DeepPredict: a state-of-the-art web server for protein secondary structure and relative solvent accessibility prediction
DeepPredict is a freely accessible web server that integrates Porter6 and PaleAle6, two state-of-the-art deep learning models designed for protein secondary structure prediction (PSSP) and relative solvent accessibility (RSA) prediction, respectively. Built on an advanced deep learning framework, DeepPredict leverages pre-trained protein language models (PLMs), specifically ESM-2, to eliminate the need for multiple sequence alignments (MSAs), enabling rapid and accurate predictions. Compared to existing methods, DeepPredict outperforms in both PSSP and RSA prediction tasks, delivering state-of-the-art performance. The server offers a user-friendly interface, catering to both computational biologists and experimental researchers. DeepPredict is available at [ https://pcrgwd.ucd.ie/wafa/] with comprehensive online documentation and downloadable example datasets.
Authors
- Gianluca Pollastri (ORCID: https://orcid.org/0000-0002-5825-4949)
- Di Meng (ORCID: https://orcid.org/0009-0003-6063-7951)
- Wafa Alanazi
Institutions
- University College Dublin (IE)
- Northern Border University (SA)
Publication Details
- Journal
- Frontiers in Bioinformatics
- Published
- 2025-06-06
- DOI
- https://doi.org/10.3389/fbinf.2025.1607402
- Citations
- 1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.50
Funders
- Science Foundation Ireland
- Northern Border University