A residual level potential of mean force based approach to predict protein-protein interaction affinity

Xue Ling Li, Mei Ling Hou, Shu Lin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

We develop a knowledge-based statistical energy function on residual level for quantitatively predicting the affinity of protein-protein complexes by using 20 residue types and a distance-free reference state. The correlation coefficients between experimentally measured protein-protein binding affinities (PPIA) and the predicted affinities by our approach are 0.74 for 82 protein-protein (peptide) complexes. Compared to the published results of two other volume corrected knowledge-based scoring functions on atomic level, the proposed approach not only is the simplest but also yields the comparable correlation between theoretical and experimental binding affinities of the test sets with the reported best methods.

Original languageEnglish (US)
Title of host publicationAdvanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings
Pages680-686
Number of pages7
DOIs
StatePublished - 2010
Externally publishedYes
Event6th International Conference on Intelligent Computing, ICIC 2010 - Changsha, China
Duration: Aug 18 2010Aug 21 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6215 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Intelligent Computing, ICIC 2010
Country/TerritoryChina
CityChangsha
Period8/18/108/21/10

Keywords

  • Protein-protein interaction
  • affinity
  • mean force of potential
  • protein complex
  • quaternary structure
  • residue level

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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