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Computational Compensatory Mutation Discovery Approach: Predicting a PARP1 Variant Rescue Mutation

View ORCID ProfileKrithika Ravishankar, View ORCID ProfileXianli Jiang, View ORCID ProfileEmmett M. Leddin, View ORCID ProfileFaruck Morcos, View ORCID ProfileG. Andrés Cisneros
doi: https://doi.org/10.1101/2021.11.21.469407
Krithika Ravishankar
1Department of Chemistry, University of North Texas, Denton, TX 76201
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Xianli Jiang
2Department of Biological Sciences, The University of Texas at Dallas, Richardson, TX 75080
3Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center
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Emmett M. Leddin
1Department of Chemistry, University of North Texas, Denton, TX 76201
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Faruck Morcos
2Department of Biological Sciences, The University of Texas at Dallas, Richardson, TX 75080
4Department of Bioengineering, The University of Texas at Dallas, Richardson, TX 75080
5Center for Systems Biology, The University of Texas at Dallas, Richardson, TX 75080
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G. Andrés Cisneros
1Department of Chemistry, University of North Texas, Denton, TX 76201
6Department of Physics, The University of Texas at Dallas, Richardson, TX 75080
7Department of Chemistry, The University of Texas at Dallas, Richardson, TX 75080
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  • For correspondence: andres@unt.edu
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Abstract

The prediction of protein mutations that affect function may be exploited for multiple uses. In the context of disease variants, the prediction of compensatory mutations that reestablish functional phenotypes could aid in the development of genetic therapies. In this work, we present an integrated approach that combines coevolutionary analysis and molecular dynamics (MD) simulations to discover functional compensatory mutations. This approach is employed to investigate possible rescue mutations of a poly(ADP-ribose) polymerase 1 (PARP1) variant, PARP1 V762A, associated with lung cancer and follicular lymphoma. MD simulations show PARP1 V762A exhibits noticeable changes in structural and dynamical behavior compared with wild type PARP1. Our integrated approach predicts A755E as a possible compensatory mutation based on coevolutionary information, and molecular simulations indicate that the PARP1 A755E/V762A double mutant exhibits similar structural and dynamical behavior to WT PARP1. Our methodology can be broadly applied to a large number of systems where single nucleotide polymorphisms (SNPs) have been identified as connected to disease and can shed light on the biophysical effects of such changes as well as provide a way to discover potential mutants that could restore wild type-like functionality. This can in turn be further utilized in the design of molecular therapeutics that aim to mimic such compensatory effect.

Significance Statement Discovering protein mutations with desired phenotypes can be challenging due to its combinatorial nature. Herein we employ a methodology combining gene SNP association to disease, direct coupling analysis and molecular dynamics simulations to systematically predict rescue mutations. Our workflow identifies A755E as a potential rescue for the PARP1 V762A mutation, which has been associated with cancer. This methodology is general and can be applied broadly.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* faruckm{at}utdallas.edu, andres{at}utdallas.edu

  • Figure 1 added, figure quality/captions improved, additional MD details, and supplemental files updated.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 18, 2022.
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Computational Compensatory Mutation Discovery Approach: Predicting a PARP1 Variant Rescue Mutation
Krithika Ravishankar, Xianli Jiang, Emmett M. Leddin, Faruck Morcos, G. Andrés Cisneros
bioRxiv 2021.11.21.469407; doi: https://doi.org/10.1101/2021.11.21.469407
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Computational Compensatory Mutation Discovery Approach: Predicting a PARP1 Variant Rescue Mutation
Krithika Ravishankar, Xianli Jiang, Emmett M. Leddin, Faruck Morcos, G. Andrés Cisneros
bioRxiv 2021.11.21.469407; doi: https://doi.org/10.1101/2021.11.21.469407

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