Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements

Mostra el registre complet Registre parcial de l'ítem

  • dc.contributor.author Fornés Crespo, Oriol, 1983-
  • dc.contributor.author Meseguer, Alberto
  • dc.contributor.author Aguirre Plans, Joaquim, 1993-
  • dc.contributor.author Gohl, Patrick
  • dc.contributor.author Bota, Patricia M.
  • dc.contributor.author Molina Fernández, Rubén
  • dc.contributor.author Bonet Martínez, Jaume, 1982-
  • dc.contributor.author Chinchilla Hernández, Altair
  • dc.contributor.author Pegenaute Pérez, Ferran
  • dc.contributor.author Gallego, Oriol
  • dc.contributor.author Fernández Fuentes, Narcís
  • dc.contributor.author Oliva Miguel, Baldomero
  • dc.date.accessioned 2025-07-07T06:34:18Z
  • dc.date.available 2025-07-07T06:34:18Z
  • dc.date.issued 2024
  • dc.description.abstract Transcription 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.
  • dc.description.sponsorship The work was supported by grants PID2020-113203RB-I00 and ‘Unidad de Excelencia María de Maeztu’ (ref: CEX2018-000792-M), funded by the MCIN and the AEI (DOI: 10.13039/501100011033). BO acknowledges support of Generalitat de Catalunya 4413015318-J.SELENT/SGR-22. OG acknowledge support from PGC2018-095745-B-I00 and PID2021-127773NB-I00/MICIN/AEI/10.13039/501100011033/ FEDER, EU (MICIN, AEI and FEDER), and support from RGP0017/2020 (HFSP ).
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Fornes 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/lqae068
  • dc.identifier.doi http://dx.doi.org/10.1093/nargab/lqae068
  • dc.identifier.issn 2631-9268
  • dc.identifier.uri http://hdl.handle.net/10230/70849
  • dc.language.iso eng
  • dc.publisher Oxford University Press
  • dc.relation.ispartof NAR Genom Bioinform. 2024 Jun 12;6(2):lqae068
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-113203RB-I00
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PGC2018-095745-B-I00
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-127773NB-I00
  • dc.rights © 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.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.other Transcripció genètica--Regulació
  • dc.title Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

Col·leccions

Articles (Hospital del Mar Research Institute)
Articles (Departament de Medicina i Ciències de la Vida)