On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach

PLoS ONE

Volumn 12, Issue 12, 2017, Pages

  Qu, Yu Hui ^a   Yu, Hua ^a   Gong, Xiu Jun ^a   Xu, Jia Hui ^b   Lee, Hong Shun ^a  

^a TIANJIN UNIVERSITY   (China)

^b Beijing Kedong Power Control System Limited Liability Company   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DNA BINDING PROTEIN;

AMINO ACID SEQUENCE; ARABIDOPSIS; AREA UNDER THE CURVE; ARTICLE; CONTROLLED STUDY; CORRELATION COEFFICIENT; ENTROPY; GENE EXPRESSION PROFILING; HUMAN; MACHINE LEARNING; NONHUMAN; PHYSICAL CHEMISTRY; PROTEIN ANALYSIS; PROTEIN DNA BINDING; PROTEIN DOMAIN; PROTEIN MOTIF; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; YEAST; CHEMISTRY; GENETICS; METABOLISM; REPRODUCIBILITY; THEORETICAL MODEL;

AMINO ACID SEQUENCE; ARABIDOPSIS; DNA-BINDING PROTEINS; MODELS, THEORETICAL; REPRODUCIBILITY OF RESULTS; YEASTS;

EID: 85039863846     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0188129     Document Type: Article

References (35)

1. Kumar M, Gromiha MM, Raghava GPS. Identification of DNA-binding proteins using support vector machines and evolutionary profiles. BMC bioinformatics. 2007; 8:463. https://doi.org/10.1186/1471-2105-8-463 PMID: 18042272
2. Cai CZ, Han LY, Ji ZL, Chen X, Chen YZ. SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Research. 2003; 31 (13):3692–3697. https://doi.org/10.1093/nar/gkg600 PMID: 12824396
3. Lin WZ, Fang JA, Xiao X, Chou KC. iDNA-Prot: identification of DNA binding proteins using random forest with grey model. PloS one. 2011; 6(9):e24756. https://doi.org/10.1371/journal.pone.0024756 PMID: 21935457
4. Zou C, Gong J, Li H. An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis. BMC Bioinformatics. 2013; 14(1):90. https://doi.org/10.1186/1471-2105-14-90 PMID: 23497329
5. Zhou C., Yu H., Ding Y., Guo F., Gong X. Multi-Scale Encoding of Amino Acid Sequences for Predicting Protein Interactions Using Gradient Boosting Decision Tree. PLoS ONE, 2017.
6. Lou W, Wang X, Chen F, Chen Y, Jiang B, Zhang H. Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian NaÃve Bayes. PLoS ONE. 2014; 9(1):1–10. https://doi.org/10.1371/journal.pone.0086703
7. Ma X, Guo J, Sun X. DNABP: Identification of DNA-binding proteins based on feature selection using a random forest and predicting binding residues. PLoS ONE. 2016; 11(12):1–20. https://doi.org/10.1371/journal.pone.0167345
8. Liu B, Xu J, Lan X, Xu R, Zhou J, Wang X, et al. iDNA-Prot| dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PloS one. 2014 Sep 3; 9(9):e106691. https://doi.org/10.1371/journal.pone.0106691PMID: 25184541
9. Liu B, Xu J, Fan S, Xu R, Zhou J, Wang X. PseDNA-Pro: DNA-binding protein identification by combining Chou’s PseAAC and physicochemical distance transformation. Molecular Informatics. 2015 Jan 1; 34(1):8–17. https://doi.org/10.1002/minf.201400025 PMID: 27490858
10. Liu B, Wang S, Wang X. DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation. Scientific Reports. 2015; 5(October):15479. https://doi.org/10.1038/srep15479 PMID: 26482832
11. Liu B, Wang S, Dong Q, Li S, Liu X. Identification of DNA-Binding Proteins by Combining Auto-Cross Covariance Transformation and Ensemble Learning. IEEE Transactions on Nanobioscience. 2016; 15(4):328–334. https://doi.org/10.1109/TNB.2016.2555951
12. Liu B, Liu F, Wang X, Chen J, Fang L, Chou KC. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic acids research. 2015 May 9; 43(W1):W65–71. https://doi.org/10.1093/nar/gkv458 PMID: 25958395
13. Liu B, Wu H, Zhang D, Wang X, Chou KC. Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods. Oncotarget. 2017 Feb 21; 8(8):13338. https://doi.org/10.18632/oncotarget.14524 PMID: 28076851
14. Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks. In Proceedings of NIPS[C] 2012.
15. Graves A, Mohamed A R, Hinton G. Speech recognition with deep recurrent neural networks. In Proceedings of ICASSP [C] 2013.
16. Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. NIPS, 2014.
17. Angermueller C, Pärnamaa T, Parts L, Oliver S. Deep Learning for Computational Biology. Molecular Systems Biology. 2016; (12):878. https://doi.org/10.15252/msb.20156651 PMID: 27474269
18. Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015; 33(8):831–838. https://doi.org/10.1038/nbt. 3300 PMID: 26213851
19. Zeng H, Edwards MD, Liu G, Gifford DK. Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics. 2016; 32(12):i121–i127. https://doi.org/10.1093/bioinformatics/ btw255 PMID: 27307608
20. Pan X, Shen HB. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. bioRxiv. 2016;
21. Wang S, Sun S, Li Z, Zhang R, Xu J. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model. arXiv q-bioBM. 2016; 9:00680.
22. Jo T, Hou J, Eickholt J, Cheng J. Improving Protein Fold Recognition by Deep Learning Networks. Scientific reports. 2015; 5:17573. https://doi.org/10.1038/srep17573 PMID: 26634993
23. Wang S, Peng J, Ma J, Xu J. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields. Scientific reports. 2016; 6:18962. https://doi.org/10.1038/srep18962 PMID: 26752681
24. Asgari E, Mofrad MRK. Continuous distributed representation of biological sequences for deep proteo-mics and genomics. PLoS ONE. 2015; 10(11):1–15. https://doi.org/10.1371/journal.pone.0141287
25. Motion G B, Howden A J M, Huitema E, et al. DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool. Nucleic Acids Research, 2015, 43(22):e158–e158. https://doi.org/10.1093/nar/gkv805 PMID: 26304539
26. Graves A. Generating sequences with recurrent neural networks. In Arxiv preprint arXiv: 1308–0850, 2013.
27. Shore J E, Johnson R W. Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. Information Theory IEEE Transactions on, 1980, 26(1):26–37. https://doi.org/10.1109/TIT.1980.1056144
28. Lin C, Zou Y, Qin J, et al. Hierarchical classification of protein folds using a novel ensemble classifier. Plos One, 2012, 8(2).
29. Guo Y, Yu L, Wen Z, Li M. Using support vector machine combined with auto covariance to predict protein—protein interactions from protein sequences. Nucleic acids research. 2008 May 1; 36(9):3025–30. https://doi.org/10.1093/nar/gkn159 PMID: 18390576
30. Shen J, Zhang J, Luo X, Zhu W, Yu K, Chen K, et al. Predicting protein—protein interactions based only on sequences information. Proceedings of the National Academy of Sciences. 2007 Mar 13; 104 (11):4337–41. https://doi.org/10.1073/pnas.0607879104
31. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Science, 2014.
32. Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015.
33. Wang W, Sun L, Zhang S, Zhang H, Shi J, Xu T, et al. Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences. BMC Bioinformatics. 2017 Jun 12; 18(1):300. https://doi.org/10.1186/s12859-017-1715-8 PMID: 28606086
34. Liu B, Zhang D, Xu R, Xu J, Wang X, Chen Q, et al. Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics. 2013 Dec 5; 30(4):472–9. https://doi.org/10.1093/bioinformatics/btt709 PMID: 24318998
35. Liu B, Liu F, Fang L, Wang X, Chou KC. repRNA: a web server for generating various feature vectors of RNA sequences. Molecular Genetics and Genomics. 2016 Feb 1; 291(1):473–81. https://doi.org/10.1007/s00438-015-1078-7 PMID: 26085220