Quantitative modeling of transcription factor binding specificities using DNA shape

Proceedings of the National Academy of Sciences of the United States of America

Volumn 112, Issue 15, 2015, Pages 4654-4659

  Zhou, Tianyin ^a   Shen, Ning ^b,c   Yang, Lin ^a   Abe, Namiko ^d   Horton, John ^b,c   Mann, Richard S ^d   Bussemaker, Harmen J ^d   Gordân, Raluca ^b,c   Rohs, Remo ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^b DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c DUKE UNIVERSITY   (United States)

^d Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

DNA structure; Protein binding microarray; Protein DNA recognition; Statistical machine learning; Support vector regression

Indexed keywords

DNA; NUCLEOTIDE; TRANSCRIPTION FACTOR; PROTEIN BINDING;

ARTICLE; BINDING AFFINITY; BINDING SITE; DNA BINDING; DNA FLANKING REGION; DNA SEQUENCE; DNA STRUCTURE; GENOMICS; HIGH THROUGHPUT SEQUENCING; HUMAN; IN VITRO SELECTION; MOLECULAR BIOLOGY; MOLECULAR MODEL; NONHUMAN; NUCLEOTIDE SEQUENCE; PRIORITY JOURNAL; PROTEIN DNA BINDING; PROTEIN MICROARRAY; QUANTITATIVE ANALYSIS; SUPPORT VECTOR MACHINE; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; BIOLOGY; CHEMISTRY; CONFORMATION; GENETICS; KINETICS; METABOLISM; MOUSE; PROCEDURES;

MAMMALIA;

ALGORITHMS; ANIMALS; BASE SEQUENCE; BINDING SITES; COMPUTATIONAL BIOLOGY; DNA; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; KINETICS; MICE; MODELS, GENETIC; NUCLEIC ACID CONFORMATION; PROTEIN ARRAY ANALYSIS; PROTEIN BINDING; TRANSCRIPTION FACTORS;

EID: 84928033171     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1422023112     Document Type: Article

References (41)

1. Shlyueva D, Stampfel G, Stark A (2014) Transcriptional enhancers: From properties to genome-wide predictions. Nat Rev Genet 15(4):272-286.
2. Levo M, Segal E (2014) In pursuit of design principles of regulatory sequences. Nat Rev Genet 15(7):453-468.
3. Slattery M, et al. (2014) Absence of a simple code: How transcription factors read the genome. Trends Biochem Sci 39(9):381-399.
4. Stormo GD, Zhao Y (2010) Determining the specificity of protein-DNA interactions. Nat Rev Genet 11(11):751-760.
5. Stormo GD (2013) Modeling the specificity of protein-DNA interactions. Quantitative Biology (Springer, Berlin), Vol 1, pp 115-130.
6. Rohs R, et al. (2010) Origins of specificity in protein-DNA recognition. Annu Rev Biochem 79:233-269.
7. Kim S, et al. (2013) Probing allostery through DNA. Science 339(6121):816-819.
8. Watson LC, et al. (2013) The glucocorticoid receptor dimer interface allosterically transmits sequence-specific DNA signals. Nat Struct Mol Biol 20(7):876-883.
9. Joshi R, et al. (2007) Functional specificity of a Hox protein mediated by the recognition of minor groove structure. Cell 131(3):530-543.
10. Rohs R, et al. (2009) The role of DNA shape in protein-DNA recognition. Nature 461(7268):1248-1253.
11. White MA, Myers CA, Corbo JC, Cohen BA (2013) Massively parallel in vivo enhancer assay reveals that highly local features determine the cis-regulatory function of ChIPseq peaks. Proc Natl Acad Sci USA 110(29):11952-11957.
12. Berger MF, et al. (2006) Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nat Biotechnol 24(11):1429-1435.
13. Foat BC, Morozov AV, Bussemaker HJ (2006) Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics 22(14):e141-e149.
14. Zhao Y, Stormo GD (2011) Quantitative analysis demonstrates most transcription factors require only simple models of specificity. Nat Biotechnol 29(6):480-483.
15. Orenstein Y, Shamir R (2014) A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data. Nucleic Acids Res 42(8):e63.
16. Badis G, et al. (2009) Diversity and complexity in DNA recognition by transcription factors. Science 324(5935):1720-1723.
17. Weirauch MT, et al.; DREAM5 Consortium (2013) Evaluation of methods for modeling transcription factor sequence specificity. Nat Biotechnol 31(2):126-134.
18. Bussemaker HJ, Foat BC, Ward LD (2007) Predictive modeling of genome-wide mRNA expression: From modules to molecules. Annu Rev Biophys Biomol Struct 36:329-347.
19. Stormo GD (2000) DNA binding sites: Representation and discovery. Bioinformatics 16(1):16-23.
20. Zhao Y, Ruan S, Pandey M, Stormo GD (2012) Improved models for transcription factor binding site identification using nonindependent interactions. Genetics 191(3):781-790.
21. Mordelet F, Horton J, Hartemink AJ, Engelhardt BE, Gordân R (2013) Stability selection for regression-based models of transcription factor-DNA binding specificity. Bioinformatics 29(13):i117-i125.
22. Gordân R, et al. (2013) Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape. Cell Reports 3(4):1093-1104.
23. Sharon E, Lubliner S, Segal E (2008) A feature-based approach to modeling protein-DNA interactions. PLOS Comput Biol 4(8):e1000154.
24. Agius P, Arvey A, Chang W, Noble WS, Leslie C (2010) High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions. PLOS Comput Biol 6(9).
25. Zhou T, et al. (2013) DNAshape: A method for the high-throughput prediction of DNA structural features on a genomic scale. Nucleic Acids Res 41(web server issue): W56-W62.
26. Lazarovici A, et al. (2013) Probing DNA shape and methylation state on a genomic scale with DNase I. Proc Natl Acad Sci USA 110(16):6376-6381.
27. Chen Y, et al. (2013) Structure of p53 binding to the BAX response element reveals DNA unwinding and compression to accommodate base-pair insertion. Nucleic Acids Res 41(17):8368-8376.
28. Chang YP, et al. (2013) Mechanism of origin DNA recognition and assembly of an initiator-helicase complex by SV40 large tumor antigen. Cell Reports 3(4):1117-1127.
29. Vapnik VN (1995) The Nature of Statistical Learning Theory (Springer, New York).
30. Slattery M, et al. (2011) Cofactor binding evokes latent differences in DNA binding specificity between Hox proteins. Cell 147(6):1270-1282.
31. Yang L, et al. (2014) TFBSshape: A motif database for DNA shape features of transcription factor binding sites. Nucleic Acids Res 42(database issue):D148-D155.
32. Brownlie P, et al. (1997) The crystal structure of an intact human Max-DNA complex: New insights into mechanisms of transcriptional control. Structure 5(4):509-520.
33. Kosloff M, Kolodny R (2008) Sequence-similar, structure-dissimilar protein pairs in the PDB. Proteins 71(2):891-902.
34. Dror I, Zhou T, Mandel-Gutfreund Y, Rohs R (2014) Covariation between homeodomain transcription factors and the shape of their DNA binding sites. Nucleic Acids Res 42(1):430-441.
35. von Hippel PH (2007) From "simple" DNA-protein interactions to the macromolecular machines of gene expression. Annu Rev Biophys Biomol Struct 36:79-105.
36. Chiu TP, et al. (2015) GBshape: A genome browser database for DNA shape annotations. Nucleic Acids Res 43(database issue):D103-D109.
37. Liu LA, Bradley P (2012) Atomistic modeling of protein-DNA interaction specificity: Progress and applications. Curr Opin Struct Biol 22(4):397-405.
38. Hancock SP, et al. (2013) Control of DNA minor groove width and Fis protein binding by the purine 2-amino group. Nucleic Acids Res 41(13):6750-6760.
39. Maerkl SJ, Quake SR (2007) A systems approach to measuring the binding energy landscapes of transcription factors. Science 315(5809):233-237.
40. Chang CC, Lin CJ (2011) LIBSVM: A Library for Support Vector Machines. ACM Trans Intell Syst Technol 2(3):27.
41. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Neural Information Processing Systems 9 (MIT Press, Cambridge, MA), pp 155-161.