title: Novel nonlinear knowledge-based mean force potentials based on machine learning.
abstract: The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of known structures. Current knowledge-based potentials are almost in the form of weighted linear sum of interaction pairs. In this study, a class of novel nonlinear knowledge-based mean force potentials is presented. The potential parameters are obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced and their corresponding nonlinear potentials are implemented. They are the DIH potential (single-body residue-level Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential (two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3 potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed discriminative framework is effective in developing knowledge-based mean force potentials. The nonlinear potentials can be widely used for ab initio protein structure prediction, model quality assessment, protein docking, and other challenging problems in computational biology.
authors.affiliation: Shanghai Key Lab of Intelligent Information Processing and the School of Computer Science, Fudan University, Old Yifu Building, Room 202-5, 220 Handan Road, Shanhai 200433, China. qwdong@fudan.edu.cn
authors.forename: Qiwen
authors.initials: Q
authors.lastname: Dong
authors.forename: Shuigeng
authors.initials: S
authors.lastname: Zhou
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D051188:Knowledge Bases; D017711:Nonlinear Dynamics; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins