===== ColabFold =====

LocalColabFold runs **AlphaFold2** (and DeepFold) with cluster-local weights and MMseqs2 MSA databases.

Usage: [[https://github.com/sokrypton/ColabFold|ColabFold]] / [[https://github.com/YoshitakaMo/localcolabfold|LocalColabFold]].

<code>module load colabfold</code>

This puts ''colabfold_search'' / ''colabfold_batch'' on ''PATH'' and sets ''COLABFOLD_DATA'' to ''/mnt/scratch/colabfold''. Always pass ''%%--%%data $COLABFOLD_DATA'' to ''colabfold_batch''.

==== Starting a Job ====

Two jobs: MSA on CPU, then folding on GPU.

**1.** Create a working directory and put sequences in //query.fasta//. One chain per record; for complexes, separate chains with '':'' in a single sequence (ColabFold convention).

**2.** Create a Slurm job from the [[https://portal.darwin.bioeng.ru/pun/sys/myjobs/workflows/new|ColabFold MSA template]].

MSA needs about **128 %%GB%% RAM** and no GPU. Guest partition (16 %%GB%%) is not enough. Prefer high-RAM nodes.

**3.** After the MSA job finishes, create a job from the [[https://portal.darwin.bioeng.ru/pun/sys/myjobs/workflows/new|ColabFold template]]. Use NVIDIA RTX A5000 or A6000.

**Do not pass a FASTA file to ''colabfold_batch''.** That queries the public MSA server. Pass the ''msa_out'' directory (a3m files) from the local search.

**4.** Adjust the job script (for more details see [[portal_guides:slurm|Slurm Guide]]) and start the job.

==== Optional flags ====

''colabfold_batch'': ''%%--%%model-type auto'' (default; monomers use AF2-ptm, complexes use multimer v3), ''%%--%%num-models 5'', ''%%--%%templates'', ''%%--%%amber'' / ''%%--%%use-gpu-relax''.

''colabfold_search'': default prefilter is k-mer (high RAM). ''%%--%%prefilter-mode 1'' uses ungapped search (more CPU, less RAM). The databases include GPU MMseqs2 indexes; ''%%--%%gpu 1'' uses them if the job also requests a GPU. The MSA template stays CPU-only (128 %%GB%%), as in the ColabFold batch-search docs.