MSA Transformer
MSA-conditioned mutation scoring and optional Gibbs sequence generation.
Overview
MSA Transformer (ESM-MSA) uses an uploaded multiple sequence alignment to score substitutions or generate protein sequences with Sean Johnson's protein Gibbs sampler. The first row is the query; the remaining rows provide fixed evolutionary context.
Run MSA Transformer on Neurosnap
The MSA Transformer online webserver allows anybody with a Neurosnap account to run and access MSA Transformer, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- ESM-MSA-1b masked mutation scoring without Gibbs sampling.
- Optional Gibbs sampling of the query with a fixed MSA context using protein_gibbs_sampler.
- Aligned FASTA and A3M inputs, with query residue and alignment column numbering in scores.
- CSV substitution scores or designed sequences with an ungapped FASTA export.
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.
| Statistic | Value |
|---|---|
| Credit Usage Rate | loading... |
| Estimated Total Cost | loading... |
| Runtime Mean | loading... |
| Runtime Median | loading... |
| Runtime Standard Deviation | loading... |
| Runtime 90th Percentile | loading... |
| Runtime Longest | loading... |
API Request
Access MSA Transformer 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.
Similar Tools
Explore tools with similar features, categories, and use cases.
Citations
Please cite the original work when using MSA Transformer in publications or research outputs.
|
Rao, R. M. et al. MSA Transformer. Proceedings of ICML, PMLR 139:8844–8856 (2021). https://proceedings.mlr.press/v139/rao21a.html |
|
Johnson, S. R., Monaco, S., Massie, K. & Syed, Z. Generating novel protein sequences using Gibbs sampling of masked language models. bioRxiv (2021). https://doi.org/10.1101/2021.01.26.428322 |
|
Johnson, S. R. et al. Computational scoring and experimental evaluation of enzymes generated by neural networks. Nature Biotechnology (published online 2024). https://doi.org/10.1038/s41587-024-02214-2 |
|
Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |