Electrostatic Complementarity at Ligand Binding Sites: Application to Chorismate Mutase

Electrostatics-Based Computational Design and Experimental Analysis of Buforin II Antimicrobial Peptide Variants with Increased DNA Affinities

Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics in the treatment of bacterial infections in part due to their targeting of generic bacterial structures that make it more difficult to develop drug resistance. In this study, we introduce and implement a design workflow to develop more potent AMPs by improving their electrostatic interactions with DNA, which is a putative intracellular target. Using the existing membrane-translocating AMP buforin II (BF2) as a starting point, we use a computational workflow that integrates electrostatic charge optimization, continuum electrostatics, and molecular dynamics simulations to suggest peptide positions at which a neutral BF2 residue could be substituted with arginine to increase DNA-binding affinity either significantly or minimally, with the latter choice done to determine whether AMP binding affinity depends on charge distribution and not just overall monopole. Our analyses predicted that T1R and L8R BF2 variants would yield substantial and minimal increases in DNA-binding affinity, respectively. These predictions were validated with experimental peptide-DNA binding assays with additional computational analyses providing structural insights. Additionally, experimental measurements of antimicrobial potency showed that a design to increase DNA binding can also yield greater potency. As a whole, this study takes initial steps to support the idea that (i) a design strategy aimed to increase AMP binding affinity to DNA by focusing only on electrostatic interactions can improve AMP potency and (ii) the effect on DNA binding of increasing the overall peptide monopole via arginine substitution depends on the position of the substitution. More broadly, this design strategy is a novel way to increase the potency of other membrane-translocating AMPs that target nucleic acids.

Exploring the interactive mechanism of acarbose with the amylase SusG in the starch utilization system of the human gut symbiont Bacteroides thetaiotaomicron through molecular modeling

The α-amylase, SusG, is a principal component of the Bacteroides thetaiotaomicron (Bt) starch utilization system (Sus) used to metabolize complex starch molecules in the human gastrointestinal (GI) tract. We previously reported the non-microbicidal growth inhibition of Bt by the acarbose-mediated arrest of the Sus as a potential therapeutic strategy. Herein, we report a computational approach using density functional theory (DFT), molecular docking, and molecular dynamics (MD) simulation to explore the interactive mechanism between acarbose and SusG at the atomic level in an effort to understand how acarbose shuts down the Bt Sus. The docking analysis reveals that acarbose binds orthosterically to SusG with a binding affinity of -8.3 kcal/mol. The MD simulation provides evidence of conformational variability of acarbose at the active site of SusG and also suggests that acarbose interacts with the main catalytic residues via a general acid-base double-displacement catalytic mechanism. These results suggest that small molecule competitive inhibition against the SusG protein could impact the entire Bt Sus and eliminate or reduce the system's ability to metabolize starch. This computational strategy could serve as a potential avenue for structure-based drug design to discover other small molecules capable of inhibiting the Sus of Bt with high potency, thus providing a holistic approach for selective modulation of the GI microbiota.

De novo design of peptides that coassemble into β sheet-based nanofibrils

Peptides’ hierarchical coassembly into nanostructures enables controllable fabrication of multicomponent biomaterials. In this work, we describe a computational and experimental approach to design pairs of charge-complementary peptides that selectively coassemble into β-sheet nanofibers when mixed together but remain unassembled when isolated separately. The key advance is a peptide coassembly design (PepCAD) algorithm that searches for pairs of coassembling peptides. Six peptide pairs are identified from a pool of ~10⁶ candidates via the PepCAD algorithm and then subjected to DMD/PRIME20 simulations to examine their co-/self-association kinetics. The five pairs that spontaneously aggregate in kinetic simulations selectively coassemble in biophysical experiments, with four forming β-sheet nanofibers and one forming a stable nonfibrillar aggregate. Solid-state NMR, which is applied to characterize the coassembling pairs, suggests that the in silico peptides exhibit a higher degree of structural order than the previously reported CATCH(+/−) peptides.

Structure based design, stability study and synthesis of the dinitrophenylhydrazone derivative of the oxidation product of lanosterol as a potential P. falciparum transketolase inhibitor and in-vivo antimalarial study

The growing resistance to the current antimalarial drugs in the absence of a vaccine can be effectively tackled by identifying new metabolic pathways that are essential to the survival of the malaria parasite and developing new drugs against them. Triterpenes and steroids are the most abundant group of natural products with a great variety of biological activities. However, lanosterol is not known to possess any significant biological activity. In this study the binding and interactions of a dinitrophenyl hydrazine (DNP) derivative of lanosterol, LAN (a derivative that incorporates a substantially polar moiety into the steroid) with P. falciparum transketolase was studied by molecular docking and MD simulation with the view to exploit the DNP derivative as a lead in antimalarial chemotherapy development considering that the P. falciparum transketolase (PfTk) is a novel target in antimalarial chemotherapy. The enzyme catalyses the production of ribose sugars needed for nucleic acid synthesis; it lacks a three-dimensional (3D) structure necessary for docking because it is difficult to obtain a crystalline form. A homology model of PfTk was constructed using Saccharomyces cerevisiae transketolase (protein data bank ID of 1TRK) as the template. The compound was observed to have Free Energy of Binding higher than that of the cofactor of the protein (Thiamine Pyrophosphate, TPP) and a synthetic analog (SUBTPP) used as reference compounds after MD Simulation. The compound was synthesized in a two-step, one-pot reaction, utilizing a non-acidic and mild oxidant to oxidize the lanosterol in order to avoid the rearrangement that accompanies the oxidation of sterols using acidic oxidants. The LAN was characterized using IR spectroscopy and NMR experiments and tested in-vivo for its antimalarial chemo suppression using a murine model with Chloroquine as a standard. The LAN at a concentration of 25 mg/kg was found to have a comparable activity with Chloroquine at 10 mg/kg and no mortality was observed among the test animals 24 days post drug administration showing that the compound indeed has potential as an antimalarial agent and a likely inhibitor of PfTk considering that there is a strong agreement between the in-silico results and biological study.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40203-021-00097-8.

Similarities and Differences between Crystal and Enzyme Environmental Effects on the Electron Density of Drug Molecules

The crystal interaction density is generally assumed to be a suitable measure of the polarization of a low-molecular weight ligand inside an enzyme, but this approximation has seldomly been tested and has never been quantified before. In this study, we compare the crystal interaction density and the interaction electrostatic potential for a model compound of loxistatin acid (E64c) with those inside cathepsin B, in solution, and in vacuum. We apply QM/MM calculations and experimental quantum crystallography to show that the crystal interaction density is indeed very similar to the enzyme interaction density. Less than 0.1 e are shifted between these two environments in total. However, this difference has non-negligible consequences for derived properties.

Identification of Optimal Ligand Growth Vectors Using an Alchemical Free-Energy Method

In this work, a novel method to rationally design inhibitors with improved steric contacts and enhanced binding free energies is presented. This new method uses alchemical single step perturbation calculations to rapidly optimize the van der Waals interactions of a small molecule in a protein-ligand complex in order to maximize its binding affinity. The results of the optimizer are used to predict beneficial growth vectors on the ligand, and good agreement is found between the predictions from the optimizer and a more rigorous free energy calculation, with a Spearman's rank order correlation of 0.59. The advantage of the method presented here is the significant speed up of over 10-fold compared to traditional free energy calculations and sublinear scaling with the number of growth vectors assessed. Where experimental data were available, mutations from hydrogen to a methyl group at sites highlighted by the optimizer were calculated with MBAR, and the mean unsigned error between experimental and calculated values of the binding free energy was 0.83 kcal/mol.

Structure-Activity Relationships for the Affinity of Chaotropic Polyoxometalate Anions towards Proteins

The influence of the composition of chaotropic polyoxometalate (POM) anions on their affinity to biological systems was studied by means of atomistic molecular dynamics (MD) simulations. The variations in the affinity to hen egg-white lysozyme (HEWL) were analyzed along two series of POMs whereby the charge or the size and shape of the metal cluster are modified systematically. Our simulations revealed a quadratic relationship between the charge of the POM and its affinity to HEWL as a consequence of the parabolic growth of POM⋅⋅⋅water interaction with the charge. As the charge increases, POMs become less chaotropic (more kosmotropic) increasing the number and the strength of POM-water hydrogen bonds and structuring the solvation shell around the POM. This atomistic description explains the proportionally larger desolvation energies and less protein affinity for highly charged POMs, and consequently, the preference for moderate charge densities (q/M=0.33). Also, our simulations suggest that POM⋅⋅⋅protein interactions are size-specific. The cationic pockets of HEWL protein show a preference for Keggin-like structures, which display the optimal dimensions (≈1 nm). Finally, we developed a quantitative multidimensional model for protein affinity with predictive ability (r² =0.97; q² =0.88) using two molecular descriptors that account for the charge density (charge per metal atom ratio; q/M) and the size and shape (shape weighted-volume; V_S ).

Optimization of Protein-Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method

We present an explicit solvent alchemical free-energy method for optimizing the partial charges of a ligand to maximize the binding affinity with a receptor. This methodology can be applied to known ligand–protein complexes to determine an optimized set of ligand partial atomic changes. Three protein–ligand complexes have been optimized in this work: FXa, P38, and the androgen receptor. The sets of optimized charges can be used to identify design principles for chemical changes to the ligands which improve the binding affinity for all three systems. In this work, beneficial chemical mutations are generated from these principles and the resulting molecules tested using free-energy perturbation calculations. We show that three quarters of our chemical changes are predicted to improve the binding affinity, with an average improvement for the beneficial mutations of approximately 1 kcal/mol. In the cases where experimental data are available, the agreement between prediction and experiment is also good. The results demonstrate that charge optimization in explicit solvent is a useful tool for predicting beneficial chemical changes such as pyridinations, fluorinations, and oxygen to sulfur mutations.

Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein-Ligand Complexes

Electrostatic interactions between small molecules and their respective receptors are essential for molecular recognition and are also key contributors to the binding free energy. Assessing the electrostatic match of protein-ligand complexes therefore provides important insights into why ligands bind and what can be changed to improve binding. Ideally, the ligand and protein electrostatic potentials at the protein-ligand interaction interface should maximize their complementarity while minimizing desolvation penalties. In this work, we present a fast and efficient tool to calculate and visualize the electrostatic complementarity (EC) of protein-ligand complexes. We compiled benchmark sets demonstrating electrostatically driven structure-activity relationships (SAR) from literature data, including kinase, protein-protein interaction, and GPCR targets, and used these to demonstrate that the EC method can visualize, rationalize, and predict electrostatically driven ligand affinity changes and help to predict compound selectivity. The methodology presented here for the analysis of EC is a powerful and versatile tool for drug design.

Globally Optimal Catalytic Fields - Inverse Design of Abstract Embeddings for Maximum Reaction Rate Acceleration

The search for, and understanding of, good catalysts for chemical reactions is a central issue for chemists. Here, we present first steps toward developing a general computational framework to better support this task. This framework combines efficient, unbiased global optimization techniques with an abstract representation of the catalytic environment, to shrink the search space. To analyze the resulting catalytic embeddings, we employ dimensionality reduction and clustering techniques. This not only provides an inverse design approach to new catalytic embeddings but also illuminates the actual interactions behind catalytic effects. All this is illustrated here with a strictly electrostatic model for the environment and with two versions of a selected example reaction. We close with detailed discussions of future improvements of our framework.

Influential Mutations in the SMAD4 Trimer Complex Can Be Detected from Disruptions of Electrostatic Complementarity

This article examines three techniques for rapidly assessing the electrostatic contribution of individual amino acids to the stability of protein-protein complexes. Whereas the energetic minimization of modeled oligomers may yield more accurate complexes, we examined the possibility that simple modeling may be sufficient to identify amino acids that add to or detract from electrostatic complementarity. The three methods evaluated were (a) the elimination of entire side chains (e.g., glycine scanning), (b) the elimination of the electrostatic contribution from the atoms of a side chain, called nullification, and (c) side chain structure prediction using SCWRL4. These techniques generate models in seconds, enabling large-scale mutational scanning. We evaluated these techniques on the SMAD2/SMAD4 heterotrimer, whose formation plays a crucial role in antitumor pathways. Many studies have documented the clinical and structural effect of specific mutations on trimer formation. Our results describe how glycine scanning yields more specific predictions, although nullification may be more sensitive, and how side chain structure prediction enables the identification of uncharged-to-charge mutations.

Mycobacterium tuberculosis chorismate mutase: A potential target for TB

Mycobacterium tuberculosis chorismate mutase (MtbCM) catalyzes the rearrangement of chorismate to prephenate in the shikimate biosynthetic pathway to form the essential amino acids, phenylalanine and tyrosine. Two genes encoding chorismate mutase have been identified in Mtb. The secretory form,∗MtbCM (encoded by Rv1885c) is assumed to play a key role in pathogenesis of tuberculosis. Also, the inhibition of MtbCM may hinder the supply of nutrients to the organism. Indeed, the existence of chorismate mutase (CM) in bacteria, fungi and higher plants but not in human and low sequence homology among known CM makes it an interesting target for the discovery of anti-tubercular agents. The present article mainly focuses on the recent developments in the structure, function and inhibition of MtbCM. The understanding of various aspects of MtbCM as presented in the current article may facilitate the design and subsequent chemical synthesis of new inhibitors against ∗MtbCM, that could lead to the discovery and development of novel and potent anti-tubercular agents in future.

Sensitivity Analysis and Charge-Optimization for Flexible Ligands: Applicability to Lead Optimization

Sensitivity analysis and charge-optimization have been suggested as methods to guide the optimization of lead compounds in early-stage drug discovery. However, applications to date have been restricted by the simplifying assumption of a rigid ligand. The present study applies both formalisms to the case of a flexible ligand in a model application to an HIV-protease inhibitor. The results suggest that sensitivity analysis is a fast and robust method for guiding charge changes in both a rigid and a flexible ligand, although its accuracy is limited by the fact that it represents a linear approximation. The more complete quadratic analysis provided by charge-optimization produces unexpected results when the ligand is considered to be flexible. For example, it can yield atomic charges which powerfully stabilize the bound conformation of the ligand relative to the conformation assumed for the free state, thus markedly destabilizing the assumed free conformation. Such results are traceable to the fact that the energy matrix possesses negative eigenvalues. However, optimizing charges under the assumption that the ligand does not change conformation upon binding leads to a set of charges that robustly improve affinity, even when the free conformation is later allowed to vary. Thus, both sensitivity analysis and charge-optimization appear to be useful techniques.

The chemistry and pharmacology of privileged pyrroloquinazolines

The advent of next-generation sequencing (NGS) technology has plummeted the cost of whole genome sequencing, which has provided a long list of putative drug targets for a variety of diseases ranging from infectious diseases to cancers. The majority of these drug targets are still awaiting high-quality small molecule ligands to validate their therapeutic potential and track their druggability. Screening compound libraries based on privileged scaffolds is an efficient strategy to identify potential ligands to distinct biological targets. 7H-Pyrrolo[3,2-f]quinazoline (PQZ) is a potential privileged heterocyclic scaffold with diverse pharmacological properties. A number of biological targets have been identified for different derivatives of PQZ. This review summarized the synthetic strategies to access the chemical space associated with PQZ and discussed their unique biological profiles.

Substrate tunnels in enzymes: structure-function relationships and computational methodology

In enzymes, the active site is the location where incoming substrates are chemically converted to products. In some enzymes, this site is deeply buried within the core of the protein, and, in order to access the active site, substrates must pass through the body of the protein via a tunnel. In many systems, these tunnels act as filters and have been found to influence both substrate specificity and catalytic mechanism. Identifying and understanding how these tunnels exert such control has been of growing interest over the past several years because of implications in fields such as protein engineering and drug design. This growing interest has spurred the development of several computational methods to identify and analyze tunnels and how ligands migrate through these tunnels. The goal of this review is to outline how tunnels influence substrate specificity and catalytic efficiency in enzymes with buried active sites and to provide a brief summary of the computational tools used to identify and evaluate these tunnels.

VASP-E: specificity annotation with a volumetric analysis of electrostatic isopotentials

Algorithms for comparing protein structure are frequently used for function annotation. By searching for subtle similarities among very different proteins, these algorithms can identify remote homologs with similar biological functions. In contrast, few comparison algorithms focus on specificity annotation, where the identification of subtle differences among very similar proteins can assist in finding small structural variations that create differences in binding specificity. Few specificity annotation methods consider electrostatic fields, which play a critical role in molecular recognition. To fill this gap, this paper describes VASP-E (Volumetric Analysis of Surface Properties with Electrostatics), a novel volumetric comparison tool based on the electrostatic comparison of protein-ligand and protein-protein binding sites. VASP-E exploits the central observation that three dimensional solids can be used to fully represent and compare both electrostatic isopotentials and molecular surfaces. With this integrated representation, VASP-E is able to dissect the electrostatic environments of protein-ligand and protein-protein binding interfaces, identifying individual amino acids that have an electrostatic influence on binding specificity. VASP-E was used to examine a nonredundant subset of the serine and cysteine proteases as well as the barnase-barstar and Rap1a-raf complexes. Based on amino acids established by various experimental studies to have an electrostatic influence on binding specificity, VASP-E identified electrostatically influential amino acids with 100% precision and 83.3% recall. We also show that VASP-E can accurately classify closely related ligand binding cavities into groups with different binding preferences. These results suggest that VASP-E should prove a useful tool for the characterization of specific binding and the engineering of binding preferences in proteins.

Proteins, the ubiquitous worker molecules of the cell, are a diverse class of molecules that perform very specific tasks. Understanding how proteins achieve specificity is a critical step towards understanding biological systems and a key prerequisite for rationally engineering new proteins. To examine electrostatic influences on specificity in proteins, this paper presents VASP-E, a software tool that generates solid representations of the electrostatic potential fields that surround proteins. VASP-E compares solids with constructive solid geometry, a class of techniques developed first for modeling complex machine parts. We observed that solid representations could quantify the degree of charge complementarity in protein-protein interactions and identify key residues that strengthen or weaken them. VASP-E correctly identified amino acids with established experimental influences on protein-protein binding specificity. We also observed that solid representations of electrostatic fields could identify electrostatic conservations and variations that relate to similarities and differences in binding specificity between proteins and small molecules.

Isolating influential regions of electrostatic focusing in protein and DNA structure

Electrostatic focusing is a general phenomenon that occurs in cavities and grooves on the molecular surface of biomolecules. Narrow surface features can partially shield charged atoms from the high-dielectric solvent, enhancing electrostatic potentials inside the cavity and projecting electric field lines outward into the solvent. This effect has been observed in many instances and is widely considered in the human examination of molecular structure, but it is rarely integrated into the digital representations used in protein structure comparison software. To create a computational representation of electrostatic focusing, that is compatible with structure comparison algorithms, this paper presents an approach that generates three-dimensional solids that approximate regions where focusing occurs. We verify the accuracy of this representation against instances of focusing in proteins and DNA. Noting that this representation also identifies thin focusing regions on the molecular surface that are unlikely to affect binding, we describe a second algorithm that conservatively isolates larger focusing regions. The resulting 3D solids can be compared with Boolean set operations, permitting a new range of analyses on the regions where electrostatic focusing occurs. They also represent a novel integration of molecular shape and electrostatic focusing into the same structure comparison framework.

Discovery of a Potent Anti-tumor Agent through Regioselective Mono-N-acylation of 7H-Pyrrolo[3,2-f]quinazoline-1,3-diamine

7H-Pyrrolo[3,2-f]quinazoline-1,3-diamine (1) is a privileged chemical scaffold with significant biological activities. However, the currently accessible chemical space derived from 1 is rather limited. Here we expanded the chemical space related to 1 by developing efficient methods for regioselective monoacylation at N¹ , N³ and N⁷ , respectively. With this novel methodology, a focused library of mono-N-acylated pyrroloquinazoline-1,3-diamines were prepared and screened for anti-breast cancer activity. The structure-activity relationship (SAR) results showed that N³ -acylated compounds were in general more potent than N¹ -acylated compounds while N⁷ -acylation significantly reduced their solubility. Among the compounds evaluated, 7f possessed 8-fold more potent activity than 1 in MDA-MB-468 cells. More importantly, 7f was not toxic to normal human cells. These results suggest that 7f is a novel compound as a potential anti-breast cancer agent without harming normal cells.

Charge Optimization Theory for Induced-Fit Ligands

The design of ligands with high affinity and specificity remains a fundamental challenge in understanding molecular recognition and developing therapeutic interventions. Charge optimization theory addresses this problem by determining ligand charge distributions that produce the most favorable electrostatic contribution to the binding free energy. The theory has been applied to the design of binding specificity as well. However, the formulations described only treat a rigid ligand-one that does not change conformation upon binding. Here, we extend the theory to treat induced-fit ligands for which the unbound ligand conformation may differ from the bound conformation. We develop a thermodynamic pathway analysis for binding contributions relevant to the theory, and we illustrate application of the theory using HIV-1 protease with our previously designed and validated subnanomolar inhibitor. Direct application of rigid charge optimization approaches to nonrigid cases leads to very favorable intramolecular electrostatic interactions that are physically unreasonable, and analysis shows the ligand charge distribution massively stabilizes the preconformed (bound) conformation over the unbound. After analyzing this case, we provide a treatment for the induced-fit ligand charge optimization problem that produces physically realistic results. The key factor is introducing the constraint that the free energy of the unbound ligand conformation be lower or equal to that of the preconformed ligand structure, which corresponds to the notion that the unbound structure is the ground unbound state. Results not only demonstrate the applicability of this methodology to discovering optimized charge distributions in an induced-fit model, but also provide some insights into the energetic consequences of ligand conformational change on binding. Specifically, the results show that, from an electrostatic perspective, induced-fit binding is not an adaptation designed to enhance binding affinity; at best, it can only achieve the same affinity as optimized rigid binding.

Hybrid schemes based on quantum mechanics/molecular mechanics simulations goals to success, problems, and perspectives

The development of characterization techniques, advanced synthesis methods, as well as molecular modeling has transformed the study of systems in a well-established research field. The current research challenges in biocatalysis and biotransformation evolve around enzyme discovery, design, and optimization. How can we find or create enzymes that catalyze important synthetic reactions, even reactions that may not exist in nature? What is the source of enzyme catalytic power? To answer these and other related questions, the standard strategies have evolved from trial-and-error methodologies based on chemical knowledge, accumulated experience, and common sense into a clearly multidisciplinary science that allows one to reach the molecular design of tailor-made enzyme catalysts. This is even more so when one refers to enzyme catalysts, for which the detailed structure and composition are known and can be manipulated to introduce well-defined residues which can be implicated in the chemical rearrangements taking place in the active site. The methods and techniques of theoretical and computational chemistry are becoming more and more important in both understanding the fundamental biological roles of enzymes and facilitating their utilization in biotechnology. Improvement of the catalytic function of enzymes is important from scientific and industrial viewpoints, and to put this fact in the actual perspective as well as the potentialities, we recommend the very recent report of Sanderson [Sanderson, K. (2011). Chemistry: enzyme expertise. Nature 471, 397.]. Great fundamental advances have been made toward the ab initio design of enzyme catalysts based on molecular modeling. This has been based on the molecular mechanistic knowledge of the reactions to be catalyzed, together with the development of advanced synthesis and characterization techniques. The corresponding molecular mechanism can be studied by means of powerful quantum chemical calculations. The catalytic active site can be optimized to improve the transition state analogues (TSA) and to enhance the catalytic activity, even improve the active site to favor a desired direction of some promiscuous enzymes. In this chapter, we give a brief introduction, the state of the art, and future prospects and implications of enzyme design. Current computational tools to assist experimentalists for the design and engineering of proteins with desired catalytic properties are described. The interplay between enzyme design, molecular simulations, and experiments will be presented to emphasize the interdisciplinary nature of this research field. This text highlights the recent advances and examples selected from our laboratory are shown, of how the applications of these tools are a first attempt to de novo design of protein active sites. Identification of neutral/advantageous/deleterious mutation platforms can be exploited to penetrate some of Nature's closely guarded secrets of chemical reactivity. In this chapter, we give a brief introduction, the state of the art, and future prospects and implications of enzyme design. The first part describes briefly how the molecular modeling is carried out. Then, we discuss the requirements of hybrid quantum mechanical/molecular mechanics molecular dynamics (QM/MM MD) simulations, analyzing what are the basis of these theoretical methodologies, how we can use them with a view to its application in the study of enzyme catalysis, and what are the best methodologies for assessing its catalytic potential. In the second part, we focus on some selected examples, taking as a common guide the chorismate to prephenate rearrangement, studying the corresponding molecular mechanism in vacuo, in solution and in an enzyme environment. In addition, examples involving catalytic antibodies (CAs) and promiscuous enzymes will be presented. Finally, a special emphasis is made to provide some hints about the logical evolution that can be anticipated in this research field. Moreover, it helps in understanding the open directions in this area of knowledge and highlights the importance of computational approaches in discovering specific drugs and the impact on the rational design of tailor-made enzymes.

Energetic decomposition with the generalized-born and Poisson-Boltzmann solvent models: lessons from association of G-protein components

Continuum electrostatic models have been shown to be powerful tools in providing insight into the energetics of biomolecular processes. While the Poisson-Boltzmann (PB) equation provides a theoretically rigorous approach to computing electrostatic free energies of solution in such a model, computational cost makes its use for large ensembles of states impractical. The generalized-Born (GB) approximation provides a much faster alternative, although with a weaker theoretical framework. While much attention has been given to how GB recapitulates PB energetics for the overall stability of a biomolecule or the affinity of a complex, little attention has been given to how the contributions of individual functional groups are captured by the two methods. Accurately capturing these individual electrostatic components is essential both for the development of a mechanistic understanding of biomolecular processes and for the design of variant sequences and structures with desired properties. Here, we present a detailed comparison of the group-wise decomposition of both PB and GB electrostatic free energies of binding, using association of various components of the heterotrimeric-G-protein complex as a model. We find that, while net binding free energies are strongly correlated in the two models, the correlations of individual group contributions are highly variable; in some cases, strong correlation is seen, while in others, there is essentially none. Structurally, the GB model seems to capture the magnitude of direct, short-range electrostatic interactions quite well but performs more poorly with moderate-range "action-at-a-distance" interactions--GB has a tendency to overestimate solvent screening over moderate distances, and to underestimate the costs of desolvating charged groups somewhat removed from the binding interface. Despite this, however, GB does seem to be quite effective as a predictor of those groups that will be computed to be most significant in a PB-based model.

On the diversity of physicochemical environments experienced by identical ligands in binding pockets of unrelated proteins

Most function prediction methods that identify cognate ligands from binding site analyses work on the assumption of molecular complementarity. These approaches build on the conjectured complementarity of geometrical and physicochemical properties between ligands and binding sites so that similar binding sites will bind similar ligands. We found that this assumption does not generally hold for protein-ligand interactions and observed that it is not the chemical composition of ligand molecules that dictates the complementarity between protein and ligand molecules, but that the ligand's share within the functional mechanism of a protein determines the degree of complementarity. Here, we present for a set of cognate ligands a descriptive analysis and comparison of the physicochemical properties that each ligand experiences in various nonhomologous binding pockets. The comparisons in each ligand set reveal large variations in their experienced physicochemical properties, suggesting that the same ligand can bind to distinct physicochemical environments. In some protein ligand complexes, the variation was found to correlate with the electrochemical characteristic of ligand molecules, whereas in others it was disclosed as a prerequisite for the biochemical function of the protein. To achieve binding, proteins were observed to engage in subtle balancing acts between electrostatic and hydrophobic interactions to generate stabilizing free energies of binding. For the presented analysis, a new method for scoring hydrophobicity from molecular environments was developed showing high correlations with experimental determined desolvation energies. The presented results highlight the complexities of molecular recognition and underline the challenges of computational structural biology in developing methods to detect these important subtleties.

Binding of small-molecule ligands to proteins: "what you see" is not always "what you get"

We review insights from computational studies of affinities of ligands binding to proteins. The power of structural biology is in translating knowledge of protein structures into insights about their forces, binding, and mechanisms. However, the complementary power of computer modeling is in showing "the rest of the story" (i.e., how motions and ensembles and alternative conformers and the entropies and forces that cannot be seen in single molecular structures also contribute to binding affinities). Upon binding to a protein, a ligand can bind in multiple orientations; the protein or ligand can be deformed by the binding event; waters, ions, or cofactors can have unexpected involvement; and conformational or solvation entropies can sometimes play large and otherwise unpredictable roles. Computer modeling is helping to elucidate these factors.

Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems

Drug resistance is a significant obstacle in the effective treatment of diseases with rapidly mutating targets, such as AIDS, malaria, and certain forms of cancer. Such targets are remarkably efficient at exploring the space of functional mutants and at evolving to evade drug binding while still maintaining their biological role. To overcome this challenge, drug regimens must be active against potential target variants. Such a goal may be accomplished by one drug molecule that recognizes multiple variants or by a drug "cocktail"--a small collection of drug molecules that collectively binds all desired variants. Ideally, one wants the smallest cocktail possible due to the potential for increased toxicity with each additional drug. Therefore, the task of designing a regimen for multiple target variants can be framed as an optimization problem--find the smallest collection of molecules that together "covers" the relevant target variants. In this work, we formulate and apply this optimization framework to theoretical model target ensembles. These results are analyzed to develop an understanding of how the physical properties of a target ensemble relate to the properties of the optimal cocktail. We focus on electrostatic variation within target ensembles, as it is one important mechanism by which drug resistance is achieved. Using integer programming, we systematically designed optimal cocktails to cover model target ensembles. We found that certain drug molecules covered much larger regions of target space than others, a phenomenon explained by theory grounded in continuum electrostatics. Molecules within optimal cocktails were often dissimilar, such that each drug was responsible for binding variants with a certain electrostatic property in common. On average, the number of molecules in the optimal cocktails correlated with the number of variants, the differences in the variants' electrostatic properties at the binding interface, and the level of binding affinity required. We also treated cases in which a subset of target variants was to be avoided, modeling the common challenge of closely related host molecules that may be implicated in drug toxicity. Such decoys generally increased the size of the required cocktail and more often resulted in infeasible optimizations. Taken together, this work provides practical optimization methods for the design of drug cocktails and a theoretical, physics-based framework through which useful insights can be achieved.

Evaluation of electrostatic interactions

Described here are several computational procedures for the analysis of electrostatic interactions in molecular complexes, all based on a continuum model of solvation. The first section describes how to compute the residual potential, a measure of how electrostatically complementary a ligand is for its receptor. The second procedure describes electrostatic component analysis, a method by which the electrostatic contribution to the binding free energy can be broken up into terms directly attributable to individual chemical groups. Finally, electrostatic affinity optimization is described. This procedure is particularly useful in determining what portions of a ligand are the most suboptimal, and thus provide the greatest opportunity for the design of improvements.

Computational design and experimental study of tighter binding peptides to an inactivated mutant of HIV-1 protease

Drug resistance in HIV-1 protease, a barrier to effective treatment, is generally caused by mutations in the enzyme that disrupt inhibitor binding but still allow for substrate processing. Structural studies with mutant, inactive enzyme, have provided detailed information regarding how the substrates bind to the protease yet avoid resistance mutations; insights obtained inform the development of next generation therapeutics. Although structures have been obtained of complexes between substrate peptide and inactivated (D25N) protease, thermodynamic studies of peptide binding have been challenging due to low affinity. Peptides that bind tighter to the inactivated protease than the natural substrates would be valuable for thermodynamic studies as well as to explore whether the structural envelope observed for substrate peptides is a function of weak binding. Here, two computational methods-namely, charge optimization and protein design-were applied to identify peptide sequences predicted to have higher binding affinity to the inactivated protease, starting from an RT-RH derived substrate peptide. Of the candidate designed peptides, three were tested for binding with isothermal titration calorimetry, with one, containing a single threonine to valine substitution, measured to have more than a 10-fold improvement over the tightest binding natural substrate. Crystal structures were also obtained for the same three designed peptide complexes; they show good agreement with computational prediction. Thermodynamic studies show that binding is entropically driven, more so for designed affinity enhanced variants than for the starting substrate. Structural studies show strong similarities between natural and tighter-binding designed peptide complexes, which may have implications in understanding the molecular mechanisms of drug resistance in HIV-1 protease.

Novel method for probing the specificity binding profile of ligands: applications to HIV protease

A detailed understanding of factors influencing the binding specificity of a ligand to a set of desirable targets and undesirable decoys is a key step in the design of potent and selective therapeutics. We have developed a general method for optimizing binding specificity in ligand-receptor complexes based on the theory of electrostatic charge optimization. This methodology can be used to tune the binding of a ligand to a panel of potential targets and decoys, along the continuum from narrow binding to only one partner to broad binding to the entire panel. Using HIV-1 protease as a model system, we probe specificity in three distinct ways. First, we probe interactions that could make the promiscuous protease inhibitor pepstatin more selective toward HIV-1 protease. Next, we study clinically approved HIV-1 protease inhibitors and probe ways to broaden the binding profiles toward both wild-type HIV-1 protease and drug-resistant mutants. Finally, we study a conformational ensemble of wild-type HIV-1 protease to 'design in' broad specificity to known drugs before resistance mutations arise. The results from this conformational ensemble were similar to those from the drug-resistant ensemble, suggesting the use of a conformational wild-type ensemble as a tool to develop escape-mutant-resistant inhibitors.

Exploring inhibitor binding at the S' subsites of cathepsin L

We report a series of noncovalent, reversible inhibitors of cathepsin L that have been designed to explore additional binding interactions with the S' subsites. The design was based on our previously reported crystal structure that suggested the possibility of engineering increased interactions with the S' subsites ( Chowdhury et al. J. Med. Chem. 2002, 45, 5321-5329 ). A representative of these new inhibitors has been co-crystallized with mature cathepsin L, and the structure has been solved and refined at 2.2 A. The inhibitors described in this work extend farther into the S' subsites of cathepsins than any inhibitors reported in the literature thus far. These interactions appear to make use of a S3' subsite that can potentially be exploited for enhanced specificity and/or affinity.

Specificity in molecular design: a physical framework for probing the determinants of binding specificity and promiscuity in a biological environment

Binding specificity is an important consideration in drug design. An effective drug molecule often must bind with high specificity to its intended target in the body; lower specificity implies the possibility of significant binding to unintended partners, which could instigate deleterious side effects. However, if the target is a rapidly mutating agent, a drug that is too specific will quickly lose its efficacy by not binding well to functional mutants. Therefore, in molecular design, it is crucial to tailor the binding specificity of a drug to the problem at hand. In practice, specificity is often studied on a case-by-case basis, and it is difficult to create general understanding of the determinants of specificity from the union of such available cases. In this work, we undertook a comprehensive, general study of molecular binding with emphasis on understanding the determinants of specificity from a physical standpoint. By extending a theoretical framework grounded in continuum electrostatics and creating an abstracted lattice model that captures key physical aspects of binding interactions, we systematically explored the relationship between a molecule's physical characteristics and its binding specificity toward potential partners. The theory and simulated binding interactions suggested that charged molecules are more specific binders than their hydrophobic counterparts for several reasons. First, the biological spectrum of possible binding characteristics includes more partners that bind equally well to hydrophobic ligands than to charged ligands. Also, charged ligands, whose electrostatic potentials have strong orientational dependence, are more sensitive to shape complementarity than their hydrophobic counterparts. Ligand conformational and orientational flexibility can further influence a charged molecule's ability to bind specifically. Interestingly, we found that conformational flexibility can increase the specificity of polar and charged ligands, by allowing them to greatly lower the binding free energy to a select few partners relative to others. Additionally, factors such as a molecule's size and the ionic strength of the solution were found to predictably affect binding specificity. Taken together, these results, all of which stem from a unified theoretical framework, provide valuable physical insight into the general determinants of binding specificity and promiscuity in a biological environment. The general principles discussed here could prove useful in the design of molecules with tailored specificities, leading to more effective therapeutics.

Concepts in receptor optimization: targeting the RGD peptide

Synthetic receptors have a wide range of potential applications, but it has been difficult to design low molecular weight receptors that bind ligands with high, "proteinlike" affinities. This study uses novel computational methods to understand why it is hard to design a high-affinity receptor and to explore the limits of affinity, with the bioactive peptide RGD as a model ligand. The M2 modeling method is found to yield excellent agreement with experiment for a known RGD receptor and then is used to analyze a series of receptors generated in silico with a de novo design algorithm. Forces driving binding are found to be systematically opposed by proportionate repulsions due to desolvation and entropy. In particular, strong correlations are found between Coulombic attractions and the electrostatic desolvation penalty and between the mean energy change on binding and the cost in configurational entropy. These correlations help explain why it is hard to achieve high affinity. The change in surface area upon binding is found to correlate poorly with affinity within this series. Measures of receptor efficiency are formulated that summarize how effectively a receptor uses surface area, total energy, and Coulombic energy to achieve affinity. Analysis of the computed efficiencies suggests that a low molecular weight receptor can achieve proteinlike affinity. It is also found that macrocyclization of a receptor can, unexpectedly, increase the entropy cost of binding because the macrocyclic structure further restricts ligand motion.

Coupled atomic charge selectivity for optimal ligand-charge distributions at protein binding sites

Charge optimization as a tool for both analyzing and enhancing binding electrostatics has become an attractive approach over the past few years. An interesting feature of this method for molecular design is that it provides not only the optimal charge magnitudes, but also the selectivity of a particular atomic center for its optimal charge. The current approach to compute the charge selectivity at a given atomic center of a ligand in a particular binding process is based on the binding-energy cost incurred upon the perturbation of the optimal charge distribution by a unit charge at the given atomic center, while keeping the other atomic partial charges at their optimal values. A limitation of this method is that it does not take into account the possible concerted changes in the other atomic charges that may incur a lower energetic cost than perturbing a single charge. Here, we describe a novel approach for characterizing charge selectivity in a concerted manner, taking into account the coupling between the ligand charge centers in the binding process. We apply this novel charge selectivity measure to the celecoxib molecule, a nonsteroidal anti-inflammatory agent binding to cyclooxygenase 2 (COX2), which has been recently shown to also exhibit cross-reactivity toward carbonic anhydrase II (CAII), to which it binds with nanomolar affinity. The uncoupled and coupled charge selectivity profiles over the atomic centers of the celecoxib ligand, binding independently to COX2 and CAII, are analyzed comparatively and rationalized with respect to available experimental data. Very different charge selectivity profiles are obtained for the uncoupled versus coupled selectivity calculations.

Optimal charges in lead progression: a structure-based neuraminidase case study

Collective experience in structure-based lead progression has found electrostatic interactions to be more difficult to optimize than shape-based ones. A major reason for this is that the net electrostatic contribution observed includes a significant nonintuitive desolvation component in addition to the more intuitive intermolecular interaction component. To investigate whether knowledge of the ligand optimal charge distribution can facilitate more intuitive design of electrostatic interactions, we took a series of small-molecule influenza neuraminidase inhibitors with known protein cocrystal structures and calculated the difference between the optimal and actual charge distributions. This difference from the electrostatic optimum correlates with the calculated electrostatic contribution to binding (r(2) = 0.94) despite small changes in binding modes caused by chemical substitutions, suggesting that the optimal charge distribution is a useful design goal. Furthermore, detailed suggestions for chemical modification generated by this approach are in many cases consistent with observed improvements in binding affinity, and the method appears to be useful despite discrete chemical constraints. Taken together, these results suggest that charge optimization is useful in facilitating generation of compound ideas in lead optimization. Our results also provide insight into design of neuraminidase inhibitors.

Testing electrostatic complementarity in enzyme catalysis: hydrogen bonding in the ketosteroid isomerase oxyanion hole

A longstanding proposal in enzymology is that enzymes are electrostatically and geometrically complementary to the transition states of the reactions they catalyze and that this complementarity contributes to catalysis. Experimental evaluation of this contribution, however, has been difficult. We have systematically dissected the potential contribution to catalysis from electrostatic complementarity in ketosteroid isomerase. Phenolates, analogs of the transition state and reaction intermediate, bind and accept two hydrogen bonds in an active site oxyanion hole. The binding of substituted phenolates of constant molecular shape but increasing p K_a models the charge accumulation in the oxyanion hole during the enzymatic reaction. As charge localization increases, the NMR chemical shifts of protons involved in oxyanion hole hydrogen bonds increase by 0.50–0.76 ppm/p K_a unit, suggesting a bond shortening of ˜0.02 Å/p K_a unit. Nevertheless, there is little change in binding affinity across a series of substituted phenolates (ΔΔG = −0.2 kcal/mol/p K_a unit). The small effect of increased charge localization on affinity occurs despite the shortening of the hydrogen bonds and a large favorable change in binding enthalpy (ΔΔH = −2.0 kcal/mol/p K_a unit). This shallow dependence of binding affinity suggests that electrostatic complementarity in the oxyanion hole makes at most a modest contribution to catalysis of ˜300-fold. We propose that geometrical complementarity between the oxyanion hole hydrogen-bond donors and the transition state oxyanion provides a significant catalytic contribution, and suggest that KSI, like other enzymes, achieves its catalytic prowess through a combination of modest contributions from several mechanisms rather than from a single dominant contribution.

Enzymatic reactions require exquisitely detailed molecular interactions. Here the authors show that geometric complementarity is likely more important than electrostatic charge in contributing to the binding necessary for catalytic reactions.

Salting the charged surface: pH and salt dependence of protein G B1 stability

This study shows significant effects of protein surface charges on stability and these effects are not eliminated by salt screening. The stability for a variant of protein G B1 domain was studied in the pH-range of 1.5-11 at low, 0.15 M, and 2 M salt. The variant has three mutations, T2Q, N8D, and N37D, to guarantee an intact covalent chain at all pH values. The stability of the protein shows distinct pH dependence with the highest stability close to the isoelectric point. The stability is pH-dependent at all three NaCl concentrations, indicating that interactions involving charged residues are important at all three conditions. We find that 2 M salt stabilizes the protein at low pH (protein net charge is +6 and total number of charges is 6) but not at high pH (net charge is <or=-6 and total number of charges is >or=18). Furthermore, 0.15 M salt slightly decreases the stability of the protein over the pH range. The results show that a net charge of the protein is destabilizing and indicate that proteins contain charges for reasons other than improved stability. Salt seems to reduce the electrostatic contributions to stability under conditions with few total charges, but cannot eliminate electrostatic effects in highly charged systems.

All Electron Quantum Chemical Calculation of the Entire Enzyme System Confirms a Collective Catalytic Device in the Chorismate Mutase Reaction

To elucidate the catalytic power of enzymes, we analyzed the reaction profile of Claisen rearrangement of Bacillus subtilis chorismate mutase (BsCM) by all electron quantum chemical calculations using the fragment molecular orbital (FMO) method. To the best of our knowledge, this is the first report of ab initio-based quantum chemical calculations of the entire enzyme system, where we provide a detailed analysis of the catalytic factors that accomplish transition-state stabilization (TSS). FMO calculations deliver an ab initio-level estimate of the intermolecular interaction between the substrate and the amino acid residues of the enzyme. To clarify the catalytic role of Arg90, we calculated the reaction profile of the wild-type BsCM as well as Lys90 and Cit90 mutant BsCMs. Structural refinement and the reaction path determination were performed at the ab initio QM/MM level, and FMO calculations were applied to the QM/MM refined structures. Comparison between three types of reactions established two collective catalytic factors in the BsCM reaction: (1) the hydrogen bonds connecting the Glu78-Arg90-substrate cooperatively control the stability of TS relative to the ES complex and (2) the positive charge on Arg90 polarizes the substrate in the TS region to gain more electrostatic stabilization.

Design of improved protein inhibitors of HIV-1 cell entry: Optimization of electrostatic interactions at the binding interface

Continuum electrostatic methods are a powerful tool for the analysis and design of biomolecular complexes, with methodologies that allow for the detailed analysis of the electrostatic contributions to binding affinities and procedures for computing the properties of electrostatically optimal ligands. We have applied these methods to the design of improved inhibitors of HIV-1 cell entry. HIV infection of a cell requires viral-cell membrane fusion, an event partially driven by a large-scale conformational change in the viral membrane glycoprotein gp41. This transformation involves the docking of a helix from the C-terminal region of three gp41 chains against a pre-formed trimeric-coiled coil; several protein constructs that inhibit membrane fusion act by binding to an isolated C-terminal helix and blocking the formation of the fusogenic structure. A detailed analysis of the electrostatic contributions to the binding of one such inhibitor (5-Helix) to a C-terminal helix was performed using the X-ray crystal structure of the core of the HIV-1 gp41 ectodomain as a structural model, and several residues on 5-Helix that make substantial contributions to binding, both favorable and unfavorable, were identified. An electrostatic affinity optimization methodology was applied to the side chains of 5-Helix, with the results showing that significant improvements in binding affinity are possible if the electrostatic contributions to the binding free energy are optimized. Several mutations accessible by experimental methods are suggested, with calculated improvements in binding affinity of as much as 500-fold and greater.

Investigation of Ligand Binding and Protein Dynamics in Bacillus subtilis Chorismate Mutase by Transverse Relaxation Optimized Spectroscopy−Nuclear Magnetic Resonance,

The structural and dynamical consequences of ligand binding to a monofunctional chorismate mutase from Bacillus subtilis have been investigated by solution NMR spectroscopy. TROSY methods were employed to assign 98% of the backbone (1)H(N), (1)H(alpha), (15)N, (13)C', and (13)C(alpha) resonances as well as 86% of the side chain (13)C resonances of the 44 kDa trimeric enzyme at 20 degrees C. This information was used to map chemical shift perturbations and changes in intramolecular mobility caused by binding of prephenate or a transition state analogue to the X-ray structure. Model-free interpretation of backbone dynamics for the free enzyme and its complexes based on (15)N relaxation data measured at 600 and 900 MHz showed significant structural consolidation of the protein in the presence of a bound ligand. In agreement with earlier structural and biochemical studies, substantial ordering of 10 otherwise highly flexible residues at the C-terminus is particularly notable. The observed changes suggest direct contact between this protein segment and the bound ligand, providing support for the proposal that the C-terminus can serve as a lid for the active site, limiting diffusion into and out of the pocket and possibly imposing conformational control over substrate once bound. Other regions of the protein that experience substantial ligand-induced changes also border the active site or lie along the subunit interfaces, indicating that the enzyme adapts dynamically to ligands by a sort of induced fit mechanism. It is believed that the mutase-catalyzed chorismate-to-prephenate rearrangement is partially encounter controlled, and backbone motions on the millisecond time scale, as seen here, may contribute to the reaction barrier.

Characterization of the secreted chorismate mutase from the pathogen Mycobacterium tuberculosis

The gene encompassing ORF Rv1885c with weak sequence similarity to AroQ chorismate mutases (CMs) was cloned from the genome of Mycobacterium tuberculosis and expressed in Escherichia coli. The gene product (*MtCM) complements a CM-deficient E. coli strain, but only if produced without the predicted N-terminal signal sequence typical of M. tuberculosis. The mature *MtCM, which was purified by exploiting its resistance to irreversible thermal denaturation, possesses high CM activity in vitro. The enzyme follows simple Michaelis-Menten kinetics, having a k(cat) of 50 s(-1) and a K(m) of 180 microM (at 30 degrees C and pH 7.5). *MtCM was shown to be a dimer by analytical ultracentrifugation and size-exclusion chromatography. Secondary-structure prediction and CD spectroscopy confirmed that *MtCM is a member of the all-alpha-helical AroQ class of CMs, but it seems to have a topologically rearranged AroQ fold. Because CMs are normally intracellular metabolic enzymes required for the biosynthesis of phenylalanine and tyrosine, the existence of an exported CM in Gram-positive M. tuberculosis is puzzling. The observation that homologs of *MtCM with a predicted export sequence are generally only present in parasitic or pathogenic organisms suggests that secreted CMs may have evolved to participate in some aspect of parasitism or pathogenesis yet to be unraveled.

Differential Transition-State Stabilization in Enzyme Catalysis: Quantum Chemical Analysis of Interactions in the Chorismate Mutase Reaction and Prediction of the Optimal Catalytic Field

Chorismate mutase is a key model system in the development of theories of enzyme catalysis. To analyze the physical nature of catalytic interactions within the enzyme active site and to estimate the stabilization of the transition state (TS) relative to the substrate (differential transition state stabilization, DTSS), we have carried out nonempirical variation-perturbation analysis of the electrostatic, exchange, delocalization, and correlation interactions of the enzyme-bound substrate and transition-state structures derived from ab initio QM/MM modeling of Bacillus subtilis chorismate mutase. Significant TS stabilization by approximately -23 kcal/mol [MP2/6-31G(d)] relative to the bound substrate is in agreement with that of previous QM/MM modeling and contrasts with suggestions that catalysis by this enzyme arises purely from conformational selection effects. The most important contributions to DTSS come from the residues, Arg90, Arg7, Glu78, a crystallographic water molecule, Arg116, and Arg63, and are dominated by electrostatic effects. Analysis of the differential electrostatic potential of the TS and substrate allows calculation of the catalytic field, predicting the optimal location of charged groups to achieve maximal DTSS. Comparison with the active site of the enzyme from those of several species shows that the positions of charged active site residues correspond closely to the optimal catalytic field, showing that the enzyme has evolved specifically to stabilize the TS relative to the substrate.

Escherichia coli glutaminyl-tRNA synthetase is electrostatically optimized for binding of its cognate substrates

Natural evolution has resulted in protein molecules displaying a wide range of binding properties that include extremes of affinity and specificity. A detailed understanding of the principles underlying protein structure-function relationships, particularly with respect to binding properties, would greatly enhance molecular engineering and ligand design studies. Here, we have analyzed the interactions of an aminoacyl-tRNA synthetase for which strong evolutionary pressure has enforced high specificity for substrate binding and catalysis. Electrostatic interactions have been identified as one efficient mechanism for enhancing binding specificity; as such, the effects of charged and polar groups were the focus of this study. The binding of glutaminyl-tRNA synthetase from Escherichia coli to several ligands, including the natural substrates, was analyzed. The electrostatic complementarity of the enzyme to its ligands was assessed using measures derived from affinity optimization theory. The results were independent of the details of the calculational parameters, including the value used for the protein dielectric constant. Glutamine and ATP, two of the natural ligands, were found to be extremely complementary to their binding sites, particularly in regions seen to make electrostatic interactions in the structure. These data suggest that the optimization of electrostatic interactions has played an important role in guiding the evolution of this enzyme. The results also show that the enzyme is able to effectively select for high affinity and specificity for the same chemical moieties both in the context of smaller substrates, and in that of a larger reactive intermediate. The regions of greatest non-complementarity between the enzyme and ligands are the portions of the ligand that make few polar contacts with the binding site, as well as the sites of chemical reaction, where overly strong electrostatic binding interactions with the substrate could hinder catalysis. The results also suggest that the negative charge on the phosphorus center of glutaminyl-adenylate plays an important role in the tight binding of this intermediate, and thus that adenylate analogs that preserve the negative charge in this region may bind substantially tighter than analogs where this group is replaced with a neutral group, such as the sulfamoyl family, which can make similar hydrogen bonds but is uncharged.

Charge optimization of the interface between protein kinases and their ligands

Examining the potential for electrostatic complementarity between a ligand and a receptor is a useful technique for rational drug design, and can demonstrate how a system prioritizes interactions when allowed to optimize its charge distribution. In this computational study, we implemented the previously developed, continuum solvent-based charge optimization theory with a simple, quadratic programming algorithm and the UHBD Poisson-Boltzmann solver. This method allows one to compute the best set of point charges for a ligand or ligand region based on the ligand and receptor shape, and the receptor partial charges, by optimizing the binding free energy obtained from a continuum-solvent model. We applied charge optimization to a fragment of the heat-stable protein kinase inhibitor (PKI) of protein kinase A (PKA), to three flavopiridol inhibitors of CDK2, and to cyclin A which interacts with CDK2 to regulate the cell cycle. We found that a combination of global (involving every charge) and local (involving only charges in a local region) optimization can give useful hints for designing better inhibitors. Although some parts of an inhibitor may already contribute significantly to binding, we found that they could still be the most important targets for modifications to obtain stronger binders. In studying the binding of flavopiridol inhibitors to CDK2, comparable binding affinity could be obtained regardless of whether the net charges of the inhibitors were constrained to -2, -1, 0, 1, or 2 during the optimization. This provides flexibility in inhibitor design when a certain net charge of the inhibitor is desired in addition to strong binding affinity. For the study of the PKA-PKI and CDK2-cyclin A interfaces, we identified residues whose charge distributions are already close to optimal and those whose charge distributions could be refined to further improve binding.

Theoretical insights in enzyme catalysis

In this tutorial review we show how the methods and techniques of computational chemistry have been applied to the understanding of the physical basis of the rate enhancement of chemical reactions by enzymes. This is to answer the question: Why is the activation free energy in enzyme catalysed reactions smaller than the activation free energy observed in solution? Two important points of view are presented: Transition State (TS) theories and Michaelis Complex (MC) theories. After reviewing some of the most popular computational methods employed, we analyse two particular enzymatic reactions: the conversion of chorismate to prephenate catalysed by Bacillus subtilis chorismate mutase, and a methyl transfer from S-adenosylmethionine to catecholate catalysed by catechol O-methyltransferase. The results and conclusions obtained by different authors on these two systems, supporting either TS stabilisation or substrate preorganization, are presented and compared. Finally we try to give a unified view, where a preorganized enzyme active site, prepared to stabilise the TS, also favours those reactive conformations geometrically closer to the TS.