Evolutionary context-integrated deep sequence modeling for protein engineering

Yunan Luo, Lam Vo, Hantian Ding, Yufeng Su, Yang Liu, Wesley Wei Qian, Huimin Zhao, Jian Peng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Protein engineering seeks to design proteins with improved or novel functions. Compared to rational design and directed evolution approaches, machine learning-guided approaches traverse the fitness landscape more effectively and hold the promise for accelerating engineering and reducing the experimental cost and effort.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings
EditorsRussell Schwartz
PublisherSpringer
Pages261-263
Number of pages3
ISBN (Print)9783030452568
DOIs
StatePublished - 2020
Event24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 - Padua, Italy
Duration: May 10 2020May 13 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12074 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020
CountryItaly
CityPadua
Period5/10/205/13/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Evolutionary context-integrated deep sequence modeling for protein engineering'. Together they form a unique fingerprint.

Cite this