A comprehensive comparative review of sequencebased predictors of DNA- and RNA-binding residues

Briefings in Bioinformatics

Volumn 17, Issue 1, 2016, Pages 88-105

  Yan, Jing ^a   Friedrich, Stefanie ^b,c   Kurgan, Lukasz ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b STOCKHOLM UNIVERSITY   (Sweden)

^c Life Laboratory in Solna   (Sweden)

Author keywords

DNA binding proteins; Protein DNA binding; Protein nucleic acids binding; Protein RNA binding; RNA binding proteins; Transcription factors

Indexed keywords

DNA; DNA BINDING PROTEIN; PROTEIN BINDING; RNA; RNA BINDING PROTEIN;

AMINO ACID SEQUENCE; BINDING SITE; BIOLOGY; CHEMISTRY; COMPARATIVE STUDY; CONSENSUS SEQUENCE; GENETICS; HUMAN; MACHINE LEARNING; METABOLISM; MOLECULAR GENETICS; PROCEDURES; PROTEIN DATABASE; SEQUENCE HOMOLOGY; STATISTICS AND NUMERICAL DATA;

AMINO ACID SEQUENCE; BINDING SITES; COMPUTATIONAL BIOLOGY; CONSENSUS SEQUENCE; DATABASES, PROTEIN; DNA; DNA-BINDING PROTEINS; HUMANS; MACHINE LEARNING; MOLECULAR SEQUENCE DATA; PROTEIN BINDING; RNA; RNA-BINDING PROTEINS; SEQUENCE HOMOLOGY, AMINO ACID;

EID: 84960083427     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbv023     Document Type: Article

References (63)

1. Luscombe NM, Austin SE, Berman HM, et al. An overview of the structures of protein-DNA complexes. Genome Biol 2000;1: REVIEWS001.
2. Charoensawan V, Wilson D, Teichmann SA. Genomic repertoires of DNA-binding transcription factors across the tree of life. Nucleic Acids Res 2010;38:7364-77.
3. Re A, Joshi T, Kulberkyte E, et al. RNA-protein interactions: an overview. Methods Mol Biol 2014;1097:491-521.
4. Noller HF. RNA structure: reading the ribosome. Science 2005; 309:1508-14.
5. Glisovic T, Bachorik JL, Yong J, et al. RNA-binding proteins and post-transcriptional gene regulation. FEBS Lett 2008;582: 1977-86.
6. Pruitt KD, Brown GR, Hiatt SM, et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res 2014;42: D756-63.
7. Zhao H, Yang Y, Zhou Y. Prediction of RNA binding proteins comes of age from low resolution to high resolution. Mol Biosyst 2013;9:2417-25.
8. Fornes O, Garcia-Garcia J, Bonet J, et al. On the use of knowledge-based potentials for the evaluation of models of protein-protein, protein-DNA, and protein-RNA interactions. Adv Protein Chem Struct Biol 2014;94:77-120.
9. Kauffman C, Karypis G. Computational tools for protein-DNA interactions. Wiley Interdiscip Rev Data Min Knowl Discov 2012; 2:14-28.
10. Liu LA, Bradley P. Atomistic modeling of protein-DNA interaction specificity: progress and applications. Curr Opin Struct Biol 2012;22:397-405.
11. Choi S, Han K. Predicting protein-binding RNA nucleotides using the feature-based removal of data redundancy and the interaction propensity of nucleotide triplets. Comput Biol Med 2013;43:1687-97.
12. Panwar B, Raghava GP. Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides. Genomics 2015;105:197-203.
13. Si J, Zhang Z, Lin B, et al. MetaDBSite: a meta approach to improve protein DNA-binding sites prediction. BMC Syst Biol 2011;5 (Suppl 1):S7.
14. Nagarajan R, Ahmad S, Gromiha MM. Novel approach for selecting the best predictor for identifying the binding sites in DNA binding proteins. Nucleic Acids Res 2013;41:7606-14.
15. Puton T, Kozlowski L, Tuszynska I, et al. Computational methods for prediction of protein-RNA interactions. J Struct Biol 2012;179:261-8.
16. Walia RR, Caragea C, Lewis BA, et al. Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art. BMC Bioinformatics 2012;13:89.
17. Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res 2000;28:235-42.
18. Ahmad S, Gromiha MM, Sarai A. Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information. Bioinformatics 2004;20:477-86.
19. Ahmad S, Sarai A. PSSM-based prediction of DNA binding sites in proteins. BMC Bioinformatics 2005;6:33.
20. Wang LJ, Brown SJ. BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. Nucleic Acids Res 2006;34:W243-8.
21. Ho SY, Yu FC, Chang CY, et al. Design of accurate predictors for DNA-binding sites in proteins using hybrid SVM-PSSM method. Biosystems 2007;90:234-41.
22. Kuznetsov IB, Gou ZK, Li R, et al. Using evolutionary and structural information to predict DNA-binding sites on DNAbinding proteins. Proteins Struct Funct Bioinform 2006;64:19-27.
23. Hwang S, Gou ZK, Kuznetsov IB. DP-Bind: a Web server for sequence-based prediction of DNA-binding residues in DNAbinding proteins. Bioinformatics 2007;23:634-6.
24. Ofran Y, Mysore V, Rost B. Prediction of DNA-binding residues fromsequence. Bioinformatics 2007;23:I347-53.
25. Yan CH, Terribilini M, Wu FH, et al. Predicting DNA-binding sites of proteins from amino acid sequence. BMC Bioinformatics 2006;7:262.
26. Lee JH, Hamilton M, Gleeson C, et al. Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches. Pac Symp Biocomput 2008:501-12.
27. Wang LJ, Yang MQ, Yang JY. Prediction of DNA-binding residues from protein sequence information using random forests. BMC Genomics 2009;10:S1.
28. Wu JS, Liu HD, Duan XY, et al. Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature. Bioinformatics 2009;25: 30-5.
29. Gao M, Skolnick J. A Threading-based method for the prediction of DNA-binding proteins with application to the human genome. PLoS Comput Biol 2009;5:e1000567.
30. Chu WY, Huang YF, Huang CC, et al. ProteDNA: a sequencebased predictor of sequence-specific DNA-binding residues in transcription factors. Nucleic Acids Res 2009;37: W396-401.
31. Wang L, Huang C, Yang MQ, et al. BindNρ for accurate prediction of DNA and RNA-binding residues from protein sequence features. BMC Syst Biol 2010;4 (Suppl 1):S3.
32. Carson MB, Langlois R, Lu H. NAPS: a residue-level nucleic acid-binding prediction server. Nucleic Acids Res 2010;38: W431-5.
33. Ma X, Guo J, Liu HD, et al. Sequence-based prediction of DNAbinding residues in proteins with conservation and correlation information. IEEE/ACM Trans Comput Biol Bioinform 2012; 9:1766-75.
34. Jeong E, Chung IF, Miyano S. A neural network method for identification of RNA-interacting residues in protein. Genome Inform 2004;15:105-16.
35. Jeong EN, Miyano S. A weighted profile based method for protein- RNA interacting residue prediction. Trans Comput Syst Biol Iv 2006;3939:123-39.
36. Wang Y, Xue Z, Shen G, et al. PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles. Amino Acids 2008; 35:295-302.
37. Tong J, Jiang P, Lu ZH. RISP: A web-based server for prediction of RNA-binding sites in proteins. Comput Methods Program Biomed 2008;90:148-53.
38. KumarM, Gromiha AM, Raghava GPS. Prediction of RNA binding sites in a protein using SVM and PSSM profile. Proteins Struct Funct Bioinform 2008;71:189-94.
39. Cheng CW, Su ECY, Hwang JK, et al. Predicting RNA-binding sites of proteins using support vector machines and evolutionary information. BMC Bioinformatics 2008;9:S6.
40. Spriggs RV, Murakami Y, Nakamura H, et al. Protein function annotation from sequence: prediction of residues interacting with RNA. Bioinformatics 2009;25:1492-7.
41. Murakami Y, Spriggs RV, Nakamura H, et al. PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences. Nucleic Acids Res 2010;38:W412-16.
42. Huang YF, Chiu LY, Huang CC, et al. Predicting RNA-binding residues from evolutionary information and sequence conservation. BMC Genomics 2010;11:S2.
43. Zhang T, Zhang H, Chen K, et al. Analysis and prediction of RNA-binding residues using sequence, evolutionary conservation, and predicted secondary structure and solvent accessibility. Curr Protein Peptide Sci 2010;11:609-28.
44. Wang CC, Fang YP, Xiao JM, et al. Identification of RNAbinding sites in proteins by integrating various sequence information. Amino Acids 2011;40:239-48.
45. Ma X, Guo J, Wu JS, et al. Prediction of RNA-binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature. Proteins Struct Func Bioinform 2011;79:1230-9.
46. Zhao HY, Yang YD, Zhou YQ. Highly accurate and highresolution function prediction of RNA binding proteins by fold recognition and binding affinity prediction. RNA Biology 2011;8:988-96.
47. Terribilini M, Lee JH, Yan CH, et al. Prediction of RNA binding sites in proteins from amino acid sequence. RNA Pub RNA Soc 2006;12:1450-62.
48. Terribilini M, Sander JD, Lee JH, et al. RNABindR: a server for analyzing and predicting RNA-binding sites in proteins. Nucleic Acids Res 2007;35:W578-84.
49. Pupko T, Bell RE, Mayrose I, et al. Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues. Bioinformatics 2002;18 (Suppl 1):S71-7.
50. Gao M, Skolnick J. A threading-based method for the prediction of DNA-binding proteins with application to the human genome. PLoS Comput Biol 2009;5:e1000567.
51. Chen K, Mizianty MJ, Gao J, et al. A critical comparative assessment of predictions of protein-binding sites for biologically relevant organic compounds. Structure 2011;19:613-21.
52. Zhang Y, Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 2005;33: 2302-9.
53. Altschul SF, Madden TL, Schaffer AA, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389-402.
54. Huang Y, Niu B, Gao Y, et al. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 2010;26:680-2.
55. UniProt C. UniProt: a hub for protein information. Nucleic Acids Res 2015;43:D204-12.
56. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000;25:25-9.
57. Baldi P, Brunak S, Chauvin Y, et al. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000;16:412-24.
58. Anderson TW, Darling DA. Asymptotic theory of certain goodness of fit criteria based on stochastic processes. Ann Math Stat 1952;23:193-212.
59. Kurgan L, Disfani FM. Structural protein descriptors in 1-dimension and their sequence-based predictions. Curr Protein Pept Sci 2011;12:470-89.
60. Lichtarge O, Bourne HR, Cohen FE. An evolutionary trace method defines binding surfaces common to protein families. J Mol Biol 1996;257:342-58.
61. Zvelebil MJ, Barton GJ, Taylor WR, et al. Prediction of protein secondary structure and active-sites using the alignment of homologous sequences. J Mol Biol 1987;195:957-61.
62. Hsu CM, Chen CY, Hsu CC, et al. Efficient discovery of structural motifs from protein sequences with combination of flexible intra- and inter-block gap constraints. Adv Knowl Discov Data Mining Proc 2006;3918:530-9.
63. Schneider R, Sander C. The HSSP database of protein structuresequence alignments. Nucleic Acids Res 1996;24:201-5.