Phosphopeptide interactions with BRCA1 BRCT domains: More than just a motif.
Prog Biophys Mol Biol. 2015 Mar;117(2-3):143-8
Authors: Wu Q, Jubb H, Blundell TL
BRCA1 BRCT domains function as phosphoprotein-binding modules for recognition of the phosphorylated protein-sequence motif pSXXF. While the motif interaction interface provides strong anchor points for binding, protein regions outside the motif have recently been found to be important for binding affinity. In this review, we compare the available structural data for BRCA1 BRCT domains in complex with phosphopeptides in order to gain a more complete understanding of the interaction between phosphopeptides and BRCA1-BRCT domains.
PMID: 25701377 [PubMed - indexed for MEDLINE]
A fragment merging approach towards the development of small molecule inhibitors of Mycobacterium tuberculosis EthR for use as ethionamide boosters.
Org Biomol Chem. 2016 Jan 25;
Authors: Nikiforov PO, Surade S, Blaszczyk M, Delorme V, Brodin P, Baulard AR, Blundell TL, Abell C
With the ever-increasing instances of resistance to frontline TB drugs there is the need to develop novel strategies to fight the worldwide TB epidemic. Boosting the effect of the existing second-line antibiotic ethionamide by inhibiting the mycobacterial transcriptional repressor protein EthR is an attractive therapeutic strategy. Herein we report the use of a fragment based drug discovery approach for the structure-guided systematic merging of two fragment molecules, each binding twice to the hydrophobic cavity of EthR from M. tuberculosis. These together fill the entire binding pocket of EthR. We elaborated these fragment hits and developed small molecule inhibitors which have a 100-fold improvement of potency in vitro over the initial fragments.
PMID: 26806381 [PubMed - as supplied by publisher]
In silico functional dissection of saturation mutagenesis: Interpreting the relationship between phenotypes and changes in protein stability, interactions and activity.
Sci Rep. 2016;6:19848
Authors: Pires DE, Chen J, Blundell TL, Ascher DB
Despite interest in associating polymorphisms with clinical or experimental phenotypes, functional interpretation of mutation data has lagged behind generation of data from modern high-throughput techniques and the accurate prediction of the molecular impact of a mutation remains a non-trivial task. We present here an integrated knowledge-driven computational workflow designed to evaluate the effects of experimental and disease missense mutations on protein structure and interactions. We exemplify its application with analyses of saturation mutagenesis of DBR1 and Gal4 and show that the experimental phenotypes for over 80% of the mutations correlate well with predicted effects of mutations on protein stability and RNA binding affinity. We also show that analysis of mutations in VHL using our workflow provides valuable insights into the effects of mutations, and their links to the risk of developing renal carcinoma. Taken together the analyses of the three examples demonstrate that structural bioinformatics tools, when applied in a systematic, integrated way, can rapidly analyse a given system to provide a powerful approach for predicting structural and functional effects of thousands of mutations in order to reveal molecular mechanisms leading to a phenotype. Missense or non-synonymous mutations are nucleotide substitutions that alter the amino acid sequence of a protein. Their effects can range from modifying transcription, translation, processing and splicing, localization, changing stability of the protein, altering its dynamics or interactions with other proteins, nucleic acids and ligands, including small molecules and metal ions. The advent of high-throughput techniques including sequencing and saturation mutagenesis has provided large amounts of phenotypic data linked to mutations. However, one of the hurdles has been understanding and quantifying the effects of a particular mutation, and how they translate into a given phenotype. One approach to overcome this is to use robust, accurate and scalable computational methods to understand and correlate structural effects of mutations with disease.
PMID: 26797105 [PubMed - in process]
Developing Antagonists for the Met-HGF/SF Protein-Protein Interaction Using a Fragment-Based Approach.
Mol Cancer Ther. 2015 Dec 28;
Authors: Winter A, Sigurdardottir AG, DiCara D, Valenti G, Blundell TL, Gherardi E
In many cancers, aberrant activation of the Met receptor tyrosine kinase leads to dissociation of cells from the primary tumor, causing metastasis. Accordingly, Met is a high-profile target for the development of cancer therapies, and progress has been made through development of small molecule kinase inhibitors and antibodies. However, both approaches pose significant challenges with respect to either target specificity (kinase inhibitors) or the cost involved in treating large patient cohorts (antibodies). Here, we use a fragment-based approach in order to target the protein-protein interaction (PPI) between the α-chain of hepatocyte growth factor/scatter factor (HGF/SF; the NK1 fragment) and its high-affinity binding site located on the Met Sema domain. Surface plasmon resonance was used for initial fragment library screening and hits were developed into larger compounds using substructure (similarity) searches. We identified compounds able to interfere with NK1 binding to Met, disrupt Met signaling, and inhibit tumorsphere generation and cell migration. Using molecular docking, we concluded that some of these compounds inhibit the PPI directly, whereas others act indirectly. Our results indicate that chemical fragments can efficiently target the HGF/SF-Met interface and may be used as building blocks for generating biologically active lead compounds. This strategy may have broad application for the development of a new class of Met inhibitors, namely receptor antagonists, and in general for the development of small molecule PPI inhibitors of key therapeutic targets when structural information is not available. Mol Cancer Ther; 15(1); 1-12. ©2015 AACR.
PMID: 26712116 [PubMed - as supplied by publisher]