AF-Cluster

Code and data corresponding to Wayment-Steele*, Ojoawo*, ... Ovchinnikov, Colwell, Kern (2023) "Predicting multiple conformations via sequence clustering with AlphaFold2" Nature. link

original bioRxiv

[Aug 2024] AF-Cluster can be combined with random seeds and MSA subsampling in this Colab Notebook

[Jan 2024] An exact set of methods to reproduce every structure prediction in the paper can be found here.

[Jan 2023] (DEP, use ColabDesign based notebook above). Run the entirety of the script in a Colab Notebook here!

Usage

To generate MSA:

All MSAs used in this manuscript were generated using the ColabFold notebook.

To cluster MSA and generate subsampled MSA files:

python scripts/ClusterMSA.py EX -i initial_msa.a3m -o msas

Outputs a directory named msas that contains

- msas/EX_000.a3m
- msas/EX_001.a3m
...
- msas/EX_REF.a3m
- msas/EX_U10-000.a3m
- msas/EX_U10-001.a3m
...
- msas/EX_U100-000.a3m
- msas/EX_U100-001.a3m
...
- msas/EX_REF.a3m
- msas/EX_clustering_assignments.tsv
-msas/EX_cluster_metadata.tsv

EX_000.a3m, EX_001.a3m ... are the clusters identified by DBSCAN.

EX_U10-000.a3m, ... EX_U10-009.a3m are uniformly sampled control MSAs of size 10 (Default is to generate 10).

EX_U100-000.a3m, ... EX_U100-009.a3m are uniformly sampled control MSAs of size 100 (Default is to generate 10).

EX_REF.a3m is a copy of the original MSA.

EX_clustering_assignments.tsv contains a list of original sequences and the cluster index they were assigned to (-1 means they were not assigned).

EX_cluster_metadata.tsv contains metadata corresponding to clusters.

To also perform PCA and/or tSNE embedding at the same time and save it in EX_clustering_assignments.tsv for later analysis:

python scripts/ClusterMSA.py -i <my_alignment.a3m> -o <outdir> --run_PCA

or

python scripts/ClusterMSA.py -i <my_alignment.a3m> -o <outdir> --run_tSNE

Example output for KaiB:

2OUG
2006 seqs removed for too many gaps, 4745 remaining
eps	n_clusters	n_not_clustered
3.00	1	1186
4.00	1	1186
5.00	3	1180
6.00	8	1161
7.00	12	1147
8.00	36	1045
9.00	39	950
10.00	62	825
Selected eps=10.00
4745 total seqs
315 clusters, 2280 of 4745 not clustered (0.48)
avg identity to query of unclustered: 0.30
avg identity to query of clustered: 0.37
wrote clustering data to msas/2OUG_clustering_assignments.tsv
wrote cluster metadata to msas/2OUG_cluster_metadata.tsv
writing 10 size-10 uniformly sampled clusters
writing 10 size-100 uniformly sampled clusters

To run AF2:

python scripts/RunAF2.py

See https://github.com/jproney/AF2Rank for more information on compiling an AlphaFold2 installation.

To run MSA Transformer:

python scripts/runESM.py -i <my_subMSA.a3m> -o <outdir>

To calculate RMSD to provided reference structure(s):

python scripts/CalculateModelFeatures.py path/to/pdbs/* -o <my_output_file>.json.zip --ref_struct REF_PDB_1.pdb REF_PDB_2.pdb

To reproduce figures in preprint:

See .ipynb files included in relevant folders in data.