ESMFold

ESMFold is a cutting-edge deep learning model for protein structure prediction, based on large language model architecture. Unlike AlphaFold, which relies on evolutionary information from multiple sequence alignments (MSAs), ESMFold can predict the structure of a protein from a single amino acid sequence alone. This results in a massive acceleration in prediction speed, increasing throughput by up to 60 times.

The underlying model, ESM-2, was trained on millions of protein sequences to learn the 'language of biology'. By leveraging this internal knowledge of the physicochemical properties of amino acids, the system can calculate complex spatial folding in seconds. The ESM Metagenomic Atlas provides these predictions for over 600 million proteins derived from metagenomic data for research purposes.

The application primarily targets bioinformatics and structural biology research to identify uncharacterized proteins in genomic databases more rapidly. By integrating into a web platform, it allows scientists to quickly develop hypotheses regarding protein function without waiting for computationally intensive MSA calculations. It thus acts as a key tool for decoding the 'dark matter' of the protein universe.

• Single-Sequence Prediction — ESMFold does not require time-consuming sequence comparisons or MSA data to calculate precise 3D structures. This enables a significantly faster workflow when analyzing entire protein families.
• ESM-2 Language Model Foundation — The model uses ESM-2, one of the most powerful protein language models, which recognizes internal patterns in amino acid sequences. This 'intuition' replaces classical evolutionary analysis with direct pattern recognition.
• Metagenomic Database — The platform provides access to the ESM Metagenomic Atlas, which contains millions of predictions for proteins previously underrepresented in traditional databases like UniProt.

Bioinformaticians and structural biologists engaged in fundamental research or drug discovery. Also suitable for metagenomics researchers who need to annotate large volumes of sequence data.

A researcher wants to investigate the function of a newly discovered protein from a soil sample. Instead of investing weeks in complex laboratory experiments or long MSA computing runs, they input the sequence into ESMFold. The tool generates a 3D model within seconds, visualizing active sites or binding pockets. Based on this, the researcher can plan targeted mutagenesis experiments to confirm the protein's biological function.

• Bioinformaticians and structural biologists engaged in fundamental research or drug discovery. Also suitable for metagenomics researchers who need to annotate large volumes of sequence data.
• A researcher wants to investigate the function of a newly discovered protein from a soil sample.
• Single-Sequence Prediction: ESMFold does not require time-consuming sequence comparisons or MSA data to calculate precise 3D structures. This enables a significantly faster workflow when analyzing entire protein families.
• ESM-2 Language Model Foundation: The model uses ESM-2, one of the most powerful protein language models, which recognizes internal patterns in amino acid sequences. This 'intuition' replaces classical evolutionary analysis with direct pattern recognition.
• Metagenomic Database: The platform provides access to the ESM Metagenomic Atlas, which contains millions of predictions for proteins previously underrepresented in traditional databases like UniProt.

Not suitable for applications requiring 100% experimental validation, as these are computational predictions. Additionally, highly critical or proprietary sensitive sequences should not be uploaded to public web tools without proper protection.

• AlphaFold — Open Source · HQ: London, United Kingdom · GDPR / EU
• Cradle — Paid · HQ: Amsterdam, Netherlands · GDPR / EU
• Owkin — Paid · HQ: Paris, France · Unclear
• RoseTTAFold — Open Source · HQ: Seattle, United States · US Cloud
• Schrödinger Maestro — Paid · HQ: New York, United States · Unclear