320 results found
Alhuzimi E, Leal LG, Sternberg MJE, et al., 2018, Properties of human genes guided by their enrichment in rare and common variants., Hum Mutat, Vol: 39, Pages: 365-370
We analyzed 563,099 common (minor allele frequency, MAF≥0.01) and rare (MAF < 0.01) genetic variants annotated in ExAC and UniProt and 26,884 disease-causing variants from ClinVar and UniProt occurring in the coding region of 17,975 human protein-coding genes. Three novel sets of genes were identified: those enriched in rare variants (n = 32 genes), in common variants (n = 282 genes), and in disease-causing variants (n = 800 genes). Genes enriched in rare variants have far greater similarities in terms of biological and network properties to genes enriched in disease-causing variants, than to genes enriched in common variants. However, in half of the genes enriched in rare variants (AOC2, MAMDC4, ANKHD1, CDC42BPB, SPAG5, TRRAP, TANC2, IQCH, USP54, SRRM2, DOPEY2, and PITPNM1), no disease-causing variants have been identified in major, publicly available databases. Thus, genetic variants in these genes are strong candidates for disease and their identification, as part of sequencing studies, should prompt further in vitro analyses.
Cornish AJ, David A, Sternberg MJE, 2018, PhenoRank: reducing study bias in gene prioritisation through simulation., Bioinformatics
Motivation: Genome-wide association studies have identified thousands of loci associated with human disease, but identifying the causal genes at these loci is often difficult. Several methods prioritise genes most likely to be disease causing through the integration of biological data, including protein-protein interaction and phenotypic data. Data availability is not the same for all genes however, potentially influencing the performance of these methods. Results: We demonstrate that whilst disease genes tend to be associated with greater numbers of data, this may be at least partially a result of them being better studied. With this observation we develop PhenoRank, which prioritises disease genes whilst avoiding being biased towards genes with more available data. Bias is avoided by comparing gene scores generated for the query disease against gene scores generated using simulated sets of phenotype terms, which ensures that differences in data availability do not affect the ranking of genes. We demonstrate that whilst existing prioritisation methods are biased by data availability, PhenoRank is not similarly biased. Avoiding this bias allows PhenoRank to effectively prioritise genes with fewer available data and improves its overall performance. PhenoRank outperforms three available prioritisation methods in cross-validation (PhenoRank area under receiver operating characteristic curve [AUC]=0.89, DADA AUC=0.87, EXOMISER AUC=0.71, PRINCE AUC=0.83, P < 2.2 × 10-16). Availability: PhenoRank is freely available for download at https://github.com/alexjcornish/PhenoRank. Contact: firstname.lastname@example.org. Supplementary information: Supplementary data are available at Bioinformatics online.
Reynolds CR, Islam SA, Sternberg MJE, 2018, EzMol: A web server wizard for the rapid visualisation and image production of protein and nucleic acid structures., J Mol Biol
EzMol is a molecular visualisation web server in the form of a software wizard, located at http://www.sbg.bio.ic.ac.uk/ezmol/. It is designed for easy and rapid image manipulation and display of protein molecules, and is intended for users who need to quickly produce high-resolution images of protein molecules but do not have the time or inclination to use a software molecular visualisation system. EzMol allows the upload of molecular structure files in PDB format to generate a web page including a representation of the structure that the user can manipulate. EzMol provides intuitive options for chain display, adjusting the colour/transparency of residues, side chains and protein surfaces, and for adding labels to residues. The final adjusted protein image can then be downloaded as a high-resolution image. There are a range of applications for rapid protein display, including the illustration of specific areas of a protein structure and the rapid prototyping of images.
Ainsworth D, Sternberg MJE, Raczy C, et al., 2017, k-SLAM: accurate and ultra-fast taxonomic classification and gene identification for large metagenomic data sets, NUCLEIC ACIDS RESEARCH, Vol: 45, Pages: 1649-1656, ISSN: 0305-1048
Bryant WA, Stentz R, Le Gall G, et al., 2017, In Silico Analysis of the Small Molecule Content of Outer Membrane Vesicles Produced by Bacteroides thetaiotaomicron Indicates an Extensive Metabolic Link between Microbe and Host, FRONTIERS IN MICROBIOLOGY, Vol: 8, ISSN: 1664-302X
Greener JG, Filippis I, Sternberg MJE, 2017, Predicting Protein Dynamics and Allostery Using Multi-Protein Atomic Distance Constraints, STRUCTURE, Vol: 25, Pages: 546-558, ISSN: 0969-2126
Greener JG, Sternberg MJ, 2017, Structure-based prediction of protein allostery., Curr Opin Struct Biol, Vol: 50, Pages: 1-8
Allostery is the functional change at one site on a protein caused by a change at a distant site. In order for the benefits of allostery to be taken advantage of, both for basic understanding of proteins and to develop new classes of drugs, the structure-based prediction of allosteric binding sites, modulators and communication pathways is necessary. Here we review the recently emerging field of allosteric prediction, focusing mainly on computational methods. We also describe the search for cryptic binding pockets and attempts to design allostery into proteins. The development and adoption of such methods is essential or the long-preached potential of allostery will remain elusive.
Ittisoponpisan S, Alhuzimi E, Sternberg MJE, et al., 2017, Landscape of Pleiotropic Proteins Causing Human Disease: Structural and System Biology Insights, HUMAN MUTATION, Vol: 38, Pages: 289-296, ISSN: 1059-7794
Ostankovitch MI, Sternberg MJE, 2017, Computation Resources for Molecular Biology: Special Issue 2017, JOURNAL OF MOLECULAR BIOLOGY, Vol: 429, Pages: 345-347, ISSN: 0022-2836
Scales M, Chubb D, Dobbins SE, et al., 2017, Search for rare protein altering variants influencing susceptibility to multiple myeloma, ONCOTARGET, Vol: 8, Pages: 36203-36210, ISSN: 1949-2553
Sundriyal S, Moniot S, Mahmud Z, et al., 2017, Thienopyrimidinone Based Sirtuin-2 (SIRT2)-Selective Inhibitors Bind in the Ligand Induced Selectivity Pocket., J Med Chem, Vol: 60, Pages: 1928-1945
Sirtuins (SIRTs) are NAD-dependent deacylases, known to be involved in a variety of pathophysiological processes and thus remain promising therapeutic targets for further validation. Previously, we reported a novel thienopyrimidinone SIRT2 inhibitor with good potency and excellent selectivity for SIRT2. Herein, we report an extensive SAR study of this chemical series and identify the key pharmacophoric elements and physiochemical properties that underpin the excellent activity observed. New analogues have been identified with submicromolar SIRT2 inhibtory activity and good to excellent SIRT2 subtype-selectivity. Importantly, we report a cocrystal structure of one of our compounds (29c) bound to SIRT2. This reveals our series to induce the formation of a previously reported selectivity pocket but to bind in an inverted fashion to what might be intuitively expected. We believe these findings will contribute significantly to an understanding of the mechanism of action of SIRT2 inhibitors and to the identification of refined, second generation inhibitors.
Sundriyal S, Moniot S, Mahmud Z, et al., 2017, Thienopyrimidinone Based Sirtuin-2 (SIRT2)-Selective Inhibitors Bind in the Ligand Induced Selectivity Pocket, JOURNAL OF MEDICINAL CHEMISTRY, Vol: 60, Pages: 1928-1945, ISSN: 0022-2623
Waese J, Fan J, Pasha A, et al., 2017, ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology, PLANT CELL, Vol: 29, Pages: 1806-1821, ISSN: 1040-4651
Howard SR, Guasti L, Ruiz-Babot G, et al., 2016, IGSF10 mutations dysregulate gonadotropin-releasing hormone neuronal migration resulting in delayed puberty, EMBO MOLECULAR MEDICINE, Vol: 8, Pages: 626-642, ISSN: 1757-4676
Jiang Y, Oron TR, Clark WT, et al., 2016, An expanded evaluation of protein function prediction methods shows an improvement in accuracy, GENOME BIOLOGY, Vol: 17, ISSN: 1474-760X
Metherell LA, Guerra-Assuncao JA, Sternberg MJ, et al., 2016, Three-Dimensional Model of Human Nicotinamide Nucleotide Transhydrogenase (NNT) and Sequence-Structure Analysis of its Disease-Causing Variations, HUMAN MUTATION, Vol: 37, Pages: 1074-1084, ISSN: 1059-7794
Mezulis S, Sternberg MJE, Kelley LA, 2016, PhyreStorm: A Web Server for Fast Structural Searches Against the PDB, JOURNAL OF MOLECULAR BIOLOGY, Vol: 428, Pages: 702-708, ISSN: 0022-2836
Sternberg MJE, Ostankovitch MI, 2016, Computation Resources for Molecular Biology: A Special Issue, JOURNAL OF MOLECULAR BIOLOGY, Vol: 428, Pages: 669-670, ISSN: 0022-2836
Cornish AJ, Filippis I, David A, et al., 2015, Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types, GENOME MEDICINE, Vol: 7, ISSN: 1756-994X
David A, Sternberg MJ, 2015, The contribution of missense mutations in core and rim residues of protein-protein interfaces to human disease., Journal of Molecular Biology, Vol: 427, Pages: 2886-2898, ISSN: 1089-8638
Missense mutations at protein-protein interaction (PPIs) sites, called interfaces, are important contributors to human disease. Interfaces are non-uniform surface areas characterized by two main regions, 'core' and 'rim', which differ in terms of evolutionary conservation and physico-chemical properties. Moreover, within interfaces, only a small subset of residues ('hot spots') is crucial for the binding free energy of the protein-protein complex. We performed a large-scale structural analysis of human single amino acid variations (SAVs) and demonstrated that disease-causing mutations are preferentially located within the interface core, as opposed to the rim (p< 0.01). In contrast, the interface rim is significantly enriched in polymorphisms, similar to the remaining non-interacting surface. Energetic hot spots tend to be enriched in disease-causing mutations compared to non-hot spots (p=0.05), regardless of their occurrence in core or rim residues. For individual amino acids, the frequency of substitution into a polymorphism or disease-causing mutation differed to other amino acids and was related to its structural location, as was the type of physico-chemical change introduced by the SAV. In conclusion, this study demonstrated the different distribution and properties of disease-causing SAVs and polymorphisms within different structural regions and in relation to the energetic contribution of amino acid in protein-protein interfaces, thus highlighting the importance of a structural system biology approach for predicting the effect of SAVs.
Di Fruscia P, Zacharioudakis E, Liu C, et al., 2015, The Discovery of a Highly Selective 5,6,7,8-Tetrahydrobenzo[4,5]thieno[ 2,3-d] pyrimidin-4(3H)-one SIRT2 Inhibitor that is Neuroprotective in an in vitro Parkinson's Disease Model, CHEMMEDCHEM, Vol: 10, Pages: 69-82, ISSN: 1860-7179
Greener JG, Sternberg MJE, 2015, AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis, BMC BIOINFORMATICS, Vol: 16, ISSN: 1471-2105
Kelley LA, Mezulis S, Yates CM, et al., 2015, The Phyre2 web portal for protein modeling, prediction and analysis., Nature Protocols, Vol: 10, Pages: 845-858, ISSN: 1754-2189
Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.
Kelley LA, Sternberg MJ, 2015, Partial protein domains: evolutionary insights and bioinformatics challenges., Genome Biology, Vol: 16, Pages: 100-100, ISSN: 1474-760X
Protein domains are generally thought to correspond to units of evolution. New research raises questions about how such domains are defined with bioinformatics tools and sheds light on how evolution has enabled partial domains to be viable.
Lewis TE, Sillitoe I, Andreeva A, et al., 2015, Genome3D: exploiting structure to help users understand their sequences, NUCLEIC ACIDS RESEARCH, Vol: 43, Pages: D382-D386, ISSN: 0305-1048
Reynolds CR, Muggleton SH, Sternberg MJE, 2015, Incorporating Virtual Reactions into a Logic-based Ligand-based Virtual Screening Method to Discover New Leads, MOLECULAR INFORMATICS, Vol: 34, Pages: 615-625, ISSN: 1868-1743
Irimia M, Weatheritt RJ, Ellis JD, et al., 2014, A Highly Conserved Program of Neuronal Microexons Is Misregulated in Autistic Brains, Cell, Vol: 159, Pages: 1511-1523, ISSN: 0092-8674
Alternative splicing (AS) generates vast transcriptomicand proteomic complexity. However, whichof the myriad of detected AS events provide importantbiological functions is not well understood.Here, we define the largest program of functionallycoordinated, neural-regulated AS described to datein mammals. Relative to all other types of AS withinthis program, 3-15 nucleotide ‘‘microexons’’ displaythe most striking evolutionary conservation andswitch-like regulation. These microexons modulatethe function of interaction domains of proteinsinvolved in neurogenesis. Most neural microexonsare regulated by the neuronal-specific splicing factornSR100/SRRM4, through its binding to adjacentintronic enhancer motifs. Neural microexons arefrequently misregulated in the brains of individualswith autism spectrum disorder, and this misregulationis associated with reduced levels of nSR100.The results thus reveal a highly conserved programof dynamic microexon regulation associated withthe remodeling of protein-interaction networks duringneurogenesis, the misregulation of which islinked to autism.
Talman AM, Prieto JH, Marques S, et al., 2014, Proteomic analysis of the Plasmodium male gamete reveals the key role for glycolysis in flagellar motility, MALARIA JOURNAL, Vol: 13, ISSN: 1475-2875
Yates CM, Filippis I, Kelley LA, et al., 2014, SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features, JOURNAL OF MOLECULAR BIOLOGY, Vol: 426, Pages: 2692-2701, ISSN: 0022-2836
Adzhubei AA, Sternberg MJE, Makarov AA, 2013, Polyproline-II Helix in Proteins: Structure and Function, JOURNAL OF MOLECULAR BIOLOGY, Vol: 425, Pages: 2100-2132, ISSN: 0022-2836
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