Computational Methods for Analysis of the DNA-Binding Preferences of Cys2His2 Zinc-Finger Proteins

Abstract

Cys₂His₂ zinc-finger proteins (C2H2-ZFPs) constitute the largest class of human transcription factors (TFs) and also the least characterized one. Determining the DNA sequence preferences of C2H2-ZFPs is an important first step toward elucidating their roles in transcriptional regulation. Among the most promising approaches for obtaining the sequence preferences of C2H2-ZFPs are those that combine machine-learning predictions with in vivo binding maps of these proteins. Here, we provide a protocol and guidelines for predicting the DNA-binding preferences of C2H2-ZFPs from their amino acid sequences using a machine learning-based recognition code. This protocol also describes the tools and steps to combine these predictions with ChIP-seq data to remove inaccuracies, identify the zinc-finger domains within each C2H2-ZFP that engage with DNA in vivo, and pinpoint the genomic binding sites of the C2H2-ZFPs.

Keywords: ChIP-seq; Cys2His2 zinc-finger proteins; DNA motifs; Machine learning; Recognition code; Transcription factors.