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Motivation: The large-scale comparison of protein–ligand binding sites is problematic, in that measures of structural similarity are difficult to quantify and are not easily understood in terms of statistical similarity that can ultimately be related to structure and function. We present a binding site matching score the Poisson Index (PI) based upon a well-defined statistical model. PI requires only the number of matching atoms between two sites and the size of the two sites—the same information used by the Tanimoto Index (TI), a comparable and widely used measure for molecular similarity. We apply PI and TI to a previously automatically extracted set of binding sites to determine the robustness and usefulness of both scores.Results: We found that PI outperforms TI; moreover, site similarity is poorly defined for TI at values around the 99.5% confidence level for which PI is well defined. A difference map at this confidence level shows that PI gives much more meaningful information than TI. We show individual examples where TI fails to distinguish either a false or a true site paring in contrast to PI, which performs much better. TI cannot handle large or small sites very well, or the comparison of large and small sites, in contrast to PI that is shown to be much more robust. Despite the difficulty of determining a biological ‘ground truth’ for binding site similarity we conclude that PI is a suitable measure of binding site similarity and could form the basis for a binding site classification scheme comparable to existing protein domain classification schema.Availability: PI is implemented in SitesBase www.modelling.leeds.ac.uk/sb/Contact: r.m.jackson@leeds.ac.uk

Motivation: Due to the limitations in experimental methods for determining binary interactions and structure determination of protein complexes, the need exists for computational models to fill the increasing gap between genome sequence information and protein annotation. Here we describe a novel method that uses structural models to reduce a large number of in silico predictions to a high confidence subset that is amenable to experimental validation. Results: A two-stage evaluation procedure was developed, first, a sequence-based method assessed the conservation of protein interface patches used in the original in silico prediction method, both in terms of position within the primary sequence, and in terms of sequence conservation. When applying the most stringent conditions it was found that 20.5% of the data set being assessed passed this test. Secondly, a high-throughput structure-based docking evaluation procedure assessed the soundness of three dimensional models produced for the putative interactions. Of the data set being assessed, 8264 interactions or over 70% could be modelled in this way, and 27% of these can be considered ‘valid’ by the applied criteria. In all, 6.9% of the interactions passed both the tests and can be considered to be a high confidence set of predicted interactions, several of which are described. Availability: http://bioinformatics.leeds.ac.uk/~bmb4sjc Contact: r.m.jackson@leeds.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Structure Based Drug Design (SBDD) is a computational approach to lead discovery that uses the three-dimensional structure of a protein to fit drug-like molecules into a ligand binding site to modulate function. Identifying the location of the binding site is therefore a vital first step in this process, restricting the search space for SBDD or virtual screening studies. The detection and characterisation of functional sites on proteins has increasingly become an area of interest. Structural genomics projects are increasingly yielding protein structures with unknown functions and binding sites. Binding site prediction was pioneered by pocket detection, since the binding site is often found in the largest pocket. More recent methods involve phylogenetic analysis, identifying structural similarity with proteins of known function and identifying regions on the protein surface with a potential for high binding affinity. Binding site prediction has been used in several SBDD projects and has been incorporated into several docking tools. We discuss different methods of ligand binding site prediction, their strengths and weaknesses, and how they have been used in SBDD.

Unlike nuclear localization signals, there is no obvious consensus sequence for the targeting of proteins to the nucleolus. The nucleolus is a dynamic subnuclear structure which is crucial to the normal operation of the eukaryotic cell. Studying nucleolar trafficking signals is problematic as many nucleolar retention signals (NoRSs) are part of classical nuclear localization signals (NLSs). In addition, there is no known consensus signal with which to inform a study. The avian infectious bronchitis virus (IBV), coronavirus nucleocapsid (N) protein, localizes to the cytoplasm and the nucleolus. Mutagenesis was used to delineate a novel eight amino acid motif that was necessary and sufficient for nucleolar retention of N protein and colocalize with nucleolin and fibrillarin. Additionally, a classical nuclear export signal (NES) functioned to direct N protein to the cytoplasm. Comparison of the coronavirus NoRSs with known cellular and other viral NoRSs revealed that these motifs have conserved arginine residues. Molecular modelling, using the solution structure of severe acute respiratory (SARS) coronavirus N-protein, revealed that this motif is available for interaction with cellular factors which may mediate nucleolar localization. We hypothesise that the N-protein uses these signals to traffic to and from the nucleolus and the cytoplasm.

Motivation: Protein assemblies are currently poorly represented in structural databases and their structural elucidation is a key goal in biology. Here we analyse clefts in protein surfaces, likely to correspond to binding ‘hot-spots’, and rank them according to sequence conservation and simple measures of physical properties including hydrophobicity, desolvation, electrostatic and van der Waals potentials, to predict which are involved in binding in the native complex. Results: The resulting differences between predicting binding-sites at protein–protein and protein–ligand interfaces are striking. There is a high level of prediction accuracy (≤93%) for protein–ligand interactions, based on the following attributes: van der Waals potential, electrostatic potential, desolvation and surface conservation. Generally, the prediction accuracy for protein–protein interactions is lower, with the exception of enzymes. Our results show that the ease of cleft desolvation is strongly predictive of interfaces and strongly maintained across all classes of protein-binding interface. Contact: r.m.jackson@leeds.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Angiotensin-converting enzyme-2 (ACE2) may play an important role in cardiorenal disease and it has also been implicated as a cellular receptor for the severe acute respiratory syndrome (SARS) virus. The ACE2 active-site model and its crystal structure, which was solved recently, highlighted key differences between ACE2 and its counterpart angiotensin-converting enzyme (ACE), which are responsible for their differing substrate and inhibitor sensitivities. In this study the role of ACE2 active-site residues was explored by site-directed mutagenesis. Arg273 was found to be critical for substrate binding such that its replacement causes enzyme activity to be abolished. Although both His505 and His345 are involved in catalysis, it is His345 and not His505 that acts as the hydrogen bond donor/acceptor in the formation of the tetrahedral peptide intermediate. The difference in chloride sensitivity between ACE2 and ACE was investigated, and the absence of a second chloride-binding site (CL2) in ACE2 confirmed. Thus ACE2 has only one chloride-binding site (CL1) whereas ACE has two sites. This is the first study to address the differences that exist between ACE2 and ACE at the molecular level. The results can be applied to future studies aimed at unravelling the role of ACE2, relative to ACE, in vivo.

Motivation: Given that association and dissociation of protein molecules is crucial in most biological processes several in silico methods have been recently developed to predict protein--protein interactions. Structural evidence has shown that usually interacting pairs of close homologs (interologs) physically interact in the same way. Moreover, conservation of an interaction depends on the conservation of the interface between interacting partners. In this article we make use of both, structural similarities among domains of known interacting proteins found in the Database of Interacting Proteins (DIP) and conservation of pairs of sequence patches involved in protein--protein interfaces to predict putative protein interaction pairs. Results: We have obtained a large amount of putative protein--protein interaction (∼130 000). The list is independent from other techniques both experimental and theoretical. We separated the list of predictions into three sets according to their relationship with known interacting proteins found in DIP. For each set, only a small fraction of the predicted protein pairs could be independently validated by cross checking with the Human Protein Reference Database (HPRD). The fraction of validated protein pairs was always larger than that expected by using random protein pairs. Furthermore, a correlation map of interacting protein pairs was calculated with respect to molecular function, as defined in the Gene Ontology database. It shows good consistency of the predicted interactions with data in the HPRD database. The intersection between the lists of interactions of other methods and ours produces a network of potentially high-confidence interactions. Contact: boliva@imim.es Supplementary information: http://sbi.imim.es/sup_mat/BioinformaticsO5_1/Supplementary_material.pdf

A method is described for the rapid comparison of protein binding sites using geometric matching to detect similar three-dimensional structure. The geometric matching detects common atomic features through identification of the maximum common sub-graph or clique. These features are not necessarily evident from sequence or from global structural similarity giving additional insight into molecular recognition not evident from current sequence or structural classification schemes. Here we use the method to produce an all-against-all comparison of phosphate binding sites in a number of different nucleotide phosphate-binding proteins. The similarity search is combined with clustering of similar sites to allow a preliminary structural classification. Clustering by site similarity produces a classification of binding sites for the 476 representative local environments producing ten main clusters representing half of the representative environments. The similarities make sense in terms of both structural and functional classification schemes. The ten main clusters represent a very limited number of unique structural binding motifs for phosphate. These are the structural P-loop, di-nucleotide binding motif [FAD/ NAD(P)-binding and Rossman-like fold] and FAD-binding motif. Similar classification schemes for nucleotide binding proteins have also been arrived at independently by others using different methods.

Methods for the prediction of protein function from structure are of growing importance in the age of structural genomics. Here, we focus on the problem of identifying sites of potential serine protease inhibitor interactions on the surface of proteins of known structure. Given that there is no sequence conservation within canonical loops from different inhibitor families, we first compare representative loops to all fragments of equal length among proteins of known structure by calculating main-chain RMS deviation. Fragments with RMS deviation below a certain threshold (hits) are removed if residues have solvent accessibilities appreciably lower than those observed in the search structure. These remaining hits are further filtered to remove those occurring largely within secondary structure elements. Likely functional significance is restricted further by considering only extracellular protein domains. By comparing different canonical loop structures to the protein structure database, we show that the method is able to detect previously known inhibitors. In addition, we discuss potentially new canonical loop structures found in secreted hydrolases, toxins, viral proteins, cytokines and other proteins. We discuss the possible functional significance of several of the examples found, and comment on implications for the prediction of function from protein 3D structure.

A computationally tractable strategy has been developed to refine protein-protein interfaces that models the effects of side-chain conformational change, solvation and limited rigid-body movement of the subunits. The proteins are described at the atomic level by a multiple copy representation of side-chains modelled according to a rotamer library on a fixed peptide backbone. The surrounding solvent environment is described by “soft” sphere Langevin dipoles for water that interact with the protein via electrostatic, van der Waals and field-dependent hydrophobic terms. Energy refinement is based on a two-step process in which (1) a probability-based conformational matrix of the protein side-chains is refined iteratively by a mean field method. A side-chain interacts with the protein backbone and the probability-weighted average of the surrounding protein side-chains and solvent molecules. The resultant protein conformations then undergo (2) rigid-body energy minimization to relax the protein interface. Steps (1) and (2) are repeated until convergence of the interaction energy. The influence of refinement on side-chain conformation starting from unbound conformations found improvement in the RMSD of side-chains in the interface of protease-inhibitor complexes, and shows that the method leads to an improvement in interface geometry. In terms of discriminating between docked structures, the refinement was applied to two classes of protein-protein complex: five protease-protein inhibitor and four antibody-antigen complexes. A large number of putative docked complexes have already been generated for the test systems using our rigid-body docking program, FTDOCK. They include geometries that closely resemble the crystal complex, and therefore act as a test for the refinement procedure. In the protease-inhibitors, geometries that resemble the crystal complex are ranked in the top four solutions for four out of five systems when solvation is included in the energy function, against a background of between 26 and 364 complexes in the data set. The results for the antibody-antigen complexes are not as encouraging, with only two of the four systems showing discrimination. It would appear that these results reflect the somewhat different binding mechanism dominant in the two types of protein-protein complex. Binding in the protease-inhibitors appears to be “lock and key” in nature. The fixed backbone and mobile side-chain representation provide a good model for binding. Movements in the backbone geometry of antigens on binding represent an “induced-fit” and provides more of a challenge for the model. Given the limitations of the conformational sampling, the ability of the energy function to discriminate between native and non-native states is encouraging. Development of the approach to include greater conformational sampling could lead to a more general solution to the protein docking problem.

A protein docking study was performed for two classes of biomolecular complexes: six enzyme/inhibitor and four antibody/antigen. Biomolecular complexes for which crystal structures of both the complexed and uncomplexed proteins are available were used for eight of the ten test systems. Our docking experiments consist of a global search of translational and rotational space followed by refinement of the best predictions. Potential complexes are scored on the basis of shape complementarity and favourable electrostatic interactions using Fourier correlation theory. Since proteins undergo conformational changes upon binding, the scoring function must be sufficiently soft to dock unbound structures successfully. Some degree of surface overlap is tolerated to account for side-chain flexibility. Similarly for electrostatics, the interaction of the dispersed point charges of one protein with the Coulombic field of the other is measured rather than precise atomic interactions. We tested our docking protocol using the native rather than the complexed forms of the proteins to address the more scientifically interesting problem of predictive docking. In all but one of our test cases, correctly docked geometries (interface Calpha RMS deviation

The CASP blind trials (Critical Assessment of techniques for protein Structure Prediction) assess the accuracy of protein prediction that includes evaluation of comparative model building of protein structures. Comparative models of four proteins (T0001, T0003, T0017, and T0028) for CASP2 (held during 1996) were constructed using computer algorithms combined with visual inspection. Essentially the main-chain modelling involves construction of the target structure from rigid-body segments of homologues and loop fragments extracted from homologous and nonredundant databases. Side-chains were initially constructed by inheritance from the parent or from a rotamer library. Side-chain conformations were then refined using a novel mean field approach that includes solvation. Comparison of the models with the subsequently released X-ray structures identified the successes and limitations of our approach. The most problematic area is the quality of the sequence alignments between parent(s) and target. In this respect the overinterpretation of the conserved features within homologous families can be misleading. Several features of our approach have a positive effect on the accuracy of the models. For T0003, inspection correctly identified that a lower sequence identity parent provides the best framework for this model. Loop selection worked well where a homologous protein fragment was used, but that the use of nonredundant fragment library remains problematic for hinge movements and displacements in secondary structure elements relative to the parent. Side-chain refinement improved residue conformations relative to the initial model. Use of limited energy minimization improved the stereochemical quality of the model without increasing the RMS deviation. This study has identified methods that are effective and areas requiring further attention to improve model building by comparison. Proteins, Suppl. 1:59–67, 1997. © 1998 Wiley-Liss, Inc.

We report a computational approach that integrates structural bioinformatics, molecular modelling and systems biology to construct a drug-target network on a structural proteome-wide scale. The approach has been applied to the genome of Mycobacterium tuberculosis (M.tb), the causative agent of one of today's most widely spread infectious diseases. The resulting drug-target interaction network for all structurally characterized approved drugs bound to putative M.tb receptors, we refer to as the ‘TB-drugome’. The TB-drugome reveals that approximately one-third of the drugs examined have the potential to be repositioned to treat tuberculosis and that many currently unexploited M.tb receptors may be chemically druggable and could serve as novel anti-tubercular targets. Furthermore, a detailed analysis of the TB-drugome has shed new light on the controversial issues surrounding drug-target networks [1]–[3]. Indeed, our results support the idea that drug-target networks are inherently modular, and further that any observed randomness is mainly caused by biased target coverage. The TB-drugome (http://funsite.sdsc.edu/drugome/TB) has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally the methodology may be applied to other pathogens of interest with results improving as more of their structural proteomes are determined through the continued efforts of structural biology/genomics., Author Summary The worldwide increase in multi-drug resistant TB poses a great threat to human health and highlights the need to identify new anti-tubercular agents. We have developed a computational strategy to link the structural proteome of Mycobacterium tuberculosis, the causative agent of tuberculosis, to all structurally characterized approved drugs, and hence construct a proteome-wide drug-target network – the TB-drugome. The TB-drugome has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally, the proteome-wide and multi-scale view of target and drug space may facilitate a systematic drug discovery process, which concurrently takes into account the disease mechanism and druggability of targets, the drug-likeness and ADMET properties of chemical compounds, and the genetic dispositions of individuals. Ultimately it may help to reduce the high attrition rate in drug development through a better understanding of drug-receptor interactions on a large scale.

The proposal that the active site vacuole of NAD(+)-S-lactate dehydrogenase is unable to accommodate any imbalance in electrostatic charge was tested by genetically manipulating the cDNA coding for human muscle lactate dehydrogenase to make a protein with an aspartic acid introduced at position 140 instead of the wild-type asparagine. The Asn 140-Asp mutant enzyme has the same kcat as the wild type (Asn 140) at low pH (4.5), and at higher pH the Km for pyruvate increases 10-fold for each unit increase in pH up to pH 9. We conclude that the anion of Asp 140 is completely inactive and that it binds pyruvate with a Km that is over 1,000 times that of the Km of the neutral, protonated aspartic-140. Experimental results and molecular modeling studies indicate the pKa of the active site histidine-195 in the enzyme-NADH complex is raised to greater than 10 by the presence of the anion at position 140. Energy minimization and molecular dynamics studies over 36 ps suggest that the anion at position 140 promotes the opening of and the entry of mobile solvent beneath the polypeptide loop (98-110), which normally seals off the internal active site vacuole from external bulk solvent.

Current homology-modelling methods do not consider small molecules in their automated processes. Therefore, the development of a reliable tool for protein-ligand homology modelling is an important next step in generating plausible models for molecular interactions. Two automated protein-ligand homology-modelling strategies, requiring no expert knowledge from the user, are investigated here. Both employ the "induced fit" concept with flexibility in side chains and ligand. The most successful strategy superimposes the new ligand over the original ligand before homology modelling, allowing the new ligand to be taken into consideration during protein modelling (rather than after), facilitating conformational change in the local backbone if necessary. We show that this approach results in successful modelling of the ligand and key binding-site residues of angiotensin-converting enzyme 2 (ACE2) from its homologue ACE, which is not possible via conventional homology modelling or by homology modelling followed by docking. Several other difficult target complexes are also successfully modelled, reproducing native protein-ligand contacts with significantly different biological substrates and different binding-site conformations. These include the modelling of Cdk5 (cyclin-dependent kinase 5) from Cdk2, thymidine phosphorylase from a bacterial homologue, and dihydrofolate reductase from a recombinant variant with a markedly different inhibitor. In terms of average modelling quality across 82 targets, the ligand RMSD with respect to the experimental structure is 1.4 A (and 2.0 A for the protein binding site) for "easy" cases and 2.9 A for the ligand (and 2.7 A for the protein binding site) in "hard" cases. This demonstrates the importance of selecting an optimal template. Ligand-modelling accuracy is strongly dependent on target-template ligand structural similarity, rather than target-template sequence identity. However, protein-modelling accuracy is dependent on both. Our automated protein-ligand homology-modelling strategy generates a higher degree of accuracy than homology modelling followed by docking, generating an average ligand RMSD that is 1-2 A better than docking with homology models.

The structure of the type I fructose 1,6-bisphosphate aldolase from human muscle has been extended from 3 A to 2 A resolution. The improvement in the resulting electron density map is such that the 20 or so C-terminal residues, known to be associated with activity and isozyme specificity, have been located. The side-chain of the Schiff's base-forming lysine 229 is located towards the centre of an eight-stranded β-barrel type structure. The C-terminal “tail” extends from the rim of the β-barrel towards lysine 229, thus forming part of the active site of the enzyme. This structural arrangement appears to explain the difference in activity and specificity of the three tissue-specific human aldolases and helps with our understanding of the type I aldolase reaction mechanism.

Motivation: There are two main areas of difficulty in homology modelling that are particularly important when sequence identity between target and template falls below 50%: sequence alignment and loop building. These problems become magnified with automatic modelling processes, as there is no human input to correct mistakes. As such we have benchmarked several stand-alone strategies that could be implemented in a workflow for automated high-throughput homology modelling. These include three new sequence-structure alignment programs: 3D-Coffee, Staccato and SAlign, plus five homology modelling programs and their respective loop building methods: Builder, Nest, Modeller, SegMod/ENCAD and Swiss-Model. The SABmark database provided 123 targets with at least five templates from the same SCOP family and sequence identities ≤50%.Results: When using Modeller as the common modelling program, 3D-Coffee outperforms Staccato and SAlign using both multiple templates and the best single template, and across the sequence identity range 20–50%. The mean model RMSD generated from 3D-Coffee using multiple templates is 15 and 28% (or using single templates, 3 and 13%) better than those generated by Staccato and Salign, respectively. 3D-Coffee gives equivalent modelling accuracy from multiple and single templates, but Staccato and SAlign are more successful with single templates, their quality deteriorating as additional lower sequence identity templates are added. Evaluating the different homology modelling programs, on average Modeller performs marginally better in overall modelling than the others tested. However, on average Nest produces the best loops with an 8% improvement by mean RMSD compared to the loops generated by Builder.Contact: r.m.jackson@leeds.ac.uk.Supplementary information: Supplementary data are available at Bioinformatics online.

The rapid growth in protein structural data and the emergence of structural genomics projects have increased the need for automatic structure analysis and tools for function prediction. Small molecule recognition is critical to the function of many proteins; therefore, determination of ligand binding site similarity is important for understanding ligand interactions and may allow their functional classification. Here, we present a binding sites database (SitesBase) that given a known protein-ligand binding site allows rapid retrieval of other binding sites with similar structure independent of overall sequence or fold similarity. However, each match is also annotated with sequence similarity and fold information to aid interpretation of structure and functional similarity. Similarity in ligand binding sites can indicate common binding modes and recognition of similar molecules, allowing potential inference of function for an uncharacterised protein or providing additional evidence of common function where sequence or fold similarity is already known. Alternatively, the resource can provide valuable information for detailed studies of molecular recognition including structure-based ligand design and in understanding ligand cross-reactivity. Here, we show examples of atomic similarity between superfamily or more distant fold relatives as well as between seemingly unrelated proteins. Assignment of unclassified proteins to structural superfamiles is also undertaken and in most cases substantiates assignments made using sequence similarity. Correct assignment is also possible where sequence similarity fails to find significant matches, illustrating the potential use of binding site comparisons for newly determined proteins.

Motivation: Identifying the location of ligand binding sites on a protein is of fundamental importance for a range of applications including molecular docking, de novo drug design and structural identification and comparison of functional sites. Here, we describe a new method of ligand binding site prediction called Q-SiteFinder. It uses the interaction energy between the protein and a simple van der Waals probe to locate energetically favourable binding sites. Energetically favourable probe sites are clustered according to their spatial proximity and clusters are then ranked according to the sum of interaction energies for sites within each cluster. Results: There is at least one successful prediction in the top three predicted sites in 90% of proteins tested when using Q-SiteFinder. This success rate is higher than that of a commonly used pocket detection algorithm (Pocket-Finder) which uses geometric criteria. Additionally, Q-SiteFinder is twice as effective as Pocket-Finder in generating predicted sites that map accurately onto ligand coordinates. It also generates predicted sites with the lowest average volumes of the methods examined in this study. Unlike pocket detection, the volumes of the predicted sites appear to show relatively low dependence on protein volume and are similar in volume to the ligands they contain. Restricting the size of the pocket is important for reducing the search space required for docking and de novo drug design or site comparison. The method can be applied in structural genomics studies where protein binding sites remain uncharacterized since the 86% success rate for unbound proteins appears to be only slightly lower than that of ligand-bound proteins. Availability: Both Q-SiteFinder and Pocket-Finder have been made available online at http://www.bioinformatics.leeds.ac.uk/qsitefinder and http://www.bioinformatics.leeds.ac.uk/pocketfinder Contact: r.m.jackson@leeds.ac.uk

Computational methods for the detection and characterisation of protein ligand-binding sites have increasingly become an area of interest now that large amounts of protein structural information are becoming available prior to any knowledge of protein function. There have been particularly interesting recent developments in the following areas: first, functional site detection, whereby protein evolutionary information has been used to locate binding sites on the protein surface; second, functional site similarity, whereby structural similarity and three-dimensional templates can be used to compare and classify and potentially locate new binding sites; and third, ligand docking, which is being used to find and validate functional sites, in addition to having more conventional uses in small-molecule lead discovery.

Recent developments in algorithms to predict the docking of two proteins have considered both the initial rigid-body global search and subsequent screening and refinement. The result of two blind trials of protein docking are encouraging--for complexes that are not too large and do not undergo sizeable conformational change upon association, the algorithms are now able to suggest reasonably accurate models.

The prediction of protein–protein interactions in solution is a major goal of theoretical structural biology. Here, we implement a continuum description of the thermodynamic processes involved. The model differs considerably from previous models in its use of ‘ molecular surface’' area to describe the hydrophobic component to the free energy of conformational change in solution. We have applied this model to a data set of alternative docked conformations of protein–protein complexes which were generated independently of this work. It was found previously that commonly used energy evaluation techniques fail to distinguish between near-native and certain non-native complexes in this data set. Here, we found that an energy function that takes into account (1) total electrostatic free energy, (2) hydrophobic free energy and (3) loss in side-chain conformational energy was able to reliably discriminate between near-native and non-native configurations but only when molecular surface is used as a descriptor of the hydrophobic effect. It is shown that the molecular surface and the more conventional surface descriptor ‘ solvent accessible surface’' give very different quantitative measures of hydrophobicity. In terms of the contribution of different energy components to the free energy of complex formation it was found that loss in side-chain conformational entropy is a second order effect. Electrostatic interaction energy (which is commonly used to score docked conformations) was a poor indicator of complementarity when starting from unbound conformations. It was found that electrostatic desolvation energy and the hydrophobic contribution (based on a molecular surface area descriptor) are much less sensitive to local fluctuations in atomic structure than point-to-point interaction energies and thus may be more suited for use as a scoring function when docking unbound conformations, where atomic complementarity is much less apparent. Whilst a combined energy function was able to distinguish near-native from non-native energy function was able to distinguish near-native from non-native conformations in the six systems studied here, it remains to be determined to what extent more sizeable conformational changes would influence the results.f2

The energetics of alkane dissolution and partition between water and organic solvent are described in terms of the energy of cavity formation and solute-solvent interaction using scaled particle theory. Thermodynamic arguments are proposed that allow comparison of experimental measurements of the surface area with values calculated from an all-atom representation of the solute. While the surface tension relating to the accessible surface is shape dependent, it is found that for the molecular surface it is not. This model rationalizes the change in surface tension between the microscopic (20-30 cal/mol/A2) and macroscopic (70-75 cal/mol/A2) regimes without the need to invoke Flory-Huggins theory or to apply other corrections. The difference in the values arises (i) to a small extent as a result of the curvature dependence of surface tension and (ii) to a large extent due to the difference in the molecular surface derived from the experiment and that calculated from an extended all-atom model. The model suggests that the primary driving force for alkane association in water is due to the tendency of water to reduce the solute cavity surface. It is argued that to model the energetics of alkane association, the surface tension should be related to the molecular surface (rather than the accessible surface) with a surface tension near the macroscopic limit for water. This model is compared with results from theoretical simulations of the hydrophobic effect for two well-studied systems. The implications for antibody-antigen interactions and the effect of hydrophobic amino acid deletion on protein stability are discussed. The approach can be used to model the solute cavity formation energy in solution as a first step in the continuum modelling of biomolecular interactions.

The prediction of binding energies from the three-dimensional (3D) structure of a protein-ligand complex is an important goal of biophysics and structural biology. Here, we critically assess the use of empirical, solvent-accessible surface area-based calculations for the prediction of the binding of Src-SH2 domain with a series of tyrosyl phosphopeptides based on the high-affinity ligand from the hamster middle T antigen (hmT), where the residue in the pY+ 3 position has been changed. Two other peptides based on the C-terminal regulatory site of the Src protein and the platelet-derived growth factor receptor (PDGFR) are also investigated. Here, we take into account the effects of proton linkage on binding, and test five different surface area-based models that include different treatments for the contributions to conformational change and protein solvation. These differences relate to the treatment of conformational flexibility in the peptide ligand and the inclusion of proximal ordered solvent molecules in the surface area calculations. This allowed the calculation of a range of thermodynamic state functions (deltaCp, deltaS, deltaH, and deltaG) directly from structure. Comparison with the experimentally derived data shows little agreement for the interaction of SrcSH2 domain and the range of tyrosyl phosphopeptides. Furthermore, the adoption of the different models to treat conformational change and solvation has a dramatic effect on the calculated thermodynamic functions, making the predicted binding energies highly model dependent. While empirical, solvent-accessible surface area based calculations are becoming widely adopted to interpret thermodynamic data, this study highlights potential problems with application and interpretation of this type of approach. There is undoubtedly some agreement between predicted and experimentally determined thermodynamic parameters: however, the tolerance of this approach is not sufficient to make it ubiquitously applicable.