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Nucleic Acids Res. 2014 Apr;42(8):4800-12. doi: 10.1093/nar/gku132. Epub 2014 Feb 12.

An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins.

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  • 1Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01605, USA, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA, Department of Genetics, Washington University School of Medicine, St Louis, MO 63108, USA, Department of Biochemistry and Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA 01609, USA, Molecular Pathology Unit, Center for Computational and Integrative Biology, and Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA 02129, USA, Department of Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA and Department of Pathology, Harvard Medical School, Boston, MA 02115, USA.

Abstract

Cys(2)-His(2) 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.

PMID:
24523353
PMCID:
PMC4005693
DOI:
10.1093/nar/gku132
[PubMed - indexed for MEDLINE]
Free PMC Article
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