Abstract
The Structural Classification of Proteins (SCOP) database is a classification of protein domains organised according to their evolutionary and structural relationships. We report a major ...effort to increase the coverage of structural data, aiming to provide classification of almost all domain superfamilies with representatives in the PDB. We have also improved the database schema, provided a new API and modernised the web interface. This is by far the most significant update in coverage since SCOP 1.75 and builds on the advances in schema from the SCOP 2 prototype. The database is accessible from http://scop.mrc-lmb.cam.ac.uk.
We present 'dcGO' (http://supfam.org/SUPERFAMILY/dcGO), a comprehensive ontology database for protein domains. Domains are often the functional units of proteins, thus instead of associating ...ontological terms only with full-length proteins, it sometimes makes more sense to associate terms with individual domains. Domain-centric GO, 'dcGO', provides associations between ontological terms and protein domains at the superfamily and family levels. Some functional units consist of more than one domain acting together or acting at an interface between domains; therefore, ontological terms associated with pairs of domains, triplets and longer supra-domains are also provided. At the time of writing the ontologies in dcGO include the Gene Ontology (GO); Enzyme Commission (EC) numbers; pathways from UniPathway; human phenotype ontology and phenotype ontologies from five model organisms, including plants; anatomy ontologies from three organisms; human disease ontology and drugs from DrugBank. All ontological terms have probabilistic scores for their associations. In addition to associations to domains and supra-domains, the ontological terms have been transferred to proteins, through homology, providing annotations of >80 million sequences covering 2414 complete genomes, hundreds of meta-genomes, thousands of viruses and so forth. The dcGO database is updated fortnightly, and its website provides downloads, search, browse, phylogenetic context and other data-mining facilities.
Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease ...or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source.
We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions.
We present the 'dnet' package and apply it to the 'TCGA' mutation and clinical data of >3,000 patients. We uncover the existence of an underlying gene network that at least partially controls cancer ...'survivalness', with mutations that are significantly correlated with patient survival, yet independent of tumour origin and type. The survivalness network has natural community structure corresponding to tumour hallmarks, and contains genes that are potentially druggable in the clinic. This network has evolutionary roots in Deuterostomia identifying PTK2 and VAV1 as under-valued relative to more studied genes from that era. The 'dnet' R package is available at http://cran.r-project.org/package=dnet.
It has been known for more than 35 years that, during evolution, new proteins are formed by gene duplications, sequence and structural divergence and, in many cases, gene combinations. The genome ...projects have produced complete, or almost complete, descriptions of the protein repertoires of over 600 distinct organisms. Analyses of these data have dramatically increased our understanding of the formation of new proteins. At the present time, we can accurately trace the evolutionary relationships of about half the proteins found in most genomes, and it is these proteins that we discuss in the present review. Usually, the units of evolution are protein domains that are duplicated, diverge and form combinations. Small proteins contain one domain, and large proteins contain combinations of two or more domains. Domains descended from a common ancestor are clustered into superfamilies. In most genomes, the net growth of superfamily members means that more than 90% of domains are duplicates. In a section on domain duplications, we discuss the number of currently known superfamilies, their size and distribution, and superfamily expansions related to biological complexity and to specific lineages. In a section on divergence, we describe how sequences and structures diverge, the changes in stability produced by acceptable mutations, and the nature of functional divergence and selection. In a section on domain combinations, we discuss their general nature, the sequential order of domains, how combinations modify function, and the extraordinary variety of the domain combinations found in different genomes. We conclude with a brief note on other forms of protein evolution and speculations of the origins of the duplication, divergence and combination processes.
We present the Database of Disordered Protein Prediction (D(2)P(2)), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, ...PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D(2)P(2) will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life.
The most diverse marine ecosystems, coral reefs, depend upon a functional symbiosis between a cnidarian animal host (the coral) and intracellular photosynthetic dinoflagellate algae. The molecular ...and cellular mechanisms underlying this endosymbiosis are not well understood, in part because of the difficulties of experimental work with corals. The small sea anemone Aiptasia provides a tractable laboratory model for investigating these mechanisms. Here we report on the assembly and analysis of the Aiptasia genome, which will provide a foundation for future studies and has revealed several features that may be key to understanding the evolution and function of the endosymbiosis. These features include genomic rearrangements and taxonomically restricted genes that may be functionally related to the symbiosis, aspects of host dependence on alga-derived nutrients, a novel and expanded cnidarian-specific family of putative pattern-recognition receptors that might be involved in the animal-algal interactions, and extensive lineage-specific horizontal gene transfer. Extensive integration of genes of prokaryotic origin, including genes for antimicrobial peptides, presumably reflects an intimate association of the animal-algal pair also with its prokaryotic microbiome.
ABSTRACT
The rate at which nonsynonymous single nucleotide polymorphisms (nsSNPs) are being identified in the human genome is increasing dramatically owing to advances in whole‐genome/whole‐exome ...sequencing technologies. Automated methods capable of accurately and reliably distinguishing between pathogenic and functionally neutral nsSNPs are therefore assuming ever‐increasing importance. Here, we describe the Functional Analysis Through Hidden Markov Models (FATHMM) software and server: a species‐independent method with optional species‐specific weightings for the prediction of the functional effects of protein missense variants. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two separate benchmarks. Furthermore, in one benchmark, we achieve performance accuracies that outperform current state‐of‐the‐art prediction methods (i.e., SNPs&GO and MutPred). We demonstrate that FATHMM can be efficiently applied to high‐throughput/large‐scale human and nonhuman genome sequencing projects with the added benefit of phenotypic outcome associations. To illustrate this, we evaluated nsSNPs in wheat (Triticum spp.) to identify some of the important genetic variants responsible for the phenotypic differences introduced by intense selection during domestication. A Web‐based implementation of FATHMM, including a high‐throughput batch facility and a downloadable standalone package, is available at http://fathmm.biocompute.org.uk.
Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical ...interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.
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► An atlas of human and mouse transcription factor interactions ► Quantification of transcription factor expression across human and mouse tissues ► A network of 15 transcription factors predicts tissue type specification ► SMAD3/FLI1 forms a repressor complex that controls monocyte differentiation
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