MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data

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• dc.contributor.author Faure, Andre J.
• dc.contributor.author Lehner, Ben, 1978-
• dc.date.accessioned 2025-02-07T08:07:49Z
• dc.date.available 2025-02-07T08:07:49Z
• dc.date.issued 2024
• dc.description.abstract We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for specified biophysical models, including from multimodal phenotypic data. When a user-specified model is unavailable, global nonlinearities (epistasis) can be estimated from the data. MoCHI also leverages ensemble, background-averaged epistasis to learn sparse models that can incorporate higher-order epistatic terms. MoCHI is freely available as a Python package ( https://github.com/lehner-lab/MoCHI ) relying on the PyTorch machine learning framework and allows biophysical measurements at scale, including the construction of allosteric maps of proteins.
• dc.description.sponsorship This work was funded by European Research Council (ERC) Advanced grant (883742), the Spanish Ministry of Science and Innovation (LCF/PR/HR21/52410004, EMBL Partnership, Severo Ochoa Centre of Excellence), the Bettencourt Schueller Foundation, the AXA Research Fund, Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322), and the CERCA Program/Generalitat de Catalunya. A.J.F. was funded by a Ramón y Cajal fellowship (RYC2021-033375-I) financed by the Spanish Ministry of Science and Innovation (MCIN/AEI/https://doi.org/10.13039/501100011033) and the European Union (NextGenerationEU/PRTR).
• dc.format.mimetype application/pdf
• dc.identifier.citation Faure AJ, Lehner B. MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data. Genome Biol. 2024 Dec 2;25(1):303. DOI: 10.1186/s13059-024-03444-y
• dc.identifier.doi http://dx.doi.org/10.1186/s13059-024-03444-y
• dc.identifier.issn 1474-7596
• dc.identifier.uri http://hdl.handle.net/10230/69520
• dc.language.iso eng
• dc.publisher BioMed Central
• dc.relation.ispartof Genome Biol. 2024 Dec 2;25(1):303
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/883742
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/RYC2021-033375-I
• dc.rights © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Allostery
• dc.subject.keyword Deep mutational scanning
• dc.subject.keyword Epistasis
• dc.subject.keyword Neural networks
• dc.subject.keyword Thermodynamic models
• dc.title MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion