A computational methodology to diagnose sequence-variant dynamic perturbations by comparing atomic protein structures.

Lorenza Pacini, Claire Lesieur

Journal: Bioinformatics (Oxford, England) 2023;38(3):703-709

PMID: 34694373

Abstract

MOTIVATION

The objective is to diagnose dynamics perturbations caused by amino-acid mutations as prerequisite to assess protein functional health or drug failure, simply using network models of protein X-ray structures.

RESULTS

We find that the differences in the allocation of the atomic interactions of each amino acid to 1D, 2D, 3D, 4D structural levels between variants structurally robust, recover experimental dynamic perturbations. The allocation measure validated on two B-pentamers variants of AB5 toxins having 17 mutations, also distinguishes dynamic perturbations of pathogenic and non-pathogenic Transthyretin single-mutants. Finally, the main proteases of the coronaviruses SARS-CoV and SARS-CoV-2 exhibit changes in the allocation measure, raising the possibility of drug failure despite the main proteases structural similarity.

AVAILABILITY AND IMPLEMENTATION

The Python code used for the production of the results is available at github.com/lorpac/protein_partitioning_atomic_contacts. The authors will run the analysis on any PDB structures of protein variants upon request.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].

Address: AMPERE, CNRS, Université de Lyon, 69622 Lyon, France.; Institut Rhônalpin des Systèmes Complexes (IXXI), École Normale Supérieure de Lyon, 69007 Lyon, France.
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