation, one computational study and one experiment can have several offspring.

Vemparala, S., Domene, C., & Klein, M. (2010). Computational Studies on the Interactions of Inhalational Anesthetics with Proteins Accounts of Chemical Research, 43 (1), 103-110 DOI: 10.1021/ar900149j

How much chemistry can we wring out of the universe?

Chemiotics II (luysii) had a very interesting post on his blog about the number of proteins of a given length that can be constructed from the entire mass of the earth. Comparing the masses of amino acids to the mass of the earth, he demonstrated that all the earth's mass will be pretty much exhausted with all combinations of a protein that's only 41 amino acids long, which is peanuts as far as your typical protein goes. Such calculations have great relevance for the origin of life if we are to understand the design and evolution of biomolecules.

One can ask similar questions about crystals or small organic molecules. For the latter one can similarly show that the number is much more than the number of atoms in the universe. But most naturally occurring organic molecules have a preponderance of certain fragments like benzene rings. Similarly, there are only a certain rather small number of symmetry groups for crystals. Therefore it seems that in reality, we are dealing with modular units which are much smaller in number (although still quite large) rather than the bare individual units which compose proteins/small molecules/crystals. Thus once these modular units evolved, natural selection probably worked on them instead of trying out possible combinations of their individual atoms. Also remember that natural selection can work on a population of individuals- any kind of individuals- if one of them shows even the slightest advantage with respect to replication. In case of sequences of amino acids, such replicative advantages could arise from several features; stability, charge distributions that could serve to protect the sequences from aqueous hydrolysis or attract one sequence to another, or conformational flexibility that could serve to effect flexibility in the functions of the sequence. Any one of these features could serve to "fix" a particular sequence or group of sequences in a pool of sequences.

In case of proteins for instance, one should ponder how many of the many possible sequences considered could be energetically favored. Some sequences that pit bulky or similarly charged amino acids next to each other could be disfavored by steric and electrostatic factors. Also in case of proteins, the conservation of 3D structure relative to sequence must have been a boon for natural selection. For instance, there's an enormous number of sequences that can fold up into alpha helices (although certain amino acids are favored and others are disfavored) or sheets (where amino acid preferences are not as pronounced). Thus one gets the feeling that natural selection could have some flexibility in designing sequences that would fold into energetically favored secondary structural motifs. However this would not work as well for the active sites of enzymes, where very specific amino acids need to be located in very specific positions in order to effect catalysis. But even here, certain amino acids such as histidine and lysine are interchangeable in terms of their acid-base catalysis roles.

A particularly interesting case that comes to my mind is that of amyloid. Once thought to be the province of only proteins like ß-amyloid, it has now been extensively shown (most notably by Christopher Dobson of Cambridge University, for instance see Nature Chemical Biology 5, 15 - 22 2009, doi:10.1038/nchembio.131 ) that virtually any protein can form amyloid under the right conditions. Amyloid may have been evolution's dream, since it could have tremendous flexibility in picking sequences and coercing them to form amyloid-like structures under the right conditions. As work in which I participated demonstrated (Biochemistry, 2008, 47 (38), pp 10018–10026, DOI: 10.1021/bi801081c), the simplest of changes in conditions like temperature and pH are enough to drastically modulate the architecture of amyloid assemblies.

Thus, while there was potentially an infinite pool of possibilities to design proteins from, as evolution proceeded, I think that the funnel of possibilities became narrower and narrower as the units needed to achieve optimum design became more tailored and building-block like. It's a very interesting question to contemplate the details of this matter.

A biochemical parody of Bryan Adams

For some reason when I was in high school Bryan Adams was big, and we used to listen to his songs all the time. These days I find many of his songs too sappy, but I still love some of the melodies and find myself going nostalgically down memory lane when "Summer of '69" or "Everything I Do" or "Cloud Number Nine" wafts on to the air from somewhere.

So yesterday I happened to be looking at a particularly ravishing picture of dihydrofolate reductase (DHFR) and Adams's "Have You Ever Really Loved A Woman" randomly started playing on my iPod and Bam! The two topics meshed together in an ungodly union. So here is my tribute to Bryan Adams with profound apologies...an ode to that perfect protein which we can only covet. The original version is copied first to mitigate the trauma that will follow.

HAVE YOU EVER REALLY LOVED A WOMAN

To really love a woman
To understand her - you gotta know it deep inside
Hear every thought - see every dream
N' give her wings - when she wants to fly
Then when you find yourself lyin' helpless in her arms
You know you really love a woman

When you love a woman you tell her
that she's really wanted
When you love a woman you tell her
that she's the one
she needs somebody to tell her
that it's gonna last forever
So tell me have you ever really
- really really ever loved a woman?

To really love a woman
Let her hold you -
til ya know how she needs to be touched
You've gotta breathe her - really taste her
Til you can feel her in your blood
N' when you can see your unborn children in her eyes
You know you really love a woman

When you love a woman
you tell her that she's really wanted
When you love a woman
you tell her that she's the one
she needs somebody to tell her
that you'll always be together
So tell me have you ever really -
really really ever loved a woman?

You got to give her some faith - hold her tight
A little tenderness - gotta treat her right
She will be there for you, takin' good care of you
Ya really gotta love your woman...

Then when you find yourself lyin' helpless in her arms
You know you really love a woman
When you love a woman you tell her
that she's really wanted
When you love a woman
you tell her that she's the one
she needs somebody to tell her
that it's gonna last forever
So tell me have you ever really
- really really ever loved a woman?

Just tell me have you ever really,
really, really, ever loved a woman? You got to tell me
Just tell me have you ever really,
really, really, ever loved a woman?


HAVE YOU EVER REALLY LOVED A PROTEIN

To really love a protein
To understand her - you gotta know her deep inside
Hear every helix - see every sheet
N' give her energy - when she wants to jiggle
Then when you find yourself staring helpless at her domains
You know you really love a protein

When you love a protein you tell her
that she's really conformationally correct
When you love a protein you tell her
that she's catalytically perfect
she needs somebody to tell her
that her half-life?s gonna last forever
So tell me have you ever really
- really really ever loved a protein?

To really love a protein
Let her hold your high-affinity binders-
til ya know how she needs to be crystallized
You've gotta mass spec her - really sequence her
Til you can feel her atoms in your spectrometer
N' when you can see the unformed hydrogen bonds in her pockets
You know you really love a protein

When you love a protein
you tell her that she's really evolutionarily conserved
When you love a protein you tell her that she's peptidase-digestion preserved
she needs somebody to tell her
that her fold will always hold together
So tell me have you ever really -
really really ever loved a protein?

You got to give her some metal ions - hold her co-factors
A little pH-control - gotta treat her ionization state right
She will be there for you, takin' good care of your ligands
Ya really gotta love your protein...

Then when you find yourself staring helpless at her PDB coordinates
You know you really love a protein
When you love a protein you tell her
that she's really conformationally correct
When you love a protein you tell her
that she's catalytically perfect
she needs somebody to tell her
that her half-life?s gonna last forever
So tell me have you ever really
- really really ever loved a protein?

Just tell me have you ever really,
really, really, ever loved a protein? You got to tell me
Just tell me have you ever really,
really, really, ever loved (that helical, sheety, hydrogen bondalacious) protein?

So salt bridges are not stable in water? Shocking

Three salt bridges seen in this protein in the xtal structure were not observed by detailed NMR experiments in water. Here's the abstract:

NMR investigations have been carried out on the B1 domain of protein G. This protein has six lysine residues, of which three are consistently found to form surface-exposed salt bridges in crystal structures, while the other three are not. The Nζ and Hζ chemical shifts of all six lysines are similar and are not affected significantly by pH titration of the carboxylate groups in the protein, except for a relatively small titration of K39 Nζ. Deuterium isotope effects on nitrogen and proton are of the size expected for a simple hydrated amine (a result supported by density functional theory calculations), and also do not titrate with the carboxylates. The line shapes of the J-coupled 15N signals suggest rapid internal reorientation of all NH3+ groups. pKa values have been measured for all charged side chains except Glu50 and do not show the perturbations expected for salt bridge formation, except that E35 has a Hill coefficient of 0.84. The main differential effect seen is that the lysines that are involved in salt bridges in the crystal display faster exchange of the amine protons with the solvent, an effect attributed to general base catalysis by the carboxylates. This explanation is supported by varying buffer composition, which demonstrates reduced electrostatic shielding at low concentration. In conclusion, the study demonstrates that the six surface-exposed lysines in protein G are not involved in significant salt bridge interactions, even though such interactions are found consistently in crystal structures. However, the intrahelical E35−K39 (i,i+4) interaction is partially present.

The title was meant in half-jest of course and I don't mean to disparage such studies. But I think it just goes to show the kind of difficult, tedious and careful work that has to be often carried out in science even to reach "obvious" conclusions.

An an aside though, this conclusion was not at all obvious for a fair amount of time. There was a vigorous debate in the 90s kicked off by Bruce Tidor's paper arguing that salt bridges are not really that energetically important in protein stabilization, especially on surfaces. People who believed in the intense power of the holy electrostatic attraction did not really believe this. While the debate still continues, to my knowledge the general consensus is now on the side of the original Tidor proposition; salt bridges mostly provide only a marginal energetic gain (1-2 kcal/mol) to protein stability. This has been shown to be so primarily because of the loss in solvation and especially long-range solvation that formation of a salt-bridge incurs. Well, let the "obvious" research continue.

References:
1. Tomlinson, J., Ullah, S., Hansen, P., & Williamson, M. (2009). Characterization of Salt Bridges to Lysines in the Protein G B1 Domain Journal of the American Chemical Society DOI: 10.1021/ja808223p

2. Z.S. Hendsch and B. Tidor. Do salt bridges stabilize proteins? A continuum electrostatic analysis. Protein Sci. 3: 211-226 (1994)