Skip to main content


Log in

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

  • Original Paper
  • Published:
Soft Computing Aims and scope Submit manuscript


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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others


  1. As defined in Leung et al. (2010), a kmer is commonly found in a set of sequences if and only if it is a substring in more than or equal to half of the sequences.

  2. A TFBS kmer–TF kmer pair is considered binding for a PDB chain if and only if an atom of the TFBS kmer and an atom of the TF kmer are close to each other. Two atoms are considered close if and only if their distance is smaller than 3.5 angstrom. Leung et al. (2010).


  • Aerts S, Van Loo P, Thijs G, Moreau Y, De Moor B (2003) Computational detection of cis-regulatory modules. Bioinformatics 19(Suppl 2):5–14

    Article  Google Scholar 

  • 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

  • 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 

  • 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 36:5922–5932

    Article  Google Scholar 

  • 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 28–36

  • Bailey TL, Noble WS (2003) Searching for statistically significant regulatory modules. Bioinformatics 19(Suppl 2):16–25

    Article  Google Scholar 

  • 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 Francisco

  • 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–D141

    Article  Google Scholar 

  • 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 

  • 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 16:656–668

    Article  Google Scholar 

  • 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

  • Coin L, Bateman A, Durbin R (2003) Enhanced protein domain discovery by using language modeling techniques from speech recognition. Proc Natl Acad Sci USA 100:4516–4520

    Article  Google Scholar 

  • Galas DJ, Schmitz A (1987) DNAse footprinting: a simple method for the detection of protein-DNA binding specificity. Nucleic Acids Res 5(9):3157–3170

    Article  Google Scholar 

  • 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):3047–3060

    Article  Google Scholar 

  • Givant S, Halmos P (2009) Introduction to boolean algebras. Springer, Berlin

  • 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 41–49

  • Grundy WN, Bailey TL, Elkan CP, Baker ME (1997)Meta-MEME: motif-based hidden Markov models of protein families. Comput Appl Biosci 13:397–406

    Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • 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–D249

    Google Scholar 

  • Jensen ST, Liu XS, Zhou Q, Liu JS (2004) Computational discovery of gene regulatory binding motifs: a bayesian perspective. Stat Sci 19(1):188–204

    Article  MATH  MathSciNet  Google Scholar 

  • Jong KAD (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor

  • Jong KAD (2006) Evolutionary Computation. A Unified Approach. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  • Karnaugh M (1953) A map method for synthesis of combinational logic circuits. Trans AIEE Commun Electron 72 (I):593–599

    MathSciNet  Google Scholar 

  • Kato M, Hata N, Banerjee N, Futcher B, Zhang MQ (2004) Identifying combinatorial regulation of transcription factors and binding motifs. Genome Biol 5:R56

    Article  Google Scholar 

  • 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 30:332–334

    Article  Google Scholar 

  • 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

  • Krivan W, Wasserman WW (2001) A predictive model for regulatory sequences directing liver-specific transcription. Genome Res 11:1559–1566

    Article  Google Scholar 

  • Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132

    Article  Google Scholar 

  • 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)

  • 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 

  • Liu XS, Brutlag DL, Liu JS (2002) An algorithm for finding protein-DNA binding sites with applications to chromatinimmunoprecipitation microarray experiments. Nat Biotechnol 20:835–839

    Google Scholar 

  • Luscombe NM, Thornton JM (2002) Protein-DNA interactions: amino acid conservation and the effects of mutations on binding specificity. J Mol Biol 320(5):991–1009

    Article  Google Scholar 

  • Luscombe NM, Austin SE, Berman HM, Thornton JM (2000) An overview of the structures of protein-DNA complexes. Genome Biol 1(1):1–37

    Google Scholar 

  • MacIsaac KD, Fraenkel E (2006) Practical strategies for discovering regulatory DNA sequence motifs. PLoS Comput Biol 2(4):e36

    Article  Google Scholar 

  • 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–D110

    Article  Google Scholar 

  • 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 32:219–221

    Article  Google Scholar 

  • Mohan PM, Hosur RV (2009) Structure-function-folding relationships and native energy landscape of dynein light chain protein: nuclear magnetic resonance insights. J Biosci 34:465–479

    Article  Google Scholar 

  • 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 6:21

    Article  Google Scholar 

  • Nelson RJ (1953) A way to simplify truth functions. J Symb Logic 18(3):280–282

    Google Scholar 

  • Nelson VP, Nagle HT, Carroll BD, Irwin JD (1995) Digital logic circuit analysis and design. Prentice-Hall, Inc., Upper Saddle River

    Google Scholar 

  • 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 

  • Pavlidis P, Furey TS, Liberto M, Haussler D, Grundy WN (2001) Promoter region-based classification of genes. In: Pacific symposium on biocomputing, pp 151–163

  • Remenyi A, Scholer HR, Wilmanns M (2004) Combinatorial control of gene expression. Nat Struct Mol Biol 11:812–815

    Article  Google Scholar 

  • Rudell RL (1986) Multiple-valued logic minimization for pla synthesis. Tech. Rep. UCB/ERL M86/65, EECS Department, University of California, Berkeley.

  • 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–i412

    Article  Google Scholar 

  • Smyth MS, Martin JH (2000) X-ray crystallography. Mol Pathol 53(1):8–14

    Google Scholar 

  • Stormo GD (1988) Computer methods for analyzing sequence recognition of nucleic acids. Annu Rev BioChem 17:241–263

    Google Scholar 

  • Tuch BB, Galgoczy DJ, Hernday AD, Li H, Johnson AD (2008) The evolution of combinatorial gene regulation in fungi. PLoS Biol 6:e38

    Article  Google Scholar 

  • 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

  • Wegner M (1999) From head to toes: the multiple facets of Sox proteins. Nucleic Acids Res 27:1409–1420

    Article  Google Scholar 

  • White RJ (2001) Gene transcription: mechanisms and control. Blackwell, Oxford

  • Wolberger C (1998) Combinatorial transcription factors. Curr Opin Genet Dev 8:552–559

    Article  Google Scholar 

  • 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

  • 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

  • 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

  • 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 

Download references


The authors are grateful to the anonymous reviewers for their valuable comments. They would like to thank Tak-Ming Chan for his help on surveying the related works. This research is partially supported by the grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Nos. 414107 and 414708).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ka-Chun Wong.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material (PDF 1.32 mb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wong, KC., Peng, C., Wong, MH. et al. Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm. Soft Comput 15, 1631–1642 (2011).

Download citation

  • Published:

  • Issue Date:

  • DOI: