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
Oscar Alvizo, Stephen L Mayo

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 beta1 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 impr...Continue Reading

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Citations

Sep 2, 2010·Protein Engineering, Design & Selection : PEDS·Nora H BarakatJohn J Love
Dec 17, 2011·BMC Evolutionary Biology·Johan A GrahnenDavid A Liberles
Nov 27, 2015·Journal of Chemical Theory and Computation·David SimonciniThomas Schiex
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Jun 25, 2016·Journal of Chemical Theory and Computation·Christopher M TophamIsabelle André
Feb 3, 2016·Journal of Computational Chemistry·Seydou TraoréSophie Barbe
Jan 1, 2011·Genes·Jessica Siltberg-LiberlesDavid A Liberles
Apr 10, 2021·Emerging Topics in Life Sciences·Nitu SinghKinshuk Raj Srivastava
Dec 19, 2019·Journal of Chemical Information and Modeling·Xiaoqiang HuangYang Zhang

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