Capturing Protein Domain Structure and Function Using Self-Supervision on Domain Architectures

• Published in: Algorithms Vol. 14; no. 1; p. 28
• Main Authors: Melidis, Damianos P., Nejdl, Wolfgang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Amino acids; Annotations; Biological properties; Embedding; enzymatic commission class; Language; Learning; Linguistics; Methods; Molecular structure; Neural networks; Ontology; protein domain architectures; Proteins; quantitative quality assessment; SCOPe secondary structure class; word embeddings
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a14010028

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.