ThermoMPNN Online Tool

Single amino acid mutations can have a dramatic effect on protein thermostability and, therefore, function. Mutations that unfold tumor suppressors are implicated in a variety of cancers, and mutations that stabilize proteins can be used to increase their usefulness as therapeutics or research reagents. Accurate in silico prediction of the effect of point mutations on protein stability would be useful for understanding mutations implicated in disease and for engineering protein-based medicines. In this work, we present a deep learning–based method for predicting stability changes quickly and accurately given only an initial protein structure. Amino acid mutations that lower a protein’s thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a deep neural network trained to predict a protein’s amino acid sequence given its three-dimensional structure. We show that our method achieves state-of-the-art performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design.