DR-BERT
Efficiently annotate disordered protein regions with a compact language model.
Overview
DR-BERT is a compact protein language model designed to annotate intrinsically disordered regions (IDRs) in proteins. Unlike traditional tools that rely on evolutionary or biophysical features, DR-BERT leverages pretraining on large-scale protein datasets to learn contextual information. This innovative approach achieves statistically significant improvements in predicting disordered regions, offering researchers a high-accuracy, lightweight alternative for protein annotation.
Run DR-BERT on Neurosnap
The DR-BERT online webserver allows anybody with a Neurosnap account to run and access DR-BERT, 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
- Pretrained on extensive unannotated protein datasets for enhanced contextual learning.
- Does not rely on evolutionary or biophysical features, simplifying input requirements.
- Outperforms several existing methods on gold standard datasets with statistically significant accuracy.
- Compact and efficient, suitable for high-throughput protein annotation tasks.
- Web application available for easy access and real-time annotation tasks.
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 DR-BERT 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 DR-BERT in publications or research outputs.
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Nambiar, Ananthan, et al. “DR-BERT: A Protein Language Model to Annotate Disordered Regions.” Structure, vol. 32, no. 8, 2 May 2024, pp. 1260-1268.e3, www.cell.com/structure/abstract/S0969-2126(24)00136-9, https://doi.org/10.1016/j.str.2024.04.010. Accessed 15 Nov. 2024. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |