Protein-protein binding affinity prediction based on an SVR ensemble

Xueling Li, Min Zhu, Xiaolai Li, Hong Qiang Wang, Shulin Wang

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

13 Scopus citations


Accurately predicting generic protein-protein binding affinities (PPBA) is essential to analyze the outputs of protein docking and may help infer real status of cellular protein-protein interaction sub-networks. However, accurate PPBA prediction is still extremely challenging. Machine learning methods are promising to address this problem. We propose a two-layer support vector regression (TLSVR) model to implicitly capture binding contributions that are hard to explicitly model. The TLSVR circumvents both the descriptor compatibility problem and the need for problematic modeling assumptions. Input features for TLSVR in first layer are scores of 2209 interacting atom pairs within each distance bin. The base SVRs are combined by the second layer to infer the final affinities. Leave-one-out validation on our heterogeneous data shows that the TLSVR method obtains a very good result of R=0.80 and SD=1.32 with real affinities. Comparison experiment further demonstrates that TLSVR is superior to the previous state-of-art methods in predicting generic PPBA.

Original languageEnglish (US)
Title of host publicationIntelligent Computing Technology - 8th International Conference, ICIC 2012, Proceedings
Number of pages7
StatePublished - Aug 28 2012
Event8th International Conference on Intelligent Computing Technology, ICIC 2012 - Huangshan, China
Duration: Jul 25 2012Jul 29 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7389 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other8th International Conference on Intelligent Computing Technology, ICIC 2012


  • Protein-protein interaction affinity
  • machine learning
  • potential of mean force
  • two-layer support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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