title: Prediction of protein binding sites in protein structures using hidden Markov support vector machine.
abstract: BACKGROUND
  Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.
  
  
  RESULTS
  In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.
  
  
  CONCLUSIONS
  The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.
authors.affiliation: Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Wang
authors.forename: Lei
authors.initials: L
authors.lastname: Lin
authors.forename: Buzhou
authors.initials: B
authors.lastname: Tang
authors.forename: Qiwen
authors.initials: Q
authors.lastname: Dong
authors.forename: Xuan
authors.initials: X
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008390:Markov Chains; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D011506:Proteins