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Predicting the Effect of Point Mutations on Protein Structural Stability

Authors:

Roshanak Farhoodi,

Max Shelbourne,

Brian Hutchinson,

Filip JagodzinskiAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 247 - 252

https://doi.org/10.1145/3107411.3107492

Published: 20 August 2017 Publication History

Abstract

Predicting how a point mutation alters a protein's stability can guide drug design initiatives which aim to counter the effects of serious diseases. Mutagenesis studies give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time and costs needed to assess the consequences of even a single mutation. Computational methods for predicting the effects of a mutation are available, with promising accuracy rates. In this work we study the utility of several machine learning methods and their ability to predict the effects of mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. Our approach does not require costly calculations of energy functions that rely on atomic-level statistical mechanics and molecular energetics. Our metrics are features for support vector regression, random forest, and deep neural network methods. We validate the effects of our in silico mutations against experimental Delta Delta G stability data. We attain Pearson Correlations upwards of 0.69.

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Cited By

Herr SMyers-Dean JRead HJagodzinski F(2020)PETRA: Drug Engineering via Rigidity AnalysisMolecules10.3390/molecules2506130425:6(1304)Online publication date: 12-Mar-2020
https://doi.org/10.3390/molecules25061304
Thompson EThackray TKalthoff CRapoport RJagodzinski F(2020)Using Energy-Minimization Profiles to Measure Protein Resistance to DrugsProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414703(1-6)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414703
Álvarez-Machancoses ÓDe Andrés-Galiana EFernández-Martínez JKloczkowski A(2019)Robust Prediction of Single and Multiple Point Protein Mutations Stability ChangesBiomolecules10.3390/biom1001006710:1(67)Online publication date: 31-Dec-2019
https://doi.org/10.3390/biom10010067
Show More Cited By

Index Terms

Predicting the Effect of Point Mutations on Protein Structural Stability
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Computational biology
      1. Molecular structural biology
2. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 ACM.

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Published: 20 August 2017

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BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

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ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

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Cited By

Herr SMyers-Dean JRead HJagodzinski F(2020)PETRA: Drug Engineering via Rigidity AnalysisMolecules10.3390/molecules2506130425:6(1304)Online publication date: 12-Mar-2020
https://doi.org/10.3390/molecules25061304
Thompson EThackray TKalthoff CRapoport RJagodzinski F(2020)Using Energy-Minimization Profiles to Measure Protein Resistance to DrugsProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414703(1-6)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414703
Álvarez-Machancoses ÓDe Andrés-Galiana EFernández-Martínez JKloczkowski A(2019)Robust Prediction of Single and Multiple Point Protein Mutations Stability ChangesBiomolecules10.3390/biom1001006710:1(67)Online publication date: 31-Dec-2019
https://doi.org/10.3390/biom10010067
Read HCarpenter DHerr SJagodzinski FShi XBuck MMa JVeltri P(2019)PETRAProceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3307339.3343862(568-573)Online publication date: 4-Sep-2019
https://dl.acm.org/doi/10.1145/3307339.3343862
Dehghanpoor RRicks EHursh KGunderson SFarhoodi RHaspel NHutchinson BJagodzinski F(2018)Predicting the Effect of Single and Multiple Mutations on Protein Structural StabilityMolecules10.3390/molecules2302025123:2(251)Online publication date: 27-Jan-2018
https://doi.org/10.3390/molecules23020251
Gunderson SJagodzinski FShehu AWu CBoucher CLi JLiu HPop M(2018)Ensemble Voting Schemes that Improve Machine Learning Models for Predicting the Effects of Protein MutationsProceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3233547.3233606(211-219)Online publication date: 15-Aug-2018
https://dl.acm.org/doi/10.1145/3233547.3233606
Foroughi pour ADalton LShehu AWu CBoucher CLi JLiu HPop M(2018)Biomarker Discovery via Optimal Bayesian Feature Filtering for Structured Multiclass DataProceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3233547.3233558(331-340)Online publication date: 15-Aug-2018
https://dl.acm.org/doi/10.1145/3233547.3233558
Majeske NJagodzinski F(2018)Elucidating Which Pairwise Mutations Affect Protein Stability: An Exhaustive Big Data Approach2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2018.00078(508-515)Online publication date: Jul-2018
https://doi.org/10.1109/COMPSAC.2018.00078

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