Cluster analysis of consensus water sites in thrombin and trypsin shows conservation between serine proteases and contributions to ligand specificity

Enthalpic Classification of Water Molecules in Target-Ligand Binding

Water molecules play various roles in target-ligand binding. For example, they can be replaced by the ligand and leave the surface of the binding pocket or stay conserved in the interface and form bridges with the target. While experimental techniques supply target-ligand complex structures at an increasing rate, they often have limitations in the measurement of a detailed water structure. Moreover, measurements of binding thermodynamics cannot distinguish between the different roles of individual water molecules. However, such a distinction and classification of the role of individual water molecules would be key to their application in drug design at atomic resolution. In this study, we investigate a quantitative approach for the description of the role of water molecules during ligand binding. Starting from complete hydration structures of the free and ligand-bound target molecules, binding enthalpy scores are calculated for each water molecule using quantum mechanical calculations. A statistical evaluation showed that the scores can distinguish between conserved and displaced classes of water molecules. The classification system was calibrated and tested on more than 1000 individual water positions. The practical tests of the enthalpic classification included important cases of antiviral drug research on HIV-1 protease inhibitors and the Influenza A ion channel. The methodology of classification is based on open source program packages, Gromacs, Mopac, and MobyWat, freely available to the scientific community.

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. In this chapter, we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction.The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 (angiotensin converting enzyme 2) receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

LAWS: Local alignment for water sites-Tracking ordered water in simulations

Accurate modeling of protein-water interactions in molecular dynamics (MD) simulations is important for understanding the molecular basis of protein function. Data from x-ray crystallography can be useful in assessing the accuracy of MD simulations, in particular, the locations of crystallographic water sites (CWS) coordinated by the protein. Such a comparison requires special methodological considerations that take into account the dynamic nature of proteins. However, existing methods for analyzing CWS in MD simulations rely on global alignment of the protein onto the crystal structure, which introduces substantial errors in the case of significant structural deviations. Here, we propose a method called local alignment for water sites (LAWS), which is based on multilateration-an algorithm widely used in GPS tracking. LAWS considers the contacts formed by CWS and protein atoms in the crystal structure and uses these interaction distances to track CWS in a simulation. We apply our method to simulations of a protein crystal and to simulations of the same protein in solution. Compared with existing methods, LAWS defines CWS characterized by more prominent water density peaks and a less-perturbed protein environment. In the crystal, we find that all high-confidence crystallographic waters are preserved. Using LAWS, we demonstrate the importance of crystal packing for the stability of CWS in the unit cell. Simulations of the protein in solution and in the crystal share a common set of preserved CWS that are located in pockets and coordinated by residues of the same domain, which suggests that the LAWS algorithm will also be useful in studying ordered waters and water networks in general.

Predicting Conserved Water Molecules in Binding Sites of Proteins Using Machine Learning Methods and Combining Features

Water molecules play an important role in many biological processes in terms of stabilizing protein structures, assisting protein folding, and improving binding affinity. It is well known that, due to the impacts of various environmental factors, it is difficult to identify the conserved water molecules (CWMs) from free water molecules (FWMs) directly as CWMs are normally deeply embedded in proteins and form strong hydrogen bonds with surrounding polar groups. To circumvent this difficulty, in this work, the abundance of spatial structure information and physicochemical properties of water molecules in proteins inspires us to adopt machine learning methods for identifying the CWMs. Therefore, in this study, a machine learning framework to identify the CWMs in the binding sites of the proteins was presented. First, by analyzing water molecules' physicochemical properties and spatial structure information, six features (i.e., atom density, hydrophilicity, hydrophobicity, solvent-accessible surface area, temperature B-factors, and mobility) were extracted. Those features were further analyzed and combined to reach a higher CWM identification rate. As a result, an optimal feature combination was determined. Based on this optimal combination, seven different machine learning models (including support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), discriminant analysis (DA), naïve Bayes (NB), and ensemble learning (EL)) were evaluated for their abilities in identifying two categories of water molecules, i.e., CWMs and FWMs. It showed that the EL model was the desired prediction model due to its comprehensive advantages. Furthermore, the presented methodology was validated through a case study of crystal 3skh and extensively compared with Dowser++. The prediction performance showed that the optimal feature combination and the desired EL model in our method could achieve satisfactory prediction accuracy in identifying CWMs from FWMs in the proteins' binding sites.

Multitarget, Selective Compound Design Yields Potent Inhibitors of a Kinetoplastid Pteridine Reductase 1

The optimization of compounds with multiple targets is a difficult multidimensional problem in the drug discovery cycle. Here, we present a systematic, multidisciplinary approach to the development of selective antiparasitic compounds. Computational fragment-based design of novel pteridine derivatives along with iterations of crystallographic structure determination allowed for the derivation of a structure–activity relationship for multitarget inhibition. The approach yielded compounds showing apparent picomolar inhibition of T. brucei pteridine reductase 1 (PTR1), nanomolar inhibition of L. major PTR1, and selective submicromolar inhibition of parasite dihydrofolate reductase (DHFR) versus human DHFR. Moreover, by combining design for polypharmacology with a property-based on-parasite optimization, we found three compounds that exhibited micromolar EC₅₀ values against T. brucei brucei while retaining their target inhibition. Our results provide a basis for the further development of pteridine-based compounds, and we expect our multitarget approach to be generally applicable to the design and optimization of anti-infective agents.

Water molecules at protein-drug interfaces: computational prediction and analysis methods

The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.

Characterization of the Trypanosoma brucei Pteridine Reductase Active- Site using Computational Docking and Virtual Screening Techniques

Background:

Human African trypanosomiasis is a fatal disease prevalent in approximately 36 sub-Saharan countries. Emerging reports of drug resistance in Trypanosoma brucei are a serious cause of concern as only limited drugs are available for the treatment of the disease. Pteridine reductase is an enzyme of Trypanosoma brucei.

Methods:

It plays a critical role in the pterin metabolic pathway that is absolutely essential for its survival in the human host. The success of finding a potent inhibitor in structure-based drug design lies within the ability of computational tools to efficiently and accurately dock a ligand into the binding cavity of the target protein. Here we report the computational characterization of Trypanosoma brucei pteridine reductase (Tb-PR) active-site using twenty-four high-resolution co-crystal structures with various drugs. Structurally, the Tb-PR active site can be grouped in two clusters; one with high Root Mean Square Deviation (RMSD) of atomic positions and another with low RMSD of atomic positions. These clusters provide fresh insight for rational drug design against Tb-PR. Henceforth, the effect of several factors on docking accuracy, including ligand and protein flexibility were analyzed using Fred.

Results:

The online server was used to analyze the side chain flexibility and four proteins were selected on the basis of results. The proteins were subjected to small-scale virtual screening using 85 compounds, and statistics were calculated using Bedroc and roc curves. The enrichment factor was also calculated for the proteins and scoring functions. The best scoring function was used to understand the ligand protein interactions with top common compounds of four proteins. In addition, we made a 3D structural comparison between the active site of Tb-PR and Leishmania major pteridine reductase (Lm- PR). We described key structural differences between Tb-PR and Lm-PR that can be exploited for rational drug design against these two human parasites.

Conclusion:

The results indicated that relying just on re-docking and cross-docking experiments for virtual screening of libraries isn’t enough and results might be misleading. Hence it has been suggested that small scale virtual screening should be performed prior to large scale screening.

Solvation Free Energy as a Measure of Hydrophobicity: Application to Serine Protease Binding Interfaces

Solvation and hydrophobicity play a key role in a variety of biological mechanisms. In substrate binding, but also in structure-based drug design, the thermodynamic properties of water molecules surrounding a given protein are of high interest. One of the main algorithms devised in recent years to quantify thermodynamic properties of water is the grid inhomogeneous solvation theory (GIST), which calculates these features on a grid surrounding the protein. Despite the inherent advantages of GIST, the computational demand is a major drawback, as calculations for larger systems can take days or even weeks. Here, we present a GPU accelerated version of the GIST algorithm, which facilitates efficient estimates of solvation free energy even of large biomolecular interfaces. Furthermore, we show that GIST can be used as a reliable tool to evaluate protein surface hydrophobicity. We apply the approach on a set of nine different proteases calculating localized solvation free energies on the surface of the binding interfaces as a measure of their hydrophobicity. We find a compelling agreement with the hydrophobicity of their substrates, i.e., peptides, binding into the binding cleft, and thus our approach provides a reliable description of hydrophobicity characteristics of these biological interfaces.

The role of hydration effects in 5-fluorouridine binding to SOD1: insight from a new 3D-RISM-KH based protocol for including structural water in docking simulations

Misfolded Cu/Zn superoxide dismutase enzyme (SOD1) shows prion-like propagation in neuronal cells leading to neurotoxic aggregates that are implicated in amyotrophic lateral sclerosis (ALS). Tryptophan-32 (W32) in SOD1 is part of a potential site for templated conversion of wild type SOD1. This W32 binding site is located on a convex, solvent exposed surface of the SOD1 suggesting that hydration effects can play an important role in ligand recognition and binding. A recent X-ray crystal structure has revealed that 5-Fluorouridine (5-FUrd) binds at the W32 binding site and can act as a pharmacophore scaffold for the development of anti-ALS drugs. In this study, a new protocol is developed to account for structural (non-displaceable) water molecules in docking simulations and successfully applied to predict the correct docked conformation binding modes of 5-FUrd at the W32 binding site. The docked configuration is within 0.58 Å (RMSD) of the observed configuration. The docking protocol involved calculating a hydration structure around SOD1 using molecular theory of solvation (3D-RISM-KH, 3D-Reference Interaction Site Model-Kovalenko-Hirata) whereby, non-displaceable water molecules are identified for docking simulations. This protocol was also used to analyze the hydrated structure of the W32 binding site and to explain the role of solvation in ligand recognition and binding to SOD1. Structural water molecules mediate hydrogen bonds between 5-FUrd and the receptor, and create an environment favoring optimal placement of 5-FUrd in the W32 binding site.

Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics

Water molecules are an important factor in protein-ligand binding. Upon binding of a ligand with a protein's surface, waters can either be displaced by the ligand or may be conserved and possibly bridge interactions between the protein and ligand. Depending on the specific interactions made by the ligand, displacing waters can yield a gain in binding affinity. The extent to which binding affinity may increase is difficult to predict, as the favorable displacement of a water molecule is dependent on the site-specific interactions made by the water and the potential ligand. Several methods have been developed to predict the location of water sites on a protein's surface, but the majority of methods are not able to take into account both protein dynamics and the interactions made by specific functional groups. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that explicitly accounts for the interaction of both water and small molecule probes with a protein's surface, allowing for their direct competition. This method has previously been shown to identify both active and allosteric sites on a protein's surface. Using a test set of eight systems, we have developed a method using MixMD to identify conserved and displaceable water sites. Conserved sites can be determined by an occupancy-based metric to identify sites which are consistently occupied by water even in the presence of probe molecules. Conversely, displaceable water sites can be found by considering the sites which preferentially bind probe molecules. Furthermore, the inclusion of six probe types allows the MixMD method to predict which functional groups are capable of displacing which water sites. The MixMD method consistently identifies sites which are likely to be nondisplaceable and predicts the favorable displacement of water sites that are known to be displaced upon ligand binding.

Protein conformational flexibility modulates kinetics and thermodynamics of drug binding

Structure-based drug design has often been restricted by the rather static picture of protein-ligand complexes presented by crystal structures, despite the widely accepted importance of protein flexibility in biomolecular recognition. Here we report a detailed experimental and computational study of the drug target, human heat shock protein 90, to explore the contribution of protein dynamics to the binding thermodynamics and kinetics of drug-like compounds. We observe that their binding properties depend on whether the protein has a loop or a helical conformation in the binding site of the ligand-bound state. Compounds bound to the helical conformation display slow association and dissociation rates, high-affinity and high cellular efficacy, and predominantly entropically driven binding. An important entropic contribution comes from the greater flexibility of the helical relative to the loop conformation in the ligand-bound state. This unusual mechanism suggests increasing target flexibility in the bound state by ligand design as a new strategy for drug discovery.

Exploiting the 2-Amino-1,3,4-thiadiazole Scaffold To Inhibit Trypanosoma brucei Pteridine Reductase in Support of Early-Stage Drug Discovery

Pteridine reductase-1 (PTR1) is a promising drug target for the treatment of trypanosomiasis. We investigated the potential of a previously identified class of thiadiazole inhibitors of Leishmania major PTR1 for activity against Trypanosoma brucei (Tb). We solved crystal structures of several TbPTR1-inhibitor complexes to guide the structure-based design of new thiadiazole derivatives. Subsequent synthesis and enzyme- and cell-based assays confirm new, mid-micromolar inhibitors of TbPTR1 with low toxicity. In particular, compound 4m, a biphenyl-thiadiazole-2,5-diamine with IC₅₀ = 16 μM, was able to potentiate the antitrypanosomal activity of the dihydrofolate reductase inhibitor methotrexate (MTX) with a 4.1-fold decrease of the EC₅₀ value. In addition, the antiparasitic activity of the combination of 4m and MTX was reversed by addition of folic acid. By adopting an efficient hit discovery platform, we demonstrate, using the 2-amino-1,3,4-thiadiazole scaffold, how a promising tool for the development of anti-T. brucei agents can be obtained.

Structure-Based Design of Scaffolds Targeting PDE10A by INPHARMA-NMR

Phosphodiesterases (PDE) hydrolyze both cyclic AMP and GMP (cAMP/cGMP) and are responsible for the regulation of their levels in a multitude of cellular functions. PDE10A is expressed in the brain and is a validated target for both schizophrenia and Huntington disease. Here, we address the identification of novel chemical scaffolds that may bind PDE10A via structure-based drug design. For this task, we use INPHARMA, an NMR-based method that measures protein-mediated interligand NOEs between pairs of weakly, competitively binding ligands. INPHARMA is applied to a combination of four chemically diverse PDE10A binding fragments, with the aim of merging their pharmacophoric features into a larger, tighter binding molecule. All four ligands bind the PDE10A cAMP binding domain with affinity in the micromolar range. The application of INPHARMA to identify the correct docking poses of these ligands is challenging due to the nature of the binding pocket and the high content of water-mediated intermolecular contacts. Nevertheless, ensemble docking in the presence of conserved water molecules generates docking poses that are in agreement with all sets of INPHARMA data. These poses are used to build a pharmacophore model with which we search the ZINC database.

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

Chroman-4-One Derivatives Targeting Pteridine Reductase 1 and Showing Anti-Parasitic Activity

Flavonoids have previously been identified as antiparasitic agents and pteridine reductase 1 (PTR1) inhibitors. Herein, we focus our attention on the chroman-4-one scaffold. Three chroman-4-one analogues (1–3) of previously published chromen-4-one derivatives were synthesized and biologically evaluated against parasitic enzymes (Trypanosoma brucei PTR1–TbPTR1 and Leishmania major–LmPTR1) and parasites (Trypanosoma brucei and Leishmania infantum). A crystal structure of TbPTR1 in complex with compound 1 and the first crystal structures of LmPTR1-flavanone complexes (compounds 1 and 3) were solved. The inhibitory activity of the chroman-4-one and chromen-4-one derivatives was explained by comparison of observed and predicted binding modes of the compounds. Compound 1 showed activity both against the targeted enzymes and the parasites with a selectivity index greater than 7 and a low toxicity. Our results provide a basis for further scaffold optimization and structure-based drug design aimed at the identification of potent anti-trypanosomatidic compounds targeting multiple PTR1 variants.

Solvent effects on ligand binding to a serine protease

ITC experiments and MD simulations reveal the mechanism behind enthalpy/entropy compensation upon trypsin-benzamidine binding at different solvation conditions.

Profiling of Flavonol Derivatives for the Development of Antitrypanosomatidic Drugs

Flavonoids represent a potential source of new antitrypanosomatidic leads. Starting from a library of natural products, we combined target-based screening on pteridine reductase 1 with phenotypic screening on Trypanosoma brucei for hit identification. Flavonols were identified as hits, and a library of 16 derivatives was synthesized. Twelve compounds showed EC50 values against T. brucei below 10 μM. Four X-ray crystal structures and docking studies explained the observed structure-activity relationships. Compound 2 (3,6-dihydroxy-2-(3-hydroxyphenyl)-4H-chromen-4-one) was selected for pharmacokinetic studies. Encapsulation of compound 2 in PLGA nanoparticles or cyclodextrins resulted in lower in vitro toxicity when compared to the free compound. Combination studies with methotrexate revealed that compound 13 (3-hydroxy-6-methoxy-2-(4-methoxyphenyl)-4H-chromen-4-one) has the highest synergistic effect at concentration of 1.3 μM, 11.7-fold dose reduction index and no toxicity toward host cells. Our results provide the basis for further chemical modifications aimed at identifying novel antitrypanosomatidic agents showing higher potency toward PTR1 and increased metabolic stability.

Hydration of proteins and nucleic acids: Advances in experiment and theory. A review

BACKGROUND

Most biological processes involve water, and the interactions of biomolecules with water affect their structure, function and dynamics.

SCOPE OF REVIEW

This review summarizes the current knowledge of protein and nucleic acid interactions with water, with a special focus on the biomolecular hydration layer. Recent developments in both experimental and computational methods that can be applied to the study of hydration structure and dynamics are reviewed, including software tools for the prediction and characterization of hydration layer properties.

MAJOR CONCLUSIONS

In the last decade, important advances have been made in our understanding of the factors that determine how biomolecules and their aqueous environment influence each other. Both experimental and computational methods contributed to the gradually emerging consensus picture of biomolecular hydration.

GENERAL SIGNIFICANCE

An improved knowledge of the structural and thermodynamic properties of the hydration layer will enable a detailed understanding of the various biological processes in which it is involved, with implications for a wide range of applications, including protein-structure prediction and structure-based drug design.

WATCLUST: a tool for improving the design of drugs based on protein-water interactions

MOTIVATION

Water molecules are key players for protein folding and function. On the protein surface, water is not placed randomly, but display instead a particular structure evidenced by the presence of specific water sites (WS). These WS can be derived and characterized using explicit water Molecular Dynamics simulations, providing useful information for ligand binding prediction and design. Here we present WATCLUST, a WS determination and analysis tool running on the VMD platform. The tool also allows direct transfer of the WS information to Autodock program to perform biased docking.

AVAILABILITY AND IMPLEMENTATION

The WATCLUST plugin and documentation are freely available at http://sbg.qb.fcen.uba.ar/watclust/.

CONTACT

marcelo@qi.fcen.uba.ar, adrian@qi.fcen.uba.ar.

Protein surface dynamics: interaction with water and small solutes

Previous time resolved measurements had indicated that protons could propagate on the surface of a protein, or a membrane, by a special mechanism that enhances the shuttle of the proton towards a specific site [1]. It was proposed that a proper location of residues on the surface contributes to the proton shuttling function. In the present study, this notion was further investigated using molecular dynamics, with only the mobile charge replaced by Na(+) and Cl(-) ions. A molecular dynamics simulation of a small globular protein (the S6 of the bacterial ribosome) was carried out in the presence of explicit water molecules and four pairs of Na(+) and Cl(-) ions. A 10 ns simulation indicated that the ions and the protein's surface were in equilibrium, with rapid passage of the ions between the protein's surface and the bulk. Yet it was noted that, close to some domains, the ions extended their duration near the surface, suggesting that the local electrostatic potential prevented them from diffusing to the bulk. During the time frame in which the ions were detained next to the surface, they could rapidly shuttle between various attractor sites located under the electrostatic umbrella. Statistical analysis of molecular dynamics and electrostatic potential/entropy consideration indicated that the detainment state is an energetic compromise between attractive forces and entropy of dilution. The similarity between the motion of free ions next to a protein and the proton transfer on the protein's surface are discussed.

CONSERVED WATER MOLECULES IN X-RAY STRUCTURES HIGHLIGHT THE ROLE OF WATER IN INTRAMOLECULAR AND INTERMOLECULAR INTERACTIONS

Water molecules immobilized on a protein or DNA surface are known to play an important role in intramolecular and intermolecular interactions. Comparative analysis of related three-dimensional (3D) structures allows to predict the locations of such water molecules on the protein surface. We have developed and implemented the algorithm WLAKE detecting "conserved" water molecules, i.e. those located in almost the same positions in a set of superimposed structures of related proteins or macromolecular complexes. The problem is reduced to finding maximal cliques in a certain graph. Despite exponential algorithm complexity, the program works appropriately fast for dozens of superimposed structures. WLAKE was used to predict functionally significant water molecules in enzyme active sites (transketolases) as well as in intermolecular (ETS–DNA complexes) and intramolecular (thiol–disulfide interchange protein) interactions. The program is available online at .

Analysis of water patterns in protein kinase binding sites

Deregulation of protein kinases is associated with numerous diseases, making them important targets for drug discovery. The majority of drugs target the catalytic site of these proteins, but due to the high level of similarity within the ATP binding sites of protein kinases, it is often difficult to achieve the required pharmacological selectivity. In this study, we describe the identification and subsequent analysis of water patterns in the ATP binding sites of 171 protein kinase structures, comprising 19 different kinases from various branches of the kinome, and demonstrate that structurally similar binding sites often have significantly different water patterns. We show that the observed variations in water patterns of different, but structurally similar kinases can be exploited in the structure-based design of potent and selective kinase inhibitors.

Ligand-protein cross-docking with water molecules

The accuracy of ligand-protein docking may be affected by the presence of water molecules on the surface of the protein. Cross-docking simulations have been performed on a number of ligand-protein complexes for various proteins whose crystal structures contain water molecules in their binding sites. Only common sets of water molecules found in the binding site of the proteins were considered. A statistically significant overall increase in accuracy was observed when water molecules were included in cross-docking simulations. These results confirm the importance of including water molecules whenever possible in ligand-protein docking simulations.

Computational approaches to identifying and characterizing protein binding sites for ligand design

Given the three-dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape-based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The "druggability" of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations.

Structural flexibility in hydrated proteins

The flexibility of protein structures is important in allowing the variety of motions, covering a wide range of magnitudes and frequencies, essential to biological activity. Protein flexibility is also implicated in denaturation, allowing proteins to take up nonactive conformations that have free energies close to that of the native state. High-frequency dielectric measurement can be used to study the flexibility of proteins by probing the relaxation of dipolar constituents of their structures. In this work, 14 hydrated globular proteins are investigated using this method. Four relaxation processes are identified, one of which, with a relaxation time of 19 ns, can be correlated with the sum of the number densities of protein glycine and alanine residues. A second with a relaxation time of 2 ns shows a dependence on the number of threonine residues. It is concluded that the dipolar peptide groups of the protein backbone associated with these residues are responsible for these dielectric responses, with the lower frequency dispersion being due to backbone mobility in the hydrophobic environment of the protein core and the higher frequency response being associated with mobility on the more hydrophilic protein surface. The correlation of protein backbone flexibility with particular side chains indicates that these protein motions are under the direct control of the amino acid sequence of the protein.

Ligand-protein docking with water molecules

The presence of water molecules plays an important role in the accuracy of ligand-protein docking predictions. Comprehensive docking simulations have been performed on a large set of ligand-protein complexes whose crystal structures contain water molecules in their binding sites. Only those water molecules found in the immediate vicinity of both the ligand and the protein were considered. We have investigated whether prior optimization of the orientation of water molecules in either the presence or absence of the bound ligand has any effect on the accuracy of docking predictions. We have observed a statistically significant overall increase in accuracy when water molecules are included during docking simulations and have found this to be independent of the method of optimization of the orientation of water molecules. These results confirm the importance of including water molecules whenever possible in a ligand-protein docking simulation. Our findings also reveal that prior optimization of the orientation of water molecules, in the absence of any bound ligand, does not have a detrimental effect on the improved accuracy of ligand-protein docking. This is important, given the use of docking simulations to predict the binding modes of new ligands or drug molecules.

Conserved water molecules stabilize the Omega-loop in class A beta-lactamases

A set of 49 high-resolution (<or=2.2 A) structures of the TEM, SHV, and CTX-M class A beta-lactamase families was systematically analyzed to investigate the role of conserved water molecules in the stabilization of the Omega-loop. Overall, 13 water molecules were found to be conserved in at least 45 structures, including two water positions which were found to be conserved in all structures. Of the 13 conserved water molecules, 6 are located at the Omega-loop, forming a dense cluster with hydrogen bonds to residues at the Omega-loop as well as to the rest of the protein. This layer of conserved water molecules is packed between the Omega-loop and the rest of the protein and acts as structural glue, which could reduce the flexibility of the Omega-loop. A correlation between conserved water molecules and conserved protein residues could in general not be detected, with the exception of the conserved water molecules at the Omega-loop. Furthermore, the evolutionary relationship between the three families, derived from the number of conserved water molecules, is similar to the relationship derived from phylogenetic analysis.

Dielectric studies of water clusters in cyclodextrins: Relevance to the transition between slow and fast forms of thrombin

Cyclodextrins are useful models in the study of hydrogen bonded water clusters. In alpha-cyclodextrin hexahydrate (alpha-CD.6H2O), water molecules are ordered and occupy well-defined positions whereas in the larger beta-cyclodextrin dodecahydrate (beta-CD.12H2O), there is considerable disorder with water molecules freely arranged over several possible sites. Here it is shown that beta-CD exhibits substantial structural flexibility and proton mobility compared with alpha-CD which is relatively very rigid and exhibits negligible short-range protonic conduction. These properties are directly controlled by the effective dielectric constant of the molecule, which is determined by the rotational freedom of water molecules in the hydrogen bond network. This model may be relevant to proteins where water clusters of this kind are found on the protein surface and occasionally in the protein interior. The case of thrombin, an allosteric enzyme incorporating a network of 20 internal hydrogen bonded water molecules, is discussed.

NAD+-dependent DNA ligase (Rv3014c) from Mycobacterium tuberculosis: Novel structure-function relationship and identification of a specific inhibitor

Mycobacterium tuberculosis codes for an essential NAD+-dependent DNA ligase (MtuLigA) which is a novel, validated, and attractive drug target. We created mutants of the enzyme by systematically deleting domains from the C-terminal end of the enzyme to probe for their functional roles in the DNA nick joining reaction. Deletion of just the BRCT domain from MtuLigA resulted in total loss of activity in in vitro assays. However, the mutant could form an AMP-ligase intermediate that suggests that the defects caused by deletion of the BRCT domain occur primarily at steps after enzyme adenylation. Furthermore, genetic complementation experiments using a LigA deficient E. coli strain demonstrates that the BRCT domain of MtuLigA is necessary for bacterial survival in contrast to E. coli and T. filiformis LigA, respectively. We also report the identification, through virtual screening, of a novel N-substituted tetracyclic indole that competes with NAD+ and inhibits the enzyme with IC50 in the low muM range. It exhibits approximately 15-fold better affinity for MtuLigA compared to human DNA ligase I. In vivo assays using LigA deficient S. typhimurium and E. coli strains suggest that the observed antibacterial activity of the inhibitor arises from specific inhibition of LigA over ATP ligases in the bacteria. In silico ligand-docking studies suggest that the exquisite specificity of the inhibitor arises on account of its mimicking the interactions of NAD+ with MtuLigA. An analysis of conserved water in the binding site of the enzyme suggests strategies for synthesis of improved inhibitors with better specificity and potency.

Analysis of protein hydration in ultrahigh-resolution structures of the SRP GTPase Ffh

Two new structures of the SRP GTPase Ffh have been determined at 1.1 A resolution and provide the basis for comparative examination of the extensive water structure of the apo conformation of these GTPases. A set of well defined water-binding positions have been identified in the active site of the two-domain ;NG' GTPase, as well as at two functionally important interfaces. The water hydrogen-bonding network accommodates alternate conformations of the protein side chains by undergoing local rearrangements and, in one case, illustrates binding of a solute molecule within the active site by displacement of water molecules without further disruption of the water-interaction network. A subset of the water positions are well defined in several lower resolution structures, including those of different nucleotide-binding states; these appear to function in maintaining the protein structure. Consistent arrangements of surface water between three different ultrahigh-resolution structures provide a framework for beginning to understand how local water structure contributes to protein-ligand and protein-protein binding in the SRP GTPases.

3dSS: 3D structural superposition

3dSS is a web-based interactive computing server, primarily designed to aid researchers, to superpose two or several 3D protein structures. In addition, the server can be effectively used to find the invariant and common water molecules present in the superposed homologous protein structures. The molecular visualization tool RASMOL is interfaced with the server to visualize the superposed 3D structures with the water molecules (invariant or common) in the client machine. Furthermore, an option is provided to save the superposed 3D atomic coordinates in the client machine. To perform the above, users need to enter Protein Data Bank (PDB)-id(s) or upload the atomic coordinates in PDB format. This server uses a locally maintained PDB anonymous FTP server that is being updated weekly. This program can be accessed through our Bioinformatics web server at the URL or .

Exploring structurally conserved solvent sites in protein families

Protein-bound water molecules are important components of protein structure, and therefore, protein function and energetics. Although structural conservation of solvent has been studied in a few protein families, a lack of suitable computational tools has hindered more comprehensive analyses. Herein we present a semiautomated computational approach for identifying solvent sites that are conserved among proteins sharing a common three-dimensional structure. This method is tested on six protein families: (1) monodomain cytochrome c, (2) fatty-acid binding protein, (3) lactate/malate dehydrogenase, (4) parvalbumin, (5) phospholipase A2, and (6) serine protease. For each family, the method successfully identified previously known conserved solvent sites. Moreover, the method discovered 22 novel conserved solvent sites, some of which have higher degrees of conservation than the previously known sites. All six families studied had solvent sites with more than 90% conservation and these sites were invariably located in regions of the protein with very high sequence conservation. These results suggest that highly conserved solvent sites, by virtue of their proximity to conserved residues, should be considered as one of the defining three-dimensional structural characteristics of protein families and folds.

Did protein kinase regulatory mechanisms evolve through elaboration of a simple structural component?

Statistical analysis of the functional constraints acting on eukaryotic protein kinases (EPKs) and on distantly related kinases suggests that EPK regulatory mechanisms evolved around an ancient structural component whose most distinctive features include the HxD-motif adjoining the catalytic loop, the F-helix, an F-helix aspartate, and the DFG-motif adjoined to the activation loop. The HxD-histidine constitutes a convergence point for signal integration, as conserved interactions link it to key catalytic residues, to the F-helix aspartate, and to both ends of the DFG-motif. These and other conserved features appear to be associated with DFG conformational changes and with coordinated movements possibly associated with phosphate transfer and ADP release. The EPKs have acquired structural features that link this core component to likely substrate-interacting regions at either end of the F-helix (most notably involving an F-helix tryptophan) and to three regions undergoing conformational changes upon kinase activation: the activation segment, the C-helix, and the nucleotide-binding pocket.

Optimal clustering for detecting near-native conformations in protein docking

Clustering is one of the most powerful tools in computational biology. The conventional wisdom is that events that occur in clusters are probably not random. In protein docking, the underlying principle is that clustering occurs because long-range electrostatic and/or desolvation forces steer the proteins to a low free-energy attractor at the binding region. Something similar occurs in the docking of small molecules, although in this case shorter-range van der Waals forces play a more critical role. Based on the above, we have developed two different clustering strategies to predict docked conformations based on the clustering properties of a uniform sampling of low free-energy protein-protein and protein-small molecule complexes. We report on significant improvements in the automated prediction and discrimination of docked conformations by using the cluster size and consensus as a ranking criterion. We show that the success of clustering depends on identifying the appropriate clustering radius of the system. The clustering radius for protein-protein complexes is consistent with the range of the electrostatics and desolvation free energies (i.e., between 4 and 9 Angstroms); for protein-small molecule docking, the radius is set by van der Waals interactions (i.e., at approximately 2 Angstroms). Without any a priori information, a simple analysis of the histogram of distance separations between the set of docked conformations can evaluate the clustering properties of the data set. Clustering is observed when the histogram is bimodal. Data clustering is optimal if one chooses the clustering radius to be the minimum after the first peak of the bimodal distribution. We show that using this optimal radius further improves the discrimination of near-native complex structures.

Molecular dynamics of a protein surface: ion-residues interactions

Time-resolved measurements indicated that protons could propagate on the surface of a protein or a membrane by a special mechanism that enhanced the shuttle of the proton toward a specific site. It was proposed that a suitable location of residues on the surface contributes to the proton shuttling function. In this study, this notion was further investigated by the use of molecular dynamics simulations, where Na(+) and Cl(-) are the ions under study, thus avoiding the necessity for quantum mechanical calculations. Molecular dynamics simulations were carried out using as a model a few Na(+) and Cl(-) ions enclosed in a fully hydrated simulation box with a small globular protein (the S6 of the bacterial ribosome). Three independent 10-ns-long simulations indicated that the ions and the protein's surface were in equilibrium, with rapid passage of the ions between the protein's surface and the bulk. However, it was noted that close to some domains the ions extended their duration near the surface, thus suggesting that the local electrostatic potential hindered their diffusion to the bulk. During the time frame in which the ions were detained next to the surface, they could rapidly shuttle between various attractor sites located under the electrostatic umbrella. Statistical analysis of the molecular dynamics and electrostatic potential/entropy consideration indicated that the detainment state is an energetic compromise between attractive forces and entropy of dilution. The similarity between the motion of free ions next to a protein and the proton transfer on the protein's surface are discussed.

Molecular dissection of Na+ binding to thrombin

Na(+) binding near the primary specificity pocket of thrombin promotes the procoagulant, prothrombotic, and signaling functions of the enzyme. The effect is mediated allosterically by a communication between the Na(+) site and regions involved in substrate recognition. Using a panel of 78 Ala mutants of thrombin, we have mapped the allosteric core of residues that are energetically linked to Na(+) binding. These residues are Asp-189, Glu-217, Asp-222, and Tyr-225, all in close proximity to the bound Na(+). Among these residues, Asp-189 shares with Asp-221 the important function of transducing Na(+) binding into enhanced catalytic activity. None of the residues of exosite I, exosite II, or the 60-loop plays a significant role in Na(+) binding and allosteric transduction. X-ray crystal structures of the Na(+)-free (slow) and Na(+)-bound (fast) forms of thrombin, free or bound to the active site inhibitor H-d-Phe-Pro-Arg-chloromethyl-ketone, document the conformational changes induced by Na(+) binding. The slow --> fast transition results in formation of the Arg-187:Asp-222 ion pair, optimal orientation of Asp-189 and Ser-195 for substrate binding, and a significant shift of the side chain of Glu-192 linked to a rearrangement of the network of water molecules that connect the bound Na(+) to Ser-195 in the active site. The changes in the water network and the allosteric core explain the thermodynamic signatures linked to Na(+) binding and the mechanism of thrombin activation by Na(+). The role of the water network uncovered in this study establishes a new paradigm for the allosteric regulation of thrombin and other Na(+)-activated enzymes involved in blood coagulation and the immune response.