ATRIPPI: An atom-residue preference scoring function for protein-protein interactions

Kang Ping Liu, Kai Cheng Hsu, Jhang Wei Huang, Lu Shian Chang, Jinn Moon Yang

Research output: Contribution to journalArticle

Abstract

We present an ATRIPPI model for analyzing protein-protein interactions. This model is a 167-atom-type and residue-specific interaction preferences with distance bins derived from 641 co-crystallized protein-protein interfaces. The ATRIPPI model is able to yield physical meanings of hydrogen bonding, disulfide bonding, electrostatic interactions, van der Waals and aromatic-aromatic interactions. We applied this model to identify the native states and near-native complex structures on 17 bound and 17 unbound complexes from thousands of decoy structures. On average, 77.5% structures (155 structures) of top rank 200 structures are closed to the native structure. These results suggest that the ATRIPPI model is able to keep the advantages of both atomatom and residueresidue interactions and is a potential knowledge-based scoring function for protein-protein docking methods. We believe that our model is robust and provides biological meanings to support proteinprotein interactions.

Original languageEnglish
Pages (from-to)251-266
Number of pages16
JournalInternational Journal on Artificial Intelligence Tools
Volume19
Issue number3
DOIs
Publication statusPublished - Jun 2010
Externally publishedYes

Keywords

  • atomatom interacting preference
  • knowledge-based scoring matrix
  • Protein-protein interaction
  • residueresidue interaction preference

ASJC Scopus subject areas

  • Artificial Intelligence

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