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.

Crystallography and chemistry: The culture issue

Image: Charles Reynolds and ACS Med Chem Letters

As the old saying goes, beware of crystallographers bearing ligands. Charles Reynolds who is a well-known structure-based drug design expert has an editorial in ACS Medicinal Chemistry Letters touching on an issue that lies at the confluence of crystallography, medicinal chemistry and modeling: flaws in protein ligand co-crystal structures. It's a problem with major ramifications for drug design, especially since it sits at the apex of the process and has the power to influence all subsequent steps. It's also an issue that has come up many times before, but like many deep-seated issues this is one that has not quite disappeared from the palette of the structure-based design scientist.

In 2003 Davis, Teague and Gerard Kleywegt (who is incidentally also one of the wittiest conference speakers I have come across) wrote an article pointing out one simple observation: in several PDB structures of proteins co-crystallized with small molecule druglike ligands, the protein seems to be well-resolved and assigned, but the small molecule is often strained, with unrealistic bond lengths, planar aromatic ring atoms, non-planar amide bonds, rings in boat or pseudo chair conformations and clashes between protein and ligand atoms. Now the protein can also be misassigned, and so can water molecules, but it turns out that the problem looms much larger for ligands.

Reynolds's editorial takes another, 2014 look at this 2003 problem. And it seems that while some people have actually become more cognizant of issues in crystal structures, things aren't exactly rosy at this point in time. He points out a 2009 study that located 75% of the structures in the data set whose geometries could be improved by using better restraints.

The first and foremost pitfall that non-specialists fall into when taking a crystal structure at face value is is to assume that whatever they see on that fancy computer screen is...real. The fact though is that, barring any structure solved to better than 1 Å (when was the last time you saw that?) every crystal structure is a model (and while we are on the topic, Morpheus's definition of "real" may also be somewhat relevant here). The raw data is those dots that you see in the x-ray diffraction; everything after that, including the pretty picture that you visualize in Pymol, comes from a series of steps undertaken by the crystallographer that involve intuition, parameter fitting, expert judgement and the divining of complete information from incomplete data. That's potentially a lot of guesswork and approximation, and so it shouldn't be surprising that it often leads to flaws in the results.

So is this problem primarily a technology issue? Not really. Reynolds points out several programs that can now fit ligands to the electron density better and get rid of strain and artifacts; Schrodinger's PrimeX and OpenEye's AFITT are only two prominent examples. Nor is it complicated to find out in the first place whether a ligand might be strained; any scientist who has access to a good molecular mechanics energy minimization program can take the ligand structure out of the protein, minimize it to the nearest local minimum, look at the energy difference (usually > 5kcal/mol for a strained ligand), visualize steric clashes between atoms and reach a reasonable conclusion regarding the feasibility of that particular ligand conformation.

The abundance of methods for both figuring out strained ligand conformations and refining them seems to point to something other than technology as the operative factor in the misinterpretation of crystal structures. I believe the problem, in significant part, is culture. Reynolds alludes to this when he says that "Crystallographers are not chemists". When you are a crystallographer and are in hot pursuit of a protein structure, you are rightly going to experience a moment of ecstasy when that huge hulking hunk of sheets and strands finally appears on your screen. But most crystallographers don't care about that little blimp in the binding site - a small molecule that's often crystallized with the purpose of stabilizing the protein as much as for aiding drug discovery - as they do about their beloved protein. In addition, many crystallographers don't have the knee-jerk, intuitive reaction to, say, rings in boat conformations that a good medicinal chemist or a medicinal chemistry-aware modeler would have.

The unfortunate consequence of all this is that the ligand often just comes along for the ride and the protein's gory structural details are exquisitely teased apart at the expense of the ligand's. Protein love often inevitably translates into ligand hate. For an organic chemist a cyclohexane boat may be a textbook violation of conformational preferences, but for a crystallographer it's a big, hydrophobic group filling up a big, fuzzy halo of electron density. Crystallographers are not chemists.

However, an honest assessment of the problem would not unfairly pin the blame for bad ligand structures on crystallographers alone. The fact is that structure-based drug design is an intimate covenant between crystallographers, medicinal chemists and modelers and true appreciation and progress can only come from each side speaking or at least understanding the other's language. To this end, chemists and modelers need to be aware of crystallographic parameters and need to ask the right questions to the crystallographer, beginning with a simple question about the resolution (even this question is rarer than you may think). A medicinal chemist or modeler who simply plucks the provided structure out of the PDB file and starts using it to design drugs is as guilty as a chemistry-challenged crystallographer.

A typical set of questions a modeler or medicinal chemist might ask the crystallographer is: 

- What's the resolution?
- What are the R-factors and the B-factors? 
- Do you have equal confidence in all parts of the structure? Which parts are more uncertain?
- Are the amides non-planar? 
- Where are the water molecules located? How much confidence do you have in their placement?
- Are atoms supposed to be planar non-planar? 
- Are there any gauche or eclipsed interactions? 
- Are there boats in rings? 
- Have you looked at the strain energy of the ligand?
- How did you refine the ligand?

These questions are not meant to be posed to the crystallographer by men in dark suits in a dimly lit room with bars on the windows, but rather are supposed to provide a reality check on the fidelity of the structure and its potential utility in drug design for all three arms of the SBDD process. The questions are part of a process that allows all three departments to confer and reach an agreement; anyone can and should ask them. They are meant to bring hands together, not to point fingers.

One of the cultural problems in drug discovery is still the reluctance of one group of scientists to adopt at least parts of the cultural behavior of other groups. Organic chemists are quick to look at stereochemistry or unstable functional groups, modelers are not. Modelers are much more prone to look at conformation, organic chemists are not. Crystallographers are far more likely to bear multiple conformations of loops and flexible protein side chains in their minds, the other two parties are not.

The best way to fill these gaps is for each group to speak the language of the other, but until then the optimal solution is to have all of them look at the evidence and emphasize what they think is the most important part. But for that to happen each party has to make as many details of its own domain accessible to the others, and that is partly what is being said here.

Update: As usual, the Yoda of chemistry blogging got there first.

Why the same can be different: The case of the two enantiomers

The R enantiomer (green) allows Tyr337 to adopt
two different orientations. The S (yellow) does not.

Since we were discussing thermodynamics in biological systems the other day, here's a neat example from Angewandte Chemie of a system where thermodynamics reveals something surprising. The authors from Umeå University in Sweden were looking at two enantiomers of a ligand binding acetylcholinesterase. It's a robust, well-studied system and you don't really expect anything unexpected.

Except that it does do something unexpected. The first surprise was that both enantiomers bound with the same binding affinity. This is an observation that violates a central general tenet of biochemistry, namely that ligands and receptors are both chiral and therefore enantiomeric ligands will bind differently. The second surprise was that when they dissected the similar free energy of binding into entropic and enthalpic components, they found that the S enantiomer had a much more unfavorable entropy (1.5 e.u) than the R (8.5 e.u). Since the free energies were the same, this meant that there was enthalpy-entropy compensation, which meant in turn that the S enantiomer must have the more favorable enthalpy.

To investigate the origins of these differences, the two enantiomers were crystallized with the protein. Observation of the binding site indicated something interesting; the R enantiomer bound in a way that allowed a critical tyrosine residue (Tyr337) to adopt two different orientations. However, the S enantiomer shoved an ethyl group next to the tyrosine, essentially precluding this movement. Greater conformational flexibility for the tyrosine translated to greater disorder, hence the more favorable entropy for the R. What about enthalpy? Here it turns out that the S enantiomer, while sacrificing entropic freedom for the tyrosine, compensates by making stronger interactions with it. This was analyzed by quantum chemical calculations on a "reduced" version of the protein. Interestingly, the interactions are not "normal", respectable hydrogen bonds but "unnatural" C-H---O hydrogen bonds. For the R enantiomer, even these relatively weak interactions were enough to confer an enthalpic advantage that offset the entropic disadvantage.

This is why chemistry in general and biochemistry in particular are endlessly interesting; conventional wisdom is always being challenged even in well-studied systems, weak can be important, every example is unique and best of all, surprises lurk around almost every corner. As Arthur Kornberg put it, "I never met a dull enzyme".

Overturning hydrophobic assumptions

One of the most fun things about chemistry is that for every laundry list of examples, there is always a counterexample. The counterexample does not really violate any general principles, but it enriches our understanding of the principle by demonstrating its richness and complexity. And it keeps chemists busy.

One such key principle is the hydrophobic effect, an effect with an astounding range of applicability, from the origin of life to cake baking to drug design. Textbook definitions will tell you that the signature of the "classical" hydrophobic effect is a negative heat capacity change resulting from the union of two unfavorably solvated molecular entities. The nonpolar surface area of the solute is usually proportional to the change in heat capacity. The textbooks will also tell you that the hydrophobic effect is favorable principally because of entropy; the displacement of "unhappy" water molecules that are otherwise uncomfortably bound up in solvating a solute contributes to a net favorable change in free energy. Remember, free energy is composed of both enthalpy and entropy (∆G = ∆H - T∆S) and it's the latter term that's thought to lead to hydrophobic heaven.

But not always. Here's a nice example of a protein-ligand interaction where the improvements in free energy across a series of similar molecules comes not from entropy but from improved enthalpy with the entropy actually being unfavorable. A group from the University of Texas tested the binding of a series of tripeptides against the Grb2 protein SH2 domain. The exact details of the protein are not important; what's important is that the molecules only differed in the size of the cycloalkane ring in the central residue of the peptide- going from a cyclopropane to a cyclohexane. They found that the free energy of binding improves as you go from a 3-membered to a 5-membered ring but not for the reason you expect, namely a greater hydrophobic effect and entropic gain from the larger and more lipophilic rings.

Instead, when they experimentally break down the free energy into enthalpy and entropy using isothermal titration calorimetry (ITC), they find that all the gain in free energy is from enthalpy. They find that every extra methylene group contributes about 0.7 kcal/mol to the interaction. In fact the entropy becomes unfavorable, not favorable as you move up the series. There's another surprise waiting in the crystal structures of the complexes. There are a couple of ordered water molecules stuck in some of the complexes. Ordered water molecules are fixed in one place and are "unhappy", so you would expect these complexes to display unfavorable free energy. Again, you would be surprised. It's the ones without ordered water molecules that have worse free energy. The nail in the coffin of conventional hydrophobic thinking is driven by the observation that the free energy does not even correlate with decreased heat capacity, something that's supposed to be a hallmark of the "classical" hydrophobic effect.

Now it's probably not too surprising to find the enthalpy being favorable; after all as they note, you are making more Van der Waals contacts with the protein with larger rings and greater nonpolar surface area. But in most general cases this value is small, and the dominant contribution to the free energy is supposed to come from the "classical" hydrophobic effect with attendant displacement of waters. Not in this case where enthalpy dominates and entropy worsens. They don't really speculate much on why this may be happening. One factor that comes to my mind is the flexibility of the protein. The improved contacts between the larger rings and the protein may well be enforcing rigidity in the protein, leading to a sort of "ligand enthalpy - protein entropy" compensation. Unfortunately a comparison between bound and unbound protein is precluded by the fact that the free protein forms not a monomer but a domain-swapped dimer. In this case I think that molecular dynamics simulations might be able to shed some light on the flexibility of the free protein compared to the bound structures; it might especially be worthwhile to do this exercise in the absence of the apo structure

Nonetheless, this study provides a nice counterexample to the conventional thermodynamic signature of the hydrophobic effect. The textbooks probably don't need to be rewritten anytime soon, but chemists will continue to be frustrated, busy and amused as they keep trying to tame these unruly creatures, the annoying wrinkles in the data, into an organized whole.

Myslinski, J., DeLorbe, J., Clements, J., & Martin, S. (2011). Protein–Ligand Interactions: Thermodynamic Effects Associated with Increasing Nonpolar Surface Area Journal of the American Chemical Society DOI: 10.1021/ja2068752

Xtreme C-H functionalization: Natural Edition

Blogging has been swamped lately by that miracle called life but I could not help but be drawn to a paper in this week's Science which describes a most unholy and unexpected stabilizing alliance in a protein's innards.

Proteins are known to form cross-links such as disulfide bonds to stabilize interactions with ligands and substrates. Any reasonable chemist would expect these kinds of interactions to be mediated between polar residues. But nature usurps us low-lifes once again. In this week's Science, a group led by Andrew Karplus reveals a stabilizing covalent cross-link between, hold your breath, a valine and a phenylalanine. Who could have imagined these two otherwise blissfully aloof and stable partners suddenly deciding to...bond?

As chemists know however, there is only one kind of chemical entity that can create such havoc with stable functional groups- a metal. It turns out that the protein is a four-helix bundle diiron protein with two Fe atoms bound in proximity to the Val and Phe. The two irons apparently create their own cofactor by neatly supplying electrons to bond the Val and Phe to each other and molding a cosy bed for themselves. The resolution is 1.2 A so the electron density is unambiguous. The function of the unusual cross-link seems to provide a barrier to protect the iron from potential iron chelators; experiments indicate that the iron is rapidly mopped up by chelators in mutants lacking the cross-link. Intriguingly, the real function of the protein itself remains unknown.

Organometallic chemists who are keeping the midnight oil burning trying to use metals to functionalize unreactive C-H bonds would not be too surprised that a metal is mediating such strange interactions. But the observation demonstrates something that chemists are all too familiar with by now- Nature has been there, and it's done that.

Cooley, R., Rhoads, T., Arp, D., & Karplus, P. (2011). A Diiron Protein Autogenerates a Valine-Phenylalanine Cross-Link Science, 332 (6032), 929-929 DOI: 10.1126/science.1205687

The details do matter

Consider a protein-ligand binding model. How easy is it to predict the best and worst binders in terms of affinity? Now, how hard is it to quantitatively rank these binders in terms of free energy of binding?

The former, while not an easy problem, has been solved in various ways multiple times for individual problems. In fact a new docking program is expected to at least achieve the minimal goal of ranking the most active ligands at the top and the least active at the bottom.

However, in spite of impressive advances, the latter problem is still regarded as a holy grail.

Now consider molecular dynamics simulations of proteins. Coarse-grained MD approximates atomistic details by subsuming them into a broader framework; for instance, "united atom" force fields will sometimes treat the hydrogen atoms attached to carbons implicitly without explicitly representing them. Coarse-grained MD has been indispensable for simulating large systems where explicit representation of fine details would be prohibitively time-consuming. But coarse grained MD would not always be able to shed light on cases where the fine details do matter, such as proton transfer in enzyme active sites and the general detailed modeling of enzymatic reactions.

Finally, consider solvation models in molecular simulations, a topic of perpetual development and high interest. Implicit models where the solvent and solute are considered as mean dielectrics and their interaction is modeled as a sum of electrostatic and non-electrostatic interactions are all the rage. They frequently work very well and I have myself used them numerous times. But consider cases where the detailed thermodynamics of individual water molecules in protein active sites need to be modeled. Implicit solvation can be of scant use in such circumstances. The use of implicit solvation often makes general predictions about qualitative differences between protein-ligand interactions possible, but it can mask the detailed reasons for those differences and indeed cannot even account for such differences many times.

Something similar seems to be happening for climate change. It is relatively easy to make general statements about extreme events occurring. It is generally true that putting all that buried CO2 back into the atmosphere as a high entropy substance is probably a bad idea, and that cutting emissions is probably a good idea. My problem is not so much about politicians suggesting such general solutions as it is about them sounding crystal clear about all the scientific details. It's much harder to predict the details about individual effects and 'rank' them in terms of their severity, nor is it easier to rank individual solutions to the problem in terms of relative impact. That is something that is an inherent limitation of the science at this point, and any good scientist worth his salt should acknowledge this. Nonetheless, the science has been declared 'settled' and politicians seem to suggest implementing concrete policies in spite of the coarse-grained nature of the problem. As I mentioned before, the fathers of empirical inquiry Newton, Bacon, Locke, Boyle and Hume would have been rather chagrined with this state of affairs.

It's even more disconcerting to realize that activists propose solar and wind power which could be useful in limited amounts but have by no means proven to be robust, as global high energy density solutions. On the other hand there is nuclear power, a proven existing technology that packs more energy than any other, is clean, decidedly CO2-free and highly efficient and deployable. Yet the same politicians who condemn fossil fuels and talk about climate change constantly fail to tout the one solution that could solve the problem they are trying to address. What can you say when someone ignores a solution to a problem that's staring them in the face?

Freeman Dyson, who has been duly and gratuitously vilified for his skepticism about climate models, said that he lost interest in climate change when the issue turned from scientific to political. One can understand why he said that. The exemplar of tentative scientific understanding was Niels Bohr, and Einstein's quip about him captures the perfect attitude we should all have about complex scientific issues, an attitude that is sadly lost on many climate modelers; Einstein said of Bohr that "He looks like someone who never behaves as if he is in possession of the truth, but one who is perpetually groping".

Gropers are especially encouraged to apply to The Academy.

Assessing the known and unknown unknowns: WYSI(N)WYG

Ken Dill and David Mobley from UCSF have a really nice review in Structure on computational modeling of protein-drug interactions and the problems inherent in the process. I would strongly recommend anyone interested in the challenges of calculating protein-drug binding to read the review, if not for anything else for the copious references provided. The holy grail of most such modeling is to accurately calculate the free energy of binding. For doing this we frequently start with a known structure of a protein-ligand complex. The main point that the authors emphasize is that when we are looking at a single protein-ligand complex, deduced either through crystallography or NMR, we are missing a lot of important things.

Perhaps the most important factor is entropy which is not at all obvious in a single structure. Typically both the protein and the ligand will populate several different conformations in solution. Both will have to pay complex entropic penalties to bind one another. The ligand strain energy (usually estimated at 2-3 kcal/mol for most ligands) also plays an important role. The desolvation cost for the ligand also can prominently figure. In addition both protein and ligand will have some residual entropy even in the bound state. As if this were not enough of a problem, much of the binding energy can come from the entropic gain that the release of water molecules from active sites engenders. Calculating all these entropies for protein, ligand and solvent is important for accurately calculating the free energy of protein-ligand binding. But there are few methods that can accomplish this complex task.

Among the methods reviewed in the article are most of the important methods used currently. Usually the tradeoff for each method is between cost and accuracy. Methods like docking are fast but inaccurate although they can work well on relatively rigid and well-parameterized systems. Docking also typically does not take protein motion and induced-fit effects into account. Slightly better methods are MM-PBSA or MM-GBSA which as the names indicate, combine docking poses with an implicit solvent model (PBSA or GBSA). Entropy and especially protein entropy is largely ignored, but since we are usually comparing similar ligands, such errors are expected to cancel. Going to more advanced techniques, relative free-energy calculations use molecular dynamics (MD) to try to map the detailed potential energy surfaces for both protein and ligand. Absolute free-energy perturbation calculations are perhaps the gold standard in calculating free energies but are hideously expensive. They work best for ligands that are simple.

There is clearly a long way to go before calculation of ∆Gs becomes a practical endeavor in the pharmaceutical industry. There are essentially two factors that contribute to the recalcitrance of the problem. The first factor as indicated is the sheer complexity of the problem; assessing the thermodynamic features of protein, ligand and solvent in multiple configurational and conformational states. The second problem is a problem inherent in nature; the sensitivity of the binding constant to the free energy. As iterated before, the all-holy relation ∆G = -RT ln K ensures that an error of even 1 kcal/mol in calculation will translate to a large error in the binding constant. The myriad complex factors noted above ensure that errors of 2-3 kcal/mol already constitute the limit of what the best methods can give us. Recall that an error of 3 kcal/mol means that you are dead and buried.

But we push on. One equal temper of heroic hearts. Made weak by time and fate, but strong in will. To strive, to seek, to find, and not to yield. At some point we will reach 1 kcal/mol. And then we will sail.

Reference:
Mobley, D., & Dill, K. (2009). Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get” Structure, 17 (4), 489-498 DOI: 10.1016/j.str.2009.02.010