Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design

Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization, and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This Perspective aims to showcase the current status of applications in pharmaceutical industry and also to discuss and celebrate the innovative approaches in protein science by highlighting their potential in selected recent developments and offering thoughts on future opportunities for biocatalysis. It also critically assesses the technology's limitations, unanswered questions, and unmet challenges.

Investigation of the Catalytic Mechanism of a Soluble N-glycosyltransferase Allows Synthesis of N-glycans at Noncanonical Sequons

The soluble N-glycosyltransferase from Actinobacillus pleuropneumoniae (ApNGT) can establish an N-glycosidic bond at the asparagine residue in the Asn-Xaa-Ser/Thr consensus sequon and is one of the most promising tools for N-glycoprotein production. Here, by integrating computational and experimental strategies, we revealed the molecular mechanism of the substrate recognition and following catalysis of ApNGT. These findings allowed us to pinpoint a key structural motif (215DVYM218) in ApNGT responsible for the peptide substrate recognition. Moreover, Y222 and H371 of ApNGT were found to participate in activating the acceptor Asn. The constructed models were supported by further crystallographic studies and the functional roles of the identified residues were validated by measuring the glycosylation activity of various mutants against a library of synthetic peptides. Intriguingly, with particular mutants, site-selective N-glycosylation of canonical or noncanonical sequons within natural polypeptides from the SARS-CoV-2 spike protein could be achieved, which were used to investigate the biological roles of the N-glycosylation in membrane fusion during virus entry. Our study thus provides in-depth molecular mechanisms underlying the substrate recognition and catalysis for ApNGT, leading to the synthesis of previously unknown chemically defined N-glycoproteins for exploring the biological importance of the N-glycosylation at a specific site.