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Adapting Poisson-Boltzmann to the self-consistent mean field theory: Application to protein side-chain modeling

Abstract : We present an extension of the self-consistent mean field theory for protein side-chain modeling in which solvation effects are included based on the Poisson-Boltzmann (PB) theory. In this approach, the protein is represented with multiple copies of its side chains. Each copy is assigned a weight that is refined iteratively based on the mean field energy generated by the rest of the protein, until self-consistency is reached. At each cycle, the variational free energy of the multi-copy system is computed; this free energy includes the internal energy of the protein that accounts for vdW and electrostatics interactions and a solvation free energy term that is computed using the PB equation. The method converges in only a few cycles and takes only minutes of central processing unit time on a commodity personal computer. The predicted conformation of each residue is then set to be its copy with the highest weight after convergence. We have tested this method on a database of hundred highly refined NMR structures to circumvent the problems of crystal packing inherent to x-ray structures. The use of the PB-derived solvation free energy significantly improves prediction accuracy for surface side chains. For example, the prediction accuracies for χ(1) for surface cysteine, serine, and threonine residues improve from 68%, 35%, and 43% to 80%, 53%, and 57%, respectively. A comparison with other side-chain prediction algorithms demonstrates that our approach is consistently better in predicting the conformations of exposed side chains.
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Submitted on : Friday, July 5, 2019 - 11:55:26 AM
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Patrice Koehl, Henri Orland, Marc Delarue. Adapting Poisson-Boltzmann to the self-consistent mean field theory: Application to protein side-chain modeling. Journal of Chemical Physics, American Institute of Physics, 2011, 135 (5), pp.055104. ⟨10.1063/1.3621831⟩. ⟨pasteur-02174733⟩



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