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
Open AccessArticle

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

Department of Computer Science, University of Massachusetts Boston, Boston, MA 02125, USA
Department of Computer Science, Western Washington University, Bellingham, WA 98225, USA
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;
Received: 24 December 2017 / Revised: 15 January 2018 / Accepted: 19 January 2018 / Published: 27 January 2018
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

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop