DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Liu, Bin ^a   Wang, Shanyi ^a   Wang, Xiaolong ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID; DNA BINDING PROTEIN;

ALGORITHM; BIOLOGY; CHEMISTRY; GENETICS; INFORMATION PROCESSING; PROCEDURES; PROTEIN DATABASE; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; SUPPORT VECTOR MACHINE;

ALGORITHMS; AMINO ACIDS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; DATASETS AS TOPIC; DNA-BINDING PROTEINS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SUPPORT VECTOR MACHINE;

EID: 84944886528     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep15479     Document Type: Article

References (48)

1. Jones, K. A., Kadonaga, J. T., Rosenfeld, P. J., Kelly, T. J., Tjian, R. A cellular DNA-binding protein that activates eukaryotic transcription and DNA replication. Cell 48, 79-89, doi: http://dx.doi.org/10.1016/0092-8674(87)90358-8 (1987).
2. Helwa, R., Hoheisel, J. Analysis of DNA-protein interactions: from nitrocellulose filter binding assays to microarray studies. Anal Bioanal Chem 398, 2551-2561, doi: 10.1007/s00216-010-4096-7 (2010).
3. Jaiswal, R., Singh, S. K., Bastia, D., Escalante, C. R. Crystallization and preliminary X-ray characterization of the eukaryotic replication terminator Reb1-Ter DNA complex. Acta Crystallographica Section F 71, 414-418, doi: doi: 10.1107/S2053230X15004112 (2015).
4. Omichinski, J. et al. NMR structure of a specific DNA complex of Zn-containing DNA binding domain of GATA-1. Science 261, 438-446, doi: 10.1126/science.8332909 (1993).
5. Lin, C. et al. LibD3C: Ensemble Classifiers with a Clustering and Dynamic Selection Strategy. Neurocomputing 123, 424-435 (2014).
6. Li, P., Guo, M., Wang, C., Liu, X., Zou, Q. An overview of SNP interactions in genome-wide association studies. Briefings in Functional Genomics 14, 143-155 (2015).
7. Bowie, J., Luthy, R., Eisenberg, D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 253, 164-170, doi: 10.1126/science.1853201 (1991).
8. Gao, M., Skolnick, J. DBD-Hunter: a knowledge-based method for the prediction of DNA-protein interactions. Nucleic Acids Research 36, 3978-3992, doi: 10.1093/nar/gkn332 (2008).
9. Ohlendorf, D. H., Anderson, W. F., Fisher, R. G., Takeda, Y., Matthews, B. W. The molecular basis of DNA-protein recognition inferred from the structure of cro repressor. Nature 298, 718-723 (1982).
10. Stawiski, E. W., Gregoret, L. M., Mandel-Gutfreund, Y. Annotating Nucleic Acid-Binding Function Based on Protein Structure. Journal of Molecular Biology 326, 1065-1079, doi: http://dx.doi.org/10.1016/S0022-2836(03)00031-7 (2003).
11. Liu, B. et al. PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation. Molecular Informatics 34, 8-17, (2015).
12. Wang, L., Brown, S. J. BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. Nucleic Acids Research 34, W243-W248, doi: 10.1093/nar/gkl298 (2006).
13. Hwang, S., Gou, Z., Kuznetsov, I. B. DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNAbinding proteins. Bioinformatics 23, 634-636, doi: 10.1093/bioinformatics/btl672 (2007).
14. Ofran, Y., Mysore, V., Rost, B. Prediction of DNA-binding residues from sequence. Bioinformatics 23, i347-i353, doi: 10.1093/bioinformatics/btm174 (2007).
15. Wu, J. et al. Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature. Bioinformatics 25, 30-35, doi: 10.1093/bioinformatics/btn583 (2009).
16. Kern, S. et al. Identification of p53 as a sequence-specific DNA-binding protein. Science 252, 1708-1711, doi: 10.1126/science.2047879 (1991).
17. Cai, Y.-d., Lin, S. L. Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics 1648, 127-133, http://dx.doi.org/10.1016/S1570-9639(03)00112-2 (2003).
18. Lin, C. et al. Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier. PLoS ONE 8, e56499 (2013).
19. Wei, L., Liao, M., Gao, X., Zou, Q. An Improved Protein Structural Prediction Method by Incorporating Both Sequence and Structure Information. IEEE Transactions on Nanobioscience 14, 339-349 (2015).
20. Liu, B. 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 9, e106691 (2014).
21. Liu, B. et al. Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics 30, 472-479 (2014).
22. Andrea, S., Ondej, K., Filip, Ž., Jakub, T. Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search. BMC Bioinformatics 13, S3 (2012).
23. Kumar, M., Gromiha, M., Raghava, G. Identification of DNA-binding proteins using support vector machines and evolutionary profiles. BMC Bioinformatics 8, 463 (2007).
24. Kumar, K. K., Pugalenthi, G., Suganthan, P. N. DNA-Prot: Identification of DNA Binding Proteins from Protein Sequence Information using Random Forest. Journal of Biomolecular Structure and Dynamics 26, 679-686, doi: 10.1080/07391102.2009.10507281 (2009).
25. Lin, W.-Z., Fang, J.-A., Xiao, X., Chou, K.-C. iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model. PLoS ONE 6, e24756, doi: 10.1371/journal.pone.0024756 (2011).
26. Wei, L. et al. Improved and Promising Identification of Human MicroRNAs by Incorporating a High-quality Negative Set. IEEE/ACM Transactions on Computational Biology and Bioinformatics 11, 192-201 (2014).
27. Lou, W. et al. Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naive Bayes. PLoS ONE 9, e86703, doi: 10.1371/journal.pone.0086703 (2014).
28. Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25, 3389-3402, doi: 10.1093/nar/25.17.3389 (1997).
29. Zou, Q., Hu, Q., Guo, M., Wang, G. HAlign: Fast Multiple Similar DNA/RNA Sequence Alignment Based on the Centre Star Strategy. Bioinformatics, doi: 10.1093/bioinformatics/btv177 (2015).
30. Song, L., Li, D., Zeng, X., Yunfeng Wu, L. G., Zou, Q. nDNA-prot: Identification of DNA-binding Proteins Based on Unbalanced Classification. BMC Bioinformatics 15, 298 (2014).
31. Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Research 28, 235-242 (2000).
32. Wang, C., Hu, L., Guo, M., Liu, X., Zou, Q. imDC: an ensemble learning method for imbalanced classification with miRNA data. Genetics and Molecular Research 14, 123-133 (2015).
33. Zhao, X., Zou, Q., Liu, B., Liu, X. Exploratory predicting protein folding model with random forest and hybrid features. Current Proteomics 11, 289-299 (2014).
34. Zou, Q., Li, J., Song, L., Zeng, X., Wang, G. Similarity computation strategies in the microRNA-disease network: A Survey. Briefings in Functional Genomics, doi: 10.1093/bfgp/elv024 (2015).
35. Zeng, X., Zhang, X., Zou, Q. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Briefings in Bioinformatics, doi: 10.1093/bib/bbv033 (2015).
36. Zou, Q. et al. Prediction of microRNA-disease associations based on social network analysis methods. BioMed Research International 2015, 810514 (2015).
37. Shi, H., Wu, Y., Zeng, Z., Zou, Q. A Discussion of MicroRNAs in Cancers. Current Bioinformatics 9, 453-462 (2014).
38. Zou, Q., Li, J., Wang, C., Zeng, X. Approaches for recognition disease genes based on Network. BioMed Research International 2014, 416323 (2014).
39. Wang, Q. et al. Briefing in family characteristics of microRNAs and their applications in cancer research. BBA-Proteins and Proteomics 1844, 191-197 (2014).
40. Wang, G., Dunbrack, R. L. PISCES: recent improvements to a PDB sequence culling server. Nucleic Acids Research 33, W94-W98, doi: 10.1093/nar/gki402 (2005).
41. Liu, B. et al. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Research W1, W65-W71 (2015).
42. Liu, B., Liu, F., Fang, L., Wang, X., Chou, K.-C. repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics 31, 1307-1309 (2015).
43. Chou, K.-C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins: Structure, Function, and Bioinformatics 43, 246-255, doi: 10.1002/prot.1035 (2001).
44. Suykens, J. A. K., Vandewalle, J. Least Squares Support Vector Machine Classifiers. Neural Processing Letters 9, 293-300, doi: 10.1023/A:1018628609742 (1999).
45. Liu, B. et al. Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS ONE 10, e0121501 (2015).
46. Liu, B., Chen, J., Wang, X. Application of Learning to Rank to protein remote homology detection. Bioinformatics, doi: 10.1093/bioinformatics/btv413 (2015).
47. Szilágyi, A., Skolnick, J. Efficient Prediction of Nucleic Acid Binding Function from Low-resolution Protein Structures. Journal of Molecular Biology 358, 922-933http://dx.doi.org/10.1016/j.jmb.2006.02.053 (2006).
48. Gao, M., Skolnick, J. A Threading-Based Method for the Prediction of DNA-Binding Proteins with Application to the Human Genome. PLoS Computational Biology 5, e1000567, doi: 10.1371/journal.pcbi.1000567 (2009).