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Knowledge-based energy functions for computational studies of proteins

Xiang Li·Jie Liang
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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.

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BibTeXRIS

Xiang Li, Jie Liang. 2006-01-19. Knowledge-based energy functions for computational studies of proteins. https://doi.org/10.1007/978-0-387-68372-0_3

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