SPIN2: Predicting sequence profiles from protein structures using deep neural networks-Reference-Cited by-同舟云学术

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SPIN2: Predicting sequence profiles from protein structures using deep neural networks

Published:2018-03-25 Issue:6 Volume:86 Page:629-633
ISSN:0887-3585
Container-title:Proteins: Structure, Function, and Bioinformatics
language:en
Short-container-title:Proteins

Author:

O'Connell James¹,Li Zhixiu²³,Hanson Jack¹^ORCID,Heffernan Rhys¹,Lyons James¹,Paliwal Kuldip¹,Dehzangi Abdollah¹⁴,Yang Yuedong²⁵,Zhou Yaoqi²^ORCID

Affiliation:

1. Signal Processing LaboratoryGriffith UniversityNathan Australia

2. Institute for Glycomics, Griffith UniversityGold Coast Australia

3. Translational Genomics Group, Queensland University of Technology Translational Research InstituteBrisbane Australia

4. Department of Computer ScienceMorgan State UniversityBaltimore Maryland

5. School of Data and Computer ScienceSun Yat‐Sen UniversityGuangzhou China

Funder

Australian Research Council

National Natural Science Foundation of China

Publisher

Wiley

Subject

Molecular Biology,Biochemistry,Structural Biology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/prot.25489

Reference25 articles.

1. Function and structure of inherently disordered proteins

2. Principles that Govern the Folding of Protein Chains

3. De Novo Protein Design: Fully Automated Sequence Selection

4. A Large Scale Test of Computational Protein Design: Folding and Stability of Nine Completely Redesigned Globular Proteins

5. Progress in computational protein design

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