An improved predictive recognition model for Cys2-His 2 zinc finger proteins

Ankit Gupta, Ryan G. Christensen, Heather A. Bell, Mathew Goodwin, Ronak Y. Patel, Manishi Pandey, Metewo Selase Enuameh, Amy L. Rayla, Cong Zhu, Stacey Thibodeau-Beganny, Michael H. Brodsky, J. Keith Joung, Scot A. Wolfe, Gary D. Stormo

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

Cys2-His2 zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.

Original languageEnglish
Pages (from-to)4800-4812
Number of pages13
JournalNucleic acids research
Volume42
Issue number8
DOIs
StatePublished - Apr 2014

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    Gupta, A., Christensen, R. G., Bell, H. A., Goodwin, M., Patel, R. Y., Pandey, M., Enuameh, M. S., Rayla, A. L., Zhu, C., Thibodeau-Beganny, S., Brodsky, M. H., Joung, J. K., Wolfe, S. A., & Stormo, G. D. (2014). An improved predictive recognition model for Cys2-His 2 zinc finger proteins. Nucleic acids research, 42(8), 4800-4812. https://doi.org/10.1093/nar/gku132