Protein binding site prediction by combining hidden markov support vector machine and profile-based propensities

Bin Liu*, Bingquan Liu, Fule Liu, Xiaolong Wang

*Corresponding author for this work

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Abstract

Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC.

Original languageEnglish
Article number464093
JournalThe Scientific World Journal
Volume2014
DOIs
Publication statusPublished - 2014
Externally publishedYes

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Liu, B., Liu, B., Liu, F., & Wang, X. (2014). Protein binding site prediction by combining hidden markov support vector machine and profile-based propensities. The Scientific World Journal, 2014, Article 464093. https://doi.org/10.1155/2014/464093