Predicting Protein Stability with Machine Learning

Abstract: Protein stability is a property of high importance and is of interest in a variety of fields. It determines if a protein has its native fold and can be of influence in certain diseases such as Parkinson's and Alzheimer's disease. It can also be of interest in an industrial setting to optimise the stability of enzymes in certain physicochemical environments. Recent developments in machine learning have yielded novel methods able to predict protein characteristics with surprising accuracy solely from sequence information. However, few such models have been proposed for predicting protein stability. The aim of this project was to create a model able to predict protein stability from sequence information, by utilising a multiple sequence alignment based protein language model. Different models were developed to predict two quantities relating to protein stability, Gibbs free energy of unfolding and the heat denaturation temperature. Due to limited training data the performance of the models predicting Gibbs free energy was poor. One of the models predicting heat denaturation temperature, proved more promising, with higher performance than a previously published model trained on similar data. However its ability to predict conventionally obtained heat denaturation temperatures was poor.