Title: Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis,
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.