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Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data.

ABSTRACT: PATH algorithms for identifying conformational transition states provide computational parameters-time to the transition state, conformational free energy differences, and transition state activation energies-for comparison to experimental data and can be carried out sufficiently rapidly to use in the "high throughput" mode. These advantages are especially useful for interpreting results from combinatorial mutagenesis experiments. This report updates the previously published algorithm with enhancements that improve correlations between PATH convergence parameters derived from virtual variant structures generated by RosettaBackrub and previously published kinetic data for a complete, four-way combinatorial mutagenesis of a conformational switch in Tryptophanyl-tRNA synthetase.

SUBMITTER: Chandrasekaran SN

PROVIDER: S-EPMC5315668 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data.

Chandrasekaran Srinivas Niranj SN Carter Charles W CW

Structural dynamics (Melville, N.Y.) 20170216 3

PATH algorithms for identifying conformational transition states provide computational parameters-time to the transition state, conformational free energy differences, and transition state activation energies-for comparison to experimental data and can be carried out sufficiently rapidly to use in the "high throughput" mode. These advantages are especially useful for interpreting results from combinatorial mutagenesis experiments. This report updates the previously published algorithm with enhan ...[more]

PMID: 28289692

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