title: Variable context Markov chains for HIV protease cleavage site prediction.
abstract: Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.
authors.affiliation: Department of Information and Computer Science, Helsinki University of Technology, FI-02015 TKK, Espoo, Finland. hasan.ogul@hut.fi
authors.forename: Hasan
authors.initials: H
authors.lastname: Oğul
mesh_terms: D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D004789:Enzyme Activation; D016333:HIV Protease / Q000737:chemistry*; D008390:Markov Chains; D008956:Models, Chemical; D008969:Molecular Sequence Data; D011485:Protein Binding; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D016333:HIV Protease