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The goal of assessment in a Universal Design for Learning (UDL) approach is to provide the kinds of information that will improve instruction for each learner. Whereas traditional tests and diagnostics tend to focus on identifying weaknesses and disabilities in the individual learner, diagnostics in a UDL approach focus much more on identifying weaknesses and barriers in the design of the learning context itself, making it possible to probe whether a different set of options, a different path, or a different design might lead to better learning for any given learner. A UDL approach to assessment assumes the fundamental centrality of emotions as part of the anticipated variability among all learners and asserts that when we place emotions front and center in assessment, we obtain more accurate and meaningful assessment results. In addition, a UDL approach incorporates recurring and flexible assessment throughout instruction to provide ongoing, actionable feedback to educators and students before failure takes place, when taking action can make a real difference for all. Teachers, students, parents, administrators, and assessment designers/developers all need accurate assessments and timely results to use as feedback to inform next steps. Instructional approaches with a foundation in UDL will reduce the inadvertent barriers to learning that many students currently face, making the assessment of progress toward expertise more accurate, informative, and useful, and enable the mosaic of all learners to become masters of learning itself.

Ligand promiscuity, which is now recognized as an extremely common phenomenon, is a major underlying cause of drug toxicity. We have developed a new reverse virtual screening (VS) method called ReverseScreen3D, which can be used to predict the potential protein targets of a query compound of interest. The method uses a 2D fingerprint-based method to select a ligand template from each unique binding site of each protein within a target database. The target database contains only the structurally determined bioactive conformations of known ligands. The 2D comparison is followed by a 3D structural comparison to the selected query ligand using a geometric matching method, in order to prioritize each target binding site in the database. We have evaluated the performance of the ReverseScreen2D and 3D methods using a diverse set of small molecule protein inhibitors known to have multiple targets, and have shown that they are able to provide a highly significant enrichment of true targets in the database. Furthermore, we have shown that the 3D structural comparison improves early enrichment when compared with the 2D method alone, and that the 3D method performs well even in the absence of 2D similarity to the template ligands. By carrying out further experimental screening on the prioritized list of targets, it may be possible to determine the potential targets of a new compound or determine the off-targets of an existing drug. The ReverseScreen3D method has been incorporated into a Web server, which is freely available at http://www.modelling.leeds.ac.uk/ReverseScreen3D .

Docking scoring functions are notoriously weak predictors of binding affinity. They typically assign a common set of weights to the individual energy terms that contribute to the overall energy score; however, these weights should be gene family dependent. In addition, they incorrectly assume that individual interactions contribute toward the total binding affinity in an additive manner. In reality, noncovalent interactions often depend on one another in a nonlinear manner. In this paper, we show how the use of support vector machines (SVMs), trained by associating sets of individual energy terms retrieved from molecular docking with the known binding affinity of each compound from high-throughput screening experiments, can be used to improve the correlation between known binding affinities and those predicted by the docking program eHiTS. We construct two prediction models: a regression model trained using IC(50) values from BindingDB, and a classification model trained using active and decoy compounds from the Directory of Useful Decoys (DUD). Moreover, to address the issue of overrepresentation of negative data in high-throughput screening data sets, we have designed a multiple-planar SVM training procedure for the classification model. The increased performance that both SVMs give when compared with the original eHiTS scoring function highlights the potential for using nonlinear methods when deriving overall energy scores from their individual components. We apply the above methodology to train a new scoring function for direct inhibitors of Mycobacterium tuberculosis (M.tb) InhA. By combining ligand binding site comparison with the new scoring function, we propose that phosphodiesterase inhibitors can potentially be repurposed to target M.tb InhA. Our methodology may be applied to other gene families for which target structures and activity data are available, as demonstrated in the work presented here.

Protein-protein interfaces are highly attractive targets for drug discovery because they are involved in a large number of disease pathways where therapeutic intervention would bring widespread benefit. Recent successes have challenged the widely held belief that these targets are 'undruggable'. The pocket finding algorithms described here show marked differences between the binding pockets that define protein-protein interactions (PPIs) and those that define protein-ligand interactions (PLIs) of currently marketed drugs. In the case of PPIs, drug discovery methods that simultaneously target several small pockets at the protein-protein interface are likely to increase the chances of success in this new and important field of therapeutics.

Methods for analyzing complete gene families are becoming of increasing importance to the drug discovery process, because similarities and differences within a family are often the key to understanding functional differences that can be exploited in drug design. We undertake a large-scale structural comparison of protein kinase ATP-binding sites using a geometric hashing method. Subsequently, we propose a relevant classification of the protein kinase family based on the structural similarity of its binding sites. Our classification is not only able to reveal the great diversity of different protein kinases and therefore their different potential for inhibitor selectivity but it is also able to distinguish subtle differences within binding site conformation reflecting the protein activation state. Furthermore, using experimental inhibition profiling, we demonstrate that our classification can be used to identify protein kinase binding sites that are known experimentally to bind the same drug, demonstrating that it has potential as an inverse (protein) virtual screening tool, by identifying which other sites have the potential to bind a given drug. In this way the cross-reactivities of the anticancer drugs Tarceva and Gleevec are rationalized.

The development and validation of a new knowledge based scoring function (SIScoreJE) to predict binding energy between proteins and ligands is presented. SIScoreJE efficiently predicts the binding energy between a small molecule and its protein receptor. Protein-ligand atomic contact information was derived from a Non-Redundant Data set (NRD) of over 3000 X-ray crystal structures of protein-ligand complexes. This information was classified for individual "atom contact pairs" (ACP) which is used to calculate the atomic contact preferences. In addition to the two schemes generated in this study we have assessed a number of other common atom-type classification schemes. The preferences were calculated using an information theoretic relationship of joint entropy. Among 18 different atom-type classification schemes "ScoreJE Atom Type set2" (SATs2) was found to be the most suitable for our approach. To test the sensitivity of the method to the inclusion of solvent, Single-body Solvation Potentials (SSP) were also derived from the atomic contacts between the protein atom types and water molecules modeled using AQUARIUS2. Validation was carried out using an evaluation data set of 100 protein-ligand complexes with known binding energies to test the ability of the scoring functions to reproduce known binding affinities. In summary, it was found that a combined SSP/ScoreJE (SIScoreJE) performed significantly better than ScoreJE alone, and SIScoreJE and ScoreJE performed better than GOLD::GoldScore, GOLD::ChemScore, and XScore.

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.

There are many components which govern the function of a protein within a cell. Here, we focus on the molecular recognition of small molecules and the prediction of common recognition by similarity between protein-ligand binding sites. SitesBase is an easily accessible database which is simple to use and holds information about structural similarities between known ligand binding sites found in the Protein Data Bank. These similarities are presented to the wider community enabling full analysis of molecular recognition and potentially protein structure-function relationships. SitesBase is accessible at http://www.bioinformatics.leeds.ac.uk/sb.

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.

Familial exudative vitreoretinopathy (FEVR) is an inherited blinding disorder of the retinal vascular system. Autosomal dominant FEVR is genetically heterogeneous, but its principal locus, EVR1, is on chromosome 11q13-q23. The gene encoding the Wnt receptor frizzled-4 (FZD4) was recently reported to be the EVR1 gene, but our mutation screen revealed fewer patients harboring mutations than expected. Here, we describe mutations in a second gene at the EVR1 locus, low-density-lipoprotein receptor–related protein 5 (LRP5), a Wnt coreceptor. This finding further underlines the significance of Wnt signaling in the vascularization of the eye and highlights the potential dangers of using multiple families to refine genetic intervals in gene-identification studies.

A new method is presented that docks molecular fragments to a rigid protein receptor. It uses a probabilistic procedure based on statistical thermodynamic principles to place ligand atom triplets at the lowest energy sites. The probabilistic method ranks receptor binding modes so that the lowest energy ones are sampled first. This allows constraints to be introduced to limit the depth of the search leading to a computationally efficient method of sampling low energy conformational space. This is combined with energy minimization of the initial fragment placement to arrive at a low energy conformation for the molecular fragment. Two different search methods are tested involving (i) geometric hashing and (ii) pose clustering methods. Ten molecular fragments were docked that have commonly been used to test docking methods. The success rate was 8/10 and 10/10 for generating a close solution ranked first using the two different sampling procedures. In general, all five of the top ranked solutions reproduce the observed binding mode, which increases confidence in the predictions. A set of ten molecular fragments that have previously been identified as problematic were docked. Success was achieved in 3/10 and 4/10 using the two different methods. Again there is a high level of agreement between the two methods and again in the successful cases the top ranked solutions are correct whilst in the case of the failures none are. The geometric hashing and pose clustering methods are fast averaging ∼ 13 and ∼ 11 s per placement respectively using conservative parameters. The results are very encouraging and will facilitate the process of finding novel small molecule lead compounds by virtual screening of chemical databases.

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.

Crystallization of the 1:1 molecular complex between the beta-lactamase TEM-1 and the beta-lactamase inhibitory protein BLIP has provided an opportunity to put a stringent test on current protein-docking algorithms. Prior to the successful determination of the structure of the complex, nine laboratory groups were given the refined atomic coordinates of each of the native molecules. Other than the fact that BLIP is an effective inhibitor of a number of beta-lactamase enzymes (KI for TEM-1 approximately 100 pM) no other biochemical or structural data were available to assist the practitioners in their molecular docking. In addition, it was not known whether the molecules underwent conformational changes upon association or whether the inhibition was competitive or non-competitive. All six of the groups that accepted the challenge correctly predicted the general mode of association of BLIP and TEM-1.

We use molecular docking and free energy calculations to estimate the relative free energy of binding of six arylamide compounds designed to inhibit the hDM2-p53 interaction. We show that using docking methods to predict or rank the binding affinity of a series of arylamide inhibitors of the hDM2-p53 interaction is problematic. However, using free energy calculations, we show that we can achieve levels of accuracy that can guide the development of novel arylamide compounds. We perform alchemical free energy calculations using the Desmond molecular dynamics package with the same arylamide inhibitors of the hDM2-p53 system and illustrate the challenges of performing accurate free energy calculations for realistic systems. To our knowledge, these are the first calculations for inhibitors of the hDM2 system that employ a full treatment of statistical mechanics including explicit water representation and full protein flexibility. We show that mutating three functional groups in a single transformation can be more efficient than mutating the groups one by one if proper intermediates are used. We also show that Hamiltonian exchanges can improve the efficiency of the calculation compared to standard alchemical methods, with a novel use of the phase space overlap to monitor sampling extent. We show that, despite sampling limitations, this approach can achieve levels of accuracy sufficient to bias further inhibitor modification toward binding, and identifies antiparallel configurations as stable or more stable than the parallel configurations that are typically considered.

Inhibition of amyloid fibril formation by stabilization of the native form of the protein transthyretin (TTR) is a viable approach for the treatment of familial amyloid polyneuropathy that has been gaining momentum in the field of amyloid research. The TTR stabilizer molecules discovered to date have shown efficacy at inhibiting fibrilization in vitro but display impairing issues of solubility, affinity for TTR in the blood plasma and/or adverse effects. In this study we present a benchmark of four protein- and ligand-based virtual screening (VS) methods for identifying novel TTR stabilizers: (i) two-dimensional (2D) similarity searches with chemical hashed, pharmacophore, and UNITY fingerprints, (ii) 3D searches based on shape, chemical, and electrostatic similarity, (iii) LigMatch, a new ligand-based method which uses multiple templates and combines 3D geometric hashing with a 2D preselection process, and (iv) molecular docking to consensus X-ray crystal structures of TTR. We illustrate the potential of the best-performing VS protocols to retrieve promising new leads by ranking a tailored library of 2.3 million commercially available compounds. Our predictions show that the top-scoring molecules possess distinctive features from the known TTR binders, holding better solubility, fraction of halogen atoms, and binding affinity profiles. To the best of our knowledge, this is the first attempt to rationalize the utilization of a large battery of in silico screening techniques toward the identification of a new generation of TTR amyloid inhibitors.

One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application, we develop a Bayesian hierarchical model for the derivation of pharmacophore templates from multiple configurations of point sets, partially labeled by the atom type of each point. The model is implemented through a multistage template hunting algorithm that produces a series of templates that capture the geometrical relationship of atoms matched across multiple configurations. Chemical information is incorporated by distinguishing between atoms of different elements, whereby different elements are less likely to be matched than atoms of the same element. We illustrate our method through examples of deriving templates from sets of ligands that all bind structurally related protein active sites and show that the model is able to retrieve the key pharmacophore features in two test cases.

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.

Carbohydrate binding sites are considered important for cellular recognition and adhesion and are important targets for drug design. In this paper we present a new method called InCa-SiteFinder for predicting non-covalent inositol and carbohydrate binding sites on the surface of protein structures. It uses the van der Waals energy of a protein–probe interaction and amino acid propensities to locate and predict carbohydrate binding sites. The protein surface is searched for continuous volume envelopes that correspond to a favorable protein–probe interaction. These volumes are subsequently analyzed to demarcate regions of high cumulative propensity for binding a carbohydrate moiety based on calculated amino acid propensity scores. InCa-SiteFinder 1 was tested on an independent test set of 80 protein–ligand complexes. It efficiently identifies carbohydrate binding sites with high specificity and sensitivity. It was also tested on a second test set of 80 protein–ligand complexes containing 40 known carbohydrate binders (having 40 carbohydrate binding sites) and 40 known drug-like compound binders (having 58 known drug-like compound binding sites) for the prediction of the location of the carbohydrate binding sites and to distinguish these from the drug-like compound binding sites. At 73% sensitivity the method showed 98% specificity. Almost all of the carbohydrate and drug-like compound binding sites were correctly identified with an overall error rate of 12%.

The development and validation of a new knowledge based scoring function (SIScoreJE) to predict binding energy between proteins and ligands is presented. SIScoreJE efficiently predicts the binding energy between a small molecule and its protein receptor. Protein−ligand atomic contact information was derived from a Non-Redundant Data set (NRD) of over 3000 X-ray crystal structures of protein−ligand complexes. This information was classified for individual “atom contact pairs” (ACP) which is used to calculate the atomic contact preferences. In addition to the two schemes generated in this study we have assessed a number of other common atom-type classification schemes. The preferences were calculated using an information theoretic relationship of joint entropy. Among 18 different atom-type classification schemes “ScoreJE Atom Type set2” (SATs2) was found to be the most suitable for our approach. To test the sensitivity of the method to the inclusion of solvent, Single-body Solvation Potentials (SSP) were al...

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.

Background Mammalian angiotensin converting enzyme (ACE) plays a key role in blood pressure regulation. Although multiple ACE-like proteins exist in non-mammalian organisms, to date only one other ACE homologue, ACE2, has been identified in mammals. Results Here we report the identification and characterisation of the gene encoding a third homologue of ACE, termed ACE3, in several mammalian genomes. The ACE3 gene is located on the same chromosome downstream of the ACE gene. Multiple sequence alignment and molecular modelling have been employed to characterise the predicted ACE3 protein. In mouse, rat, cow and dog, the predicted protein has mutations in some of the critical residues involved in catalysis, including the catalytic Glu in the HEXXH zinc binding motif which is Gln, and ESTs or reverse-transcription PCR indicate that the gene is expressed. In humans, the predicted ACE3 protein has an intact HEXXH motif, but there are other deletions and insertions in the gene and no ESTs have been identified. Conclusion In the genomes of several mammalian species there is a gene that encodes a novel, single domain ACE-like protein, ACE3. In mouse, rat, cow and dog ACE3, the catalytic Glu is replaced by Gln in the putative zinc binding motif, indicating that in these species ACE3 would lack catalytic activity as a zinc metalloprotease. In humans, no evidence was found that the ACE3 gene is expressed and the presence of deletions and insertions in the sequence indicate that ACE3 is a pseudogene.

Protein-ligand docking and de novo drug design have become increasingly important tools in aiding the drug design process, particularly with the recent increase in the number of pharmaceutically relevant macromolecular structures that have become available. Here, we review current methods and their applications, and highlight some recent successes in structure-based drug design. Keywords: virtual screening; de novo drug design; scoring functions; docking search procedures

The rapid expansion of structural information for protein-ligand binding sites is potentially an important source of information in structure-based drug design and in understanding ligand cross reactivity and toxicity. We have developed a large database of ligand binding sites extracted automatically from the Protein Data Bank. This has been combined with a method for calculating binding site similarity based on geometric hashing to create a relational database for the retrieval of site similarity and binding site superposition. It contains an all-against-all comparison of binding sites and holds known protein-ligand binding sites, which are made accessible to data mining. Here we demonstrate its utility in two structure-based applications: in determining site similarity and in aiding the derivation of a receptor-based pharmacophore model. The database is available from http://www.bioinformatics.leeds.ac.uk/sb/.

The conformation of a bisindolylmaleimide may be controlled by the size of a macrocyclic ring in which it is constrained. A range of techniques were used to demonstrate that the tether controls both the ratio of the two limiting conformers (syn and anti) in solution and the extent of conjugation between the maleimide and indole rings. Screening the conformationally diverse bisindolylmaleimides against a panel of protein kinases allowed their ATP binding sites to be compared using a chemical approach which, like sequence alignment, does not require detailed structural information. This approach lead to the conclusion that several AGC group protein kinases (including PKCalpha, PKCbeta, MSK1, p70 S6K, PDK-1, and MAPKAP-K1alpha) may be best inhibited by bisindolylmaleimides which adopt a compressed approximately C2-symmetric anti conformation; in constrast, GSK3beta may be best inhibited by bisindolylmaleimides whose ground state is a distorted syn conformation. It is concluded that PDK-1, whose structure has been determined by X-ray crystallography, and its mutants, may serve as particularly useful surrogates for the study of PKC inhibitors.

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

Angiotensin-converting enzyme 2 (ACE2), a homologue of ACE, represents a new and potentially important target in cardio-renal disease. A model of the active site of ACE2, based on the crystal structure of testicular ACE, has been developed and indicates that the catalytic mechanism of ACE2 resembles that of ACE. Structural differences exist between the active site of ACE (dipeptidyl carboxypeptidase) and ACE2 (carboxypeptidase) that are responsible for the differences in specificity. The main differences occur in the ligand-binding pockets, particularly at the S2' subsite and in the binding of the peptide carboxy-terminus. The model explains why the classical ACE inhibitor lisinopril is unable to bind to ACE2. On the basis of the ability of ACE2 to cleave a variety of biologically active peptides, a consensus sequence of Pro-X-Pro-hydrophobic/basic for the protease specificity of ACE2 has been defined that is supported by the ACE2 model. The dipeptide, Pro-Phe, completely inhibits ACE2 activity at 180 microM with angiotensin II as the substrate. As with ACE, the chloride dependence of ACE2 is substrate-specific such that the hydrolysis of angiotensin I and the synthetic peptide substrate, Mca-APK(Dnp), are activated in the presence of chloride ions, whereas the cleavage of angiotensin II is inhibited. The ACE2 model is also suggestive of a possible mechanism for chloride activation. The structural insights provided by these analyses for the differences in inhibition pattern and substrate specificity among ACE and its homologue ACE2 and for the chloride dependence of ACE/ACE2 activity are valuable in understanding the function and regulation of ACE2.

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.

A new method is presented that docks molecular fragments to a rigid protein receptor. It uses a probabilistic procedure based on statistical thermodynamic principles to place ligand atom triplets at the lowest energy sites. The probabilistic method ranks receptor binding modes so that the lowest energy ones are sampled first. This allows constraints to be introduced to limit the depth of the search leading to a computationally efficient method of sampling low energy conformational space. This is combined with energy minimization of the initial fragment placement to arrive at a low energy conformation for the molecular fragment. Two different search methods are tested involving (i) geometric hashing and (ii) pose clustering methods. Ten molecular fragments were docked that have commonly been used to test docking methods. The success rate was 8/10 and 10/10 for generating a close solution ranked first using the two different sampling procedures. In general, all five of the top ranked solutions reproduce the observed binding mode, which increases confidence in the predictions. A set of ten molecular fragments that have previously been identified as problematic were docked. Success was achieved in 3/10 and 4/10 using the two different methods. Again there is a high level of agreement between the two methods and again in the successful cases the top ranked solutions are correct whilst in the case of the failures none are. The geometric hashing and pose clustering methods are fast averaging approximately 13 and approximately 11 s per placement respectively using conservative parameters. The results are very encouraging and will facilitate the process of finding novel small molecule lead compounds by virtual screening of chemical databases.

As is now well documented, the numbers of children of color, poor children, and children with identified disabilities are on the rise in the United States, and in some places “minority” groups are now the majority of the school population (National Education Goals Panel, 1997). At the same time there is mounting evidence that the present educational system is failing to serve disproportionately large numbers of children who are not part of the mainstream (Christensen & Dorn, 1997; Darling-Hammond, 1995; Kozol, 1991). In response, there have been many calls for reform of public schooling and of teacher education (Banks, 1997; Dilworth, 1998; Rice-Jordan, 1995). A number of scholars have argued that we need teachers (and teacher educators) who enter and remain in the teaching force not to carry on business as usual but to work for social change and social justice (Ayers, Hunt & Quinn, 1998;Cochran-Smith, 1995, 1998;Oakes & Lipton, 1999; Skrtic & Sailor, 1996).

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 design of novel α-helix mimetic inhibitors of protein-protein interactions is of interest to pharmaceuticals and chemical genetics researchers as these inhibitors provide a chemical scaffold presenting side chains in the same geometry as an α-helix. This conformational arrangement allows the design of high affinity inhibitors mimicking known peptide sequences binding specific protein substrates. We show that GAFF and AutoDock potentials do not properly capture the conformational preferences of α-helix mimetics based on arylamide oligomers and identify alternate parameters matching solution NMR data and suitable for molecular dynamics simulation of arylamide compounds. Results from both docking and molecular dynamics simulations are consistent with the arylamides binding in the p53 peptide binding pocket. Simulations of arylamides in the p53 binding pocket of hDM2 are consistent with binding, exhibiting similar structural dynamics in the pocket as simulations of known hDM2 binders Nutlin-2 and a benzodiazepinedione compound. Arylamide conformations converge towards the same region of the binding pocket on the 20 ns time scale, and most, though not all dihedrals in the binding pocket are well sampled on this timescale. We show that there are two putative classes of binding modes for arylamide compounds supported equally by the modeling evidence. In the first, the arylamide compound lies parallel to the observed p53 helix. In the second class, not previously identified or proposed, the arylamide compound lies anti-parallel to the p53 helix.

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.