Fornés Crespo, Oriol, 1983-Meseguer, AlbertoAguirre Plans, Joaquim, 1993-Gohl, PatrickBota, Patricia M.Molina Fernández, RubénBonet Martínez, Jaume, 1982-Chinchilla Hernández, AltairPegenaute Pérez, FerranGallego, OriolFernández Fuentes, NarcísOliva Miguel, Baldomero2025-07-072025-07-072024Fornes O, Meseguer A, Aguirre-Plans J, Gohl P, Bota PM, Molina-Fernández R, et al. Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements. NAR Genom Bioinform. 2024 Jun 12;6(2):lqae068. DOI: 10.1093/nargab/lqae0682631-9268http://hdl.handle.net/10230/70849Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ∼25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the classical nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs that are then used to scan a DNA sequence for occurrences. The best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Co-operativity is modelled by: (i) the co-localization of TFs and (ii) the structural modeling of protein-protein interactions between TFs and with co-factors. We have applied our approach to automatically model the interferon-β enhanceosome and the pioneering complexes of OCT4, SOX2 (or SOX11) and KLF4 with a nucleosome, which are compared with the experimentally known structures.application/pdfeng© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Transcripció genètica--Regulacióinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1093/nargab/lqae068info:eu-repo/semantics/openAccess