Jan 252012

Imagine that you could get an injection of a protein that would chop up arterial plaques. Imagine that you could drop a plastic bottle into a pool of bacteria that would transform it back into high-grade oil. Imagine that you could take any organic material at all and, with a minimum of planning, transform it into any kind of desired organic chemical with a bare minimum of energy input and no need to purify intermediates. This is the vision behind the applied structural biology of protein design, the holy grail of which is to come up with a way to make enzymes that will perform novel chemistry. A study recently published online in Nature Biotechnology by David Baker’s group (1) suggests that the design process could be improved by crowdsourcing certain parts of the problem to gamers (the paper is paywalled at Nature but freely available via the Foldit site).

To do this, the Baker group used their program Foldit, which they have used previously for predicting three-dimensional protein structures from their amino acid sequences. Rather than predicting a structure from a known sequence, however, the Baker group asked the Foldit players to figure out an amino acid sequence that would generate a desired structure. The goal was to enhance an enzyme that would perform the chemically useful Diels-Alder reaction.

An enzyme is a protein that increases the rate of (catalyzes) a chemical reaction, often by incredible amounts. The best enzymes can increase reaction rates by factors of up to 1017 relative to the same reaction occurring in pure water. Protein design aims to produce artificial enzymes with rate enhancements comparable to their natural counterparts. To do this, biochemists try to design an active site that stabilizes the transition state of a chemical reaction. The transition state is the point of a reaction where the molecules are in their least stable state, and equally likely to revert to substrates or continue on and become products.

Unfortunately, it’s not just as simple as stabilizing a transition state. Enzymes have to bind and release their substrates and products, producing energy landscapes that are at least as complex as the one I have drawn below. Using a protein design protocol they had described in previous publications, Baker’s group managed to produce a weak enzyme. They then asked the Foldit players to help out, by posing some specific challenges to try and stabilize the bound substrates. The Foldit players eventually produced an 18-fold improvement in the enzyme’s kcat/KM value. To understand what that means and what the players accomplished, let’s examine this reaction coordinate:

That’s a busy little figure, but it’s not as bad as it looks. The position up or down in the figure indicates how much energy a state has. The more energy, the less likely the system is to occupy that state. Left to right positions show us how close we are to the desired state of the system, which is to have the product (P) we want separate from the enzyme (E) that catalyzed its production from substrate (S). To move from one stable state to another stable state, you have to push the system over hills (energy barriers) in the landscape, just like pushing a car up a hill. The higher the barrier, the slower that step becomes. For simplicity, this diagram shows only one substrate, but the artificial enzyme had two. We can pretend that the Foldit effort started with an enzyme that resembled the blue curve.

We start with E and S separate from each other in solution (E+S). E and S bind to each other to form ES, releasing binding energy. Here I’ve shown a small barrier between E+S and ES, but in many cases there is no barrier here, or it is negligible. Next S is converted to P, and as you can see there is usually a large energy barrier, at the top of which is the transition state (TS). The height of the barrier is determined by the activation energy, which is affected by the structure of the enzyme-substrate complex. Once P has been formed, the complex dissociates so we have free enzyme and product (E+P). Here I have shown E+P to be a lower-energy state than EP, but this won’t necessarily be true.

In the language of Michaelis-Menten kinetics, this landscape is described by two main parameters. KM, also called the Michaelis constant, describes the balance between E+S and ES, and therefore primarily reflects the binding energy. The larger the binding energy, the more ES will be favored, and the lower KM will be. The turnover number, or kcat (maybe we should call this the Menten constant?) describes the creation of product over time, and in this diagram it depends on the activation energy. Again, the larger the activation energy, the lower kcat will be. However, kcat really just depends on the slowest step of the catalytic cycle. If the largest energy barrier was between EP and E+P, kcat would depend on that barrier. Because kcat/KM is something like a normal rate constant, and combines the values in an easy-to-understand way (a higher kcat/KM means a better enzyme), it’s often used to describe an enzyme’s activity.

So how did the Foldit players improve the activity by a factor of 18? The original enzyme design left part of the active site open to water. Through a series of iterations, the Foldit players filled in this void with a self-stabilizing helix-loop-helix motif (Figure 1b). The upshot of this was that the affinity of the enzyme for both substrates increased. Thus, KM decreased, as shown in Table 1, for both substrates. At the end of the process, the diene bound six times as tightly and the affinity for the dienophile improved by about a factor of three. This accounts for all the observed change in kcat/KM, because kcat was not improved.

Although it may not seem like it, we can also learn a great deal from the fact that kcat did not change. This observation shows that the changes made by the Foldit players did stabilize the TS. Otherwise, the energy barrier would have increased when they stabilized the ES complex. However, the best-case scenario would have been for them to uniquely stabilize TS without improving the energy of ES, because this would effectively lower the energy barrier and increase the reaction rate. Because this didn’t happen, the situation follows the orange curve in the figure above: the ES and TS states have shifted down in energy by the same amount, with no change to the activation energy.

The lack of change in kcat also indicates that the Diels-Alder reaction itself, rather than product dissociation, is rate-limiting for the enzyme. My reasoning here is that the increase in affinity is general. We know that both the ES and TS complexes were stabilized by the changes, so EP probably was too, as shown in the orange curve. If the EP → E+P transition were rate-limiting, these stabilizing mutations would have made the enzyme slower.

The Foldit players made this a better enzyme, but that doesn’t exactly mean that it’s an impressive one. The observed kcat is significantly slower than almost any natural enzyme, and the overall rate enhancement is on the order of 103-104, which is not much better than catalytic antibodies. The success of the Foldit players at improving the affinity of the enzyme for all the bound states suggests that it might be possible to use crowdsourced systems like Foldit to accomplish the more difficult feat of stabilizing a TS, or at least to generate folds that support a pre-defined TS. The ultimate goal is to produce something like the green curve, where substrate binding is stronger and activation energy is lower. I hope that such efforts will be taking place among the Foldit players soon, if they haven’t started already.

Disclaimer: I am part of an ongoing collaboration with David Baker’s group unrelated to the Foldit program.

1) Eiben, C., Siegel, J., Bale, J., Cooper, S., Khatib, F., Shen, B., Players, F., Stoddard, B., Popovic, Z., & Baker, D. (2012). Increased Diels-Alderase activity through backbone remodeling guided by Foldit players Nature Biotechnology DOI: 10.1038/nbt.2109 Also available for free from the Foldit site.

Mar 252008
ResearchBlogging.orgBiological systems have the interesting property that most of the reactions enabling life processes are, when left to their own devices, exceedingly slow. To reach the timescales that we associate with “living”, these reactions must be sped up, which requires the presence of enzymes. Because they significantly enhance reaction rates under conditions that can be encountered almost anywhere, the design of artificial enzymes is an active area of research. In two papers this month, David Baker’s lab describes notable success in designing enzymes in silico to have specific activities with significant (106-fold) rate enhancement.

As a graduate student at UNC, I was fortunate to interact frequently with Richard Wolfenden, who did a great deal of work to find out just how good enzymes are at what they do (1). The fact is that there is a wide range of activities and rate enhancements. The proline isomerase cyclophilin, for instance, achieves a modest 106-fold rate increase, depending on the substrate. In contrast, arginine decarboxylase achieves an amazing rate enhancement of about 1019. Many reactions we think nothing of, such as hydrolysis of a phosphodiester bond (found in nucleic acids) would take millions of years in pure neutral water at 25° C. Of course, deviations from neutral pH and the presence of other molecules greatly enhance these rates, and obviously the same is true of changes in temperature, but this is a useful starting point for comparing enzymes to basal rates.

In the works at hand, collaborative teams involving several labs coordinated by David Baker designed enzymes to perform a novel retro-aldol reaction (2) and the Kemp elimination from 5-nitro-benzisoxazole (3) (a proton abstraction causing a ring to open). The retro-aldol paper is fascinating, particularly because of the multi-step nature of the reaction, but I’m going to focus on the Nature paper because its results are more complete, in that they implemented an appropriate wet-lab extension to the computational procedure.

The fundamental strategy of both papers is the same. For most enzymes it is believed that catalysis occurs because the transition state, the moment when the chemical reaction has the highest energy, is stabilized by the functional groups of the enzyme (see (1), among others). Using their knowledge of chemistry, the researchers of these groups predicted a transition state, and then positioned functional groups of side chains in such a way that they would stabilize this predicted state. They also placed potential bases in an appropriate geometry to attack protons as necessary. This done, they used a program based on Baker’s ROSETTA to predict sequences that would fold to produce this geometry. This required a somewhat more complicated process in the case of the retro-aldol reaction due to its multiple steps.

One interesting outcome was that TIM barrels were a popular choice of this algorithm in both papers. The final results in the Röthlisberger paper are all based on backbones identified by CATH as TIM-barrel folds (explore these scaffolds at the PDB: 1thf, 1a53, 1h61, 1jcl). As the authors note, the TIM barrel is a very common catalytic scaffold in nature, in part because the central β-strands provide a convenient way to orient side-chains towards the catalytic pocket. In both papers, the structures predicted using the ROSETTA algorithm were shown to be very close to the actual result, although they only checked successful catalysts. A comparison of the failed designs to their predicted structures may be of great use in refining the computational approach.

As the above paragraph implies, the groups did in fact succeed in designing enzymes that achieved significant rate enhancements. In the case of the Kemp elimination, the eight enzymes reported had ~5×103 – 2×105 -fold increases in rate over the spontaneous reaction in a very slightly basic solution. This amounted to actual kcat (reaction rate) values of 0.006 – 0.29 s-1, which is significantly slower than is common for enzymes.

In order to improve these results, Röthlisberger et al. turned to the process that produced our own prodigious enzymes in the first place, i.e. evolution. Using a relatively standard in vitro evolution approach, they altered one of the early successes, KE07, which had a kcat of 0.018 s-1. Keep in mind, this was not the best computational design result, just one of the first that worked. This in vitro evolution procedure, in just a few rounds, produced an enzyme with a kcat of 1.37 s-1. While this is still slow for an enzyme, it represents a rate enhancement of ~1×106 over the spontaneous reaction in solution, an acceleration comparable to that of a modest enzyme like cyclophilin.

This is nowhere near a complete journey. I’ve already mentioned that the enzymes produced in these experiments are still quite slow in comparison to the genuine article, and the rate enhancements are still modest. The specificity of the enzymes also has yet to be proven—can these proteins distinguish their targets from a sea of similar molecules, or are they promiscuous catalysts? A further dissection of the failed designs is essential to refining the computational approach employed. More careful consideration of effects beyond the secondary shell, and (as the authors note) backbone dynamics and loop positioning may prove particularly helpful in future iterations.

So, we are not all the way to the creation of a truly proficient man-made enzyme, but this is a tremendous step in that direction. The combination of wet lab and computational approaches proved to be very successful in this case. In principle, it should be possible to incorporate all that was learned in the in vitro evolution experiments into the design algorithm from the start. We will not be designing custom catalysts for biofuel production and bioremediation tomorrow or next week. These results, however, demonstrate substantial promise for the future.

In particular, the retro-aldol paper suggests that this approach will work for multi-step reactions. However, provided that the intermediates are stable and soluble this will not be strictly necessary. So long as efficient catalysts can be designed for each step, the ability of ROSETTA to design protein-protein interfaces will make it possible to assemble functional synthetic or catabolic enzyme cassettes to achieve very complex chemistry with tremendous accelerations over basal rates.

1. Wolfenden, R., Snider, M. (2001). The Depth of Chemical Time and the Power of Enzymes as Catalysts. Accounts of Chemical Research, 34 (12), 938-945. DOI: 10.1021/ar000058i

2. Jiang, L., Althoff, E.A., Clemente, F.R., Doyle, L., Rothlisberger, D., Zanghellini, A., Gallaher, J.L., Betker, J.L., Tanaka, F., Barbas, C.F., Hilvert, D., Houk, K.N., Stoddard, B.L., Baker, D. (2008). De Novo Computational Design of Retro-Aldol Enzymes. Science, 319(5868), 1387-1391. DOI: 10.1126/science.1152692

3. Röthlisberger, D., Khersonsky, O., Wollacott, A.M., Jiang, L., DeChancie, J., Betker, J., Gallaher, J.L., Althoff, E.A., Zanghellini, A., Dym, O., Albeck, S., Houk, K.N., Tawfik, D.S., Baker, D. (2008). Kemp elimination catalysts by computational enzyme design. Nature DOI: 10.1038/nature06879

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