Biology subjects

Carpenter, E. J.

Publications and source records attributed to Carpenter, E. J..

10 recordsLinked to original sources

bioRxiv2026

Biological recognition of mirror-image glycans

Recent synthesis of essential enzymes, such as DNA and RNA polymerases with opposite chirality, has boosted the feasibility of creating mirror-image life. Such life, if ever produced, will undoubtedly be coated by a dense display of glycans (glycoproteins, glycolipids, polysaccharides) built from enantiomers of common monosaccharides. Recognition of mirror-image glycans by extant glycan-binding proteins (GBPs) may be critical for colonization by or immune response to mirror life organisms. We evaluated recognition of enantiomers of common glycans by a diverse set of purified GBPs (plant and human derived), antibodies (including IgM from human plasma), mammalian cells (including immune cells), and organs in live animals. We found that GBP binding to enantiomers of naturally prevalent glycans is widespread. Notably, L-glucose and L-galactose interact with fucose-binding lectins, including DC-SIGN, a C-type lectin expressed on immune cells. These interactions can be inhibited by soluble "natural" glycan ligands and enantiomeric ones confirming specificity. Binding of L-glycans to diverse immune cell repertoires revealed preferences for specific glycan enantiomers. IgM antibodies from human serum showed donor-specific recognition of L-glycans. We propose that the recognition of L-glycans by extant GBPs arises from their co-evolution over millennia with the L-glycans that are present in the glycocalyx of many microorganisms.

biochemistry↗

bioRxiv2026

Development of Deep-Learning Models that Predict Quantitative Protein-Ligand Interac-tions in Glycobiology as a part of a Capstone Course

Glycans coat the surface of all cells, and every glycan is recognised by specific glycan-binding proteins (GBPs). There are no general tools that can accurately estimate the binding strength between glycan and GBP from the amino acid sequence of the GBP and the molecular structure of the glycan, represented as SMILES string. We describe models for predicting such binding strengths developed as a part of a Capstone Course at the University of Alberta. The models are trained on a dataset that combines BindingDB, a published database of small-molecule protein interactions, and data from glycan arrays measured by Consortium of Functional Glycomics (CFG). In this hybrid dataset of protein-ligand interactions the ligands are both glycans from CFG and small molecules from BindingDB; similarly, proteins include GBP and proteins from BindingDB. Three models are presented (i) ProMax which fuses ESM-2, MolFormer, and MolCLR features; (ii) APEX which constrains learning to a predetermined form, a physical model of binding; (iii) UltraMax adds inter-atomic distances for the ligands. To address the datasets severe long-tail distribution, the models employ tail-aware losses for rare high-binding instances. Trained and evaluated on approximately one million protein-ligand pairs using hold-out splits for unseen molecules, the three models provide a unified framework for quantitative glycan-protein binding prediction. We observed that learning glycan-protein binding is harder than the similar task of learning small-molecule-protein interactions. Simple mirror-inversion tests led us to postulate that insufficient use of chiral features is an important source of difficulty in learning these interactions.

bioinformatics↗

bioRxiv2026

Single-Cell and Spatial Methods for Multimodal Functional Glycan Profiling in Tissues

Glycans regulate multiple physiological processes, including immune recognition and cancer progression. In disease, altered glycan landscapes are interpreted by human lectins. Functional glycan-lectin interactions are difficult to profile because glycans are not genome-encoded and their changes are poorly captured by existing multimodal methods. We present two platforms, single-cell outlining and transcriptome sequencing (scGOAT-seq) and GlycoScope, which use human lectins to enable functional glycan accessibility into single-cell and spatial multiomic measurements. ScGOAT-seq quantifies lectin-accessible glycan states with gene expression, while GlycoScope enables multiplexed in situ co-detection of glycans and proteins in tissues. Applying these approaches to immune cells, we identify stimulus-specific glycan remodeling and show that distinct Siglec-ligand-defined programs stratify immune activation states not captured by traditional methods; in follicular lymphoma, GlycoScope, resolves spatial glycan programs associated with malignant B cells and localized immune microenvironments. The presented methods provide a general framework for integrating functional glycan accessibility into single-cell and spatial multiomics.

molecular biology↗

bioRxiv2025

Evaluation of Multiplexed Liquid Glycan Array (LiGA) for Serological Detection of Glycan-binding Antibodies

We test the performance of the multiplexed liquid glycan array (LiGA) technology in serological assays. Specifically, we use LiGA to detect ABO blood group antibodies in human serum. This LiGA, which we name ABO-LiGA, contains ABO blood group trisaccharide glycans with an ethylazido aglycone conjugated to groups of ten multi-barcoded M13 particles carrying dibenzocyclooctyne (DBCO) on p8 proteins. ELISA clonal binding assays to anti-A/B antibodies confirmed the functional performance of ABO-clones and aligned with next-generation sequencing (NGS) of the mixed clones. Multiple DNA-barcoded technical replicates in LiGA allow for quantification of reproducibility and robustness as determined by the Z-score using NGS. We then tested ABO-LiGA for specific detection of IgG and IgM anti-A and anti-B IgG and IgM antibodies in human serum samples. Comparison of antibody binding responses in sera from 31 healthy donors to ABO-LiGA with an ABO-Luminex-based method revealed consistent responses to LiGA-ABO but also minor deficiencies of ABO-LiGA such as low robustness of the current assay format and a limited ability to detect low intensity antibody responses. Some results point to undesired interactions of serum antibodies with small-footprint glycans conjugated to phage via the bulky DBCO moiety. This report illuminates the path for future development of LiGA-based serological assays and suggests the need to develop alternative methods for conjugating glycans to phage to avoid liabilities of the hydrophobic DBCO moiety.

biochemistry↗

bioRxiv2025

Atom-level Machine of Protein-glycan Interactions and Cross-chiral Recognition in Glycobiology

Cross-chiral recognition in glycobiology is the interactions between biologically conventional proteins and the enantiomers of biological glycans (e.g., L-proteins binding with L-hexoses) from organisms across all kingdoms of life. By symmetry, it also describes the interactions of chirally mirrored proteins with normal D-glycans. Knowledge of cross-chiral recognition is critical to understanding the potential interactions of existing life forms with artificial mirror-life forms, but currently known rules of protein-glycan interaction are insufficient. To build a methodology for learning such interactions, we used machine learned models that can predict binding strength between a set of proteins and glycans represented as graphs of atoms, rather than monosaccharides. Atomic q-gram and Morgan fingerprint (MF) based representation of glycans made it possible to train ML models that predict lectin binding properties of glycans, glycomimetic compounds, and enantiomers of all natural glycans. Critical to this training was merging disparate data--some with relative fluorescence units (RFU) from glycan microarrays and others with Kd values from ITC--using a universal "fraction bound" parameter f at a specific lectin concentration. A fully-connected neural network architecture, MCNet takes a MF and concentration (C) as inputs and returns f for 147 lectins. Performance of MCNet is comparable to the GlyNet models, and by proxy to other state-of-the art models that predict strength of protein-glycan interactions. MCNet effectively predicts binding of glycomimetic compounds to Galectins 1, 3, and 7. Breaking from a monosaccharide-based description makes it possible for MCNet to predict cross-chiral recognition. We employed a Liquid Glycan Array to validate some predictions, such as the lack of interactions of L-mannose with D-mannose binding lectins, purified ConA, and DC-SIGN displayed on cells, and weak binding of L-Man to galactose-binding lectins and L-Glc binding by canonical fucose binding lectins. MCNets atom-level input makes it possible to agglomerate protein-glycan data from diverse glycans across all kingdoms of life and non-glycan structures (e.g. glycomimetic compounds). The universal fraction bound parameter makes it possible to unify disparate quantitative observations (Kd/IC50, RFU, chromatographic retention times, etc.). We believe that such an approach will facilitate a merger of knowledge from diverse glycobiology datasets and predict protein interactions with uncommon/unnatural glycans not attainable from current ML models.

biochemistry↗

bioRxiv2023

Multivalent DNA-encoded lectins on phage enable detecting compositional glycocalyx differences

Selective detection of disease-associated changes in the cellular glycocalyx is a foundation of modern targeted therapies. Detecting minor changes in the density and identity of glycans on the cell surface is a technological challenge exacerbated by lack of 1:1 correspondence between cellular DNA/RNA and glycan structures on cell surface. We demonstrate that multivalent displays of up to 300 lectins on DNA-barcoded M13 phage on a liquid lectin array (LiLA), detects subtle differences in composition and density of glycans on cells ex vivo and in immune cells or organs in animals. For example, constructs displaying 73 copies of diCBM40 lectin per 700x5 nm virion ({varphi}-CBM73) exhibit non-linear ON/OFF-like recognition of sialoglycans on the surface of normal and cancer cells. In contrast, a high-valency {varphi}-CBM290 display, or soluble diCBM40, exhibit canonical progressive scaling in binding with increased epitope density; these constructs cannot amplify the subtle differences detected by {varphi}-CBM73. Similarly, multivalent displays of diCBM40 and Siglec-7 detect differences in the glycocalyx between stem-like and non-stem populations in cancer cells that are not detected with soluble lectins. Multivalent display of lectins on M13 scaffold with protected DNA inside the phage offer non-destructive detection of minor differences in glycocalyx in cells in vitro and in vivo not feasible to currently available technologies.

biochemistry↗

bioRxiv2023

Measuring carbohydrate recognition profile of lectins on live cells using liquid glycan array (LiGA)

Glycans constitute a significant fraction of biomolecular diversity on the surface of cells across all the species in all kingdoms of life. As the structure of glycans is not encoded by the DNA of the host organisms, it is impossible to use cutting-edge DNA technology to study the role of cellular glycosylation or to understand how cell-surface glycome is recognized by glycan-binding proteins (GBPs). To address this gap, we recently described a genetically-encoded liquid glycan array (LiGA) platform that allows profiling of glycan:GBP interactions on the surface of live cells in vitro and in vivo using next-generation sequencing (NGS). LiGA is a library of DNA-barcoded bacteriophages coated with 5-1500 copies of a glycan; the DNA barcode inside each bacteriophage encodes the structure and density of the displayed glycans. Deep sequencing of the glycophages associated with live cells yields a glycan-binding profile of GBPs displayed on the surface of such cells. This protocol provides detailed instructions of using LiGA to probe cell surface receptors and includes information on the preparation of glycophages, analysis by MALDI-TOF MS, the assembly of a LiGA library, and its deep-sequencing. Using the protocol detailed in this report, we measure a glycan-binding profile of the immunomodulatory SiglecLJ1, -2, -6, -7, and -9 expressed on the surface of different cell types and uncover previously unknown environment-dependent recognition of glycans by Siglec-receptors on the surface of live cells. Protocols similar to the one described in this report will make it possible to measure the precise glycan-binding profile of any GPBs displayed on the surface of any cell types.

biochemistry↗

bioRxiv2022

Chemoenzymatic Synthesis of Genetically-Encoded Multivalent Liquid N-glycan Arrays

A hallmark of cellular glycosylation is its chemical complexity and heterogeneity, which can be challenging to capture synthetically. Using chemoenzymatic synthesis on M13 phage, we produce a genetically-encoded liquid glycan array (LiGA) of biantennary complex type N-glycans. Ligation of azido-functionalized sialylglycosyl-asparagine derived from egg yolk to phage functionalized with 50-1000 copies of dibenzocyclooctyne produced divergent intermediate that can be trimmed by glycosidases and extended by glycosyltransferases to yield a library of phages with different N-glycans. Post-reaction analysis by MALDI-TOF MS provided a rigorous approach to confirm N-glycan structure and density, both of which were encoded in the bacteriophage DNA. The binding of this N-glycan LiGA by ten lectins, including CD22 or DC-SIGN expressed on live cells, uncovered an optimal structure/density combination for recognition. Injection of the LiGA into mice identified glycoconjugates with structures and avidity necessary for enrichment in specific organs. This work provides an unprecedented quantitative evaluation of the interaction of complex N-glycans with GBPs in vitro and in vivo.

biochemistry↗

bioRxiv2021

GlyNet: A Multi-Task Neural Network for Predicting Protein-Glycan Interactions

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNets output of continuous values provides more detailed results than classification models. Such continuous outputs are easily converted by a following classifier, and in this form GlyNet outperforms reported classifiers. GlyNet is the first multi-output regression model for protein-glycan interactions and will serve as an important benchmark, facilitating development of quantitative computational glycobiology.

bioinformatics↗

bioRxiv2020

Genetically Encoded, Multivalent Liquid Glycan Array (LiGA)

The Central Dogma of Biology does not allow for the study of glycans using DNA sequencing. We report a "Liquid Glycan Array" (LiGA) platform comprising a library of DNA barcoded M13 virions that display 30-1500 copies of glycans per phage. A LiGA is synthesized by acylation of phage pVIII protein with a dibenzocyclooctyne, followed by ligation of azido-modified glycans. Pulldown of the LiGA with lectins followed by deep sequencing of the barcodes in the bound phage decodes the optimal structure and density of the recognized glycans. The LiGA is target agnostic and can measure the glycan-binding profile of lectins such as CD22 on cells in vitro and immune cells in a live mouse. From a mixture of multivalent glycan probes, LiGAs identifies the glycoconjugates with optimal avidity necessary for binding to lectins on living cells in vitro and in vivo; measurements that cannot be performed with canonical glass slide-based glycan arrays. DedicationThe paper is dedicated to Laura L. Kiessling on the occasion of her 60th birthday.

biochemistry↗