Literature DB >> 12488102

Analysing six types of protein-protein interfaces.

Yanay Ofran1, Burkhard Rost.   

Abstract

Non-covalent residue side-chain interactions occur in many different types of proteins and facilitate many biological functions. Are these differences manifested in the sequence compositions and/or the residue-residue contact preferences of the interfaces? Previous studies analysed small data sets and gave contradictory answers. Here, we introduced a new data-mining method that yielded the largest high-resolution data set of interactions analysed. We introduced an information theory-based analysis method. On the basis of sequence features, we were able to differentiate six types of protein interfaces, each corresponding to a different functional or structural association between residues. Particularly, we found significant differences in amino acid composition and residue-residue preferences between interactions of residues within the same structural domain and between different domains, between permanent and transient interfaces, and between interactions associating homo-oligomers and hetero-oligomers. The differences between the six types were so substantial that, using amino acid composition alone, we could predict statistically to which of the six types of interfaces a pool of 1000 residues belongs at 63-100% accuracy. All interfaces differed significantly from the background of all residues in SWISS-PROT, from the group of surface residues, and from internal residues that were not involved in non-trivial interactions. Overall, our results suggest that the interface type could be predicted from sequence and that interface-type specific mean-field potentials may be adequate for certain applications.

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Year:  2003        PMID: 12488102     DOI: 10.1016/s0022-2836(02)01223-8

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  126 in total

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Journal:  Proc Natl Acad Sci U S A       Date:  2003-05-01       Impact factor: 11.205

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Journal:  Protein Sci       Date:  2004-02       Impact factor: 6.725

6.  Accurate and efficient loop selections by the DFIRE-based all-atom statistical potential.

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8.  The dependence of all-atom statistical potentials on structural training database.

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9.  The PredictProtein server.

Authors:  Burkhard Rost; Guy Yachdav; Jinfeng Liu
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

10.  Protein subunit interfaces: A statistical analysis of hot spots in Sm proteins.

Authors:  Srđan D Stojanović; Božidarka L Zarić; Snežana D Zarić
Journal:  J Mol Model       Date:  2010-07-23       Impact factor: 1.810

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