ATRIPPI: AN ATOM-RESIDUE PREFERENCE SCORING FUNCTION FOR PROTEIN-PROTEIN INTERACTIONS

被引：1

作者：

Liu, Kang-Ping ^{[1
]}

Hsu, Kai-Cheng ^{[1
]}

Huang, Jhang-Wei ^{[1
]}

Chang, Lu-Shian ^{[1
]}

Yang, Jinn-Moon ^{[1
,2
,3
]}

机构：

[1] Natl Chiao Tung Univ, Inst Bioinformat, Hsinchu, Taiwan

[2] Natl Chiao Tung Univ, Dept Biol Sci & Technol, Hsinchu, Taiwan

[3] Natl Chiao Tung Univ, Core Facil Struct Bioinformat, Hsinchu, Taiwan

来源：

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS | 2010年 / 19卷 / 03期

关键词：

Protein-protein interaction; atom-atom interacting preference; knowledge-based scoring matrix; residue-residue interaction preference; INTERACTION NETWORKS; DOCKING; DESOLVATION; PREDICTION; POTENTIALS; SCALE;

D O I：

10.1142/S0218213010000169

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 atom-atom and residue-residue 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 protein-protein interactions.

引用

页码：251 / 266

页数：16

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