Prediction of protein-ATP binding residues using multi-view feature learning via contextual-based co-attention network
Copyright © 2024 Elsevier Ltd. All rights reserved..
Accurately predicting protein-ATP binding residues is critical for protein function annotation and drug discovery. Computational methods dedicated to the prediction of binding residues based on protein sequence information have exhibited notable advancements in predictive accuracy. Nevertheless, these methods continue to grapple with several formidable challenges, including limited means of extracting more discriminative features and inadequate algorithms for integrating protein and residue information. To address the problems, we propose ATP-Deep, a novel protein-ATP binding residues predictor. ATP-Deep harnesses the capabilities of unsupervised pre-trained language models and incorporates domain-specific evolutionary context information from homologous sequences. It further refines the embedding at the residue level through integration with corresponding protein-level information and employs a contextual-based co-attention mechanism to adeptly fuse multiple sources of features. The performance evaluation results on the benchmark datasets reveal that ATP-Deep achieves an AUC of 0.954 and 0.951, respectively, surpassing the performance of the state-of-the-art model. These findings underscore the effectiveness of assimilating protein-level information and deploying a contextual-based co-attention mechanism grounded in context to bolster the prediction performance of protein-ATP binding residues.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:172 |
|---|---|
| Enthalten in: |
Computers in biology and medicine - 172(2024) vom: 26. März, Seite 108227 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Wu, Jia-Shun [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
8L70Q75FXE |
|---|
| Anmerkungen: |
Date Completed 26.03.2024 Date Revised 26.03.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.compbiomed.2024.108227 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM369500415 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM369500415 | ||
| 003 | DE-627 | ||
| 005 | 20240326235755.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240310s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.compbiomed.2024.108227 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1349.xml |
| 035 | |a (DE-627)NLM369500415 | ||
| 035 | |a (NLM)38460308 | ||
| 035 | |a (PII)S0010-4825(24)00311-1 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Wu, Jia-Shun |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Prediction of protein-ATP binding residues using multi-view feature learning via contextual-based co-attention network |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 26.03.2024 | ||
| 500 | |a Date Revised 26.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Accurately predicting protein-ATP binding residues is critical for protein function annotation and drug discovery. Computational methods dedicated to the prediction of binding residues based on protein sequence information have exhibited notable advancements in predictive accuracy. Nevertheless, these methods continue to grapple with several formidable challenges, including limited means of extracting more discriminative features and inadequate algorithms for integrating protein and residue information. To address the problems, we propose ATP-Deep, a novel protein-ATP binding residues predictor. ATP-Deep harnesses the capabilities of unsupervised pre-trained language models and incorporates domain-specific evolutionary context information from homologous sequences. It further refines the embedding at the residue level through integration with corresponding protein-level information and employs a contextual-based co-attention mechanism to adeptly fuse multiple sources of features. The performance evaluation results on the benchmark datasets reveal that ATP-Deep achieves an AUC of 0.954 and 0.951, respectively, surpassing the performance of the state-of-the-art model. These findings underscore the effectiveness of assimilating protein-level information and deploying a contextual-based co-attention mechanism grounded in context to bolster the prediction performance of protein-ATP binding residues | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Contextual-based co-attention network | |
| 650 | 4 | |a Deep learning model | |
| 650 | 4 | |a Pre-trained protein language | |
| 650 | 4 | |a Protein-ATP binding residues prediction | |
| 650 | 7 | |a Proteins |2 NLM | |
| 650 | 7 | |a Adenosine Triphosphate |2 NLM | |
| 650 | 7 | |a 8L70Q75FXE |2 NLM | |
| 700 | 1 | |a Liu, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Ge, Fang |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Dong-Jun |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computers in biology and medicine |d 1970 |g 172(2024) vom: 26. März, Seite 108227 |w (DE-627)NLM000382272 |x 1879-0534 |7 nnns |
| 773 | 1 | 8 | |g volume:172 |g year:2024 |g day:26 |g month:03 |g pages:108227 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.compbiomed.2024.108227 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 172 |j 2024 |b 26 |c 03 |h 108227 | ||