IDP-ELM: Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning

IDP-ELM is a web server for predicting intrinsically disordered proteins (IDPs) from amino acid sequences, based on multiple protein language models and ensemble learning. It is designed to predict IDPs with high accuracy and speed. [GitHub | Paper | License]

Input sequence in FASTA format

Predict by IDP-ELM Example Sequence Clear

Prediction Results

The prediction process may take a few seconds since it is running on a CPU server with limited resources. The results contains 5 lines:

1. The first line is the sequence ID.
2. The second line is the input sequence.
3. The third line is the propensity of intrinsically disorder.
4. The fourth line is the propensity of disordered flexible linker.
5. The fifth line is the propensity of disordered protein binding.

References

Users are kindly requested to utilize the following citation when referencing this method:

• Shijie Xu and Akira Onoda, Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning, Journal of Chemical Information and Modeling, (2023), DOI: 10.1021/acs.jcim.3c01202

Please contact shijie.xu@ees.hokudai.ac.jp for any questions.

Changelogs

• 2024/05/28: Web server updated.
• 2023/09/12: First release.