%0 Journal Article
%T Knowledge-based energy functions for computational studies of proteins
%A Xiang Li
%A Jie Liang
%J Quantitative Biology
%D 2006
%I arXiv
%R 10.1007/978-0-387-68372-0_3
%X This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.
%U http://arxiv.org/abs/q-bio/0601026v1