DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks

Bioinformatics

Volumn 34, Issue 9, 2018, Pages 1466-1472

  Adhikari, Badri ^a   Hou, Jie ^a   Cheng, Jianlin ^a,b  

^a UNIVERSITY OF MISSOURI ST LOUIS   (United States)

^b UNIVERSITY OF MISSOURI   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CASPASE;

ARTIFICIAL NEURAL NETWORK; BIOLOGY; CHEMISTRY; MACHINE LEARNING; METABOLISM; PROCEDURES; PROTEIN CONFORMATION; SEQUENCE ANALYSIS;

CASPASES; COMPUTATIONAL BIOLOGY; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); PROTEIN CONFORMATION; SEQUENCE ANALYSIS, PROTEIN;

EID: 85047095484     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx781     Document Type: Article

References (33)

1. Adhikari,B. et al. (2016) ConEVA: a toolbox for comprehensive assessment of protein contacts. BMC Bioinformatics, 17, 517.
2. Adhikari,B. et al. (2015) CONFOLD: residue-residue contact-guided ab initio protein folding. Proteins, 83, 1436-1449.
3. Cheng,J. et al. (2005) SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res., 33, W72-W76.
4. Eickholt,J. and Cheng,J. (2013) A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks. BMC Bioinformatics, 14, S12.
5. Eickholt,J. and Cheng,J. (2012) Predicting protein residue-residue contacts using deep networks and boosting. Bioinformatics, 28, 3066-3072.
6. Ioffe,S. and Szegedy,C. (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Proc. 32nd Int. Conf. Mach. Learn., 37, 448-456.
7. Johnson,L.S. et al. (2010) Hidden Markov model speed heuristic and iterative HMMsearch procedure. BMC Bioinformatics, 11, 431.
8. Jones,D.T. et al. (2014) MetaPSICOV: Combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics, 31, btu791.
9. Jones,D.T. (2001) Predicting novel protein folds by using FRAGFOLD. Proteins Struct. Funct. Genet., 45, 127-132.
10. Jones,D.T. (1999) Protein secondary structure prediction based on position-specific scoringmatrices. J. Mol. Biol., 292, 195-202.
11. Jones,D.T. et al. (2012) PSICOV: Precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics, 28, 184-190.
12. Kaján,L. et al. (2014) FreeContact: fast and free software for protein contact prediction from residue co-evolution. BMC Bioinformatics, 15, 85.
13. Kinch,L.N. et al. (2016) CASP 11 target classification. Proteins Struct. Funct. Bioinform., 84, 20-33.
14. Kosciolek,T. and Jones,D.T. (2015) Accurate contact predictions using covariation techniques and machine learning. Proteins Struct. Funct. Bioinform., 84, 145-151.
15. Kosciolek,T. et al. (2014) De novo structure prediction of globular proteins aided by sequence variation-derived contacts. PLoS One, 9, e92197.
16. Mabrouk,M. et al. (2016) Analysis of free modeling predictions by RBO aleph in CASP11. Proteins, 84, 87-104.
17. Marks,D.S. et al. (2012) Protein structure prediction from sequence variation. Nat. Biotechnol., 30, 1072-1080.
18. Michel,M. et al. (2014) PconsFold: improved contact predictions improve protein models. Bioinformatics, 30, i482-i488.
19. Michel,M. et al. (2017a) Large-scale structure prediction by improved contact predictions and model quality assessment. Bioinformatics, 33, i23-i29.
20. Michel,M. et al. (2017b) Predicting accurate contacts in thousands of Pfam domain families using PconsC3. Bioinformatics, 33, 2859-2866.
21. Monastyrskyy,B. et al. (2014) Evaluation of residue-residue contact prediction in CASP10. Proteins Struct. Funct. Bioinform., 82, 138-153.
22. Monastyrskyy,B. et al. (2011) Evaluation of residue-residue contact predictions in CASP9. Proteins, 79, 119-125.
23. Monastyrskyy,B. et al. (2016) New encouraging developments in contact prediction: Assessment of the CASP11 results. Proteins Struct. Funct. Bioinform., 84, 131-144.
24. Nair,V. and Hinton,G.E. (2010) Rectified Linear Units Improve Restricted Boltzmann Machines. In: Proceedings of the 27th International Conference on Machine Learning, pp. 807-814.
25. Ovchinnikov,S. et al. (2016) Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta. Proteins Struct. Funct. Bioinform., 84, 67-75.
26. Pietal,M.J. et al. (2015) GDFuzz3D: a method for protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function. Bioinformatics, 31, 3499-3505.
27. Remmert,M. et al. (2011) HHblits: lightning-fast iterative protein sequence searching by HMM-HMMalignment. Nat. Methods, 9, 173-175.
28. Seemayer,S. et al. (2014) CCMpred-Fast and precise prediction of protein residue-residue contacts from correlated mutations. Bioinformatics, 30, 3128-3130.
29. Skwark,M.J. et al. (2014) Improved contact predictions using the recognition of protein like contact patterns. PLoS Comput. Biol., 10, e1003889.
30. Sutskever,I. et al. (2013) On the Importance of Initialization and Momentum in Deep Learning. In: Proceedings of the 30th International Conference on International Conference on Machine Learning-Volume 28, ICML'13. JMLR.org, p. III-1139-III-1147.
31. Wang,S. et al. (2017) Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput. Biol., 13, e1005324.
32. Xu,D. and Zhang,Y. (2012) Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins, 80, 1715-1735.
33. Zhang,W. et al. (2016) Integration of QUARK and I-TASSER for ab initio protein structure prediction in CASP11. Proteins Struct. Funct. Bioinform., 84, 76-86.