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Medientyp: Sonstige Veröffentlichung; E-Artikel Titel: Capturing protein domain structure and function using self-supervision on domain architectures Beteiligte: Melidis, Damianos P. [VerfasserIn]; Nejdl, Wolfgang [VerfasserIn] Erschienen: Basel : MDPI AG, 2021 Erschienen in: Algorithms 14 (2021), Nr. 1 ; Algorithms Ausgabe: published Version Sprache: Englisch DOI: https://doi.org/10.15488/12310; https://doi.org/10.3390/a14010028 Schlagwörter: Amino acids ; Protein prediction ; Metadata ; Quantitative quality assessment ; Forecasting ; Biological properties ; Enzymatic functions ; Domain architectures ; SCOPe secondary structure class ; Linguistics ; Biological information ; Embeddings ; Protein domain architectures ; Word embeddings ; Location prediction ; Proteins ; Enzymatic commission class ; Linguistic features ; Biological characteristic Entstehung: Anmerkungen: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen. Beschreibung: Predicting biological properties of unseen proteins is shown to be improved by the use of protein sequence embeddings. However, these sequence embeddings have the caveat that biological metadata do not exist for each amino acid, in order to measure the quality of each unique learned embedding vector separately. Therefore, current sequence embedding cannot be intrinsically evaluated on the degree of their captured biological information in a quantitative manner. We address this drawback by our approach, dom2vec, by learning vector representation for protein domains and not for each amino acid base, as biological metadata do exist for each domain separately. To perform a reliable quantitative intrinsic evaluation in terms of biology knowledge, we selected the metadata related to the most distinctive biological characteristics of a domain, which are its structure, enzymatic, and molecular function. Notably, dom2vec obtains an adequate level of performance in the intrinsic assessment—therefore, we can draw an analogy between the local linguistic features in natural languages and the domain structure and function information in domain architectures. Moreover, we demonstrate the dom2vec applicability on protein prediction tasks, by comparing it with state-of-the-art sequence embeddings in three downstream tasks. We show that dom2vec outperforms sequence embeddings for toxin and enzymatic function prediction and is comparable with sequence embeddings in cellular location prediction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Zugangsstatus: Freier Zugang Rechte-/Nutzungshinweise: Namensnennung (CC BY)