The crystal structure of fibroblast growth factor 18 (FGF18).
Protein Cell. 2014 May;5(5):343-7
Authors: Brown A, Adam LE, Blundell TL
PMID: 24668462 [PubMed - indexed for MEDLINE]
Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?
J Chem Inf Model. 2014 Mar 24;54(3):944-55
Authors: Ballester PJ, Schreyer A, Blundell TL
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.
PMID: 24528282 [PubMed - indexed for MEDLINE]
Small-Molecule Inhibitors That Target Protein-Protein Interactions in the RAD51 Family of Recombinases.
ChemMedChem. 2014 Dec 2;
Authors: Scott DE, Coyne AG, Venkitaraman A, Blundell TL, Abell C, Hyvönen M
The development of small molecules that inhibit protein-protein interactions continues to be a challenge in chemical biology and drug discovery. Herein we report the development of indole-based fragments that bind in a shallow surface pocket of a humanised surrogate of RAD51. RAD51 is an ATP-dependent recombinase that plays a key role in the repair of double-strand DNA breaks. It both self-associates, forming filament structures with DNA, and interacts with the BRCA2 protein through a common "FxxA" tetrapeptide motif. We elaborated previously identified fragment hits that target the FxxA motif site and developed small-molecule inhibitors that are approximately 500-fold more potent than the initial fragments. The lead compounds were shown to compete with the BRCA2-derived Ac-FHTA-NH2 peptide and the self-association peptide of RAD51, but they had no effect on ATP binding. This study is the first reported elaboration of small-molecular-weight fragments against this challenging target.
PMID: 25470112 [PubMed - as supplied by publisher]
Enriching the annotation of Mycobacterium tuberculosis H37Rv proteome using remote homology detection approaches: Insights into structure and function.
Tuberculosis (Edinb). 2014 Nov 6;
Authors: Ramakrishnan G, Ochoa-Montaño B, Raghavender US, Mudgal R, Joshi AG, Chandra NR, Sowdhamini R, Blundell TL, Srinivasan N
The availability of the genome sequence of Mycobacterium tuberculosis H37Rv has encouraged determination of large numbers of protein structures and detailed definition of the biological information encoded therein; yet, the functions of many proteins in M. tuberculosis remain unknown. The emergence of multidrug resistant strains makes it a priority to exploit recent advances in homology recognition and structure prediction to re-analyse its gene products. Here we report the structural and functional characterization of gene products encoded in the M. tuberculosis genome, with the help of sensitive profile-based remote homology search and fold recognition algorithms resulting in an enhanced annotation of the proteome where 95% of the M. tuberculosis proteins were identified wholly or partly with information on structure or function. New information includes association of 244 proteins with 205 domain families and a separate set of new association of folds to 64 proteins. Extending structural information across uncharacterized protein families represented in the M. tuberculosis proteome, by determining superfamily relationships between families of known and unknown structures, has contributed to an enhancement in the knowledge of structural content. In retrospect, such superfamily relationships have facilitated recognition of probable structure and/or function for several uncharacterized protein families, eventually aiding recognition of probable functions for homologous proteins corresponding to such families. Gene products unique to mycobacteria for which no functions could be identified are 183. Of these 18 were determined to be M. tuberculosis specific. Such pathogen-specific proteins are speculated to harbour virulence factors required for pathogenesis. A re-annotated proteome of M. tuberculosis, with greater completeness of annotated proteins and domain assigned regions, provides a valuable basis for experimental endeavours designed to obtain a better understanding of pathogenesis and to accelerate the process of drug target discovery.
PMID: 25467293 [PubMed - as supplied by publisher]
Polyphony: superposition independent methods for ensemble-based drug discovery.
BMC Bioinformatics. 2014;15:324
Authors: Pitt WR, Montalvão RW, Blundell TL
BACKGROUND: Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates.
RESULTS: Novel methodologies are described for the analysis of multiple structures of a protein. Statistical approaches that rely upon residue equivalence, but not superposition, are developed. Tasks that can be performed include the identification of hinge regions, allosteric conformational changes and transient binding sites. The approaches are tested on crystal structures of CDK2 and other CMGC protein kinases and a simulation of p38α. Known interaction - conformational change relationships are highlighted but also new ones are revealed. A transient but druggable allosteric pocket in CDK2 is predicted to occur under the CMGC insert. Furthermore, an evolutionarily-conserved conformational link from the location of this pocket, via the αEF-αF loop, to phosphorylation sites on the activation loop is discovered.
CONCLUSIONS: New methodologies are described and validated for the superimposition independent conformational analysis of large collections of structures or simulation snapshots of the same protein. The methodologies are encoded in a Python package called Polyphony, which is released as open source to accompany this paper [http://wrpitt.bitbucket.org/polyphony/].
PMID: 25265915 [PubMed - indexed for MEDLINE]
Genome3D: exploiting structure to help users understand their sequences.
Nucleic Acids Res. 2014 Oct 27;
Authors: Lewis TE, Sillitoe I, Andreeva A, Blundell TL, Buchan DW, Chothia C, Cozzetto D, Dana JM, Filippis I, Gough J, Jones DT, Kelley LA, Kleywegt GJ, Minneci F, Mistry J, Murzin AG, Ochoa-Montaño B, Oates ME, Punta M, Rackham OJ, Stahlhacke J, Sternberg MJ, Velankar S, Orengo C
Genome3D (http://www.genome3d.eu) is a collaborative resource that provides predicted domain annotations and structural models for key sequences. Since introducing Genome3D in a previous NAR paper, we have substantially extended and improved the resource. We have annotated representatives from Pfam families to improve coverage of diverse sequences and added a fast sequence search to the website to allow users to find Genome3D-annotated sequences similar to their own. We have improved and extended the Genome3D data, enlarging the source data set from three model organisms to 10, and adding VIVACE, a resource new to Genome3D. We have analysed and updated Genome3D's SCOP/CATH mapping. Finally, we have improved the superposition tools, which now give users a more powerful interface for investigating similarities and differences between structural models.
PMID: 25348407 [PubMed - as supplied by publisher]
Platinum: a database of experimentally measured effects of mutations on structurally defined protein-ligand complexes.
Nucleic Acids Res. 2014 Oct 16;
Authors: Pires DE, Blundell TL, Ascher DB
Drug resistance is a major challenge for the treatment of many diseases and a significant concern throughout the drug development process. The ability to understand and predict the effects of mutations on protein-ligand affinities and their roles in the emergence of resistance would significantly aid treatment and drug design strategies. In order to study and understand the impacts of missense mutations on the interaction of ligands with the proteome, we have developed Platinum (http://structure.bioc.cam.ac.uk/platinum). This manually curated, literature-derived database, comprising over 1000 mutations, associates for the first time experimental information on changes in affinity with three-dimensional structures of protein-ligand complexes. To minimize differences arising from experimental techniques and to directly compare binding affinities, Platinum considers only changes measured by the same group and with the same amino-acid sequence used for structure determination, providing a direct link between protein structure, how a ligand binds and how mutations alter the affinity of the ligand of the protein. We believe Platinum will be an invaluable resource for understanding the effects of mutations that give rise to drug resistance, a major problem emerging in pandemics including those caused by the influenza virus, in infectious diseases such as tuberculosis, in cancer and in many other life-threatening illnesses.
PMID: 25324307 [PubMed - as supplied by publisher]