Publications by authors named "Adrian Schreyer"

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.

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CREDO is a unique relational database storing all pairwise atomic interactions of inter- as well as intra-molecular contacts between small molecules and macromolecules found in experimentally determined structures from the Protein Data Bank. These interactions are integrated with further chemical and biological data. The database implements useful data structures and algorithms such as cheminformatics routines to create a comprehensive analysis platform for drug discovery.

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Efforts to increase affinity in the design of new therapeutic molecules have tended to lead to greater lipophilicity, a factor that is generally agreed to be contributing to the low success rate of new drug candidates. Our aim is to provide a structural perspective to the study of lipophilic efficiency and to compare molecular interactions created over evolutionary time with those designed by humans. We show that natural complexes typically engage in more polar contacts than synthetic molecules bound to proteins.

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Background: Ligand-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.

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The use of MP2 level quantum mechanical (QM) calculations on isolated heteroaromatic ring systems for the prediction of the tautomeric propensities of whole molecules in a crystalline environment was examined. A Polarisable Continuum Model was used in the calculations to account for environment effects on the tautomeric relative stabilities. The calculated relative energies of tautomers were compared to relative abundances within the Cambridge Structural Database (CSD) and the Protein Data Bank (PDB).

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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.

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Here we review the use of informatics in structural interactomics, with particular emphasis on understanding interfacial contacts in the development of novel therapeutics and the interpretation of genetic variation. We describe the availability and applicability of structural databases of protein interactions which facilitate this endeavour. We demonstrate the applicability of a structural interactomics approach to the study of the fibroblast growth factor (FGF)-stimulated mitogen-activated protein kinase (MAPK) pathway.

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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.

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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.

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The prediction of the effects of nonsynonymous single nucleotide polymorphisms (nsSNPs) on function depends critically on exploiting all information available on the three-dimensional structures of proteins. We describe software and databases for the analysis of nsSNPs that allow a user to move from SNP to sequence to structure to function. In both structure prediction and the analysis of the effects of nsSNPs, we exploit information about protein evolution, in particular, that derived from investigations on the relation of sequence to structure gained from the study of amino acid substitutions in divergent evolution.

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