RNA-Seq of different TadA variants to validate RNA Off-Targets
Mutation effects prediction is a fundamental challenge in biotechnology and biomedicine. State-of-the-art computational methods have demonstrated the benefits of including semantically rich representations learned from protein sequences, but leave structural constraints out of reach. Here we developed Protein Mutational Effect Predictor (ProMEP), a general and multimodal deep representation learning method that simultaneously learns sequence context and structural constraints from proteins at the scale of evolution. ProMEP markedly outperforms current leading methods and enables accurate zero-shot mutational effects prediction across a variety of deep mutational scanning experiments. The application of ProMEP in the transposon-associated TnpB enzyme engineering task further demonstrates its ability for high-throughput protein space exploration. Without prior knowledge of TnpB, ProMEP accurately identifies multiple mutations that significantly improve the editing efficiency from millions of variants. Overall design: Analysis of RNA off-target effects of TadA mutants