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

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 unavaila...

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Bibliographic Details
Published in:Genome Biology
Main Authors: Andre J. Faure, Ben Lehner
Format: Article
Language:English
Published: BMC 2024-12-01
Subjects:
Deep mutational scanning
Neural networks
Thermodynamic models
Epistasis
Allostery
Online Access:https://doi.org/10.1186/s13059-024-03444-y
Description
Summary: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.
ISSN:1474-760X

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