No abstract

Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.

No abstract

The breast cancer susceptibility protein BRCA2 controls the function of RAD51, a recombinase enzyme, in pathways for DNA repair by homologous recombination. We report here the structure of a complex between an evolutionarily conserved sequence in BRCA2 (the BRC repeat) and the RecA-homology domain of RAD51. The BRC repeat mimics a motif in RAD51 that serves as an interface for oligomerization between individual RAD51 monomers, thus enabling BRCA2 to control the assembly of the RAD51 nucleoprotein filament, which is essential for strand-pairing reactions during DNA recombination. The RAD51 oligomerization motif is highly conserved among RecA-like recombinases, highlighting a common evolutionary origin for the mechanism of nucleoprotein filament formation, mirrored in the BRC repeat. Cancer-associated mutations that affect the BRC repeat disrupt its predicted interaction with RAD51, yielding structural insight into mechanisms for cancer susceptibility.

Motivation: Mutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein–protein and protein–nucleic acid interactions.Results: We show that mCSM performs as well as or better than other methods that are used widely. The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue. We showed that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario.Availability and implementation: A web server is available at http://structure.bioc.cam.ac.uk/mcsm.Contact: dpires@dcc.ufmg.br; tom@cryst.bioc.cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Fibroblast growth factors (FGFs) are a large family of structurally related proteins with a wide range of physiological and pathological activities. Signal transduction requires association of FGF with its receptor tyrosine kinase (FGFR) and heparan sulphate proteoglycan in a specific complex on the cell surface. Direct involvement of the heparan sulphate glycosaminoglycan polysaccharide in the molecular association between FGF and its receptor is essential for biological activity. Although crystal structures of binary complexes of FGF-heparin and FGF-FGFR have been described, the molecular architecture of the FGF signalling complex has not been elucidated. Here we report the crystal structure of the FGFR2 ectodomain in a dimeric form that is induced by simultaneous binding to FGF1 and a heparin decasaccharide. The complex is assembled around a central heparin molecule linking two FGF1 ligands into a dimer that bridges between two receptor chains. The asymmetric heparin binding involves contacts with both FGF1 molecules but only one receptor chain. The structure of the FGF1-FGFR2-heparin ternary complex provides a structural basis for the essential role of heparan sulphate in FGF signalling.

No abstract

Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at http://structure.bioc.cam.ac.uk/duet.