Predicting Transcription Factor Specificity with All-Atom Models

TL;DR

This study explores the use of all-atom, structure-based energy calculations to predict transcription factor binding sites without prior knowledge of cognate sites, focusing on the PurR TF in E. coli.

Contribution

It demonstrates that atomistic models can effectively distinguish cognate from non-cognate DNA sites, highlighting the role of direct protein-DNA interactions in specificity.

Findings

Atomistic models can differentiate cognate and non-cognate sites.

Specificity is mainly driven by direct protein-DNA interactions.

DNA bending has a weak influence on specificity.

Abstract

The binding of a transcription factor (TF) to a DNA operator site can initiate or repress the expression of a gene. Computational prediction of sites recognized by a TF has traditionally relied upon knowledge of several cognate sites, rather than an ab initio approach. Here, we examine the possibility of using structure-based energy calculations that require no knowledge of bound sites but rather start with the structure of a protein-DNA complex. We study the PurR E. coli TF, and explore to which extent atomistic models of protein-DNA complexes can be used to distinguish between cognate and non-cognate DNA sites. Particular emphasis is placed on systematic evaluation of this approach by comparing its performance with bioinformatic methods, by testing it against random decoys and sites of homologous TFs. We also examine a set of experimental mutations in both DNA and the protein. Using our explicit estimates of energy, we show that the specificity for PurR is dominated by direct protein-DNA interactions, and weakly influenced by bending of DNA.