Protein-Protein interaction affinity prediction based on interface descriptors and machine learning

Authors:
Xue-Ling Li;Min Zhu;Xiao-Lai Li;Hong-Qiang Wang;Shulin Wang
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, P.R. China;Robot Sensor and Human-Machine Interaction Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, P.R. China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, P.R. China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, P.R. China;School of Computer and Communication, Hunan University, Changsha, Hunan, P.R. China
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

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Abstract

Knowing the protein-protein interaction affinity is important for accurately inferring the time dimensionality of the dynamic protein-protein interaction networks from a viewpoint of systems biology. The accumulation of the determined protein complex structures with high resolution facilitates to realize this ambitious goal. Previous methods on protein-protein interaction affinity (PPIA) prediction have achieved great success. However, there is still a great space to improve prediction accuracy. Here, we develop a support vector regression method to infer highly heterogeneous protein-protein interaction affinities based on interface properties. This method takes full advantage of the labels of the interaction pairs and greatly reduces the dimensionality of the input features. Results show that the supervised machine leaning methods are effective with R=0.80 and SD=1.41 and perform well when applied to the prediction of highly heterogeneous or generic PPIA. Comparison of different types of interface properties shows that the global interface properties have a more stable performance while the smoothed PMF obtains the best prediction accuracy.