Campus Units

Biochemistry, Biophysics and Molecular Biology, Roy J. Carver Department of, Bioinformatics and Computational Biology

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

11-29-2019

Journal or Book Title

Journal of Molecular Biology

DOI

10.1016/j.jmb.2019.11.018

Abstract

The functioning of proteins requires highly specific dynamics, which depend critically on the details of how amino acids are packed. Hinge motions are the most common type of large motion, typified by the opening and closing of enzymes around their substrates. The packing and geometries of residues are characterized here by graph theory. This characterization is sufficient to enable reliable hinge predictions from a single static structure, and notably, this can be from either the open or the closed form of a structure. This new method to identify hinges within protein structures is called PACKMAN. The predicted hinges are validated by using permutation tests on B-factors. Hinge prediction results are compared against lists of manually-curated hinge residues, and the results suggest that PACKMAN is robust enough to reproduce the known conformational changes and is able to predict hinge regions equally well from either the open or the closed forms of a protein. A group of 167 protein pairs with open and closed structures has been investigated Examples are shown for several additional proteins, including Zika virus non-structured (NS) proteins where there are 6 hinge regions in the NS5 protein, 5 hinge regions in the NS2B bound in the NS3 protease complex and 5 hinges in the NS3 helicase protein. Results obtained from this method can be important for generating conformational ensembles of protein targets for drug design. PACKMAN is freely accessible at (https://PACKMAN.bb.iastate.edu/).

Comments

This is a manuscript of an article published as Khade, Pranav M., Ambuj Kumar, and Robert L. Jernigan. "Characterizing and Predicting Protein Hinges for Mechanistic Insight." Journal of Molecular Biology (2019). doi: 10.1016/j.jmb.2019.11.018

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier Ltd.

Language

en

File Format

application/pdf

Available for download on Sunday, November 29, 2020

Published Version

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