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

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

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 t...

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Main Authors: Srinivas Niranj Chandrasekaran, Charles W. Carter Jr.
Format: Article
Language:English
Published: AIP Publishing LLC and ACA 2017-05-01
Series:Structural Dynamics
Online Access:http://dx.doi.org/10.1063/1.4976142
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spelling doaj-c7b4125f063b45fc9a9067859a173f7b2020-11-24T22:58:27ZengAIP Publishing LLC and ACAStructural Dynamics2329-77782017-05-0143032103032103-1710.1063/1.4976142005792SDYAugmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate dataSrinivas Niranj Chandrasekaran0Charles W. Carter Jr.1 Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts 01655, USA Department of Biochemistry and Biophysics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7260, USAPATH 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.http://dx.doi.org/10.1063/1.4976142
collection DOAJ
language English
format Article
sources DOAJ
author Srinivas Niranj Chandrasekaran
Charles W. Carter Jr.
spellingShingle Srinivas Niranj Chandrasekaran
Charles W. Carter Jr.
Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data
Structural Dynamics
author_facet Srinivas Niranj Chandrasekaran
Charles W. Carter Jr.
author_sort Srinivas Niranj Chandrasekaran
title Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data
title_short Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data
title_full Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data
title_fullStr Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data
title_full_unstemmed Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data
title_sort augmenting the anisotropic network model with torsional potentials improves path performance, enabling detailed comparison with experimental rate data
publisher AIP Publishing LLC and ACA
series Structural Dynamics
issn 2329-7778
publishDate 2017-05-01
description 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.
url http://dx.doi.org/10.1063/1.4976142
work_keys_str_mv AT srinivasniranjchandrasekaran augmentingtheanisotropicnetworkmodelwithtorsionalpotentialsimprovespathperformanceenablingdetailedcomparisonwithexperimentalratedata
AT charleswcarterjr augmentingtheanisotropicnetworkmodelwithtorsionalpotentialsimprovespathperformanceenablingdetailedcomparisonwithexperimentalratedata
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