Published August 26, 2024
| Version 2.0
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.
Version 2.0 added 36 finetuned METL-Local and METL-Global target models.
Files
Files
(2.3 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:b9af0e77810e65920170a3dfdc3a03b6
|
75.8 MB | Download |
|
md5:16c2f802c6f48ae0d1b475acff629ee4
|
75.8 MB | Download |
|
md5:2cdb749f219d7b4f12024428696bdd9d
|
75.8 MB | Download |
|
md5:f98d265745cc03cfee50009055be9faf
|
75.8 MB | Download |
|
md5:e359cb55df9a4f6e8de487890fd81e66
|
75.8 MB | Download |
|
md5:f3d4b3d63a48d93bb90123d1b604557d
|
75.8 MB | Download |
|
md5:ae2d69dd7c96ddf09e995f057591ffb2
|
75.8 MB | Download |
|
md5:48522902521f56564a870c0615411560
|
75.8 MB | Download |
|
md5:54aabf6e715d4ad6b4f7978274bc748f
|
75.8 MB | Download |
|
md5:0c1c9e8f3108c9c99e631ea8429ddccd
|
75.8 MB | Download |
|
md5:c3c08b89926cea5da442792d1ed10c73
|
75.8 MB | Download |
|
md5:4abbe5b9012a97847fefd9112c5d6ade
|
75.8 MB | Download |
|
md5:efe1ace2e0e4510ba25ee224c80a2790
|
75.8 MB | Download |
|
md5:c0cb31310349ebd7028499d04a2528d5
|
75.8 MB | Download |
|
md5:44919d5fc4a7db10ffcff7d94016da7d
|
75.8 MB | Download |
|
md5:a689c84e8fd4aeaac9009298e9cb42ea
|
75.8 MB | Download |
|
md5:9de26ecb1b2bbcf94268298a460b1375
|
75.8 MB | Download |
|
md5:7212bb86881333d0ead99722b56086df
|
75.8 MB | Download |
|
md5:a887648247c90fee7357515f46c41a90
|
9.8 MB | Download |
|
md5:0dd6ad783afba0d0f691eeeab49529b5
|
9.8 MB | Download |
|
md5:0ffc2f377098ad444a0c241352f3d62b
|
9.8 MB | Download |
|
md5:e2298debec18d68fc5907e5e81c76c5d
|
9.8 MB | Download |
|
md5:7169ed96b2b6559184bab8d8166e896a
|
9.8 MB | Download |
|
md5:dafe6f6bb072d65ede6354ad660f0521
|
9.8 MB | Download |
|
md5:e88a91a568848779c15010a25b333188
|
9.8 MB | Download |
|
md5:dcb6dd6eff95dc2a2af015f2cb8bec5f
|
9.8 MB | Download |
|
md5:4ce23fba05b25eec1387ff1f4998cf42
|
9.8 MB | Download |
|
md5:200b53c2cb47f68b0464410519fb360c
|
9.8 MB | Download |
|
md5:ad122a773fbd40debc913c5a2cd03bdc
|
9.8 MB | Download |
|
md5:bc8b72e1bcbedab613c075af70176562
|
9.8 MB | Download |
|
md5:de893407c9595a753f725b3c6438040c
|
9.8 MB | Download |
|
md5:09da8034e08679dce5be07c44bfa090c
|
9.8 MB | Download |
|
md5:22348747b4bc374608c8f355de3214a5
|
9.8 MB | Download |
|
md5:e06ef6b38df166a06f159cf1f06411e9
|
9.8 MB | Download |
|
md5:aa0c6e0f2687f6038d8cd0ae1a88e089
|
9.8 MB | Download |
|
md5:0c31556590497bb363044b7bab420d85
|
9.8 MB | Download |
|
md5:7ce7051cb4df088241afa978b68c3068
|
9.8 MB | Download |
|
md5:cf14f284b0c89cb667c4229d36bab4aa
|
9.8 MB | Download |
|
md5:3922a44fbe5e2fe1dfbc61008adec86e
|
9.9 MB | Download |
|
md5:548cbb2b776317031988c0c20a787838
|
9.9 MB | Download |
|
md5:bb0cde3ab6b65272450cc7624ae223e7
|
77.0 MB | Download |
|
md5:3d1a0ef982d9106fc5ce736a6e174ca3
|
76.9 MB | Download |
|
md5:e6844c08079e8c28168fa7bc3f09415c
|
203.2 MB | Download |
|
md5:6f43f638294b2cc14c1f66928f25feff
|
203.1 MB | Download |
|
md5:157886954216ef1a25ff4a9981637f3f
|
9.9 MB | Download |
|
md5:f34ce0c73e642524815d94c470e96d02
|
9.9 MB | Download |
|
md5:3ac1078b02c20e1d7704ba75647c02b2
|
9.9 MB | Download |
|
md5:8c03a3466f2521d1687d8ffec7ed1d62
|
9.9 MB | Download |
|
md5:1031b4df750b75e9ac8929c6efb45168
|
9.9 MB | Download |
|
md5:d136ab0411ef1ee73f9699dc716a13df
|
9.9 MB | Download |
|
md5:a81d85199612902253afea769ec9bbfd
|
9.9 MB | Download |
|
md5:8e3e6d74576ec298c7847af1f5365bd2
|
9.9 MB | Download |
|
md5:ce24dec175f32aee8c23104c6be8459f
|
9.9 MB | Download |
|
md5:3f91d250dc889820d8dee29b0d154564
|
9.9 MB | Download |
|
md5:5a31a952f6645dd1d1e190d94be19a0d
|
9.9 MB | Download |
|
md5:146dcb44ce16e4ff54103300d5b91adc
|
9.9 MB | Download |
|
md5:beb1bcf9b2323b18b6e9cf1fef3241f9
|
9.9 MB | Download |
|
md5:9c8ce7a2c15425ed69a470f21e828dcc
|
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
- Collaborative Research: MFB: Integrating Deep Learning and High-throughput Experimentation to Rapidly Navigate Protein Fitness Landscapes for Non-native Enzyme Catalysis 2226383
- 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 Science Foundation
- A Machine Learning Platform for Adaptive Chemical Screening R01GM135631
- National Institutes of Health
- Data-driven analysis of protein structure, function, and regulation R35GM119854
- National Institutes of Health
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