AssignSLP_GUI, a software tool exploiting AI for NMR resonance assignment of sparsely labeled proteins

Abstract

Not all proteins are amenable to uniform isotopic labeling with 13C and 15N, something needed for the widely used, and largely deductive, triple resonance assignment process. Among them are proteins expressed in mammalian cell culture where native glycosylation can be maintained, and proper formation of disulfide bonds facilitated. Uniform labeling in mammalian cells is prohibitively expensive, but sparse labeling with one or a few isotopically enriched amino acid types is an option for these proteins. However, assignment then relies on accessing the best match between a variety of measured NMR parameters and predictions based on 3D structure, often from X-ray crystallography. Finding this match is a challenging process that has benefitted from many computational tools, including trained neural nets for chemical shift prediction, genetic algorithms for searches through a myriad of assignment possibilities, and now AI-based prediction of high-quality structures for protein targets. AssignSLP_GUI, a new version of a software package for assignment of resonances from sparsely-labeled proteins, uses many of these tools. These tools and new additions to the package are highlighted in an application to a sparsely-labeled domain from a glycoprotein, CEACAM1.