Predicting Drug-Resistant Mutations of HIV Protease

Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance

In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce antibiotic binding without sacrificing too much substrate binding. The property that reflects this is the enzyme "vitality", defined here as the difference between the inhibitor and substrate binding free energies. To predict such mutations, we borrow methodology from computational protein design. We use a Monte Carlo exploration of mutation space and vitality changes, allowing us to rank thousands of mutations and identify ones that might provide resistance through the simple mechanism considered. As an illustration, we chose dihydrofolate reductase, an essential enzyme targeted by several antibiotics. We simulated its complexes with the inhibitor trimethoprim and the substrate dihydrofolate. 20 active site positions were mutated, or "redesigned" individually, then in pairs or quartets. We computed the resulting binding free energy and vitality changes. Out of seven known resistance mutations involving active site positions, five were correctly recovered. Ten positions exhibited mutations with significant predicted vitality gains. Direct couplings between designed positions were predicted to be small, which reduces the combinatorial complexity of the mutation space to be explored. It also suggests that over the course of evolution, resistance mutations involving several positions do not need the underlying point mutations to arise all at once: they can appear and become fixed one after the other.

Design of Acetohydroxyacid Synthase Herbicide-Resistant Germplasm through MB-QSAR and CRISPR/Cas9-Mediated Base-Editing Approaches

The development of herbicide-resistant germplasm is significant in solving the increasingly severe weed problem in crop fields. In this study, we, for the first time, rationally designed a predictable and effective approach to create herbicide-resistant germplasm by combining mutation-dependent biomacromolecular quantitative structure-activity relationship (MB-QSAR) and CRISPR/Cas9-mediated base-editing strategies. Our results showed that the homozygous P197F-G654D-G655S or P197F-G654N-G655S Arabidopsis plants exhibited high resistance to multiple acetohydroxyacid synthase-inhibiting herbicides, including chlorsulfuron, bispyribac-sodium, and flucarbazone-sodium. Additionally, the plants with the homozygous P197S mutant displayed increased susceptibility to bispyribac-sodium than the wild-type but more resistance to flumetsulam than other mutants. Besides, we found that the herbicide resistance levels of the gene-edited plants have a good correlation with MB-QSAR prediction.

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.

Aflatoxin B₁⁻Formamidopyrimidine DNA Adducts: Relationships between Structures, Free Energies, and Melting Temperatures

Thermal stabilities of DNA duplexes containing Gua (g), α- (a) or β-anomer of formamidopyrimidine-N7-9-hydroxy-aflatoxin B₁ (b) differ markedly (T_m: a<g<b), but the underlying molecular origin of this experimentally observed phenomenon is yet to be identified and determined. Here, by employing explicit-solvent molecular dynamics simulations coupled with free-energy calculations using a combined linear-interaction-energy/linear-response-approximation approach, we explain the quantitative differences in Tm in terms of three structural features (bulkiness, order, and compactness) and three energetical contributions (non-polar, electrostatic, and preorganized-electrostatic), and thus advance the current understanding of the relationships between structures, free energies, and thermal stabilities of DNA double helices.

Exploring the Drug Resistance of HCV Protease

Hepatitis C virus (HCV) currently affects several million people across the globe. One of the major classes of drugs against HCV inhibits the NS3/4A protease of the polyprotein chain. Efficacy of these drugs is severely limited due to the high mutation rate that results in several genetically related quasispecies. The molecular mechanism of drug resistance is frequently deduced from structural studies and binding free energies. However, prediction of new mutations requires the evaluation of both binding free energy of the drug as well as the parameters (k_cat and K_M) for the natural substrate. The vitality values offer a good approach to investigate and predict mutations that render resistance to the inhibitor. A successful mutation should only affect the binding of the drug and not the catalytic activity and binding of the natural substrate. In this article, we have calculated the vitality values for four known drug inhibitors that are either currently in use or in clinical trials, evaluating binding free energies by the relevant PDLD/S-LRA method and activation barriers by the EVB method. The molecular details pertaining to resistance are also discussed. We show that our calculations are able to reproduce the catalytic effects and binding free energies in a good agreement with the corresponding observed values. Importantly, previous computational approaches have not been able to achieve this task. The trend for the vitality values is in accordance with experimental findings. Finally, we calculate the vitality values for mutations that have either not been studied experimentally or reported for some inhibitors.

Synthesis and biological evaluations of a series of thaxtomin analogues

Thaxtomins are a unique family of phytotoxins with unique 4-nitroindole and diketopiperazine fragments possessing potential herbicidal activities. This work presents the total synthesis of natural product thaxtomin C and its analogues. The extensive structure-activity relationship study screens four effective compounds, including thaxtomin A and thaxtomin C. It is indicated that 4-nitro indole fragment is essential for phytotoxicity, while benzyl and m-hydroxybenzyl substituents on the diketopiperazine ring are favorable for the efficacy. The N-methylations on indole and diketopiperazine show weak influence on the herbicidal activities. The four selected compounds show effective herbicidal activities against Brassica campestris, Amaranthus retroflexus, and Abutilon theophrasti, which are comparable or better than dichlobenil, even at a dosage of 187.5 g ha(-1). Moreover, these four compounds show good crop-selective properties to different crops and exhibit moderate protoporphyrinogen oxidase (PPO) enzyme inhibition. The antifungal results indicate that thaxtomin C displays inhibition to a wide range of fungi.

Valence bond and enzyme catalysis: a time to break down and a time to build up

Understanding enzyme catalysis and developing ability to control of it are two great challenges in biochemistry. A few successful examples of computational-based enzyme design have proved the fantastic potential of computational approaches in this field, however, relatively modest rate enhancements have been reported and the further development of complementary methods is still required. Herein we propose a conceptually simple scheme to identify the specific role that each residue plays in catalysis. The scheme is based on a breakdown of the total catalytic effect into contributions of individual protein residues, which are further decomposed into chemically interpretable components by using valence bond theory. The scheme is shown to shed light on the origin of catalysis in wild-type haloalkane dehalogenase (wt-DhlA) and its mutants. Furthermore, the understanding gained through our scheme is shown to have great potential in facilitating the selection of non-optimal sites for catalysis and suggesting effective mutations to enhance the enzymatic rate.

Protein design algorithms predict viable resistance to an experimental antifolate

Methods to accurately predict potential drug target mutations in response to early-stage leads could drive the design of more resilient first generation drug candidates. In this study, a structure-based protein design algorithm (K* in the OSPREY suite) was used to prospectively identify single-nucleotide polymorphisms that confer resistance to an experimental inhibitor effective against dihydrofolate reductase (DHFR) from Staphylococcus aureus. Four of the top-ranked mutations in DHFR were found to be catalytically competent and resistant to the inhibitor. Selection of resistant bacteria in vitro reveals that two of the predicted mutations arise in the background of a compensatory mutation. Using enzyme kinetics, microbiology, and crystal structures of the complexes, we determined the fitness of the mutant enzymes and strains, the structural basis of resistance, and the compensatory relationship of the mutations. To our knowledge, this work illustrates the first application of protein design algorithms to prospectively predict viable resistance mutations that arise in bacteria under antibiotic pressure.

Computational study of putative residues involved in DNA synthesis fidelity checking in Thermus aquaticus DNA polymerase I

A fidelity-checking site for DNA polymerase I has been proposed based on recent single-molecule Förster resonance energy transfer studies. The checking site is believed to ensure proper base pairing of the newly inserted nucleotide. Computational studies have been utilized to predict residues involved in this putative checking site on the Klenow and Bacillus fragments. Here, we employ energy decomposition analysis, electrostatic free energy response, and noncovalent interaction plots to identify the residues involved in the hypothesized checking site in the homologous Klenow fragment from Thermus aquaticus (Klentaq). Our results indicate multiple protein residues that show altered interactions for three mispairs compared to the correctly paired DNA dimer. Many of these residues are also conserved along A family polymerases.

Multiscale modeling of biological functions: from enzymes to molecular machines (Nobel Lecture)

A detailed understanding of the action of biological molecules is a pre-requisite for rational advances in health sciences and related fields. Here, the challenge is to move from available structural information to a clear understanding of the underlying function of the system. In light of the complexity of macromolecular complexes, it is essential to use computer simulations to describe how the molecular forces are related to a given function. However, using a full and reliable quantum mechanical representation of large molecular systems has been practically impossible. The solution to this (and related) problems has emerged from the realization that large systems can be spatially divided into a region where the quantum mechanical description is essential (e.g. a region where bonds are being broken), with the remainder of the system being represented on a simpler level by empirical force fields. This idea has been particularly effective in the development of the combined quantum mechanics/molecular mechanics (QM/MM) models. Here, the coupling between the electrostatic effects of the quantum and classical subsystems has been a key to the advances in describing the functions of enzymes and other biological molecules. The same idea of representing complex systems in different resolutions in both time and length scales has been found to be very useful in modeling the action of complex systems. In such cases, starting with coarse grained (CG) representations that were originally found to be very useful in simulating protein folding, and augmenting them with a focus on electrostatic energies, has led to models that are particularly effective in probing the action of molecular machines. The same multiscale idea is likely to play a major role in modeling of even more complex systems, including cells and collections of cells.

Validating computer simulations of enantioselective catalysis; reproducing the large steric and entropic contributions in Candida Antarctica lipase B

The prospect for computer-aided refinement of stereoselective enzymes is further validated by simulating the ester hydrolysis by the wild-type and mutants of CalB, focusing on the challenge of dealing with strong steric effects and entropic contributions. This was done using the empirical valence bond (EVB) method in a quantitative screening of the enantioselectivity, considering both k(cat) and k(cat)/K(M) of the R and S stereoisomers. Although the simulations require very extensive sampling for convergence they give encouraging results and major validation, indicating that our approach offers a powerful tool for computer-aided design of enantioselective enzymes. This is particularly true in cases with large changes in steric effects where alternative approaches may have difficulties in capturing the interplay between steric clashes with the reacting substrate and protein flexibility.

Computer-Aided Protein Directed Evolution: a Review of Web Servers, Databases and other Computational Tools for Protein Engineering

The combination of computational and directed evolution methods has proven a winning strategy for protein engineering. We refer to this approach as computer-aided protein directed evolution (CAPDE) and the review summarizes the recent developments in this rapidly growing field. We will restrict ourselves to overview the availability, usability and limitations of web servers, databases and other computational tools proposed in the last five years. The goal of this review is to provide concise information about currently available computational resources to assist the design of directed evolution based protein engineering experiment.

Structure-based methods for predicting target mutation-induced drug resistance and rational drug design to overcome the problem

Drug resistance has become one of the biggest challenges in drug discovery and/or development and has attracted great research interests worldwide. During the past decade, computational strategies have been developed to predict target mutation-induced drug resistance. Meanwhile, various molecular design strategies, including targeting protein backbone, targeting highly conserved residues and dual/multiple targeting, have been used to design novel inhibitors for combating the drug resistance. In this article we review recent advances in development of computational methods for target mutation-induced drug resistance prediction and strategies for rational design of novel inhibitors that could be effective against the possible drug-resistant mutants of the target.

Computational prediction of residues involved in fidelity checking for DNA synthesis in DNA polymerase I

Recent single-molecule Förster resonance energy transfer studies of DNA polymerase I have led to the proposal of a postinsertion fidelity-checking site. This site is hypothesized to ensure proper base pairing of the newly inserted nucleotide. To help test this hypothesis, we have used energy decomposition, electrostatic free energy response, and noncovalent interaction analysis analyses to identify residues involved in this putative checking site. We have used structures of DNA polymerase I from two different organisms, the Klenow fragment from Escherichia coli and the Bacillus fragment from Bacillus stearothermophilus. Our results point to several residues that show altered interactions for three mispairs compared to the correctly paired DNA dimer. Furthermore, many of these residues are conserved among A family polymerases. The identified residues provide potential targets for mutagenesis studies for investigation of the fidelity-checking site hypothesis.

Validating the vitality strategy for fighting drug resistance

The current challenge in designing effective drugs against HIV-1 is to find novel candidates with high potency, but with a lower susceptibility to mutations associated with drug resistance. Trying to address this challenge, we developed in our previous study (Ishikita and Warshel, Angew Chem Int Ed Engl 2008; 47:697-700) a novel computational strategy for fighting drug resistance by predicting the likely moves of the virus through constraints on binding and catalysis. This has been based on calculating the ratio between the vitality values ((K(i) k(cat)/K(M))(mutant)/(K(i) k(cat)/K(M))(wild-type)) and using it as a guide for predicting the moves of the virus. The corresponding calculations of the binding affinity, K(i), were carried out using the semi-macroscopic version of the protein dipole Langevin dipole (PDLD/S) in its linear response approximation (LRA) in its β version (PDLD/S-LRA/β). We also calculate the proteolytic efficiency, k(cat)/K(M), by evaluating the transition state (TS) binding free energies using the PDLD/S-LRA/β method. Here we provide an extensive validation of our strategy by calculating the vitality of six existing clinical and experimental drug candidates. It is found that the computationally determined vitalities correlate reasonably well with those derived from the corresponding experimental data. This indicates that the calculated vitality may be used to identify mutations that would be most effective for the survival of the virus. Thus, it should be possible to use our approach in screening for mutations that would provide the most effective resistance to any proposed antiviral drug. This ability should be very useful in guiding the design of drug molecules that will lead to the slowest resistance.

Towards quantitative computer-aided studies of enzymatic enantioselectivity: the case of Candida antarctica lipase A

The prospect for consistent computer-aided refinement of stereoselective enzymes is explored by simulating the hydrolysis of enantiomers of an α-substituted ester by wild-type and mutant Candida antarctica lipase A, using several strategies. In particular, we focused on the use of the empirical valence bond (EVB) method in a quantitative screening for enantioselectivity, and evaluate both k(cat) and k(cat)/K(M) of the R and S stereoisomers. We found that an extensive sampling is essential for obtaining converging results. This requirement points towards possible problems with approaches that use a limited conformational sampling. However, performing the proper sampling appears to give encouraging results and to offer a powerful tool for the computer-aided design of enantioselective enzymes. We also explore faster strategies for identifying mutations that will help in augmenting directed-evolution experiments, but these approaches require further refinement.

The linear interaction energy method for the prediction of protein stability changes upon mutation

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.

Electrostatics in proteins and protein-ligand complexes

Accurate computational methods for predicting electrostatic energies are of major importance for our understanding of protein energetics in general for computer-aided drug design as well as for the design of novel biocatalysts and protein therapeutics. Electrostatic energies are of particular importance in such applications as virtual screening, drug design and protein-protein docking due to the high charge density of protein ligands and small-molecule drugs, and the frequent protonation state changes observed when drugs bind to their protein targets. Therefore, the development of a reliable and fast algorithm for the evaluation of electrostatic free energies, as an important contributor to the overall protein energy function, has been the focus for many scientists over the past three decades. In this review we describe the current state-of-the-art in modeling electrostatic effects in proteins and protein-ligand complexes. We focus mainly on the merits and drawbacks of the continuum methodology, and speculate on future directions in refining algorithms for calculating electrostatic energies in proteins using experimental data.

The evolution of catalytic function in the HIV-1 protease

The evolution of species is a complex phenomenon based on the optimization of a multidimensional function referred to as fitness. At the level of biomolecular evolution, the fitness function can be reduced to include physiochemical properties relevant to the biological function of a particular molecule. In this work, questions involving the physical-chemical mechanisms underlying the evolution of HIV-1 protease are addressed through molecular simulation and subsequent analysis of thermodynamic properties related to the activity of the enzyme. Specifically, the impact of 40 single amino acid mutations on the binding affinity toward the matrix/capsid (MA/CA) substrate and corresponding transition state intermediate has been characterized using a molecular mechanics Poisson-Boltzmann surface area approach. We demonstrate that this approach is capable of extracting statistically significant information relevant to experimentally determined catalytic activity. Further, no correlation was observed between the effect of mutations on substrate and transition state binding, suggesting independent evolutionary pathways toward optimizing substrate specificity and catalytic activity. In addition, a detailed analysis of calculated binding affinity data suggests that ground-state destabilization (reduced binding affinity for the substrate) could be a contributing factor in the evolutionary optimization of HIV-1 protease. A numerical model is developed to demonstrate that ground-state destabilization is a valid mechanism for activity optimization given the high concentrations of substrate experienced by the functional enzyme in vivo.

On catalytic preorganization in oxyanion holes: highlighting the problems with the gas-phase modeling of oxyanion holes and illustrating the need for complete enzyme models

Oxyanion holes play a major role in catalyzing enzymatic reactions, yet the corresponding energetics is frequently misunderstood. The main problem may be associated with the nontrivial nature of the electrostatic preorganization effect, without following the relevant formulation. That is, although the energetics of oxyanion holes have been fully quantified in early studies (which include both the enzymatic and reference solution reactions), the findings of these studies are sometimes overlooked, and, in some cases, it is assumed that gas-phase calculations with a fixed model of an oxyanion hole are sufficient for assessing the corresponding effect in the protein. Herein, we present a systematic analysis of this issue, clarifying the problems associated with modeling oxyanions by means of two fixed water molecules (or related constructs). We then re-emphasize the point that the effect of the oxyanion hole is mainly due to the fact that the relevant dipoles are already set in an orientation that stabilizes the TS charges, whereas the corresponding dipoles in solution are randomly oriented, resulting in the need to pay a very large reorganization energy. Simply calculating interaction energies with relatively fixed species cannot capture this crucial point, and considering it may help in advancing rational enzyme design.

Predicting resistance mutations using protein design algorithms

Drug resistance resulting from mutations to the target is an unfortunate common phenomenon that limits the lifetime of many of the most successful drugs. In contrast to the investigation of mutations after clinical exposure, it would be powerful to be able to incorporate strategies early in the development process to predict and overcome the effects of possible resistance mutations. Here we present a unique prospective application of an ensemble-based protein design algorithm, K*, to predict potential resistance mutations in dihydrofolate reductase from Staphylococcus aureus using positive design to maintain catalytic function and negative design to interfere with binding of a lead inhibitor. Enzyme inhibition assays show that three of the four highly-ranked predicted mutants are active yet display lower affinity (18-, 9-, and 13-fold) for the inhibitor. A crystal structure of the top-ranked mutant enzyme validates the predicted conformations of the mutated residues and the structural basis of the loss of potency. The use of protein design algorithms to predict resistance mutations could be incorporated in a lead design strategy against any target that is susceptible to mutational resistance.

Absolute binding free energy calculations: on the accuracy of computational scoring of protein-ligand interactions

Calculating the absolute binding free energies is a challenging task. Reliable estimates of binding free energies should provide a guide for rational drug design. It should also provide us with deeper understanding of the correlation between protein structure and its function. Further applications may include identifying novel molecular scaffolds and optimizing lead compounds in computer-aided drug design. Available options to evaluate the absolute binding free energies range from the rigorous but expensive free energy perturbation to the microscopic linear response approximation (LRA/beta version) and related approaches including the linear interaction energy (LIE) to the more approximated and considerably faster scaled protein dipoles Langevin dipoles (PDLD/S-LRA version) as well as the less rigorous molecular mechanics Poisson-Boltzmann/surface area (MM/PBSA) and generalized born/surface area (MM/GBSA) to the less accurate scoring functions. There is a need for an assessment of the performance of different approaches in terms of computer time and reliability. We present a comparative study of the LRA/beta, the LIE, the PDLD/S-LRA/beta, and the more widely used MM/PBSA and assess their abilities to estimate the absolute binding energies. The LRA and LIE methods perform reasonably well but require specialized parameterization for the nonelectrostatic term. The PDLD/S-LRA/beta performs effectively without the need of reparameterization. Our assessment of the MM/PBSA is less optimistic. This approach appears to provide erroneous estimates of the absolute binding energies because of its incorrect entropies and the problematic treatment of electrostatic energies. Overall, the PDLD/S-LRA/beta appears to offer an appealing option for the final stages of massive screening approaches.

A comprehensive examination of the contributions to the binding entropy of protein-ligand complexes

One of the most important requirements in computer-aided drug design is the ability to reliably evaluate the binding free energies. However, the process of ligand binding is very complex because of the intricacy of the interrelated processes that are difficult to predict and quantify. In fact, the deeper understanding of the origin of the observed binding free energies requires the ability to decompose these free energies to their contributions from different interactions. Furthermore, it is important to evaluate the relative entropic and enthalpic contributions to the overall free energy. Such an evaluation is useful for assessing temperature effects and exploring specialized options in enzyme design. Unfortunately, calculations of binding entropies have been much more challenging than calculations of binding free energies. This work is probably the first to present microscopic evaluation of all of the relevant components to the binding entropy, namely configurational, polar solvation, and hydrophobic entropies. All of these contributions are evaluated by the restraint release approach. The calculated results shed an interesting light on major compensation effects in both the solvation and hydrophobic effect and, despite some overestimate, can provide very useful insight. This study also helps in analyzing some problems with the widely used molecular mechanics/Poisson-Boltzmann surface area approach.

A binding free energy decomposition approach for accurate calculations of the fidelity of DNA polymerases

DNA polymerase beta (pol beta) is a small eukaryotic enzyme with the ability to repair short single-stranded DNA gaps that has found use as a model system for larger replicative DNA polymerases. For all DNA polymerases, the factors determining their catalytic power and fidelity are the interactions between the bases of the base pair, amino acids near the active site, and the two magnesium ions. In this report, we study effects of all three aspects on human pol beta transition state (TS) binding free energies by reproducing a consistent set of experimentally determined data for different structures. Our calculations comprise the combination of four different base pairs (incoming pyrimidine nucleotides incorporated opposite both matched and mismatched purines) with four different pol beta structures (wild type and three mutants). We generate three fragments of the incoming deoxynucleoside 5'-triphosphate-TS and run separate calculations for the neutral base part and the highly charged triphosphate part, using different dielectric constants in order to account for the specific dielectric response. This new approach improves our ability to predict the effect of matched and mismatched base pairing and of mutations in DNA polymerases on fidelity and may be a useful tool in studying the potential of DNA polymerase mutations in the development of cancer. It also supports our point of view with regards to the origin of the structural control of fidelity, allowing for a quantified description of the fidelity of DNA polymerases.

Toward accurate screening in computer-aided enzyme design

The ability to design effective enzymes is one of the most fundamental challenges in biotechnology and in some respects in biochemistry. In fact, such ability would be one of the most convincing manifestations of a full understanding of the origin of enzyme catalysis. In this work, we explore the reliability of different simulation approaches, in terms of their ability to rank different possible active site constructs. This validation is done by comparing the ability of different approaches to evaluate the catalytic contributions of various residues in chorismate mutase. It is demonstrated that the empirical valence bond (EVB) model can serve as a practical yet accurate tool in the final stages of computer-aided enzyme design (CAED). Other approaches for fast screening are also examined and found to be less accurate and mainly useful for qualitative screening of ionized residues. It is pointed out that accurate ranking of different options for enzyme design cannot be accomplished by approaches that cannot capture the electrostatic preorganization effect. This is in particular true with regard to current design approaches that use gas phase or small cluster calculations and then estimate the interaction between the enzyme and the transition state (TS) model rather than the TS binding free energy or the relevant activation free energy. The ability of the EVB model to provide a tool for quantitative ranking in the final stage of CAED may help in progressing toward the design of enzymes whose catalytic power is closer to that of native enzymes than to that of the current generation of designer enzymes.

On the relationship between folding and chemical landscapes in enzyme catalysis

Elucidating the relationship between the folding landscape of enzymes and their catalytic power has been one of the challenges of modern enzymology. The present work explores this issue by using a simplified folding model to generate the free-energy landscape of an enzyme and then to evaluate the activation barriers for the chemical step in different regions of the landscape. This approach is used to investigate the recent finding that an engineered monomeric chorismate mutase exhibits catalytic efficiency similar to the naturally occurring dimer even though it exhibits the properties of an intrinsically disordered molten globule. It is found that the monomer becomes more confined than its native-like counterpart upon ligand binding but still retains a wider catalytic region. Although the overall rate acceleration is still determined by reduction of the reorganization energy, the detailed contribution of different barriers yields a more complex picture for the chemical process than that of a single path. This work provides insight into the relationship between folding landscapes and catalysis. The computational approach used here may also provide a powerful strategy for modeling single-molecule experiments and designing enzymes.

Protein-protein docking and analysis reveal that two homologous bacterial adenylyl cyclase toxins interact with calmodulin differently

Calmodulin (CaM), a eukaryotic calcium sensor that regulates diverse biological activities, consists of N- and C-terminal globular domains (N-CaM and C-CaM, respectively). CaM serves as the activator of CyaA, a 188-kDa adenylyl cyclase toxin secreted by Bordetella pertussis, which is the etiologic agent for whooping cough. Upon insertion of the N-terminal adenylyl cyclase domain (ACD) of CyaA to its targeted eukaryotic cells, CaM binds to this domain tightly ( approximately 200 pm affinity). This interaction activates the adenylyl cyclase activity of CyaA, leading to a rise in intracellular cAMP levels to disrupt normal cellular signaling. We recently solved the structure of CyaA-ACD in complex with C-CaM to elucidate the mechanism of catalytic activation. However, the structure of the interface between N-CaM and CyaA, the formation of which contributes a 400-fold increase of binding affinity between CyaA and CaM, remains elusive. Here, we used site-directed mutations and molecular dynamic simulations to generate several working models of CaM-bound CyaA-ACD. The validity of these models was evaluated by disulfide bond cross-linking, point mutations, and fluorescence resonance energy transfer experiments. Our study reveals that a beta-hairpin region (amino acids 259-273) of CyaA-ACD likely makes contacts with the second calcium binding motif of the extended CaM. This mode of interaction differs from the interaction of N-CaM with anthrax edema factor, which binds N-CaM via its helical domain. Thus, two structurally conserved, bacterial adenylyl cyclase toxins have evolved to utilize distinct binding surfaces and modes of activation in their interaction with CaM, a highly conserved eukaryotic signaling protein.