Knowledge-based energy functions for computational studies of proteins

摘要

This chapter discusses theoretical framework and methods for developingknowledge-based potential functions essential for protein structure prediction,protein-protein interaction, and protein sequence design. We discuss in somedetails about the Miyazawa-Jernigan contact statistical potential,distance-dependent statistical potentials, as well as geometric statisticalpotentials. We also describe a geometric model for developing both linear andnon-linear potential functions by optimization. Applications of knowledge-basedpotential functions in protein-decoy discrimination, in protein-proteininteractions, and in protein design are then described. Several issues ofknowledge-based potential functions are finally discussed.