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Evaluating and optimizing computational protein design force fields using fixed composition-based negative design

Alvizo, Oscar and Mayo, Stephen L. (2008) Evaluating and optimizing computational protein design force fields using fixed composition-based negative design. Proceedings of the National Academy of Sciences of the United States of America, 105 (34). pp. 12242-12247. ISSN 0027-8424. http://resolver.caltech.edu/CaltechAUTHORS:ALVpnas08

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Abstract

An accurate force field is essential to computational protein design and protein fold prediction studies. Proper force field tuning is problematic, however, due in part to the incomplete modeling of the unfolded state. Here, we evaluate and optimize a protein design force field by constraining the amino acid composition of the designed sequences to that of a well behaved model protein. According to the random energy model, unfolded state energies are dependent only on amino acid composition and not the specific arrangement of amino acids. Therefore, energy discrepancies between computational predictions and experimental results, for sequences of identical composition, can be directly attributed to flaws in the force field's ability to properly account for folded state sequence energies. This aspect of fixed composition design allows for force field optimization by focusing solely on the interactions in the folded state. Several rounds of fixed composition optimization of the 56-residue β1 domain of protein G yielded force field parameters with significantly greater predictive power: Optimized sequences exhibited higher wild-type sequence identity in critical regions of the structure, and the wild-type sequence showed an improved Z-score. Experimental studies revealed a designed 24-fold mutant to be stably folded with a melting temperature similar to that of the wild-type protein. Sequence designs using engrailed homeodomain as a scaffold produced similar results, suggesting the tuned force field parameters were not specific to protein G.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1073/pnas.0805858105DOIUNSPECIFIED
http://www.pnas.org/content/105/34/12242.abstractPublisherUNSPECIFIED
Additional Information:Copyright ©2008 by the National Academy of Sciences. Freely available online through the PNAS open access option. Contributed by Stephen L. Mayo, June 17, 2008 (received for review November 5, 2007). We thank Marie Ary for help with the manuscript, Scott Ross and Karin Crowhurst for their assistance with NMR, and Ben Allen for preparation of the conformer library. This work was supported by the Ralph M. Parsons Foundation, the Howard Hughes Medical Institute, and the National Institutes of Health. Author contributions: O.A. and S.L.M. designed research; O.A. performed research; O.A. and S.L.M. analyzed data; and O.A. and S.L.M. wrote the paper. The authors declare no conflict of interest. This article contains supporting information online at www.pnas.org/cgi/content/full/0805858105/DCSupplemental.
Funders:
Funding AgencyGrant Number
Ralph M. Parsons FoundationUNSPECIFIED
Howard Hughes Medical InstituteUNSPECIFIED
National Institutes of HealthUNSPECIFIED
Subject Keywords:fixed amino acid composition; force field optimization; random energy model
Record Number:CaltechAUTHORS:ALVpnas08
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:ALVpnas08
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:11513
Collection:CaltechAUTHORS
Deposited By: Archive Administrator
Deposited On:27 Aug 2008 02:32
Last Modified:14 Nov 2014 19:20

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