Use ESM-IF1

Official Neurosnap webserver for accessing ESM-IF1 online.

Inverse Folding Protein Design

Overview

The ESM-IF1 inverse folding model predicts protein sequences from their backbone atom coordinates, trained with 12M protein structures predicted by AlphaFold2.

Neurosnap Overview

The ESM-IF1 online webserver allows anybody with a Neurosnap account to run and access ESM-IF1, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong terms of service and privacy policy.

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Features

  • Allows you to inverse fold any protein or complex of proteins.
  • Includes options to control which chains to design and which to keep fixed.
  • Supports different sampling techniques to better explore the protein landscape.
  • Includes per sequence metrics such as an overall score and sequence recovery.
  • Includes amino acid probabilities by position.

Statistics

Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.

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API Request

Access ESM-IF1 using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.

Job Note

Provide a name or description for your job to help you organize and track its results. This input is solely for organizational purposes and does not impact the outcome of the job.

Configuration & Options

Service Inputs

The input protein structure to predict the amino acid sequence of. Acceptable input file formats include pdb and mmcif. Input protein structure cannot exceed 2000 residues.

Specify the name of the chain(s) that you want to inverse fold and predict new sequences for. The provided name needs to match the name on the pdb/cif file. For multiple chains seperate them with a comma (e.g., "A,B,C")

The number of output sequences to generate.

Advanced Settings

Lower sampling temperature typically results in higher sequence recovery but less diversity. If you set your temperature too low then all your samples will be the same. Ideally we recommend something in between 0.1-0.5.

Check this option to enable "Training Mode" which activates drop out layers within the model. This can be used to force the model to create more diverse predictions, but at the potential cost of accuracy.

Ready to submit your job?

Once you're done just hit the submit button below and let us do the rest.

To submit a job please login or register an account.