Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein–ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein–ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data.
Growing evidence of the possibility of modulating protein-protein interactions with small molecules is opening the door to new approaches and concepts in drug discovery. In this paper, we describe the creation of TIMBAL, a hand-curated database holding an up to date collection of small molecules inhibiting multi-protein complexes. This database has been analysed and profiled in terms of molecular properties. Protein-protein modulators tend to be large lipophilic molecules with few hydrogen bond features. An analysis of TIMBAL's intersection with other structural databases, including CREDO (protein-small molecule from the PDB) and PICCOLO (protein-protein from the PDB) reveals that TIMBAL molecules tend to form mainly hydrophobic interactions with only a few hydrogen bonding contacts. With respect to potency, TIMBAL molecules are slightly less efficient than an average medicinal chemistry hit or lead. The database provides a resource that will allow further insights into the types of molecules favoured by protein interfaces and provide a background to continuing work in this area. Access at http://www-cryst.bioc.cam.ac.uk/timbal.
BackgroundLigand-based virtual screening using molecular shape is an important tool for researchers who wish to find novel chemical scaffolds in compound libraries. The Ultrafast Shape Recognition (USR) algorithm is capable of screening millions of compounds and is therefore suitable for usage in a web service. The algorithm however is agnostic of atom types and cannot discriminate compounds with similar shape but distinct pharmacophoric features. To solve this problem, an extension of USR called USRCAT, has been developed that includes pharmacophoric information whilst retaining the performance benefits of the original method.ResultsThe USRCAT extension is shown to outperform the traditional USR method in a retrospective virtual screening benchmark. Also, a relational database implementation is described that is capable of screening a million conformers in milliseconds and allows the inclusion of complex query parameters.ConclusionsUSRCAT provides a solution to the lack of atom type information in the USR algorithm. Researchers, particularly those with only limited resources, who wish to use ligand-based virtual screening in order to discover new hits, will benefit the most. Online chemical databases that offer a shape-based similarity method might also find advantage in using USRCAT due to its accuracy and performance. The source code is freely available and can easily be modified to fit specific needs.
Harnessing data from the growing number of protein-ligand complexes in the Protein Data Bank is an important task in drug discovery. In order to benefit from the abundance of three-dimensional structures, structural data must be integrated with sequence as well as chemical data and the protein-small molecule interactions characterized structurally at the inter-atomic level. In this study, we present CREDO, a new publicly available database of protein-ligand interactions, which represents contacts as structural interaction fingerprints, implements novel features and is completely scriptable through its application programming interface. Features of CREDO include implementation of molecular shape descriptors with ultrafast shape recognition, fragmentation of ligands in the Protein Data Bank, sequence-tostructure mapping and the identification of approved drugs. Selected analyses of these key features are presented to highlight a range of potential applications of CREDO. The CREDO dataset has been released into the public domain together with the application programming interface under a Creative Commons license at http:// www-cryst.bioc.cam.ac.uk/credo. We believe that the free availability and numerous features of CREDO database will be useful not only for commercial but also for academia-driven drug discovery programmes.Key words: database, drug discovery, fragments, protein-ligand interactions, shape similarity Protein-ligand interactions play important roles in mediating enzyme catalysis, signal transduction and other protein functions and consequently their disruption is often implicated in disease. In order to make progress in improving molecular therapeutic intervention, knowledge of the exponentially increasing number of detailed three-dimensional structures of protein-ligand complexes in the Protein Data Bank (PDB) (1) is invaluable and structural data together with related chemical, biological and clinical information must be organized and accessible.To meet these needs both free-to-use and commercial proteinligand interaction databases have been developed. Those that are still publicly available and not subject to Uniform Resource Locator (URL) decay (2) can be broadly classified into several different categories. BindingDB, BindingMOAD, PDBCal, AffinDB and Protein Ligand Database (3-7) host affinity data obtained from text-mining or user contributions. Protein Data Bank-Ligand (8) (not updated since 2006) and Het-PDB Navi (9) do not comprise interaction data at the atomic level and offer only simple querying and visualization tools. STITCH and LIGAND (10,11) focus on curated or predicted protein-ligand interactions, often as part of biological pathways; they complement databases comprising inter-atomic interaction data. Other databases focus on the structures of ligand-binding sites, sometimes in structurally aligned protein families (8,12,13), in order to gain new insights into molecular recognition. Still others such as Ligand-Protein Contacts (LPC) (14), which was one of the first to classify contac...
Relationships between ligand binding and the shapes of the binding sites in families of homologous enzymes are investigated by comparing matrices of distances between key binding site atoms. Multiple linear regression is used to help identify key distances that influence ligand binding affinity. In order to illustrate the utility of this generic approach, we study protein kinase binding sites for ATP and the promiscuous competitive inhibitor, staurosporine. We show that the size of the gatekeeper residue and the closure between the first glycine of the GXGXXG motif and the aspartate of the DFG loop act together to promote tight binding. Our web-based tool, ‘mapping analogous hetero-atoms onto residue interactions’ (MAHORI), indicates that the greater the number of hydrogen bonds made by the kinase around the methylamine group of staurosporine, the tighter the binding. The conservation of surrounding atoms identified using our novel grid-based method clearly demonstrates that the most structurally conserved part of the binding site for staurosporine is the main chain of the hinge region. The critical role of interactions that are not dependent on side-chain identities is consistent with the promiscuous nature of this inhibitor.
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