Predict protein structures with ESMFold on Deadline Cloud
The
esmfold_predictfacebook/esmfold_v1, MIT license). The bundle takes a FASTA
file as input and produces a .pdb file per sequence as
output, along with confidence metrics and an optional validation report
against experimental reference structures.
The job runs four steps:
-
Parse the input FASTA, validate sequences (up to 1024 amino acids, standard residues plus X), and split records across worker tasks.
-
Run ESMFold inference on each batch of sequences on GPU.
-
Render a backbone trace image of each predicted structure, colored by per-residue pLDDT confidence.
-
Optional: when you supply a directory of experimental reference PDBs, compute TM-score, RMSD, and a per-residue pLDDT/error calibration plot.
The bundle requires a farm with an NVIDIA GPU service-managed fleet
(A10G, L4, or A100; at least 16 GB VRAM and 16 GB system RAM) and a
queue with a conda queue environment that consumes the
CondaPackages and CondaChannels job
parameters. The fastest setup is the
cuda_farm
Submit the demo, which folds three short benchmark proteins (Trp-cage variants 1L2Y and 2JOF, and villin headpiece 1VII):
deadline bundle submit ./job_bundles/esmfold_predict/ \ -p InputFasta=./job_bundles/esmfold_predict/sample_inputs/demo.fasta
The first fold on a fresh worker downloads the 5.2 GB
facebook/esmfold_v1 weights into
<OutputDir>/.hf_cache/ (about three minutes on a
g5.2xlarge). Subsequent fold tasks in the same job reuse
the cache.
To validate predictions against experimental references, place
<seq_id>.pdb files in a directory and pass it as
ReferencePdbDir. The Validate step writes
validation.csv and a per-sequence
calibration.png.
