Accurate prediction of damaging missense variants is critically important for interpreting genome sequence. While many methods have been developed, their performance has been limited. Recent progress in machine learning and availability of large-scale population genomic sequencing data provide new opportunities to significantly improve computational predictions. I this talk, I will present our two methods gMVP and tMVP for predicting the damaging missense variants. gMVP is a new method based on graph attention neural networks. Its main component is a graph with nodes capturing predictive features of amino acids and edges weighted by coevolution strength, which enables effective pooling of information from local protein context and functionally correlated distal positions. Evaluated by deep mutational scan data, gMVP outperforms published methods in identifying damaging variants in TP53, PTEN, BRCA1, and MSH2. Additionally, it achieves the best separation of de novo missense variants in neurodevelopmental disorder cases from the ones in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies. I will also present our latest method tMVP for predicting damaging missense variants which is based on a deep generative language model trained on 250 million protein sequences. Additionally, I will briefly introduce AbFold, our recently developed software for antibody structure prediction
 

精准医疗；人工智能