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Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed
Srinivas Niranj Chandrasekaran1, Charles W Carter2
1Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School , Worcester, Massachusetts 01655, USA.
Structural Dynamics (Melville, N.Y.)
|March 15, 2017
Summary
Enhanced PATH algorithms improve computational predictions of protein conformational changes. This advancement aids in interpreting mutagenesis experiments by refining correlations with kinetic data for Tryptophanyl-tRNA synthetase.
Area of Science:
- Computational biology
- Biophysics
- Biochemistry
Background:
- PATH algorithms identify protein conformational transition states computationally.
- These algorithms provide parameters like transition time and activation energy for experimental comparison.
- High-throughput analysis is possible, aiding interpretation of combinatorial mutagenesis.
Purpose of the Study:
- To update and enhance the previously published PATH algorithm.
- To improve correlations between computational parameters and experimental kinetic data.
- To validate the enhanced algorithm using a four-way combinatorial mutagenesis of Tryptophanyl-tRNA synthetase.
Main Methods:
- Utilized PATH algorithms for identifying conformational transition states.
- Generated virtual variant structures using RosettaBackrub.
- Compared PATH convergence parameters with previously published kinetic data.
Main Results:
- The updated PATH algorithm demonstrated improved correlations.
- Enhanced parameters derived from virtual variants showed better agreement with kinetic data.
- Successful application to a complex four-way combinatorial mutagenesis study.
Conclusions:
- The enhanced PATH algorithm offers more accurate computational insights into protein conformational dynamics.
- This improved methodology facilitates the interpretation of mutagenesis experiments.
- The findings support the utility of PATH algorithms in biophysical research.
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