EnzBert E.C. Prediction
Enzbert predicts enzymatic classes of protein sequences in batch or individually.
Overview
Enzbert predicts enzymatic classes from sequence using an attention transformer model.
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Features
- Predict monofunctional enzyme classes from sequence alone.
- Uses state-of-the-art attention model to achieve 95% accuracy on EC40 benchmarks.
- Supports large batch predictions without long compute times.
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 EnzBert E.C. Prediction 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.
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Citations
Please cite the original work when using EnzBert E.C. Prediction in publications or research outputs.
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Cunff, L. Yann et al. "Predicting enzymatic function of protein sequences with attention", https://academic.oup.com/, 10 October 2023, https://academic.oup.com/bioinformatics/article/39/10/btad620/7329097. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |