Protein sequence design with deep generative models
Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang
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.
Topics & Concepts
Generative grammarSequence (biology)Deep learningComputer scienceArtificial intelligenceProtein engineeringProtein sequencingField (mathematics)Computational biologyProtein designMachine learningProtein–protein interactionGenerative modelSequence learningSynthetic biologyProtein structureBioinformaticsBiologyMachine Learning in BioinformaticsFractal and DNA sequence analysisProtein Structure and Dynamics