Protein design via deep learning

Author:

Ding Wenze1234ORCID,Nakai Kenta5ORCID,Gong Haipeng34ORCID

Affiliation:

1. School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

2. School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China

3. MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China

4. Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China

5. Institute of Medical Science, the University of Tokyo, Tokyo 1088639, Japan

Abstract

AbstractProteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

Funder

National Natural Science Foundation of China

Beijing Advanced Innovation Center for Structural Biology

Japan Society for the Promotion of Science

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3