Exploring the protein universe with distant similarity detection methods

During the last few years, the body of data on proteins is expanding almost exponentially with the development of advanced methods for gene sequencing, protein structure determination, particularly by cryoelectron microscopy, and structure prediction using artificial intelligence-based approaches. These developments create the potential for a comprehensive exploration of the protein universe, the entirety of the proteins existing in the biosphere. Elucidation of the relationships among proteins including the most distant ones, where only the core fold is shared, is crucial for understanding protein functions, folding mechanisms, and evolution, as well as the evolution of cellular life forms and viruses. In this brief review, we discuss methods that shaped the field of protein bioinformatics, first, through comparative sequence analysis, and the recent developments in protein structure prediction that transformed the state of the art in the comparative analysis of distantly related proteins. The combination of the rapidly growing databases of genome and metagenome sequences with sensitive methods for sequence comparison and the new generation of structure analysis tools can make charting the protein universe at the structural level a realistic goal.