The Mycobacterium tuberculosis drugome and its polypharmacological implications.

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.

Hierarchical bayesian modeling of pharmacophores in bioinformatics.

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.

ReverseScreen3D: a structure-based ligand matching method to identify protein targets.

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 .

A machine learning-based method to improve docking scoring functions and its application to drug repurposing.

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.

Toward the discovery of functional transthyretin amyloid inhibitors: application of virtual screening methods.

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.

LigMatch: a multiple structure-based ligand matching method for 3D virtual screening.

We have developed a new virtual screening (VS) method called LigMatch and evaluated its performance on 13 protein targets using a filtered and clustered version of the directory of useful decoys (DUD). The method uses 3D structural comparison to a crystallographically determined ligand in a bioactive 'template' conformation, using a geometric hashing method, in order to prioritize each database compound. We show that LigMatch outperforms several other widely used VS methods on the 13 DUD targets. We go on to demonstrate that improved VS performance can be gained from using multiple, structurally diverse templates rather than a single template ligand for a particular protein target. In this case, a 2D fingerprint-based method is used to select a ligand template from a set of known bioactive conformations. Furthermore, we show that LigMatch performs well even in the absence of 2D similarity to the template ligands, thereby demonstrating its robustness with respect to purely 2D methods and its potential for scaffold hopping.

An evaluation of automated homology modelling methods at low target template sequence similarity.

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

Structure-based evaluation of in silico predictions of protein-protein interactions using Comparative Docking.

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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SitesBase: a database for structure-based protein-ligand binding site comparisons.

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.

Identification of the REST regulon reveals extensive transposable element-mediated binding site duplication.

The genome-wide mapping of gene-regulatory motifs remains a major goal that will facilitate the modelling of gene-regulatory networks and their evolution. The repressor element 1 is a long, conserved transcription factor-binding site which recruits the transcriptional repressor REST to numerous neuron-specific target genes. REST plays important roles in multiple biological processes and disease states. To map RE1 sites and target genes, we created a position specific scoring matrix representing the RE1 and used it to search the human and mouse genomes. We identified 1301 and 997 RE1s inhuman and mouse genomes, respectively, of which >40% are novel. By employing an ontological analysis we show that REST target genes are significantly enriched in a number of functional classes. Taking the novel REST target gene CACNA1A as an experimental model, we show that it can be regulated by multiple RE1s of different binding affinities, which are only partially conserved between human and mouse. A novel BLAST methodology indicated that many RE1s belong to closely related families. Most of these sequences are associated with transposable elements, leading us to propose that transposon-mediated duplication and insertion of RE1s has led to the acquisition of novel target genes by REST during evolution.

Identification and characterisation of the angiotensin converting enzyme-3 (ACE3) gene: a novel mammalian homologue of ACE.

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.

Fold independent structural comparisons of protein-ligand binding sites for exploring functional relationships.

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.

Ligand binding: functional site location, similarity and docking.

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.

Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening.

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.

Identification of critical active-site residues in angiotensin-converting enzyme-2 (ACE2) by site-directed mutagenesis.

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.

Towards a structural classification of phosphate binding sites in protein-nucleotide complexes: an automated all-against-all structural comparison using geometric matching.

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.

BAYESIAN ALIGNMENT OF SIMILARITY SHAPES.

We develop a Bayesian model for the alignment of two point configurations under the full similarity transformations of rotation, translation and scaling. Other work in this area has concentrated on rigid body transformations, where scale information is preserved, motivated by problems involving molecular data; this is known as form analysis. We concentrate on a Bayesian formulation for statistical shape analysis. We generalize the model introduced by Green and Mardia for the pairwise alignment of two unlabeled configurations to full similarity transformations by introducing a scaling factor to the model. The generalization is not straight-forward, since the model needs to be reformulated to give good performance when scaling is included. We illustrate our method on the alignment of rat growth profiles and a novel application to the alignment of protein domains. Here, scaling is applied to secondary structure elements when comparing protein folds; additionally, we find that one global scaling factor is not in general sufficient to model these data and, hence, we develop a model in which multiple scale factors can be included to handle different scalings of shape components.

Configurational preferences of arylamide α-helix mimetics via alchemical free energy calculations of relative binding affinities.

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.

Modeling of arylamide helix mimetics in the p53 peptide binding site of hDM2 suggests parallel and anti-parallel conformations are both stable.

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.