MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data
Andre J Faure, Ben Lehner · 2024
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Description
Deep mutational scanning mutates a protein at thousands of spots and measures each change, yet turning that flood of data into physics takes a good model. Andre Faure and Ben Lehner present MoCHI, a neural network tool that fits readable models to such scans and reports energy changes for each mutation. It also extracts couplings between sites, the extra effects called epistasis, and maps of allostery, where a change in one place shifts action far away.
When no model is supplied, the tool estimates the global nonlinearity from the data itself. It runs on the PyTorch framework and is free as a Python package, aiming to make careful biophysics possible at large scale.
Catalog record
| CREATOR | Andre J Faure, Ben Lehner |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 71 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |