@@ -13,7 +13,7 @@ This guide will focus on monomer prediction, the next sections will describe Mul
### Pre-requisites:
- OpenFold Conda Environment. See [OpenFold Installation](installation.md) for instructions on how to build this environment.
- OpenFold Conda Environment. See [OpenFold Installation](Installation.md) for instructions on how to build this environment.
- Downloading sequence databases for performing multiple sequence alignments. We provide a script to download the AlphaFold databases [here](https://github.com/aqlaboratory/openfold/blob/main/scripts/download_alphafold_dbs.sh).
To run inference on a complex or multiple complexes using a set of DeepMind's pretrained parameters, you will need:
- AlphaFold Multimer v2.3 parameters
- Updated sequence databases, with UniRef and PDB Seqres databases.
## Upgrade from a previous OpenFold Installation
If you had previously downloaded OpenFold parameters and or AlphaFold databases, you will need to download updated versions. Here are some instructions for upgrading from an existing openfold installations.
### Download AlphaFold-Multimer v2.3 Model Parameters
1. Re-download the alphafold parameters to get the latest
MSA-free sequence to structure prediction using the [ESM-1b model](https://github.com/facebookresearch/esm) embeddings.
To run inference for a sequence using the SoloSeq single-sequence model, you can either precompute ESM-1b embeddings in bulk, or you can generate them during inference.
For generating ESM-1b embeddings in bulk, use the provided script: `scripts/precompute_embeddings.py`. The script takes a directory of FASTA files (one sequence per file) and generates ESM-1b embeddings in the same format and directory structure as required by SoloSeq. Following is an example command to use the script:
In the same per-label subdirectories inside `embeddings_output_dir`, you can also place `*.hhr` files (outputs from HHSearch), which can contain the details about the structures that you want to use as templates. If you do not place any such file, templates will not be used and only the ESM-1b embeddings will be used to predict the structure. If you want to use templates, you need to pass the PDB MMCIF dataset to the command.
For generating the embeddings during inference, skip the `--use_precomputed_alignments` argument. The `*.hhr` files will be generated as well if you pass the paths to the relevant databases and tools, as specified in the command below. If you skip the database and tool arguments, HHSearch will not be used to find templates and only generated ESM-1b embeddings will be used to predict the structure.
For generating template information, you will need the UniRef90 and PDB70 databases and the JackHmmer and HHSearch binaries.
SoloSeq allows you to use the same flags and optimizations as the MSA-based OpenFold. For example, you can skip relaxation using `--skip_relaxation`, save all model outputs using `--save_outputs`, and generate output files in MMCIF format using `--cif_output`.
```{note}
Due to the nature of the ESM-1b embeddings, the sequence length for inference using the SoloSeq model is limited to 1022 residues. Sequences longer than that will be truncated.
In this guide, we will OpenFold and its dependencies.
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@@ -42,16 +42,12 @@ The script is a thin wrapper around Python's `unittest` suite, and recognizes
**Alphafold Comparison tests:**
Certain tests perform equivalence comparisons with the AlphaFold implementation. Instructions to run this level of tests requires an environment with both AlphaFold 2.0.1 and OpenFold installed, and is not covered in this guide. These tests are skipped by default if no installation of AlphaFold is found.
## Modifications
## Environment specific modifications
### CUDA 11 environment
To use OpenFold on CUDA 11 environment rather than a CUDA 12 environment.
In step 1, replace the github repository link with [OpenFold version v2](https://github.com/aqlaboratory/openfold/tree/v2.0.0)
Follow the rest of the steps of [Installation Guide](#installation)
```{note}
Replace with link to last stable pre-update version
```
### CUDA 12
To use OpenFold on CUDA 12 environment rather than a CUDA 11 environment.
In step 1, use the branch [`pl_upgrades`](https://github.com/aqlaboratory/openfold/tree/pl_upgrades) rather than the main branch, i.e. replace the URL in step 1 with https://github.com/aqlaboratory/openfold/tree/pl_upgrades
Follow the rest of the steps of [Installation Guide](#Installation)
### Install OpenFold parameters without aws
If you don't have access to `aws` on your system, you can use a different download source:
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@@ -61,9 +57,7 @@ If you don't have access to `aws` on your system, you can use a different downlo
### Docker setup
```{note}
Add / check docker installation instructions
```
A [`Dockerfile`] is provided to build an OpenFold Docker image. Additional notes for setting up a docker container for OpenFold and running inference can be found [here](original_readme.md#building-and-using-the-docker-container).
