Deep Learning Model for Amyloidogenicity Prediction using a Pre-trained Protein LLM

DOI

10.2991/978-94-6463-805-9_22How to use a DOI?

Keywords

Amyloid prediction; Protein LLMs; Deep Learning

Abstract

The prediction of amyloidogenicity in peptides and proteins remains a focal point of ongoing bioinformatics. The crucial step in this field is to apply advanced computational methodologies. Many recent approaches to predicting amyloidogenicity within proteins are highly based on evolutionary motifs and the individual properties of amino acids. It is becoming increasingly evident that the sequence information-based features show high predictive performance. Consequently, our study evaluated the contextual features of protein sequences obtained from a pretrained protein large language model leveraging bidirectional LSTM and GRU to predict amyloidogenic regions in peptide and protein sequences. Our method achieved an accuracy of 84.5% on 10-fold cross-validation and an accuracy of 83% in the test dataset. Our results demonstrate competitive performance, highlighting the potential of LLMs in enhancing the accuracy of amyloid prediction.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zohra Yagoub
AU  - Hafida Bouziane
PY  - 2025
DA  - 2025/08/05
TI  - Deep Learning Model for Amyloidogenicity Prediction using a Pre-trained Protein LLM
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 194
EP  - 201
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_22
DO  - 10.2991/978-94-6463-805-9_22
ID  - Yagoub2025
ER  -