Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.

ABSTRACT: Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, which are often expensive and time consuming. The recent advancement in neural language models has provided stunning results in modeling and deciphering sequences. In this paper, we propose a Deep Novel Mutation Search (DNMS) method, using deep neural networks, to model protein sequence for mutation prediction. We use SARS-CoV-2 spike protein as the target and use a protein language model to predict novel mutations. Different from existing research which is often limited to mutating the reference sequence for prediction, we propose a parent-child mutation prediction paradigm where a parent sequence is modeled for mutation prediction. Because mu

SUBMITTER: Elkin ME 

PROVIDER: S-EPMC11751191 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature