broad-spectrum-asap-paper

Structure-based prediction of affinity across viral families.

Intro

The rapid emergence of viruses with pandemic and epidemic potential presents a continuous threat for public health worldwide. With the typical drug discovery pipeline taking an average of 6 years to reach the pre-clinical stage, there is an urgent need for new strategies to design broad-spectrum antivirals that can target multiple viral family members and variants of concern. We present a structure-based computational pipeline designed to identify and evaluate broad-spectrum inhibitors across viral family members for a given target.

Publication: In progress

Contributors

• Maria A. Castellanos
• Alexander M. Payne
• Hugo MacDermott-Opeskin

Contents

Scripts and input files for the paper on structure-based prediction of affinity across the coronavirus family.

• Run sequence search and alignment with asap-spectrum from asapdiscovery
• Run protein folding with ColabFold and structure alignment with asap-spectrum
• Ligand transfer, docking and refinement with asap-discovery
• Scoring of poses with the score_complexes cli

Installation

To install this repository, follow these steps:

1. Clone the repository, then enter the source tree:

git clone https://github.com/choderalab/broad-spectrum-asap-paper.git
cd broad-spectrum-asap-paper

2. Install the dependencies into a new conda environment, and activate it:

mamba env create -f conda-envs/test_env.yml
conda activate broad-spectrum

3. Install ColabFold. This can be done locally using localfold, or via Docker, following the instructions on the ColabFold repo. The example will assume the program is installed on a module colabfold/v1.5.2.

4. Install gnina. Also not on available in conda-forge but can be installed via a Docker image. gnina is used for scoring docked poses and it not strictly required (the alternative is AutoDock Vina), but it is more accurate.

License

• This software is licensed under the MIT license - a copy of this license is provided as SOFTWARE_LICENSE
• The data in this repository is made available under the Creative Commons CC0 (“No Rights Reserved”) License - a copy of this license is provided as DATA_LICENSE

Copyright

Copyright (c) 2025, Maria A. Castellanos