Affinity Peptide-Driven Precision Glycan Analysis: From Targeted Capture to Single-Molecule Profiling

Glycans play pivotal roles in cell-cell communication, immune recognition, signal transduction, and pathogen-host interactions. Their versatile functions stem from extraordinary structural diversity, defined by variations in composition, linkage, configuration, and branching. Such structural complexity poses substantial challenges for comprehensive glycan analysis. Complicating matters further, many glycans exist only in trace amounts in biological systems, and their nontemplate-driven biosynthesis precludes amplification. This dual constraint further intensifies the analytical difficulties. Thus, the ability to isolate and structurally determine glycans with high efficiency has become paramount for advancing glycoscience. Given the structural diversity of glycans, biological systems have evolved a repertoire of glycan-binding proteins to decode these complex structures. Lectins represent a prime example; renowned for their specific binding to glycans, they have been used to profile glycans in the form of affinity reagents. Inspired by lectins, we turned to the rational design of glycan-affinity peptides (GAPs) that mimic lectin function. Over the past decade, our group has been dedicated to the development of GAPs, driving their evolution from simple binders to high-specificity, high-affinity recognition elements. These efforts have advanced GAPs from single amino acids and dipeptides to oligopeptides, accompanied by a remarkable affinity enhancement from millimolar to nanomolar levels. In parallel, the design paradigm has shifted from empirical trial-and-error to directed evolution and now to preliminary computational design. As a result, these GAPs have evolved into versatile precision recognition engines powering two complementary analytical frontiers: first, they underpin robust affinity materials for the precision capture of glycosylated targets, including lipopolysaccharides and circulating tumor cells; second, they enable high-resolution nanopore sensors capable of precision identification of specific glycans and glycosides. Overall, it is the evolution of GAPs into reliable recognition platforms that represents a significant advance in glycan analytical capability, moving from broad-spectrum enrichment toward targeted capture, and from bulk measurements toward single-feature resolution. In this Account, we summarize our efforts in GAP design and applications, presenting a unified narrative of how we engineered these affinity engines to decipher the glycan code with high precision. We begin with an overview of the fundamental principles, design strategies, and future trends underpinning GAP evolution. Following this, we detail our advances in GAP-driven precision glycan analyses. We believe this Account will drive the development of a new generation of GAPs and facilitate their broader implementation across glycoscience.