Genome-wide prediction of minor-groove electrostatic potential enables biophysical modeling of protein–DNA binding

Abstract

Protein–DNA binding is a fundamental component of gene regulatory processes, but it is still not completely understood how proteins recognize their target sites in the genome. Besides hydrogen bonding in the major groove (base readout), proteins recognize minor-groove geometry using positively charged amino acids (shape readout). The underlying mechanism of DNA shape readout involves the correlation between minor-groove width and electrostatic potential (EP). To probe this biophysical effect directly, rather than using minor-groove width as an indirect measure for shape readout, we developed a methodology, DNAphi, for predicting EP in the minor groove and confirmed the direct role of EP in protein–DNA binding using massive sequencing data. The DNAphi method uses a sliding-window approach to mine results from non-linear Poisson–Boltzmann (NLPB) calculations on DNA structures derived from all-atom Monte Carlo simulations. We validated this approach, which only requires nucleotide sequence as input, based on direct comparison with NLPB calculations for available crystal structures. Using statistical machine-learning approaches, we showed that adding EP as a biophysical feature can improve the predictive power of quantitative binding specificity models across 27 transcription factor families. High-throughput prediction of EP offers a novel way to integrate biophysical and genomic studies of protein–DNA binding.

Crystal structures of REF6 and its complex with DNA reveal diverse recognition mechanisms

“…Several studies have suggested that DNA shape, including MGW and electrostatic potential, contributes to specific DNA recognition 33,36,37 . Our experiments show that the CTCTGTTTT motif has the highest binding affinity, while other CTCTGYTY motifs are weaker.…”

Dissection of acute stimulus-inducible nucleosome remodeling in mammalian cells

“…2. Average DNA shape feature values within the same window, computed using the R package DNAshapeR v1.5.3 (Chiu et al 2017). Thirteen DNA shape features were used in this study (Sagendorf et al 2017), along with predicted minor-groove electrostatic potential (Chiu et al 2017).…”

Dissection of acute stimulus-inducible nucleosome remodeling in mammalian cells

“…For modeling of nucleosomal symmetry patterns at enhancers, DNA sequences were extracted from the reference genome (mm9) and scored as follows. Three sets of features were considered: (1) DNA sequence content encoded as k-mers (2 ≤ k ≤ 4) within 450 bp upstream of and downstream from the PU.1 summit; (2) average DNA shape feature values within the same window, computed using the R package DNAshapeR version 1.5.3 (Chiu et al 2017)-13 DNA shape features were used in this study (Sagendorf et al 2017), along with predicted minor groove electrostatic potential (Chiu et al 2017); (3) transformed FIMO P-values [−10 * log 10 (p)] (Grant et al 2011) for a curated collection of >1700 position weight matrices (PWMs) representing mammalian TF motifs (Diaferia et al 2016), core promoter elements, 5 ′ splice site, and PAS motifs-these were computed within 300 bp of the PU.1 summit as described previously (Barozzi et al 2014) using FIMO from MEME version 4.11.3.…”