Most human extracellular proteins are post-translationally modified by N-linked glycans attached to Asn, and O- linked glycans attached to Ser/Thr. Such glycans control or fine-tune a number of biological processes including cell growth, differentiation, cell adhesion, and signaling. As a result, changes in glycosylation are also associated with mammalian pathophysiological processes like tumor metastasis, host-pathogen recognition, inflammation etc. An important impediment to understanding the role of protein-carbohydrate interactions in human health and disease is the lack of a streamlined technology to rapidly and accurately characterize glycans in arbitrary cell/ tissue systems. Carbohydrate binding lectins are commonly used to characterize cell-surface glycans, but the binding specificity and affinity of natural plant and animal lectins is poor. There has also been some success in developing novel glycan binding proteins (GBPs) by engineering, for example, sialidases in order to recognize sialic acid containing glycans, but these reagents typically only bind terminal residues with less specificity for the glycan backbone. In this proposal, we describe an alternative approach to engineering GBPs starting with glycosyltransferases (glycoT), particularly with a focus on the sialyltransferase (ST) family. We hypothesize that engineering this class of enzymes may enable specific detection of larger glycan structures with high specificity. In this regard, STs catalyze stereo and regiospecific sialylation of distinct glycan acceptors, suggesting that their engineering may yield sialoglycan binding proteins (SiaBP) recognizing both the sialic acid and the acceptor substrate. Thus, SiaBPs may have unique binding specificity that is not recapitulated by traditional lectins or engineered glycosidases. To test this concept, in Aim 1, we perform protein engineering on three different human sialyltransferases to generate three SiaBPs that recognize specific carbohydrate epitopes with high affinity and specificity. These include: ST3Gal-I mutants to recognize Neu5Ac(2-3)Gal(β1-3)GalNAcα; ST6Gal-I mutants to recognize Neu5Ac(2-6)Gal(β1-4)GlcNAcβ; and ST8Sia3 mutants to engage poly sialic acids. We will model the ligand-bound enzyme structures through computational docking and rationally design the mutations to improve binding specificity. We will also perform molecular dynamics (MD) simulation of apo-enzymes and analyze the simulated structures to identify low frequency, collective motions (principal component analysis). The analysis will enable us to introduce mutations to bias the enzyme conformation to one that favors product binding. The rationally designed mutants will be further refined using directed evolution. In Aim 2, purified SiaBPs will be characterized using glycan arrays that bear various sialoglycans. We will additionally assay the binding of SiaBPs to isogenic HEK293T clones and CRISPR-Cas9 KO cell libraries that either contain or have del...