Improving prediction of secondary structure, local backbone angles and solvent accessible surface area of proteins by iterative deep learning-Reference-Cited by-同舟云学术

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Improving prediction of secondary structure, local backbone angles and solvent accessible surface area of proteins by iterative deep learning

Published:2015-06-22 Issue:1 Volume:5 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Heffernan Rhys,Paliwal Kuldip,Lyons James,Dehzangi Abdollah,Sharma Alok,Wang Jihua,Sattar Abdul,Yang Yuedong,Zhou Yaoqi

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/srep11476.pdf

Reference48 articles.

1. Gibson, K. D. & Scheraga, H. A. Minimization of polypeptide energy. I. Preliminary structures of bovine pancreatic ribonuclease S-peptide. Proc Natl Acad Sci USA 58, 420–427 (1967).

2. Zhou, Y. Q., Duan, Y., Yang, Y. D., Faraggi, E. & Lei, H. X. Trends in template/fragment-free protein structure prediction. Theor Chem Acc 128, 3–16 (2011).

3. Rost, B. Review: Protein secondary structure prediction continues to rise. J Struct Biol 134, 204–218 (2001).

4. Faraggi, E., Zhang, T., Yang, Y., Kurgan, L. & Zhou, Y. SPINE X: Improving protein secondary structure prediction by multi-step learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Computational Chemistry 33, 259–263 (2011).

5. Yaseen, A. & Li, Y. H. Context-Based Features Enhance Protein Secondary Structure Prediction Accuracy. J Chem Inf Model 54, 992–1002 (2014).

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