Comparing pairwise-additive and many-body generalized Born models for acid/base calculations and protein design - École polytechnique Access content directly
Journal Articles Journal of Computational Chemistry Year : 2017

Comparing pairwise-additive and many-body generalized Born models for acid/base calculations and protein design

Abstract

Generalized Born (GB) solvent models are common in acid/base calculations and protein design. With GB, the interaction between a pair of solute atoms depends on the shape of the protein/solvent boundary and, therefore, the positions of all solute atoms, so that GB is a many-body potential. For compute-intensive applications, the model is often simplified further, by introducing a mean, native-like protein/solvent boundary, which removes the many-body property. We investigate a method for both acid/base calculations and protein design that uses Monte Carlo simulations in which side chains can explore rotamers, bind/release protons, or mutate. The fluctuating protein/solvent dielectric boundary is treated in a way that is numerically exact (within the GB framework), in contrast to a mean boundary. Its originality is that it captures the many-body character while retaining the residue-pairwise complexity given by a fixed boundary. The method is implemented in the Proteus protein design software. It yields a slight but systematic improvement for acid/base constants in nine proteins and a significant improvement for the computational design of three PDZ domains. It eliminates a source of model uncertainty, which will facilitate the analysis of other model limitations.
No file

Dates and versions

hal-01964492 , version 1 (22-12-2018)

Licence

Copyright

Identifiers

Cite

Francesco Villa, David Mignon, Savvas Polydorides, Thomas Simonson. Comparing pairwise-additive and many-body generalized Born models for acid/base calculations and protein design. Journal of Computational Chemistry, 2017, 38 (28), pp.2396-2410. ⟨10.1002/jcc.24898⟩. ⟨hal-01964492⟩
58 View
0 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More