MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data
Year: 2024
Type: article
Abstract: The massively parallel nature of deep mutational scanning (DMS) allows the quantification of the phenotypic effects of thousands of perturbations in a single experiment. We have developed MoCHI, a software tool that allows the parameterisation of arbitrarily complex models using DMS data. MoCHI simplifies the task of building custom models from measurements of mutant effects on any number of phenotypes. It allows the inference of free energy changes, as well as pairwise and higher-order interaction terms (energetic couplings) for specified biophysical models. When a suitable user-specified mechanistic model is not available, global nonlinearities (epistasis) can be estimated directly from the data. MoCHI also builds upon and leverages theory on ensemble (or background-averaged) epistasis to learn sparse predictive models that can incorporate higher-order epistatic terms and are informative of the genetic architecture of the underlying biological system. The combination of DMS and MoCHI allows biophysical measurements to be performed at scale, including the construction of complete allosteric maps of proteins. MoCHI is freely available ( https://github.com/lehner-lab/MoCHI ) and implemented as an easy-to-use python package relying on the PyTorch machine learning framework.
Source: Genome biology
Authors: Andre J. Faure, Ben Lehner
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,290
Funders: Centres de Recerca de Catalunya, European Commission, AXA Research Fund, Generalitat de Catalunya, Ministerio de Ciencia e Innovación, Fondation Bettencourt Schueller, Agència de Gestió d'Ajuts Universitaris i de Recerca, Agencia Estatal de Investigación, H2020 European Research Council, NextGenerationEU
Awards: G1175115578, G121809054, G1855990336, Determining in vivo protein structures and understanding genetic interactions using deep mutagenesis, G2064873908, G210041235, G2262748287, G2309174360, G2355262757, G2601397997, G2660406951, G279363373, G288263039, G3005324705, G300979063, G3288897321, G34346518, G3480869486, G3681454997, G4042783231, G425668148, G430777504, G5080475149, G5195188938, G5421974903, G5554424168, G5852091828, G6656126374, G69051668, G7084143925, G8347645112, G8637226926, G897484643, G99296377
OpenAlex ID: W4404894045