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POC Nextflow pipeline to run the Chai-1, SOTA model for biomolecular structure prediction

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# nf-chai

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## POC implementation of Chai-1 in Nextflow

## Introduction

**nf-chai** is a simple, proof-of-concept bioinformatics pipeline for running the [Chai-1](https://github.com/chaidiscovery/chai-lab) protein prediction algorithm on an input set of protein sequences in FASTA format. The pipeline has been written in Nextflow to generate results for downstream analysis in a reproducible, scalable and portable way.

## Usage

> [!NOTE]
> If you are new to Nextflow, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.

First, prepare a FASTA file with entities supported by Chai-1 in the format highlighted below. You can also provide a directory with multiple FASTA files by specifying the `--input` parameter with a directory path like this: `--input "/path/to/fasta_files/*.fasta|*.fa"`.

`multiple_entities.fa`:

```txt
>protein|name=example-of-long-protein
AGSHSMRYFSTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAASPRGEPRAPWVEQEGPEYWDRETQKYKRQAQTDRVSLRNLRGYYNQSEAGSHTLQWMFGCDLGPDGRLLRGYDQSAYDGKDYIALNEDLRSWTAADTAAQITQRKWEAAREAEQRRAYLEGTCVEWLRRYLENGKETLQRAEHPKTHVTHHPVSDHEATLRCWALGFYPAEITLTWQWDGEDQTQDTELVETRPAGDGTFQKWAAVVVPSGEEQRYTCHVQHEGLPEPLTLRWEP
>protein|name=example-of-short-protein
AIQRTPKIQVYSRHPAENGKSNFLNCYVSGFHPSDIEVDLLKNGERIEKVEHSDLSFSKDWSFYLLYYTEFTPTEKDEYACRVNHVTLSQPKIVKWDRDM
>protein|name=example-peptide
GAAL
>ligand|name=example-ligand-as-smiles
CCCCCCCCCCCCCC(=O)O
```

Run the pipeline using CPUs with the command below:

```bash
nextflow run seqeralabs/nf-chai \
   --input multiple_entities.fa \
   --outdir <OUTDIR> \
   -profile <docker/singularity>
```

Run the pipeline using GPUs with the command below:

```bash
nextflow run seqeralabs/nf-chai \
   --input multiple_entities.fa \
   --outdir <OUTDIR> \
   --use_gpus \
   -profile <docker/singularity>
```

Set the `--weights_dir` parameter to a location with the pre-downloaded weights required by Chai-1 to avoid having to download them every time you run the pipeline.

To further improve prediction performance using pre-built multiple sequence alignments (MSA) with evolutionary information, set the `--msa_dir` parameter to a location with [`*.aligned.pqt`](https://github.com/chaidiscovery/chai-lab/tree/main/examples/msas#adding-msa-evolutionary-information) format as required by Chai-1.

## Credits

nf-chai was originally written by the Seqera Team.

## Contributions and Support

If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).

## Citations

An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.

This pipeline uses code and infrastructure developed and maintained by the [nf-core](https://nf-co.re) community, reused here under the [MIT license](https://github.com/nf-core/tools/blob/main/LICENSE).

> **The nf-core framework for community-curated bioinformatics pipelines.**
>
> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
>
> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).

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