Next Article in Journal
Systematic Structure-Activity Relationship (SAR) Exploration of Diarylmethane Backbone and Discovery of A Highly Potent Novel Uric Acid Transporter 1 (URAT1) Inhibitor
Next Article in Special Issue
Exploring Protein Cavities through Rigidity Analysis
Previous Article in Journal
Coumarin: A Natural, Privileged and Versatile Scaffold for Bioactive Compounds
Previous Article in Special Issue
Analytical Approaches to Improve Accuracy in Solving the Protein Topology Problem
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Molecules
Volume 23
Issue 2
10.3390/molecules23020251
molecules-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability

by
Ramin Dehghanpoor
1,†,
Evan Ricks
2,†,
Katie Hursh
2,
Sarah Gunderson
2,
Roshanak Farhoodi
1,
Nurit Haspel
1,
Brian Hutchinson
2,3 and
Filip Jagodzinski
2,*
1
Department of Computer Science, University of Massachusetts Boston, Boston, MA 02125, USA
2
Department of Computer Science, Western Washington University, Bellingham, WA 98225, USA
3
Computing and Analytics Division, Pacific Northwest National Laboratory; Richland, WA 99354, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Molecules 2018, 23(2), 251; https://doi.org/10.3390/molecules23020251
Received: 24 December 2017 / Revised: 15 January 2018 / Accepted: 19 January 2018 / Published: 27 January 2018
(This article belongs to the Special Issue Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017))
View Full-Text Download PDF
Browse Figures

Abstract

Predicting how a point mutation alters a protein’s stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time as well as financial resources needed to assess the effect of even a single amino acid substitution. Computational methods for predicting the effects of a mutation on a protein structure can complement wet-lab work, and varying approaches are available with promising accuracy rates. In this work we compare and assess the utility of several machine learning methods and their ability to predict the effects of single and double mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. We use these as features for our Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN) methods. We validate the predictions of our in silico mutations against experimental Δ Δ G stability data, and attain Pearson Correlation values upwards of 0.71 for single mutations, and 0.81 for double mutations. We perform ablation studies to assess which features contribute most to a model’s success, and also introduce a voting scheme to synthesize a single prediction from the individual predictions of the three models. View Full-Text
Keywords: machine learning; protein mutational study; SVR; RF; DNN; rigidity analysis machine learning; protein mutational study; SVR; RF; DNN; rigidity analysis
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
SciFeed

Share and Cite

MDPI and ACS Style

Dehghanpoor, R.; Ricks, E.; Hursh, K.; Gunderson, S.; Farhoodi, R.; Haspel, N.; Hutchinson, B.; Jagodzinski, F. Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability. Molecules 2018, 23, 251. https://doi.org/10.3390/molecules23020251

AMA Style

Dehghanpoor R, Ricks E, Hursh K, Gunderson S, Farhoodi R, Haspel N, Hutchinson B, Jagodzinski F. Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability. Molecules. 2018; 23(2):251. https://doi.org/10.3390/molecules23020251

Chicago/Turabian Style

Dehghanpoor, Ramin, Evan Ricks, Katie Hursh, Sarah Gunderson, Roshanak Farhoodi, Nurit Haspel, Brian Hutchinson, and Filip Jagodzinski. 2018. "Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability" Molecules 23, no. 2: 251. https://doi.org/10.3390/molecules23020251

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop