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Protein Science (2004), 13:391-399. Published by Cold Spring Harbor Laboratory Press. Copyright © 2004 The Protein Society
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Accurate and efficient loop selections by the DFIRE-based all-atom statistical potential

Chi Zhang1, Song Liu1 and Yaoqi Zhou

Howard Hughes Medical Institute (HHMI) Center for Single Molecule Biophysics, Department of Physiology and Biophysics, State University of New York at Buffalo, Buffalo, New York 14214, USA

(RECEIVED September 3, 2003; FINAL REVISION October 17, 2003; ACCEPTED October 17, 2003)



Abstract

The conformations of loops are determined by the water-mediated interactions between amino acid residues. Energy functions that describe the interactions can be derived either from physical principles (physical-based energy function) or statistical analysis of known protein structures (knowledge-based statistical potentials). It is commonly believed that statistical potentials are appropriate for coarse-grained representation of proteins but are not as accurate as physical-based potentials when atomic resolution is required. Several recent applications of physical-based energy functions to loop selections appear to support this view. In this article, we apply a recently developed DFIRE-based statistical potential to three different loop decoy sets (RAPPER, Jacobson, and Forrest-Woolf sets). Together with a rotamer library for side-chain optimization, the performance of DFIRE-based potential in the RAPPER decoy set (385 loop targets) is comparable to that of AMBER/GBSA for short loops (two to eight residues). The DFIRE is more accurate for longer loops (9 to 12 residues). Similar trend is observed when comparing DFIRE with another physical-based OPLS/SGB-NP energy function in the large Jacobson decoy set (788 loop targets). In the Forrest-Woolf decoy set for the loops of membrane proteins, the DFIRE potential performs substantially better than the combination of the CHARMM force field with several solvation models. The results suggest that a single-term DFIRE-statistical energy function can provide an accurate loop prediction at a fraction of computing cost required for more complicate physical-based energy functions. A Web server for academic users is established for loop selection at the softwares/services section of the Web site http://theory.med.buffalo.edu/.

Keywords: Knowledge-based potential; loop decoy sets; ideal-gas reference state; loop prediction

Reprint requests to: Yaoqi Zhou, Howard Hughes Medical Institute Center for Single Molecule Biophysics and Department of Physiology and Biophysics, State University of New York at Buffalo, 124 Sherman Hall, Buffalo, NY 14214, USA; e-mail: yqzhou{at}buffalo.edu; fax: (716) 829-2344.

Supplemental material: See www.proteinscience.org

1 These authors contributed equally to this work.

Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.03411904.


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