Discovery of CRISPR-Cas12a clades using a large language model

Abstract

Abstract The identification and engineering of CRISPR-Cas systems revolutionized life science. Metagenome contains millions of unknown Cas proteins, which require precise prediction and characterization. Traditional protein mining mainly depends on protein sequence alignments. In this work, we harnessed the capability of the evolutionary scale language model (ESM) to learn the information beyond the sequence. After training with the CRISPR-Cas sequences and their functional annotation, the ESM model can identify the CRISPR-Cas proteins from the annotated genome sequences accurately and robustly without sequence alignment. However, due to the lack of experimental data, the feature prediction is limited by the small sample size. Integrated with machine learning on small size experimental data, the model is able to predict the trans-cleavage activity of novel Cas12a. Furthermore, we discovered 7 novel subtypes of Cas12a proteins with unique organization of CRISPR loci and protein sequences. Notably, structural alignments revealed that Cas1, Cas2, and Cas4 also exhibit 8 subtypes, with the absence of integrase proteins correlating with a reduction in spacer numbers within CRISPR loci. In addition, the Cas12a subtypes displayed distinct 3D foldings, a finding further corroborated by CryoEM analyses that unveiled unique interaction patterns with RNA. Accordingly, these proteins show distinct double-strand and single-strand DNA cleavage preferences and broad PAM recognition. Finally, we established a specific detection strategy for the oncogene SNP without traditional Cas12a PAM. This study shows the great potential of the language model in the novel Cas protein function exploration via gene cluster classification.