Leveraging structure for enzyme function prediction: methods, opportunities, and challenges

•Of the >50 million protein sequences, <1% have experimentally determined functions. •Protein structures can provide clues to function, such as the substrates of enzymes. •Homology modeling and ligand docking algorithms can help infer function from structure. •Recent successes include discovery of novel metabolites, enzymes, and pathways. The rapid growth of the number of protein sequences that can be inferred from sequenced genomes presents challenges for function assignment, because only a small fraction (currently <1%) has been experimentally characterized. Bioinformatics tools are commonly used to predict functions of uncharacterized proteins. Recently, there has been significant progress in using protein structures as an additional source of information to infer aspects of enzyme function, which is the focus of this review. Successful application of these approaches has led to the identification of novel metabolites, enzyme activities, and biochemical pathways. We discuss opportunities to elucidate systematically protein domains of unknown function, orphan enzyme activities, dead-end metabolites, and pathways in secondary metabolism. The rapid growth of the number of protein sequences that can be inferred from sequenced genomes presents challenges for function assignment, because only a small fraction (currently <1%) has been experimentally characterized. Bioinformatics tools are commonly used to predict functions of uncharacterized proteins. Recently, there has been significant progress in using protein structures as an additional source of information to infer aspects of enzyme function, which is the focus of this review. Successful application of these approaches has led to the identification of novel metabolites, enzyme activities, and biochemical pathways. We discuss opportunities to elucidate systematically protein domains of unknown function, orphan enzyme activities, dead-end metabolites, and pathways in secondary metabolism. a computational technique that builds an atomic model of a target protein using its sequence and an experimental 3D structure of a homologous protein (called the 'template'). The quality of a homology model depends on the accuracy of the sequence alignment between target and template, which varies (loosely) with the sequence identity (roughly speaking, pairwise identity higher than 40% is ideal, and lower than 25% is poor). a computational technique that predicts and ranks the binding poses of small molecule ligands to receptors (e.g., proteins). Docking usually comprises a sampling method that generates possible binding poses of a ligand in a binding site, and a scoring function that ranks these poses. Most scoring functions are empirical, and give only a crude estimate of the binding free energy of a ligand. biochemical pathways to produce organic molecules (i.e., secondary metabolites) that are not absolutely required for the survival of the organism. There are five particularly prevalent classes of secondary metabolite: isoprenoids, alkaloids, polyketides, nonribosomal peptides, and ribosomally synthesized and post-translationally modified peptides. Secondary metabolites are often restricted to a narrow set of species and have important ecological roles for the organisms that produce them. Many secondary metabolites are bioactive (antibacterial, anticancer, antifungal, antiviral, antioxidant, anti-inflammatory, antiparasitic, antimalaria, cytotoxic, etc.) and have been used as drugs and drug leads. an effort to determine the 3D, atomic-level structure of every protein encoded by a genome through a combination of high-throughput experimental and modeling approaches. The determination of a protein structure though a structural genomics effort often precedes knowledge of its function, motivating the development of methods to infer function from structure.