pymolfold's Introduction

PymolFold

Fold your protein in PyMOL!
Inspired by ColabFold by Sergey O.
Visualization inspired by pymol-color-alphafold.
Thanks to ESMFold by Meta and the API.

Tested under macOS Monterey Version 12.5.1, Python 3.7.12.
Open an issue if ran into any errors.

Install pymol-open-source

conda install -c conda-forge pymol-open-source

Usage

Load extension into PyMOL. In the PyMOL command prompt:

run https://raw.githubusercontent.com/JinyuanSun/PymolFold/main/predict_structure.py
# for user still using python2, it is also py3 compatible.
run https://raw.githubusercontent.com/JinyuanSun/PymolFold/py27/predict_structure.py
# try the command below in China mainland, the mirror will be delayed if modifications were just made, download the file to your computer and install it is always a good idea:
run https://raw.staticdn.net/JinyuanSun/PymolFold/main/predict_structure.py

Fold your protein!
The coloresm command also returns pymol selection object of different confidence levels. The color scheme is now compatible with plddt in range (0, 1) and (0, 100) only if they are consistent in your selection.

esmfold GENGEIPLEIRATTGAEVDTRAVTAVEMTEGTLGIFRLPEEDYTALENFRYNRVAGENWKPASTVIYVGGTYARLCAYAPYNSVEFKNSSLKTEAGLTMQTYAAEKDMRFAVSGGDEVWKKTPTANFELKRAYARLVLSVVRDATYPNTCKITKAKIEAFTGNIITANTVDISTGTEGSGTQTPQYIHTVTTGLKDGFAIGLPQQTFSGGVVLTLTVDGMEYSVTIPANKLSTFVRGTKYIVSLAVKGGKLTLMSDKILIDKDWAEVQTGTGGSGDDYDTSFN, test
coloresm
orient 
ray 1280, 960, async=1

Reference

@article{lin2022language,
  title={Language models of protein sequences at the scale of evolution enable accurate structure prediction},
  author={Lin, Zeming and Akin, Halil and Rao, Roshan and Hie, Brian and Zhu, Zhongkai and Lu, Wenting and dos Santos Costa, Allan and Fazel-Zarandi, Maryam and Sercu, Tom and Candido, Sal and others},
  journal={bioRxiv},
  year={2022},
  publisher={Cold Spring Harbor Laboratory}
}

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