Novel universal domain-centric method for protein classification

Human protein kinases constitute a large superfamily of about 500 genes, historically classified into subfamilies based on phylogenetic relationship. However, many kinases remain unclassified. Phylogeny is typically based on multiple sequence alignments, and neglects the physico-chemical properties of residues at each position of the sequence. By incorporating these properties, we can gain deeper insights beyond basic alignments. Here we use, for the first time, a detailed physico-chemical description of kinases to identify class-specific structural regions, supporting an unsupervised classification method capable of classifying previously unlabeled kinases. This novel approach aligns with existing phylogeny-based classifications while offering refinements and enhanced accuracy. Ultimately, we use machine learning techniques to classify unlabeled kinases, validated by analyzing class-specific structural regions. This new classification approach goes beyond current rankings and can be applied to any type of protein, such as immunoglobulins and G protein-coupled receptors.