ArticleEntropy (Basel, Switzerland)2020
Residue Cluster Classes: A Unified Protein Representation for Efficient Structural and Functional Classification.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- BioS2Net: Holistic Structural and Sequential Analysis of Biomolecules Using a Deep Neural Network.International journal of molecular sciences · 2022Article
- Saturation Mutagenesis of the Transmembrane Region of HokC inInternational journal of molecular sciences · 2021Article
- Protein-Protein Interactions Efficiently Modeled by Residue Cluster Classes.International journal of molecular sciences · 2020Article
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2 authors.
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Abstract
Proteins are characterized by their structures and functions, and these two fundamental aspects of proteins are assumed to be related. To model such a relationship, a single representation to model both protein structure and function would be convenient, yet so far, the most effective models for protein structure or function classification do not rely on the same protein representation. Here we provide a computationally efficient implementation for large datasets to calculate residue cluster classes (RCCs) from protein three-dimensional structures and show that such representations enable a random forest algorithm to effectively learn the structural and functional classifications of proteins, according to the CATH and Gene Ontology criteria, respectively. RCCs are derived from residue contact maps built from different distance criteria, and we show that 7 or 8 Å with or without amino acid side-chain atoms rendered the best classification models. The potential use of a unified representation of proteins is discussed and possible future areas for improvement and exploration are presented.
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