Published April 23, 2024
| Version 1.0
Model
Open
METL models
Description
This repository contains pretrained source and target METL models. The metl-pretrained GitHub repository is used to load and run these models, and its readme describes each of the model files.
Files
Files
(737.8 MB)
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md5:200b53c2cb47f68b0464410519fb360c
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md5:e6844c08079e8c28168fa7bc3f09415c
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md5:157886954216ef1a25ff4a9981637f3f
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9.9 MB | Download |
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md5:ce24dec175f32aee8c23104c6be8459f
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md5:3f91d250dc889820d8dee29b0d154564
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md5:5a31a952f6645dd1d1e190d94be19a0d
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9.9 MB | Download |
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md5:146dcb44ce16e4ff54103300d5b91adc
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9.9 MB | Download |
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md5:beb1bcf9b2323b18b6e9cf1fef3241f9
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9.9 MB | Download |
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md5:9c8ce7a2c15425ed69a470f21e828dcc
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9.9 MB | Download |
Additional details
Related works
- Is supplement to
- Preprint: 10.1101/2024.03.15.585128 (DOI)
- Software: https://github.com/gitter-lab/metl (URL)
- Software: https://github.com/gitter-lab/metl-pretrained (URL)
Funding
- U.S. National Science Foundation
- Collaborative Research: MFB: Integrating Deep Learning and High-throughput Experimentation to Rapidly Navigate Protein Fitness Landscapes for Non-native Enzyme Catalysis 2226383
- U.S. National Science Foundation
- Collaborative Research: MFB: Integrating Deep Learning and High-throughput Experimentation to Rapidly Navigate Protein Fitness Landscapes for Non-native Enzyme Catalysis 2226451
- National Institutes of Health
- A Machine Learning Platform for Adaptive Chemical Screening R01GM135631
- National Institutes of Health
- Data-driven analysis of protein structure, function, and regulation R35GM119854
References
- Sam Gelman, Bryce Johnson, Chase Freschlin, Sameer D'Costa, Anthony Gitter, Philip A Romero. Biophysics-based protein language models for protein engineering. bioRxiv, 2024. doi:10.1101/2024.03.15.585128