Protein structure prediction using global optimization by basin-hopping with NMR shift restraints

Hoffmann, Falk; Strodel, Birgit

doi:10.1063/1.4773406

Journal Article

FZJ-2013-02977

Protein structure prediction using global optimization by basin-hopping with NMR shift restraints

Hoffmann, F. (Corresponding author)FZJ* ; Strodel, B.FZJ*

2013
American Institute of Physics Melville, NY

The journal of chemical physics 138(025102), 1-7 (2013) [10.1063/1.4773406]

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Please use a persistent id in citations: http://hdl.handle.net/2128/19022 doi:10.1063/1.4773406

Abstract: Computational methods that utilize chemical shifts to produce protein structures at atomic resolution have recently been introduced. In the current work, we exploit chemical shifts by combining the basin-hopping approach to global optimization with chemical shift restraints using a penalty function. For three peptides, we demonstrate that this approach allows us to find near-native structures from fully extended structures within 10 000 basin-hopping steps. The effect of adding chemical shift restraints is that the α and β secondary structure elements form within 1000 basin-hopping steps, after which the orientation of the secondary structure elements, which produces the tertiary contacts, is driven by the underlying protein force field. We further show that our chemical shift-restraint BH approach also works for incomplete chemical shift assignments, where the information from only one chemical shift type is considered. For the proper implementation of chemical shift restraints in the basin-hopping approach, we determined the optimal weight of the chemical shift penalty energy with respect to the CHARMM force field in conjunction with the FACTS solvation model employed in this study. In order to speed up the local energy minimization procedure, we developed a function, which continuously decreases the width of the chemical shift penalty function as the minimization progresses. We conclude that the basin-hopping approach with chemical shift restraints is a promising method for protein structure prediction.

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