Soft Computing

, Volume 15, Issue 8, pp 1631–1642

Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm

  • Ka-Chun Wong
  • Chengbin Peng
  • Man-Hon Wong
  • Kwong-Sak Leung
Original Paper

Abstract

Protein-DNA bindings are essential activities. Understanding them forms the basis for further deciphering of biological and genetic systems. In particular, the protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) play a central role in gene transcription. Comprehensive TF-TFBS binding sequence pairs have been found in a recent study. However, they are in one-to-one mappings which cannot fully reflect the many-to-many mappings within the bindings. An evolutionary algorithm is proposed to learn generalized representations (many-to-many mappings) from the TF-TFBS binding sequence pairs (one-to-one mappings). The generalized pairs are shown to be more meaningful than the original TF-TFBS binding sequence pairs. Some representative examples have been analyzed in this study. In particular, it shows that the TF-TFBS binding sequence pairs are not presumably in one-to-one mappings. They can also exhibit many-to-many mappings. The proposed method can help us extract such many-to-many information from the one-to-one TF-TFBS binding sequence pairs found in the previous study, providing further knowledge in understanding the bindings between TFs and TFBSs.

Keywords

Bioinformatics Sequence Protein DNA Crowding Gene transcription TRANSFAC PDB 

Supplementary material

500_2011_692_MOESM1_ESM.pdf (1.3 mb)
Supplementary material (PDF 1.32 mb)

References

  1. Aerts S, Van Loo P, Thijs G, Moreau Y, De Moor B (2003) Computational detection of cis-regulatory modules. Bioinformatics 19(Suppl 2):5–14CrossRefGoogle Scholar
  2. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, pp 207–216. doi:10.1145/170035.170072
  3. Ahmad S, Gromiha MM, Sarai A (2004) Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information. Bioinformatics 20(4):477–486. doi:10.1093/bioinformatics/btg432 Google Scholar
  4. Ahmad S, Keskin O, Sarai A, Nussinov R (2008) Protein-DNA interactions: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins. Nucleic Acids Res 36:5922–5932CrossRefGoogle Scholar
  5. Bailey TL, Elkan C (1994) Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In: Proceedings of the 2nd international conference on intelligent systems for molecular biology, pp 28–36Google Scholar
  6. Bailey TL, Noble WS (2003) Searching for statistically significant regulatory modules. Bioinformatics 19(Suppl 2):16–25CrossRefGoogle Scholar
  7. Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic Programming—an introduction; on the automatic evolution of computer programs and its applications. Morgan Kaufmann, San FranciscoGoogle Scholar
  8. Bateman A, Coin L, Durbin R, Finn RD, Hollich V, GrifRths-Jones S, Khanna A, Marshall M, Moxon S, Sonnhammer ELL, Studholme DJ, Yeats C, Eddy SR (2004) The pfam protein families database. Nucleic Acids Res 32:D138–D141CrossRefGoogle Scholar
  9. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242. doi:10.1093/nar/28.1.235 Google Scholar
  10. Blanchette M, Bataille AR, Chen X, Poitras C, Laganiere J, Lefebvre C, Deblois G, Giguere V, Ferretti V, Bergeron D, Coulombe B, Robert F (2006) Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression. Genome Res 16:656–668CrossRefGoogle Scholar
  11. Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. SIGMOD Rec 26(2):255–264. doi:10.1145/253262.253325
  12. Coin L, Bateman A, Durbin R (2003) Enhanced protein domain discovery by using language modeling techniques from speech recognition. Proc Natl Acad Sci USA 100:4516–4520CrossRefGoogle Scholar
  13. Galas DJ, Schmitz A (1987) DNAse footprinting: a simple method for the detection of protein-DNA binding specificity. Nucleic Acids Res 5(9):3157–3170CrossRefGoogle Scholar
  14. Garner MM, Revzin A (1981) A gel electrophoresis method for quantifying the binding of proteins to specific DNA regions: application to components of the escherichia coli lactose operon regulatory system. Nucleic Acids Res 9(13):3047–3060CrossRefGoogle Scholar
  15. Givant S, Halmos P (2009) Introduction to boolean algebras. Springer, BerlinGoogle Scholar
  16. Goldberg DE, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the 2nd international conference on genetic algorithms and their application. L. Erlbaum Associates Inc., Hillsdale, pp 41–49Google Scholar
  17. Grundy WN, Bailey TL, Elkan CP, Baker ME (1997)Meta-MEME: motif-based hidden Markov models of protein families. Comput Appl Biosci 13:397–406Google Scholar
  18. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  19. Hulo N, Bairoch A, Bulliard V, Cerutti L, Cuche BA, de Castro E, Lachaize C, Langendijk-Genevaux PS, Sigrist CJA (2008) The 20 years of prosite. Nucl Acids Res 36(Suppl 1):D245–D249Google Scholar
  20. Jensen ST, Liu XS, Zhou Q, Liu JS (2004) Computational discovery of gene regulatory binding motifs: a bayesian perspective. Stat Sci 19(1):188–204MATHCrossRefMathSciNetGoogle Scholar
  21. Jong KAD (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann ArborGoogle Scholar
  22. Jong KAD (2006) Evolutionary Computation. A Unified Approach. MIT Press, Cambridge, MAMATHGoogle Scholar
  23. Karnaugh M (1953) A map method for synthesis of combinational logic circuits. Trans AIEE Commun Electron 72 (I):593–599MathSciNetGoogle Scholar
  24. Kato M, Hata N, Banerjee N, Futcher B, Zhang MQ (2004) Identifying combinatorial regulation of transcription factors and binding motifs. Genome Biol 5:R56CrossRefGoogle Scholar
  25. Kel-Margoulis OV, Kel AE, Reuter I, Deineko IV, Wingender E (2002) TRANSCompel: a database on composite regulatory elements in eukaryotic genes. Nucleic Acids Res 30:332–334CrossRefGoogle Scholar
  26. Kraft D, Petry F, Buckles B, Sadasivan T (1994) The use of genetic programming to build queries for information retrieval. In: Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence. Proceedings of the 1st IEEE conference, vol 1, pp 468–473. doi:10.1109/ICEC.1994.349905
  27. Krivan W, Wasserman WW (2001) A predictive model for regulatory sequences directing liver-specific transcription. Genome Res 11:1559–1566CrossRefGoogle Scholar
  28. Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132CrossRefGoogle Scholar
  29. Leung KS, Wong KC, Chan TM, Wong MH, Lee KH, Lau CK, Tsui SKW (2010) Discovering protein-DNA binding sequence patterns using association rule mining. Nucleic Acids Research (accepted)Google Scholar
  30. Li JP, Balazs ME, Parks GT, Clarkson PJ (2002) A species conserving genetic algorithm for multimodal function optimization. Evol Comput 10(3):207–234. doi:10.1162/106365602760234081 Google Scholar
  31. Liu XS, Brutlag DL, Liu JS (2002) An algorithm for finding protein-DNA binding sites with applications to chromatinimmunoprecipitation microarray experiments. Nat Biotechnol 20:835–839Google Scholar
  32. Luscombe NM, Thornton JM (2002) Protein-DNA interactions: amino acid conservation and the effects of mutations on binding specificity. J Mol Biol 320(5):991–1009CrossRefGoogle Scholar
  33. Luscombe NM, Austin SE, Berman HM, Thornton JM (2000) An overview of the structures of protein-DNA complexes. Genome Biol 1(1):1–37Google Scholar
  34. MacIsaac KD, Fraenkel E (2006) Practical strategies for discovering regulatory DNA sequence motifs. PLoS Comput Biol 2(4):e36CrossRefGoogle Scholar
  35. Matys V, Kel-Margoulis O, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel A, Wingender E (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 34:D108–D110CrossRefGoogle Scholar
  36. McGuire AM, De Wulf P, Church GM, Lin EC (1999) A weight matrix for binding recognition by the redox-response regulator ArcA-P of Escherichia coli. Mol Microbiol 32:219–221CrossRefGoogle Scholar
  37. Mohan PM, Hosur RV (2009) Structure-function-folding relationships and native energy landscape of dynein light chain protein: nuclear magnetic resonance insights. J Biosci 34:465–479CrossRefGoogle Scholar
  38. Moreland JL, Gramada A, Buzko OV, Zhang Q, Bourne PE (2005) The Molecular Biology Toolkit (MBT): a modular platform for developing molecular visualization applications. BMC Bioinformatics 6:21CrossRefGoogle Scholar
  39. Nelson RJ (1953) A way to simplify truth functions. J Symb Logic 18(3):280–282Google Scholar
  40. Nelson VP, Nagle HT, Carroll BD, Irwin JD (1995) Digital logic circuit analysis and design. Prentice-Hall, Inc., Upper Saddle RiverGoogle Scholar
  41. Ofran Y, Mysore V, Rost B (2007) Prediction of DNA-binding residues from sequence. Bioinformatics 23(13):i347–i353. doi:10.1093/bioinformatics/btm174 Google Scholar
  42. Pavlidis P, Furey TS, Liberto M, Haussler D, Grundy WN (2001) Promoter region-based classification of genes. In: Pacific symposium on biocomputing, pp 151–163Google Scholar
  43. Remenyi A, Scholer HR, Wilmanns M (2004) Combinatorial control of gene expression. Nat Struct Mol Biol 11:812–815CrossRefGoogle Scholar
  44. Rudell RL (1986) Multiple-valued logic minimization for pla synthesis. Tech. Rep. UCB/ERL M86/65, EECS Department, University of California, Berkeley. http://www.eecs.berkeley.edu/Pubs/TechRpts/1986/734.html
  45. Smith AD, Sumazin P, Das D, Zhang MQ (2005) Mining ChIP-chip data for transcription factor and cofactor binding sites. Bioinformatics Suppl 1(20):i403–i412CrossRefGoogle Scholar
  46. Smyth MS, Martin JH (2000) X-ray crystallography. Mol Pathol 53(1):8–14Google Scholar
  47. Stormo GD (1988) Computer methods for analyzing sequence recognition of nucleic acids. Annu Rev BioChem 17:241–263Google Scholar
  48. Tuch BB, Galgoczy DJ, Hernday AD, Li H, Johnson AD (2008) The evolution of combinatorial gene regulation in fungi. PLoS Biol 6:e38CrossRefGoogle Scholar
  49. Veitch EW (1952) A chart method for simplifying truth functions. In: Proceedings of the 1952 ACM national meeting, Pittsburgh. ACM, New York, pp 127–133. doi:10.1145/609784.609801
  50. Wegner M (1999) From head to toes: the multiple facets of Sox proteins. Nucleic Acids Res 27:1409–1420CrossRefGoogle Scholar
  51. White RJ (2001) Gene transcription: mechanisms and control. Blackwell, OxfordGoogle Scholar
  52. Wolberger C (1998) Combinatorial transcription factors. Curr Opin Genet Dev 8:552–559CrossRefGoogle Scholar
  53. Wong KC, Leung KS, Wong MH (2009) An evolutionary algorithm with species-specific explosion for multimodal optimization. In: Proceedings of the 11th Annual conference on genetic and evolutionary computation. ACM, New York, pp 923–930. doi:10.1145/1569901.1570027
  54. Wong KC, Leung KS, Wong MH (2010a) Effect of spatial locality on an evolutionary algorithm for multimodal optimization. In: Applications of Evolutionary Computation, EvoApplications 2010 Part I. Lecture notes in computer science, vol 6024. Springer, Berlin, pp 481–490. doi:10.1007/978-3-642-12239-2_50
  55. Wong KC, Leung KS, Wong MH (2010b) Protein structure prediction on a lattice model via multimodal optimization techniques. In: Proceedings of the 12th annual conference on genetic and evolutionary computation. ACM, New York, pp 155–162. doi:10.1145/1830483.1830513
  56. Zhou Q, Liu JS (2008) Extracting sequence features to predict protein-DNA interactions: a comparative study. Nucleic Acids Res 36(12):4137–4148. doi:10.1093/nar/gkn361 Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Ka-Chun Wong
    • 1
    • 2
  • Chengbin Peng
    • 2
  • Man-Hon Wong
    • 1
  • Kwong-Sak Leung
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatinHong Kong
  2. 2.Mathematical and Computer Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyJeddahKingdom of Saudi Arabia

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