Ensemble Voting Schemes that Improve Machine Learning Models for Predicting the Effects of Protein Mutations

Abstract

Understanding how a mutation affects a protein's structural stability can guide pharmaceutical drug design initiatives that aim to engineer medicines for combating a variety of diseases. Conducting wet-lab mutagenesis experiments in physical proteins can provide precise insights about the role of a residue in maintaining a protein's stability, but such experiments are time and cost intensive. Computational methods for modeling and predicting the effects of mutations are available, with several Machine Learning approaches achieving good predictions. However, most such methods, including ensemble based approaches that are based on multiple classifier models instead of a single-expert system, are dependent on large datasets for training the model. In this work, we motivate and demonstrate the utility of several voting-based models that rely on the predictions of a Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN) models for inferring the effects of single amino acid substitutions. The three models rely on rigidity analysis results for a dataset of proteins, for which we use wet lab experimental data to show prediction accuracies with Pearson Correlation values of 0.76. We show that our voting approaches achieve a higher Pearson Correlation, as well as a lower RMSE score, than any of the SVR, RF, and DNN models alone.