Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis

TL;DR

This paper presents a neural network-based method for classifying protein mutations into stabilizing, destabilizing, or inconclusive categories, improving prediction accuracy amidst experimental data variability.

Contribution

It introduces a three-way classification scheme with systematic cutoff optimization for predicting mutation effects on protein stability.

Findings

Achieved 68% accuracy in ternary classification.

Low misclassification rate between stabilizing and destabilizing mutations.

Systematic cutoff search enhances classification robustness.

Abstract

Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Machine learning and statistical analysis techniques can inform how to allocate limited resources to the considerable time and cost associated with wet lab mutagenesis experiments. In this work we explore the effectiveness of using a neural network classifier to predict the change in the stability of a protein due to a mutation. Assessing the accuracy of our approach is dependent on the use of experimental data about the effects of mutations performed in vitro. Because the experimental data is prone to discrepancies when similar experiments have been performed by multiple laboratories, the use of the data near the juncture of stabilizing and destabilizing mutations is questionable. We address this later problem via a systematic approach in which we explore the use of a three-way classification scheme with stabilizing, destabilizing, and inconclusive labels. For a systematic search of potential classification cutoff values our classifier achieved 68 percent accuracy on ternary classification for cutoff values of -0.6 and 0.7 with a low rate of classifying stabilizing as destabilizing and vice versa.