mCSM: predicting the effects of mutations in proteins using graph-based signatures.
Bioinformatics. 2014 Feb 1;30(3):335-42
Authors: Pires DE, Ascher DB, Blundell TL
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.
PMID: 24281696 [PubMed - indexed for MEDLINE]
A structure-guided fragment-based approach for the discovery of allosteric inhibitors targeting the lipophilic binding site of transcription factor EthR.
Biochem J. 2014 Mar 1;458(2):387-94
Authors: Surade S, Ty N, Hengrung N, Lechartier B, Cole ST, Abell C, Blundell TL
A structure-guided fragment-based approach was used to target the lipophilic allosteric binding site of Mycobacterium tuberculosis EthR. This elongated channel has many hydrophobic residues lining the binding site, with few opportunities for hydrogen bonding. We demonstrate that a fragment-based approach involving the inclusion of flexible fragments in the library leads to an efficient exploration of chemical space, that fragment binding can lead to an extension of the cavity, and that fragments are able to identify hydrogen-bonding opportunities in this hydrophobic environment that are not exploited in Nature. In the present paper, we report the identification of a 1 μM affinity ligand obtained by structure-guided fragment linking.
PMID: 24313835 [PubMed - indexed for MEDLINE]
Threonine 57 is required for the post-translational activation of Escherichia coli aspartate α-decarboxylase.
Acta Crystallogr D Biol Crystallogr. 2014 Apr 1;70(Pt 4):1166-72
Authors: Webb ME, Yorke BA, Kershaw T, Lovelock S, Lobley CM, Kilkenny ML, Smith AG, Blundell TL, Pearson AR, Abell C
Aspartate α-decarboxylase is a pyruvoyl-dependent decarboxylase required for the production of β-alanine in the bacterial pantothenate (vitamin B5) biosynthesis pathway. The pyruvoyl group is formed via the intramolecular rearrangement of a serine residue to generate a backbone ester intermediate which is cleaved to generate an N-terminal pyruvoyl group. Site-directed mutagenesis of residues adjacent to the active site, including Tyr22, Thr57 and Tyr58, reveals that only mutation of Thr57 leads to changes in the degree of post-translational activation. The crystal structure of the site-directed mutant T57V is consistent with a non-rearranged backbone, supporting the hypothesis that Thr57 is required for the formation of the ester intermediate in activation.
PMID: 24699660 [PubMed - in process]
The crystal structure of fibroblast growth factor 18 (FGF18).
Protein Cell. 2014 Mar 26;
Authors: Brown A, Adam LE, Blundell TL
PMID: 24668462 [PubMed - as supplied by publisher]
The spatial organization of non-homologous end joining: From bridging to end joining.
DNA Repair (Amst). 2014 Mar 10;
Authors: Ochi T, Wu Q, Blundell TL
Non-homologous end joining (NHEJ) repairs DNA double-strand breaks generated by DNA damage and also those occurring in V(D)J recombination in immunoglobulin and T cell receptor production in the immune system. In NHEJ DNA-PKcs assembles with Ku heterodimer on the DNA ends at double-strand breaks, in order to bring the broken ends together and to assemble other proteins, including DNA ligase IV (LigIV), required for DNA repair. Here we focus on structural aspects of the interactions of LigIV with XRCC4, XLF, Artemis and DNA involved in the bridging and end-joining steps of NHEJ. We begin with a discussion of the role of XLF, which interacts with Ku and forms a hetero-filament with XRCC4; this likely forms a scaffold bridging the DNA ends. We then review the well-defined interaction of XRCC4 with LigIV, and discuss the possibility of this complex interrupting the filament formation, so positioning the ligase at the correct positions close to the broken ends. We also describe the interactions of LigIV with Artemis, the nuclease that prepares the ends for ligation and also interacts with DNA-PK. Lastly we review the likely affects of Mendelian mutations on these multiprotein assemblies and their impacts on the form of inherited disease.
PMID: 24636752 [PubMed - as supplied by publisher]
Combining in silico protein stability calculations with structure-function relationships to explore the effect of polymorphic variation on cytochrome P450 drug metabolism.
Curr Drug Metab. 2013 Sep;14(7):745-63
Authors: Arendse L, Blundell TL, Blackburn J
We carried out an in silico structural analysis of 348 non-synonymous single nucleotide polymorphisms, found across nine of the major human drug metabolising cytochrome P450 isoforms, to determine the effects of mutations on enzyme structure and function. Previous functional studies in our group have delineated regions of the cytochrome P450 structure important for substrate recognition, substrate and product access and egress from the active site and interaction with the cytochrome P450 reductase. Here we combine the information from those studies with new in silico calculations on the effect of mutations on protein stability and we compare our results to experimental data in order to establish the likely causes of altered drug metabolism observed for cytochrome P450 variants in functional assays to date, in the process creating a cytochrome P450 polymorphic variant map. Using the computational tool Site Directed Mutator we predicted destabilising mutations that result in altered enzyme function in vitro with a specificity of 83%. We found that 75% of all cytochrome P450 mutations that show altered activity in vitro are either predicted to be destabilising to protein structure or are found within regions predicted to be important for catalytic activity. Furthermore, we found that 70% of the mutations that showed similar activity to the wild-type enzyme in in vitro studies lie outside of functional regions important for catalytic activity and are predicted to have no effect on protein stability. Our resultant cytochrome P450 polymorphic variant map should therefore find utility in predicting the likely functional effect of uncharacterised variants on drug metabolism.
PMID: 23826828 [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 Feb 20;
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 - as supplied by publisher]