Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences

BMC Bioinformatics

Volumn 18, Issue 1, 2017, Pages

  Wang, Wei ^a,b   Sun, Lin ^a   Zhang, Shiguang ^a   Zhang, Hongjun ^c   Shi, Jinling ^d   Xu, Tianhe ^a   Li, Keliang ^a  

^a HENAN NORMAL UNIVERSITY   (China)

^b Engineering Technology Research Center for Computing Intelligence and Data Mining   (China)

^c ANYANG UNIVERSITY   (China)

^d XUCHANG UNIVERSITY   (China)

Author keywords

Binding specificity; DSBs (Double stranded DNA binding proteins); Protein sequence; SSBs (Single stranded DNA binding proteins)

Indexed keywords

AMINO ACIDS; BINS; DECISION TREES; DNA; DNA SEQUENCES; SUPPORT VECTOR MACHINES;

10-FOLD CROSS-VALIDATION; AMINO ACID COMPOSITIONS; BINDING SPECIFICITIES; DOUBLE STRANDED DNA; POSITION SPECIFIC SCORING MATRIX; PROTEIN SEQUENCES; SINGLE-STRANDED DNA BINDING PROTEINS; SVM(SUPPORT VECTOR MACHINE);

PROTEINS;

DNA; DNA BINDING PROTEIN; PROTEIN BINDING; SINGLE STRANDED DNA;

AMINO ACID SEQUENCE; BIOLOGY; CHEMISTRY; GENETICS; METABOLISM; PROCEDURES; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE;

AMINO ACID SEQUENCE; COMPUTATIONAL BIOLOGY; DNA; DNA, SINGLE-STRANDED; DNA-BINDING PROTEINS; PROTEIN BINDING; SEQUENCE ANALYSIS, PROTEIN; SUPPORT VECTOR MACHINE;

EID: 85020470119     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-017-1715-8     Document Type: Article

References (56)

1. Edsö JR, Gustafsson C, Cohn M. Single- and double-stranded DNA binding proteins act in concert to conserve a telomeric DNA core sequence. Genome Integrity. 2011;2(1):1-9.
2. Attaiech L, Olivier A, Mortier-Barrière I, Soulet AL, Granadel C, Martin B, et al. Role of the single-stranded DNA-binding protein SsbB in pneumococcal transformation: maintenance of a reservoir for genetic plasticity. PLoS Genet. 2011;7(6):1-12.
3. Shlyakhtenko LS, Lushnikov AY, Miyagi A, Lyubchenko YL. Specificity of binding of single-stranded DNA-binding protein to its target. Biochemistry-US. 2012;51(7):1500-9.
4. Richard DJ, Bolderson E, Cubeddu L, Richard DJ, Bolderson E, Cubeddu L, et al. Single-stranded DNA-binding protein hssb1 is critical for genomic stability. Nature. 2008;453(5):677-81.
5. Delagoutte E, Heneman-Masurel A, Baldacci G. Single-stranded DNA binding proteins unwind the newly synthesized double-stranded DNA of model miniforks. Biochemistry. 2011;50(6):932-44.
6. Kur J, Olszewski M, Długołecka A, Filipkowski P. Single-stranded DNA-binding proteins (SSBs)-sources and applications in molecular biology. ACTA Biochimica Polonica-English Edition. 2005;52(3):569-74.
7. Shi H, Zhang Y, Zhang G, Guo J, Zhang X, Song H, et al. Systematic functional comparative analysis of four single-stranded DNA-binding proteins and their affection on viral RNA metabolism. PLoS One. 2013;8(1):e55076.
8. Morgan HP, Estibeiro P, Wear MA, Max KE, Heinemann U, Cubeddu L, et al. Sequence specificity of single-stranded DNA-binding proteins: a novel DNA microarray approach. Nucleic Acids Res. 2007;35(10):e75.
9. Kresten LL, Best RB, Depristo MA, Dobson CM, Michele V. Simultaneous determination of protein structure and dynamics. Nature. 2005;433(7022):128-32.
10. Rohs R, West SM, Sosinsky A, Liu P, Mann RS, Honig B. The role of DNA shape in protein-DNA recognition. Nature. 2009;461(7268):1248-53.
11. Dickey TH, Altschuler SE, Wuttke DS. Single-stranded DNA-binding proteins: multiple domains for multiple functions. Structure. 2013;21(7):1074-84.
12. Kerr ID, Wadsworth RIM, Cubeddu L, Blankenfeldt W, Naismith JH, White MF. Insights into ssDNA recognition by the OB fold from a structural and thermodynamic study of Sulfolobus SSB protein. EMBO J. 2003;22(11):2561-70.
13. Marceau AH, Bahng S, Massoni SC, George NP, Sandler SJ, Marians KJ, et al. Structure of the SSB-DNA polymerase III interface and its role in DNA replication. EMBO J. 2011;30(20):4236-47.
14. Pretto DI, Tsutakawa S, Brosey CA, Castillo A, Chagot ME, Smith JA, et al. Structural dynamics and ssDNA binding activity of the three n-terminal domains of the large subunit of replication protein a from small angle X-ray scattering. Biochemistry-US. 2010;13(49):2880-9.
15. Wakamatsu T, Kitamura Y, Kotera Y, Nakagawa N, Kuramitsu S, Masui R. Structure of RecJ exonuclease defines its specificity for single-stranded DNA. J Biol Chem. 2010;285(13):9762-9.
16. Dey S, Pal A, Guharoy M, Sonavane S, Chakrabarti P. Characterization and prediction of the binding site in DNA-binding proteins improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters. Nucleic Acids Res. 2012;40(15):7150-61.
17. Xiong Y, Liu J, Wei DQ. An accurate feature-based method for identifying DNA-binding residues on protein surfaces. Proteins Struct Funct Bioinf. 2011;79(2):509-17.
18. Xiong Y, Xia J, Zhang W, Liu J. Exploiting a reduced set of weighted average features to improve prediction of DNA-binding residues from 3D structures. PLoS One. 2011;6(12):e28440.
19. Qian ZL, Cai YD, Li YX. A novel computational method to predict transcription factor DNA binding preference. Biochem Biophys Res Commun. 2006;348(3):1034-7.
20. Zhu X, Ericksen SS, Mitchell JC. DBSI: DNA-binding site identifier. Nucleic Acids Res. 2013;41(16):e160.
21. Kuznetsov IB, Gou Z, Li R, Hwang S. Using evolutionary and structural information to predict DNA binding sites on DNA-binding proteins. Proteins Struct Funct Bioinf. 2006;64(1):19-27.
22. Wei W, Juan L, Yi X. Analysis and classification of DNA-binding sites in single-stranded and double-stranded DNA-binding proteins using protein information. IET Syst Biol. 2014;4(8):176-83.
23. Nimrod G, Szilágyi A, Leslie C, Ben-Tal N. Identification of DNA-binding proteins using structural, electrostatic and evolutionary features. J Mol Biol. 2009;387(4):1040-53.
24. 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.
25. Szabóová A, Kuželka O, Sergio ME, Železn F, Tolar J. Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search. BMC Bioinformatics. 2012;13(Suppl 10):S3.
26. Yan C, Terribilini M, Wu F, Jernigan R, Dobbs D, Honavar V. Predicting DNA-binding sites of proteins from amino acid sequence. BMC Bioinformatics. 2006;7(1):262.
27. Zhou W, Yan H. Prediction of DNA-binding protein based on statistical and geometric features and supportvector machines. Proteome Sci. 2011;9(12):1-6.
28. Shazman S, Elber G, Mandel-Gutfreund Y. From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces. Nucleic Acids Res. 2011;39(17):7390-9.
29. Jolma A, Yan J, Whitington T, Toivonen J, Nitta KR, Rastas P, et al. DNA-binding specificities of human transcription factors. Cell. 2013;152(1-2):327-39.
30. Wang W, Liu J, Zhou X. Identification of single-stranded and double-stranded DNA binding proteins based on protein structure. BMC Bioinformatics. 2014;12(15):12.
31. Cai YD, Doig AJ. Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition. Bioinformatics. 2004;20(8):1292-300.
32. Brameier M, Haan J, Krings A, Maccallum RM. Automatic discovery of cross-family sequence features associated with protein function. BMC Bioinformatics. 2006;7(1):16.
33. Yu EY, Wang F, Lei M, Lue N. A proposed OB-fold with a protein-interaction surfacein Candida albicans telomerase protein Est3. Nat Struct Mol Biol. 2008;15(9):985-9.
34. Nanni L, Brahnam S, Lumini A. High performance set of PseAAC and sequence based descriptors for protein classification. J Theor Biol. 2010;266(1):1-10.
35. Song J, Tan H, Takemoto K, Akutsu T. HSEpred: predict half-sphere exposure from protein sequences. Bioinformatics. 2008;24(13):1489-97.
36. Zhang Z, Kochhar S, Grigorov MG. Descriptor-based protein remote homology identification. Protein Sci. 2005;14(2):431-44.
37. Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT suite: a web server for clustering and comparing biological sequences. Bioinformatics. 2010;26(5):680-2.
38. Feng ZP, Zhang CT. Prediction of the subcellular location of prokaryotic proteins based on a new representation of the amino acid composition. Int J Biol Macromol. 2001;28(3):255-61.
39. Lin H, Li QZ. Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components. J Comput Chem. 2007;28(9):1463-6.
40. Garg A, Raghava GP. ESLpred2. Improved method for predicting subcellular localization of eukaryotic proteins. BMC Bioinformatics. 2008;9(1):503.
41. Huang HL, Lin IC, Liou YF, Tsai CT, Hsu KT, Huang WL, et al. Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties. BMC Bioinformatics. 2011;12(Suppl 1):S47.
42. Ahmad S, Sarai A. PSSM-based prediction of DNA binding sites in proteins. BMC Bioinformatics. 2005;6(1):33.
43. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389-402.
44. Afridi TH, Khan A, Lee YS. Mito-GSAAC: mitochondria prediction using genetic ensemble classifier and split amino acid composition. Amino Acids. 2012;42(4):1443-54.
45. Zhang W, Chen Y, Tu S, Liu F, Qu Q. Drug side effect prediction through linear neighborhoods and multiple data source integration, IEEE International Conference on Bioinformatics and Biomedicine; 2016. p. 427-34.
46. Zhang W, Chen Y, Liu F, Luo F, Tian G, Li X. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinformatics. 2017;18(1):18.
47. Zhang W, Zhu X, Fu Y, Tsuji J, Weng Z. The prediction of human splicing branchpoints by multi-label learning, IEEE International Conference on Bioinformatics and Biomedicine; 2016. p. 254-9.
48. Li D, Luo L, Zhang W, Liu F, Luo F. A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. BMC Bioinformatics. 2016;17(1):329.
49. Luo L, Li D, Zhang W, Tu S, Zhu X, Tian G. Accurate prediction of transposon-derived piRNAs by integrating various sequential and physicochemical features. PLoS One. 2016;11(4):e0153268.
50. Zhang W, Liu F, Luo L, Zhang J. Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinformatics. 2015;16:365.
51. Zhang W, Zou H, Luo L, Liu Q, Wu W. Wenyi Xiao. Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing. 2015;173(3):979-87.
52. Zhang W, Niu Y, Zou H, Luo L, Liu Q, Wu W. Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning. PLoS One. 2015;10(5):e0128194.
53. Zhang W, Niu Y, Xiong Y, Zhao M, Rongwei Yu, Juan Liu. Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning. PLoS One. 2012; 7(8): e43575.
54. Zhang W, Liu J, Zhao M, Li Q. Predicting linear B-cell epitopes by using sequence-derived structural and physicochemical features. Int J Data Mining Bioinformatics. 2012;6(5):557-69.
55. Govindan G, Nair AS. New feature vector for apoptosis protein subcellular localization prediction. In: Advances in Computing and Communications Communications, vol. 190; 2011. p. 294-301.
56. Naderi-Manesh H, Sadeghi M, Arab S, Moosavi Movahedi AA. Prediction of protein surface accessibility with information theory. Proteins Struct Funct Bioinf. 2001;42(4):452-9.