Multiple protein-DNA interfaces unravelled by evolutionary information, physico-chemical and geometrical properties

Abstract

The usage made of protein surfaces by nucleic acids still remains largely unknown, due to the lack of available structural data and the inherent complexity associated to protein surface deformability and evolution. In this work, we present a method that contributes to decipher such complexity by predicting protein-DNA interfaces and characterizing their properties. It relies on three biologically and physically meaningful descriptors, namely evolutionary conservation, physico-chemical properties and surface geometry. We carefully assessed its performance on several hundreds of protein structures. We achieve a higher sensitivity compared to state-of-the-art methods, and similar precision. Importantly, we show that our method is able to unravel 'hidden' binding sites by applying it to unbound protein structures and to proteins binding to DNA via multiple sites and in different conformations. It is implemented as a fully automated tool, JET 2 DNA , freely accessible at: http://www.lcqb.upmc.fr/JET2DNA. We also provide a new reference dataset of 187 protein-DNA complex structures, representative of all types of protein-DNA interactions, along with a subset of associated unbound structures: http://www.lcqb.upmc.fr/PDNAbenchmarks.

Understanding the structural details of APOBEC3-DNA interactions using graph-based representations

“…5 ). Per-residue evaluation of protein-DNA interfaces have previously yielded scoring systems useful for the prediction of DNA-binding propensities given a protein surface ( Corsi et al., 2020 ); here our subgraphs represent the spatial arrangement amongst residues, which will add an extra layer of description for such interfaces. The use of graph theory has been recently an area of active research in structural bioinformatics, for example in cataloguing patterns of molecular assemblies ( Heal et al., 2018 ).…”

DLA-Ranker: Evaluating protein docking conformations with many locally oriented cubes

“…1b ). The support-core-rim classification previously proved useful for the prediction and analysis of protein-protein and protein-DNA interfaces (Laine and Carbone, 2015; Raucci et al ., 2018; Corsi et al ., 2020).…”

From complete cross-docking to partners identification and binding sites predictions

“…In the past years, a lot of effort has been dedicated to describe the way in which proteins interact and, in particular, to characterise their interfaces. Depending on the type and function of the interaction, these may be evolutionary conserved, display peculiar physico-chemical properties or adopt an archetypal geometry [10,11,12,13,14,15,16,17,18,19,20]. For example, DNA-binding sites are systematically enriched in positively charged residues [10] and antigens are recognized by highly protruding loops [12].…”

“…Depending on the type and function of the interaction, these may be evolutionary conserved, display peculiar physico-chemical properties or adopt an archetypal geometry [10,11,12,13,14,15,16,17,18,19,20]. For example, DNA-binding sites are systematically enriched in positively charged residues [10] and antigens are recognized by highly protruding loops [12]. Such properties can be efficiently exploited toward an accurate detection of protein interfaces [10,11,21,22,23,24,25,26,27,12].…”

“…For example, DNA-binding sites are systematically enriched in positively charged residues [10] and antigens are recognized by highly protruding loops [12]. Such properties can be efficiently exploited toward an accurate detection of protein interfaces [10,11,21,22,23,24,25,26,27,12]. However, the large scale assessment of predicted interfaces is problematic as our knowledge of protein surface usage by multiple partners is still very limited [23].…”