Protein sequence design with deep generative models

Paper

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.

Analysis

Paper intelligence

Summary

This review highlights recent machine learning applications for protein sequence generation, focusing on deep generative methods.

Key findings

Machine learning can improve protein sequence generation by leveraging prior knowledge.
Deep generative models are an emerging area in protein sequence design.

Methods

Review of machine learning applications in protein sequence generation.
Focus on deep generative methods.

Applications

Protein engineering
Biocatalysis
Therapeutics
Materials science

Confidence100%

Discovery

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Source record

Publication metadata

Authors: Zachary Wu; Kadina E. Johnston; Frances H. Arnold; Kevin K. Yang
Published: May 26, 2021
Journal or venue: Current Opinion in Chemical Biology
DOI: 10.1016/j.cbpa.2021.04.004
Paper type: journal article
Source: Openalex

DOI OpenAlex