Knowledge-based energy functions for computational studies of proteins

Abstract

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.

57 pages, 6 figures. To be published in a book by Springer

References in corpus (1)

• Interstrand pairing patterns in $\beta$-barrel membrane proteins: the positive-outside rule, aromatic rescue, and strand registration prediction

Cited by in corpus (1)

• Structural Learning for Template-free Protein Folding