| Literature DB >> 34051682 |
Zachary Wu1, Kadina E Johnston2, Frances H Arnold3, Kevin K Yang4.
Abstract
Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.Entities:
Keywords: Deep learning; Generative models; Protein engineering
Mesh:
Year: 2021 PMID: 34051682 DOI: 10.1016/j.cbpa.2021.04.004
Source DB: PubMed Journal: Curr Opin Chem Biol ISSN: 1367-5931 Impact factor: 8.822