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PIRSF family classification system for protein functional and evolutionary analysis.

ABSTRACT: The PIRSF protein classification system (http://pir.georgetown.edu/pirsf/) reflects evolutionary relationships of full-length proteins and domains. The primary PIRSF classification unit is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). PIRSF families are curated systematically based on literature review and integrative sequence and functional analysis, including sequence and structure similarity, domain architecture, functional association, genome context, and phyletic pattern. The results of classification and expert annotation are summarized in PIRSF family reports with graphical viewers for taxonomic distribution, domain architecture, family hierarchy, and multiple alignment and phylogenetic tree. The PIRSF system provides a comprehensive resource for bioinformatics analysis and comparative studies of protein function and evolution. Domain or fold-based searches allow identification of evolutionarily related protein families sharing domains or structural folds. Functional convergence and functional divergence are revealed by the relationships between protein classification and curated family functions. The taxonomic distribution allows the identification of lineage-specific or broadly conserved protein families and can reveal horizontal gene transfer. Here we demonstrate, with illustrative examples, how to use the web-based PIRSF system as a tool for functional and evolutionary studies of protein families.

SUBMITTER: Nikolskaya AN

PROVIDER: S-EPMC2674652 | biostudies-literature | 2007 Feb

REPOSITORIES: biostudies-literature

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PIRSF family classification system for protein functional and evolutionary analysis.

Nikolskaya Anastasia N AN Arighi Cecilia N CN Huang Hongzhan H Barker Winona C WC Wu Cathy H CH

Evolutionary bioinformatics online 20070210

The PIRSF protein classification system (http://pir.georgetown.edu/pirsf/) reflects evolutionary relationships of full-length proteins and domains. The primary PIRSF classification unit is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). PIRSF families are curated systematically based on literature review and integrative sequence and functional analysis, includi ...[more]

PMID: 19455212

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Project description:BackgroundDistinguishing biologically relevant interfaces from lattice contacts in protein crystals is a fundamental problem in structural biology. Despite efforts towards the computational prediction of interface character, many issues are still unresolved.ResultsWe present here a protein-protein interface classifier that relies on evolutionary data to detect the biological character of interfaces. The classifier uses a simple geometric measure, number of core residues, and two evolutionary indicators based on the sequence entropy of homolog sequences. Both aim at detecting differential selection pressure between interface core and rim or rest of surface. The core residues, defined as fully buried residues (>95% burial), appear to be fundamental determinants of biological interfaces: their number is in itself a powerful discriminator of interface character and together with the evolutionary measures it is able to clearly distinguish evolved biological contacts from crystal ones. We demonstrate that this definition of core residues leads to distinctively better results than earlier definitions from the literature. The stringent selection and quality filtering of structural and sequence data was key to the success of the method. Most importantly we demonstrate that a more conservative selection of homolog sequences - with relatively high sequence identities to the query - is able to produce a clearer signal than previous attempts.ConclusionsAn evolutionary approach like the one presented here is key to the advancement of the field, which so far was missing an effective method exploiting the evolutionary character of protein interfaces. Its coverage and performance will only improve over time thanks to the incessant growth of sequence databases. Currently our method reaches an accuracy of 89% in classifying interfaces of the Ponstingl 2003 datasets and it lends itself to a variety of useful applications in structural biology and bioinformatics. We made the corresponding software implementation available to the community as an easy-to-use graphical web interface at http://www.eppic-web.org.

Project description:Crop diseases caused by pathogens critically affect global food security and plant ecology. Pathogens are well adapted to their host plants and have developed sophisticated mechanisms allowing successful colonization. Plants in turn have taken measures to counteract pathogen attacks resulting in an evolutionary arms race. Recent studies provided mechanistic insights into how two plant Kiwellin proteins from Zea mays mitigate the activity of the chorismate mutase Cmu1, a virulence factor secreted by the fungal pathogen Ustilago maydis during maize infection. Formerly identified as human allergens in kiwifruit, the biological function of Kiwellins is apparently linked to plant defense. We combined the analysis of proteome data with structural predictions to obtain a holistic overview of the Kiwellin protein family, that is subdivided into proteins with and without a N-terminal kissper domain. We found that Kiwellins are evolutionarily conserved in various plant species. At median five Kiwellin paralogs are encoded in each plant genome. Structural predictions revealed that Barwin-like proteins and Kiwellins cannot be discriminated purely at the sequence level. Our data shows that Kiwellins emerged in land plants (embryophyta) and are not present in fungi as suggested earlier. They evolved via three major duplication events that lead to clearly distinguishable subfamilies. We introduce a systematic Kiwellin nomenclature based on a detailed evolutionary reconstruction of this protein family. A meta-analysis of publicly available transcriptome data demonstrated that Kiwellins can be differentially regulated upon the interaction of plants with pathogens but also with symbionts. Furthermore, significant differences in Kiwellin expression levels dependent on tissues and cultivars were observed. In summary, our study sheds light on the evolution and regulation of a large protein family and provides a framework for a more detailed understanding of the molecular functions of Kiwellins.

Dataset Information

PIRSF family classification system for protein functional and evolutionary analysis.

Publications

PIRSF family classification system for protein functional and evolutionary analysis.

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