Chapter 6: Ultra-Fast Protein Folding: ESMFold, ColabFold & OmegaFold
Chapter 6: Ultra-Fast Protein Folding: ESMFold, ColabFold & OmegaFold
While AlphaFold2 is exceptionally accurate, generating large Multiple Sequence Alignments (MSAs) using JackHMMER against databases of hundreds of millions of sequences takes several minutes to hours per protein. Next-generation tools dramatically accelerate folding speeds using protein language models and fast search heuristics.
Meta AI's ESMFold utilizes ESM-2 (a 15-billion parameter transformer language model trained on millions of evolutionary protein sequences). ESM-2 learns internal structural and co-evolutionary constraints directly from single sequences, folding proteins in seconds without requiring any MSA search!
1. Folding Sequences with ESMFold in Python
import requests
import json
def fold_with_esmfold(sequence: str, output_pdb: str):
url = "https://api.esmatlas.com/v1/prediction"
headers = {"Content-Type": "text/plain"}
response = requests.post(url, data=sequence, headers=headers)
if response.status_code == 200:
with open(output_pdb, "w") as f:
f.write(response.text)
print(f"Structure saved to {output_pdb}")
else:
print(f"Error: {response.status_code} - {response.text}")
seq = "MKWVTFISLLLLFSSAYSRGVFRRDTHKSEIAHRFKDLGEEHFKGLVLIAFSQYLQQCPFDEHVKLVNELTEFAKTCVADESHAGCEK"
fold_with_esmfold(seq, "predicted_albumin.pdb")2. ColabFold: 50x Faster MSA Generation
ColabFold replaces JackHMMER with MMseqs2 (Many-against-Many sequence searching), accelerating the MSA construction phase by 40–60x without sacrificing AlphaFold accuracy.
# Run ColabFold CLI locally or on GPU server
colabfold_batch \\
input_sequences.fasta \\
./output_predictions/ \\
--num-recycle 3 \\
--use-gpu-relax