Dataset Information

Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins.

ABSTRACT: Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.

SUBMITTER: Doppelt-Azeroual O

PROVIDER: S-EPMC2867024 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins.

Doppelt-Azeroual Olivia O Delfaud François F Moriaud Fabrice F de Brevern Alexandre G AG

Protein science : a publication of the Protein Society 20100401 4

Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is b ...[more]

PMID: 20162627

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Project description:Organellar gene expression (OGE) is crucial for plant development, photosynthesis, and respiration, but our understanding of the mechanisms that control it is still relatively poor. Thus, OGE requires various nucleus-encoded proteins that promote transcription, splicing, trimming, and editing of organellar RNAs, and regulate translation. In metazoans, proteins of the mitochondrial Transcription tERmination Factor (mTERF) family interact with the mitochondrial chromosome and regulate transcriptional initiation and termination. Sequencing of the Arabidopsis thaliana genome led to the identification of a diversified MTERF gene family but, in contrast to mammalian mTERFs, knowledge about the function of these proteins in photosynthetic organisms is scarce. In this hypothesis article, I show that tandem duplications and one block duplication contributed to the large number of MTERF genes in A. thaliana, and propose that the expansion of the family is related to the evolution of land plants. The MTERF genes-especially the duplicated genes-display a number of distinct mRNA accumulation patterns, suggesting functional diversification of mTERF proteins to increase adaptability to environmental changes. Indeed, hypothetical functions for the different mTERF proteins can be predicted using co-expression analysis and gene ontology (GO) annotations. On this basis, mTERF proteins can be sorted into five groups. Members of the "chloroplast" and "chloroplast-associated" clusters are principally involved in chloroplast gene expression, embryogenesis, and protein catabolism, while representatives of the "mitochondrial" cluster seem to participate in DNA and RNA metabolism in that organelle. Moreover, members of the "mitochondrion-associated" cluster and the "low expression" group may act in the nucleus and/or the cytosol. As proteins involved in OGE and presumably nuclear gene expression (NGE), mTERFs are ideal candidates for the coordination of the expression of organelle and nuclear genomes.

Dataset Information

Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins.

Publications

Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins.

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