Zero‐shot mutation effect prediction on protein stability and function using RoseTTAFold

Predicting the effects of mutations on protein function and stability is an outstanding challenge. Here, we assess the performance of a variant of RoseTTAFold jointly trained for sequence and structure recovery, RF(joint), for mutation effect prediction. Without any further training, we achieve comparable accuracy in predicting mutation effects for a diverse set of protein families using RF(joint) to both another zero‐shot model (MSA Transformer) and a model that requires specific training on a particular protein family for mutation effect prediction (DeepSequence). Thus, although the architecture of RF(joint) was developed to address the protein design problem of scaffolding functional motifs, RF(joint) acquired an understanding of the mutational landscapes of proteins during model training that is equivalent to that of recently developed large protein language models. The ability to simultaneously reason over protein structure and sequence could enable even more precise mutation effect predictions following supervised training on the task. These results suggest that RF(joint) has a quite broad understanding of protein sequence‐structure landscapes, and can be viewed as a joint model for protein sequence and structure which could be broadly useful for protein modeling.