nach oben

Soft Computing

Erschienen in:

01.08.2011 | Original Paper

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

verfasst von: Ka-Chun Wong, Chengbin Peng, Man-Hon Wong, Kwong-Sak Leung

Erschienen in: Soft Computing | Ausgabe 8/2011

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel A fuzzy-rule-based driving architecture for non-player characters in a car racing game

Nächster Artikel β-Interval attribute reduction in variable precision rough set model

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

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.

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):5–14CrossRef

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

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 36:5922–5932CrossRef

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):16–25CrossRef

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–D141CrossRef

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

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 16:656–668CrossRef

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 100:4516–4520CrossRef

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

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):3047–3060CrossRef

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

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

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

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

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, MAMATH

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

Kato M, Hata N, Banerjee N, Futcher B, Zhang MQ (2004) Identifying combinatorial regulation of transcription factors and binding motifs. Genome Biol 5:R56CrossRef

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 30:332–334CrossRef

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 11:1559–1566CrossRef

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

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

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

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

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

MacIsaac KD, Fraenkel E (2006) Practical strategies for discovering regulatory DNA sequence motifs. PLoS Comput Biol 2(4):e36CrossRef

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–D110CrossRef

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 32:219–221CrossRef

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

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 6:21CrossRef

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

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

Ofran Y, Mysore V, Rost B (2007) Prediction of DNA-binding residues from sequence. Bioinformatics 23(13):i347–i353. doi:10.1093/bioinformatics/btm174

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 11:812–815CrossRef

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

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–i412CrossRef

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

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

Tuch BB, Galgoczy DJ, Hernday AD, Li H, Johnson AD (2008) The evolution of combinatorial gene regulation in fungi. PLoS Biol 6:e38CrossRef

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 27:1409–1420CrossRef

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

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

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

Titel: Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm
verfasst von: Ka-Chun Wong
Chengbin Peng
Man-Hon Wong
Kwong-Sak Leung
Publikationsdatum: 01.08.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 8/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-011-0692-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 8/2011

A goal programming approach to fuzzy linear regression with fuzzy input–output data

Use of the q-Gaussian mutation in evolutionary algorithms

Classification-based self-adaptive differential evolution with fast and reliable convergence performance

Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control

Advances in Computational Intelligence and Bioinformatics

Clustering of protein expression data: a benchmark of statistical and neural approaches