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Protein-Protein Binding Affinity Prediction Based on an SVR Ensemble

  • Xueling Li
  • Min Zhu
  • Xiaolai Li
  • Hong-Qiang Wang
  • Shulin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

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.

Keywords

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xueling Li
    • 1
  • Min Zhu
    • 2
  • Xiaolai Li
    • 1
  • Hong-Qiang Wang
    • 1
  • Shulin Wang
    • 3
  1. 1.Intelligent Computing Lab, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefeiP.R. China
  2. 2.Robot Sensor and Human-Machine Interaction Laboratory, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefeiP.R. China
  3. 3.School of Computer and CommunicationHunan UniversityChangshaP.R. China

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