dom2vec

dom2vec generates unsupervised low-dimensional embeddings of protein domains to capture their structural and functional characteristics.

Key Features:

• Unsupervised Learning: dom2vec produces embeddings without requiring labeled training data.
• NLP-based Embedding Method: It adapts word embedding methodologies from natural language processing by treating domains as analogous to words in a sentence.
• Low-dimensional Vector Representation: The tool maps protein domains into a low-dimensional vector space where similar domains are positioned closely.
• Domain Structure and Function Representation: Embeddings capture both structural and functional aspects of protein domains.
• Intrinsic Evaluation Strategies: Four intrinsic evaluations assess embeddings using hierarchical relationships among InterPro domains, known secondary structure classes, Enzyme Commission class information, and Gene Ontology annotations.

Scientific Applications:

• Insights into Domain Architectures: dom2vec reveals contextual relationships and collocations within protein domain architectures.
• Downstream Task Performance: The embeddings have demonstrated superior or comparable performance relative to existing state-of-the-art approaches on downstream tasks.

Methodology:

dom2vec adapts word embedding methodologies from NLP by treating domains as words in a sentence to construct low-dimensional embeddings that reflect local structural features and broader functional contexts, and evaluates embeddings intrinsically using InterPro hierarchy, secondary structure classes, Enzyme Commission classes, and Gene Ontology annotations.