Novel machine learning approaches revolutionize protein knowledge: Trends in Biochemical Sciences

The number of experimentally determined, high-resolution structures deposited in the Protein Data Banki (PDB) [1] has grown immensely since its beginning in 1976, enabling research into biological mechanisms, and in turn the development of novel therapeutics and industrial applications. This growth is, however, outpaced exponentially by that of known protein sequences increasingly impacted by high-throughput metagenomic experiments which yield billions of entries per experiment. Closing the ever-increasing gap between protein sequence and annotations of structure and function is thus a desideratum in molecular and medical biology research.