Protein backbone dihedral angle prediction based on probabilistic models

摘要：

Protein backbone dihedral angles are important descriptors of local conformation for amino acids. Protein backbone dihedral angle prediction lays the foundation for prediction of higher-order protein structure. Existing prediction methods of protein backbone angles mainly exploit traditional machine learning techniques. In this paper, we propose to use two well-known types of probabilistic models ---maximum entropy Markov models (MEMMs) and conditional random fields (CRFs) to predict the backbone dihedral angles of amino acid sequences. Experiments conducted on dataset PDB25 show that these two probabilistic models are effective in dihedral angle prediction, and CRFs outperform MEMMs.

作者: Xin Geng Jihong Guan Qiwen Dong Shuigeng Zhou

作者单位: Dept.of Computer Science and Technology Tongji University Shanghai, China Shanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University

会议类型: 国际会议

会议名称: The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-18（万方平台首次上网日期，不代表论文的发表时间）