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.
Fast access to AlphaMissense predicted Human proteins provided by hegelab.

Info
The PymolFold service is running on a A5000 instance (cost $100 a week), and the sequence length is limited to 1000aa.

Issues and Errors
If you encounter any errors or issues while using this project, please don't hesitate to open an issue here on GitHub. Your feedback helps us improve the project and make it more user-friendly for everyone.

PymolFold Server: A Shared Resource
Please note that the PymolFold server is a shared resource, and I request you to use it responsibly. Do not abuse the server, as it can affect the availability and performance of the service for other users.

18Jul2024: Add `fetch_af` to fetch AlphaFold predicted proteins in AlphaFold Database using UniProt ID. Deprecate `dms`, `singlemut`, `cpd`, and `webapp`.
10Jan2024: Add `predict_pocket` to predict ligand binding pocket of protein.
03Nov2023: Add `fetch_am` for AlphaMissense predicted Human proteins.
20Sep2023: Add `fold_batch`, a command line tool.
21Aug2023: As the ESMFold API is not stable, the job will be sent to PymolFold server if the job failed.
11Apr2023: `pf_plugin.py` is the PyMOL plugin and the `pf_pkg.py` is a pymol-free python package.
03Dec2022: Add `dms`, `singlemut`, and `webapps`. `pymolfold` allow sequence length up to 700aa.
26Nov2022: ProteinMPNN is now integrated to design proteins.
15Nov2022: I now provide an unofficial API to support user defined recycle number and allow sequence length up to 500aa!

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/pf_plugin.py
# for user still using python2, it is also py3 compatible, only esmfold supports.
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/pf_plugin.py

Other scripts:

run https://alphamissense.hegelab.org/coloram.py

Fold your protein

Also, check META's web app

The color_plddt 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.

The Meta API (up to 400 aa)

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

The PymolFold API (up to 500 aa, number of recycle can be set in range (3,24))

pymolfold GENGEIPLEIRATTGAEVDTRAVTAVEMTEGTLGIFRLPEEDYTALENFRYNRVAGENWKPASTVIYVGGTYARLCAYAPYNSVEFKNSSLKTEAGLTMQTYAAEKDMRFAVSGGDEVWKKTPTANFELKRAYARLVLSVVRDATYPNTCKITKAKIEAFTGNIITANTVDISTGTEGSGTQTPQYIHTVTTGLKDGFAIGLPQQTFSGGVVLTLTVDGMEYSVTIPANKLSTFVRGTKYIVSLAVKGGKLTLMSDKILIDKDWAEVQTGTGGSGDDYDTSFN, 4, test
color_plddt
orient 
ray 1280, 960, async=1

Use fetch_am for AlphaMissense predicted Human proteins

fetch_am cftr_human
coloram 

Use fetch_af to fetch AlphaFold predicted proteins in AlphaFold Database using UniProt ID

fetch_af P26196

Reference

ESMFold:

@article{lin2023evolutionary,
  title={Evolutionary-scale prediction of atomic-level protein structure with a language model},
  author={Lin, Zeming and Akin, Halil and Rao, Roshan and Hie, Brian and Zhu, Zhongkai and Lu, Wenting and Smetanin, Nikita and Verkuil, Robert and Kabeli, Ori and Shmueli, Yaniv and others},
  journal={Science},
  volume={379},
  number={6637},
  pages={1123--1130},
  year={2023},
  publisher={American Association for the Advancement of Science}
}

ProteinMPNN:

@article{dauparas2022robust,
  title={Robust deep learning--based protein sequence design using ProteinMPNN},
  author={Dauparas, Justas and Anishchenko, Ivan and Bennett, Nathaniel and Bai, Hua and Ragotte, Robert J and Milles, Lukas F and Wicky, Basile IM and Courbet, Alexis and de Haas, Rob J and Bethel, Neville and others},
  journal={Science},
  volume={378},
  number={6615},
  pages={49--56},
  year={2022},
  publisher={American Association for the Advancement of Science}
}

Access to AlphaMissense:

@article {Tordai2023.10.30.564807,
	author = {Hedvig Tordai and Odalys Torres and Mate Csepi and Rita Padanyi and Gergely L Lukacs and Tamas Hegedus},
	title = {Lightway access to AlphaMissense data that demonstrates a balanced performance of this missense mutation predictor},
	elocation-id = {2023.10.30.564807},
	year = {2023},
	doi = {10.1101/2023.10.30.564807},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2023/11/02/2023.10.30.564807},
	eprint = {https://www.biorxiv.org/content/early/2023/11/02/2023.10.30.564807.full.pdf},
	journal = {bioRxiv}
}

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