Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles-Reference-Cited by-同舟云学术

Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles

Published:2014-06-19 Issue:10 Volume:82 Page:2565-2573
ISSN:0887-3585
Container-title:Proteins: Structure, Function, and Bioinformatics
language:en
Short-container-title:Proteins

Author:

Li Zhixiu¹,Yang Yuedong¹²³,Faraggi Eshel¹²⁴⁵⁶,Zhan Jian¹²³,Zhou Yaoqi¹²³

Affiliation:

1. School of Informatics and Computing, Indiana University-Purdue University; Indianapolis Indiana 46202

2. Center for Computational Biology and Bioinformatics, Indiana University School of Medicine; Indianapolis Indiana 46202

3. Institute for Glycomics and School of Information and Communication Technology, Griffith University; Parklands Dr. Southport, QLD 4222

4. Department of Biochemistry and Molecular Biology; Indiana University School of Medicine; Indianapolis Indiana 46202

5. Battelle Center for Mathematical Medicine; Nationwide Children's Hospital; Columbus Ohio 43215

6. Research and Information Systems; LLC Carmel Indiana 46032

Funder

National Institutes of Health

a Project Development Team

National Health and Medical Council of Australia

Windows Azure for Research Award

Griffith University research Services Team

Queensland Cyber Infrastructure Foundation

Publisher

Wiley

Subject

Molecular Biology,Biochemistry,Structural Biology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/prot.24620/fullpdf

Reference45 articles.

1. De novo protein design: Fully automated sequence selection;Dahiyat;Science,1997

2. High-resolution protein design with backbone freedom;Harbury;Science,1998

3. From coiled coils to small globular proteins: Design of a native-like three-helix bundle;Bryson;Prot Sci: A Publ Prot Soc,1998

4. Solution structure and dynamics of a de novo designed three-helix bundle protein;Walsh;Proc Natl Acad Sci USA,1999

5. Full-sequence computational design and solution structure of a thermostable protein variant;Shah;J Mol Biol,2007

Cited by 51 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. OPUS-Design: Designing Protein Sequence from Backbone Structure with 3DCNN and Protein Language Model;2024-08-21

2. Rotation invariance and equivariance in 3D deep learning: a survey;Artificial Intelligence Review;2024-06-07

3. DIProT: A deep learning based interactive toolkit for efficient and effective Protein design;Synthetic and Systems Biotechnology;2024-06

4. A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation;Briefings in Bioinformatics;2024-05-23

5. SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition;Briefings in Bioinformatics;2024-03-27