Basic toolkit for common protein engineering tasks

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  • License: Copyright 2024 The Regents of the University of California San Diego. All Rights Reserved.
  • Author: Jonathan Parkinson
  • Tags RESP , protein_engineering
  • Requires: Python >=3.9
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Project description

resp_protein_toolkit

A toolkit designed to perform some common tasks in protein engineering, especially encoding protein sequences (in various formats), assisting in fitting uncertainty-aware models to data, and using a trained model to search for improved candidates using a simulated annealing "in silico" directed evolution algorithm from Parkinson et al. 2023 (Nature Communications).

Installation

To install this, run:

pip install resp_protein_toolkit

Numpy is a required dependency. PyTorch is not required but will be necessary to use the models contained in the package so is strongly recommended.

Usage

For usage and general guidelines, see the docs.

Citations

If using this toolkit in work intended for publication, please cite:

Parkinson, J., Hard, R. & Wang, W. The RESP AI model accelerates the identification of tight-binding antibodies. Nat Commun 14, 454 (2023).

Project details

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  • License: Copyright 2024 The Regents of the University of California San Diego. All Rights Reserved.
  • Author: Jonathan Parkinson
  • Tags RESP , protein_engineering
  • Requires: Python >=3.9

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0.2.3

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