Predicting interacting hotspots for nanobodies' binding using triplets of residues

Protein-protein interactions (PPI) are fundamental to cellular signaling, forming robust networks that govern critical biological processes such as immune response, cell growth, and signal transduction. Nanobody-based therapies have emerged as a key strategy for modulating PPIs, offering exceptional...

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Detalhes bibliográficos
Autores: Hamdani, Rahma, Cianferoni, Damiano, Reche, Raul, Delgado Blanco, Javier, Serrano Pubull, Luis, 1982-
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2025
País:España
Recursos:Universitat Pompeu Fabra
Repositório:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/71444
Acesso em linha:http://hdl.handle.net/10230/71444
http://dx.doi.org/10.1002/pro.70220
Access Level:Acceso aberto
Palavra-chave:Binding hotspots
Computational prediction
Drug discovery
Nanobodies
Protein–protein interactions
Structural biology
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spelling Predicting interacting hotspots for nanobodies' binding using triplets of residuesHamdani, RahmaCianferoni, DamianoReche, RaulDelgado Blanco, JavierSerrano Pubull, Luis, 1982-Binding hotspotsComputational predictionDrug discoveryNanobodiesProtein–protein interactionsStructural biologyProtein-protein interactions (PPI) are fundamental to cellular signaling, forming robust networks that govern critical biological processes such as immune response, cell growth, and signal transduction. Nanobody-based therapies have emerged as a key strategy for modulating PPIs, offering exceptional potential due to their high specificity, stability, and ability to access challenging epitopes on PPI interfaces inside cells. The rational design of nanobodies relies mainly on understanding and predicting their binding regions, particularly the residues that contribute the most to the binding energy (binding hotspots). Existing computational methods do not fully provide a scalable solution for hotspot identification in nanobody design, leaving a critical gap in the rational design of these therapeutics. Here, we present a scalable and structure-aware algorithm for hotspot prediction in nanobody design. The algorithm queries a curated database of triplets of interacting residues obtained from ~20,000 non-redundant PDB structures. We showed that these triplets contain structural and energetic information, being able to assess the stability effect of residue variations in protein structures, Pearson R = 0.63 (MSE = 1.58 kcal/mol). More important than effects on stability is the ability of the algorithm to predict binding hotspots of protein-protein generic complexes and more specifically in complexes containing nanobodies. HotspotPred reached an accuracy of 0.73 for hotspot residue identification in a protein interaction dataset of 1160 Alanine mutants and correctly identified in 63.4% of the cases we predicted at least 2 residues on the binding surface.This publication is part of the grant PRE2022-101389, funded by MICIU/AEI /10.13039/501100011033 and by the ESF+, and of the project PID2021-122341NB-I00, funded by MCIN/ AEI / 10.13039/501100011033 / FEDER, UE). This project has also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 101020135).Wiley202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/71444http://dx.doi.org/10.1002/pro.70220reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésProtein science: a publication of the Protein Society. 2025 Aug;34(8):e70220info:eu-repo/grantAgreement/EC/H2020/101020135info:eu-repo/grantAgreement/ES/3PE/PID2021-122341NB-I00info:eu-repo/grantAgreement/ES/3PE/PRE2022-101389© 2025 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/714442026-06-12T07:21:37Z
dc.title.none.fl_str_mv Predicting interacting hotspots for nanobodies' binding using triplets of residues
title Predicting interacting hotspots for nanobodies' binding using triplets of residues
spellingShingle Predicting interacting hotspots for nanobodies' binding using triplets of residues
Hamdani, Rahma
Binding hotspots
Computational prediction
Drug discovery
Nanobodies
Protein–protein interactions
Structural biology
title_short Predicting interacting hotspots for nanobodies' binding using triplets of residues
title_full Predicting interacting hotspots for nanobodies' binding using triplets of residues
title_fullStr Predicting interacting hotspots for nanobodies' binding using triplets of residues
title_full_unstemmed Predicting interacting hotspots for nanobodies' binding using triplets of residues
title_sort Predicting interacting hotspots for nanobodies' binding using triplets of residues
dc.creator.none.fl_str_mv Hamdani, Rahma
Cianferoni, Damiano
Reche, Raul
Delgado Blanco, Javier
Serrano Pubull, Luis, 1982-
author Hamdani, Rahma
author_facet Hamdani, Rahma
Cianferoni, Damiano
Reche, Raul
Delgado Blanco, Javier
Serrano Pubull, Luis, 1982-
author_role author
author2 Cianferoni, Damiano
Reche, Raul
Delgado Blanco, Javier
Serrano Pubull, Luis, 1982-
author2_role author
author
author
author
dc.subject.none.fl_str_mv Binding hotspots
Computational prediction
Drug discovery
Nanobodies
Protein–protein interactions
Structural biology
topic Binding hotspots
Computational prediction
Drug discovery
Nanobodies
Protein–protein interactions
Structural biology
description Protein-protein interactions (PPI) are fundamental to cellular signaling, forming robust networks that govern critical biological processes such as immune response, cell growth, and signal transduction. Nanobody-based therapies have emerged as a key strategy for modulating PPIs, offering exceptional potential due to their high specificity, stability, and ability to access challenging epitopes on PPI interfaces inside cells. The rational design of nanobodies relies mainly on understanding and predicting their binding regions, particularly the residues that contribute the most to the binding energy (binding hotspots). Existing computational methods do not fully provide a scalable solution for hotspot identification in nanobody design, leaving a critical gap in the rational design of these therapeutics. Here, we present a scalable and structure-aware algorithm for hotspot prediction in nanobody design. The algorithm queries a curated database of triplets of interacting residues obtained from ~20,000 non-redundant PDB structures. We showed that these triplets contain structural and energetic information, being able to assess the stability effect of residue variations in protein structures, Pearson R = 0.63 (MSE = 1.58 kcal/mol). More important than effects on stability is the ability of the algorithm to predict binding hotspots of protein-protein generic complexes and more specifically in complexes containing nanobodies. HotspotPred reached an accuracy of 0.73 for hotspot residue identification in a protein interaction dataset of 1160 Alanine mutants and correctly identified in 63.4% of the cases we predicted at least 2 residues on the binding surface.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/71444
http://dx.doi.org/10.1002/pro.70220
url http://hdl.handle.net/10230/71444
http://dx.doi.org/10.1002/pro.70220
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Protein science: a publication of the Protein Society. 2025 Aug;34(8):e70220
info:eu-repo/grantAgreement/EC/H2020/101020135
info:eu-repo/grantAgreement/ES/3PE/PID2021-122341NB-I00
info:eu-repo/grantAgreement/ES/3PE/PRE2022-101389
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
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application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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