Physicochemical models of protein–DNA binding with standard and modified base pairs

Abstract

DNA-binding proteins play important roles in various cellular processes, but the mechanisms by which proteins recognize genomic target sites remain incompletely understood. Functional groups at the edges of the base pairs (bp) exposed in the DNA grooves represent physicochemical signatures. As these signatures enable proteins to form specific contacts between protein residues and bp, their study can provide mechanistic insights into protein–DNA binding. Existing experimental methods, such as X-ray crystallography, can reveal such mechanisms based on physicochemical interactions between proteins and their DNA target sites. However, the low throughput of structural biology methods limits mechanistic insights for selection of many genomic sites. High-throughput binding assays enable prediction of potential target sites by determining relative binding affinities of a protein to massive numbers of DNA sequences. Many currently available computational methods are based on the sequence of standard Watson–Crick bp. They assume that the contribution of overall binding affinity is independent for each base pair, or alternatively include dinucleotides or short k -mers. These methods cannot directly expand to physicochemical contacts, and they are not suitable to apply to DNA modifications or non-Watson–Crick bp. These variations include DNA methylation, and synthetic or mismatched bp. The proposed method, DeepRec, can predict relative binding affinities as function of physicochemical signatures and the effect of DNA methylation or other chemical modifications on binding. Sequence-based modeling methods are in comparison a coarse-grain description and cannot achieve such insights. Our chemistry-based modeling framework provides a path towards understanding genome function at a mechanistic level.

DeepPBS: Geometric deep learning for interpretable prediction of protein–DNA binding specificity

“…This protein only makes direct and water-mediated H-bonds with the backbone phosphates 58 , and the DNA shape required for optimal binding gives rise to sequence specificity. Capturing such interactions and how they lead to binding specificity with protein information alone is complicated and cannot be understood in a sequence space alone 22,59 . Furthermore, for many protein families, the protein monomer is insufficient 60 for binding; a biological assembly, potentially with other interaction partners 61 , is often necessary.…”

“…Predicting binding specificity for a given protein sequence, across protein families, remains a challenging and unsolved problem, despite progress for specific protein families [14][15][16][17][18][19][20][21] . Structural changes in the context of binding, along with large mechanistic diversity, contribute to the difficulty 22 . Protein-DNA structures contain valuable information that artificial intelligence can leverage to achieve generalizability across families.…”

AAontology: An ontology of amino acid scales for interpretable machine learning

“…Moreover, we envision that AAontology serves as a cornerstone in linking physicochemical properties to protein functions and dysfunctions-as for example done for post-translational modifications [197]. Notable examples of connections to protein functions include (a) substrate cleavage facilitated by the formation of extended β-strands [198][199][200][201]; (b) epitope recognition influenced by hydrophobicity and hydrogen bonds [202,203]; and (c) protein-protein interactions [204,205] (e.g., chaperone-client interactions [206,207]), protein-peptide interactions (involving peptides with extended structures, but also α-helix or β-turn conformation [105]), and protein interactions with other biomolecules such as DNA [208], RNA [208], lipids [208], or small molecules [209]. Physiochemical properties are further important for subcellular localization [21], protein stability [210], or protein trafficking [211], next to general cellular functions such as signaling [25,26] or cell division [212].…”

Systematic Comparison of Atomistic Force Fields for the Mechanical Properties of Double-Stranded DNA

“…The elasticity of double-stranded DNA (dsDNA) is a key molecular determinant in the many cellular contexts where this molecule is found. For instance, accommodating dsDNA onto the histone core of nucleosomes comes at a significant mechanical cost, , which can be alleviated by intrinsic bending of the sequence. , Increasing evidence points also to the dsDNA mechanics dependence of transcription factors affinity, − thus adding a further layer of complexity to readout mechanisms based on a static description of the system. ,− More in general, elasticity affects the DNA response to the mechanical action exerted by proteins and ligands in the most disparate contexts, including genome organization in prokaryotes, topology regulation, DNA recombination, and toxin-induced double-stranded breaks …”

“… 3 , 4 Increasing evidence points also to the dsDNA mechanics dependence of transcription factors affinity, 5 − 8 thus adding a further layer of complexity to readout mechanisms based on a static description of the system. 5 , 9 − 11 More in general, elasticity affects the DNA response to the mechanical action exerted by proteins and ligands in the most disparate contexts, including genome organization in prokaryotes, 12 topology regulation, 13 DNA recombination, 14 and toxin-induced double-stranded breaks. 15 …”