Structure-aware protein self-supervised learning
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Le résumé fourni par la source
MOTIVATION: Protein representation learning methods have shown great potential to many downstream tasks in biological applications. A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major obstacle to effective protein representation learning. However, existing protein representation learning is usually pretrained on protein sequences without considering the important protein structural information. RESULTS: In this work, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a graph neural network model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed graph neural network model via a novel pseudo bi-level optimization scheme. We conduct experiments on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification into 10 cellular compartments, and the enzyme-catalyzed reaction classification into 384 EC numbers, and these experiments verify the effectiveness of our proposed method. AVAILABILITY AND IMPLEMENTATION: The Alphafold2 database is available in https://alphafold.ebi.ac.uk/. The PDB files are available in https://www.rcsb.org/. The downstream tasks are available in https://github.com/phermosilla/IEConv\_proteins/tree/master/Datasets. The code of the proposed method is available in https://github.com/GGchen1997/STEPS_Bioinformatics.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Structure-aware protein self-supervised learning
- Date Crossref
- 01/04/2023
- Éditeur
- Oxford University Press (OUP)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Mila - Quebec Artificial Intelligence Institute pays non établi dans la noticeStructure de recherche
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McGill University pays non établi dans la noticeUniversité ou école supérieure
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Baidu (China) pays non établi dans la noticeEntreprise
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MILA—Quebec AI Institute pays non établi dans la noticeStructure de recherche
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School of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Baidu Research pays non établi dans la noticeInstitution
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Baidu Inc. pays non établi dans la noticeEntreprise
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BCG X pays non établi dans la noticeInstitution
Mila - Quebec Artificial Intelligence Institute, McGill University et Baidu (China), avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.