Biology subjects

Derda, R.

Publications and source records attributed to Derda, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Genetically-encoded discovery and development of peptide-macrocycle imaging agents for PD-L1

The unique cell surface composition of tumor cells forms the molecular basis for many targeting and cell-based therapies. Here, we describe the development of novel peptide-based targeting agents for programmed death ligand 1 (PD-L1). Molecular imaging by peptide agents, coupled with therapeutic intervention using the same modality, represents a critical advancement in cancer management. Whole-body PET imaging of PD-L1 expression offers a superior alternative to traditional immuno-histochemistry, making PD-L1 radiodiagnostic imaging a highly sought-after modality. PD-L1 targeting modalities developed for clinical imaging to date can be divided into antibodies, protein domains, and small macrocyclic peptides with fewer than 20 amino acids. The latter modalities can address many challenges seen in antibody-based targeting vectors. All potent PD-L1 targeting peptide modalities reported to date rely extensively on non-canonical amino acids (ncAAs). Here, we report a comprehensive structure-activity relationship (SAR) analysis of a family of macrocycles discovered from an Sx2Cx8Cx2 phage-display library composed entirely of natural amino acids (x represents 19 natural amino acids excluding Cys). Using >10,000 variants in ''focused'' phage-display libraries, we optimized these macrocycles to achieve single-digit-nanomolar potency in protein- and cell-based assays. En route to this optimization, the activity of 216 synthetic macrocycles towards PD-L1 was measured in five distinct assays; two leads have been evaluated by imaging in tumor xenografts in mice, and the X-ray structure of one advanced lead in complex with PD-L1 has been determined at 2.78 [A] resolution. This publication demonstrates the development potential of PD-L1-targeting macrocycles that do not require extensive incorporation of ncAAs and the democratization of discovery by mapping the optimization path to single-digit-nanomolar assets for targeted radiopharmaceuticals via canonical phage-display technology.

pharmacology and toxicology↗

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

Mapping Glycan Binding Profiles of the Gut Bacteria using Liquid Glycan Array (LiGA)

Glycan-microbe interactions are central to gut colonization and host-microbiota communication. Here, we apply a DNA-encoded Liquid Glycan Array (LiGA) to quantify interactions between live gut bacteria and multivalent natural or mirror glycans. LiGA comprises glycosylated M13 bacteriophage bearing silent DNA barcodes that encode glycan identity and density. Using LiGA, we profiled glycan binding across 16 Limosilactobacillus reuteri strains isolated from murine, porcine, poultry, and human hosts, and then extended the approach to profile glycan binding of taxonomically diverse bacteria from three phyla Bacillota, Bacteroidota, Pseudomonadota consisting of nine different species. Recent discussion of mirror-image microorganisms raise a question whether these microorganisms could interact with present-day life by engaging naturally chiral glycans. We demonstrated that this question can be assessed by testing the binding of mirror-image glycans to natural bacteria. Evaluation of enantiomers of common glycan revealed cross-chiral recognition by Escherichia coli and L. reuteri, indicating that these bacteria can used mirror-image glycans to engage for adhesion and potential colonization. By symmetry the same arguments extends to mirror microorganisms and glycans of naturally chirality. We established that LiGA enables efficient characterization of bacterial glycan binding and provides new insights into intestinal microbial ecology.

biochemistry↗

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↗

bioRxiv2026

Universal Baseline for in vitro Selection of Genetically Encoded Libraries

Genetically encoded (GE) libraries enable identification of high-affinity ligands for diverse molecular targets through iterative in vitro selection and DNA sequencing or next-generation sequencing (NGS). Despite their impact in therapeutic development, a systematic framework for evaluating reproducibility in GE-molecular discoveries remains limited. To aid such analysis, we introduce the concept of baseline response, which reproducibly partitions active and inactive members of in vitro selection. The baseline response is provided by spiking a random DNA-barcoded population. We calibrated the baseline concept using Bioconductor EdgeR differential enrichment (DE) analysis of NGS of phage-displayed selection on oligosaccharide chitin and hepatitis virus NS3a* protease as model targets. We further show that mixing discovery campaigns also offers an effective baseline: chitin-enriched peptides serve as a baseline for DE-analysis of NS3a* selection and NS3a*-enriched peptides serve as a baseline for chitin binders. We applied baseline-stratified DE-analysis to 66 parallel selections performed in 3-5 replicates across 22 extracellular targets, including HER1-3, EpCAM, CAIX, PD-L1, and eight integrin receptors. Automated DE-analysis across hundreds of NGS files produced hits validated in a secondary screen and yielded synthetic macrocyclic ligands with mid-nanomolar affinity confirmed in 2-3 biophysical assays. For PD-L1, we further demonstrated how baseline-calibrated NGS data provide decision-enabling information for optimization of peptide macrocycles to yield potent single-digit nanomolar ligands for the cell-surface receptor. We anticipate that baseline-based analyses of NGS data from in vitro selection procedures will offer a scalable framework for reproducible hit discovery and standardized analysis across diverse in vitro selection campaigns. Significance StatementGenetically encoded selection technologies such as phage, mRNA and ribosome display, have produced FDA-approved therapeutics and numerous clinical candidates. Yet reproducibility in such in vitro discovery systems is rarely evaluated against a defined experimental baseline. Here, we establish a universal baseline by spiking unrelated, DNA-barcoded peptide sequences into selection libraries and quantifying their binding alongside target-enriched populations. This composition-agnostic strategy enables rigorous normalization, confidence assessment, and cross-target comparison of molecular discovery outcomes. Our framework introduces practical standards for reproducibility and statistical benchmarking across genetically encoded display platforms.

biochemistry↗

bioRxiv2025

Non-Carbohydrate Inhibitors of Sialic Acid-binding Immunomodulatory-type Lectin-7 (Siglec-7) Discovered from Genetically Encoded Bicyclic Peptide Libraries

Glycan-binding proteins (GBP) are among the most difficult to drug targets. This deficiency delays clinical progress for therapeutically important GBPs. We employed bicyclic genetically encoded libraries (BiGELs), produced by chemical modification of phage-displayed libraries of peptides with two-fold symmetric linchpins, to discover inhibitors of therapeutically relevant Siglec-7:GD3 interactions. Next-generation sequencing (NGS) analysis of panning of BiGEL against Siglec-7 yielded 815 candidates from which 23 hits yielded KD = 1-100 {micro}M as determined by surface plasmon resonance (SPR). Competitive enzyme-linked immunosorbent assays (ELISA) identified a subset of leads that disrupted the Siglec-7:GD3 interaction with IC50= 3-300 {micro}M. Machine learning models trained on NGS datasets identified additional inhibitors with equivalent potency. Alanine scans of 8c (SWCRPATVNC, IC50 = 3.8 {micro}M) and 12c (SFCHYPTHVC, IC50= 11 {micro}M), identified key residues as crucial for activity. Ring reshaping studies of compound 8c highlighted the critical role of bicyclic topology produced by analogue 46e (SAAAAAWCRPATVNC, IC50= 9.5 {micro}M). Multivalent display of the lead bicycles alongside [~]100 glycans in Liquid glycan Array (LiGA), made it possible to compare the binding of bicycles and glycans to Siglec-7 expressed on CHO, Jurkat, and Raji cells. LiGA assays confirmed binding of the bicycles to Siglec-7 but revealed considerable non-specific interactions with receptor-negative cells. Saturation transfer difference nuclear magnetic resonance (STD-NMR) revealed 46e binds to Siglec-7 at a site distinct from the V-Ig domain, suggesting it might inhibit binding of glycans to the glycan-binding site of Siglec-7 via an allosteric site. Together these results demonstrate that BiGEL enables the discovery of bicyclic peptides for undruggable Siglec targets but highlights future challenges in molecular discoveries that aim to identify small, non-carbohydrate inhibitors of GBPs.

biochemistry↗

bioRxiv2025

A Two-Step Synthesis of Covalent Genetically-Encoded Libraries of Peptide-Derived Macrocycles (cGELs) enables use of electrophiles with diverse reactivity

Genetically-encoded libraries of peptide-derived macrocycles containing electrophile warheads (cGELs) can be used to identify potent and selective covalent ligands for protein targets. Such cGELs are synthesized either by incorporation of unnatural amino acids that display mild electrophiles on their side chains or by chemical post-translational modification (cPTM) of mRNA or phage-displayed peptide libraries. Here we investigate fundamental barriers to the synthesis of cGELs. We observe that a previously reported cPTM that proceeds in neutral-to-basic conditions creates mixtures of regioisomers. The complexity of the resulting mixture scales with the electrophilicity of the warhead used in the linker, with some electrophiles being not suitable for use under basic conditions. In contrast, use of a Knorr-pyrazole cPTM enables attachment of electrophiles in acidic pH, thus preventing unwanted reactions with nucleophilic sidechains. The Electrophile is activated only upon mixture with the desired protein target in neutral pH. We use this approach to generate a cGEL with alkyne-bearing macrocycles and use it to identify covalent macrocyclic ligands for pyruvate kinase 2 (PKM2). Our results suggest that construction of cGELs should be performed in conditions that silence the electrophiles (e.g., acidic environment) to prevent unwanted side reactions. In addition to the Knorr-pyrazole method, many other biocompatible bond-forming processes that proceed in mildly acidic pH are likely to be equally effective in constructing cGELs.

biochemistry↗

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

An ultrasensitive and modular platform to detect Siglec ligands and control immune cell function

Siglecs are immunomodulatory receptors that regulate immune cell function. A fundamental challenge in studying Siglec-ligand interactions is the low affinity of Siglecs for their ligands. Inspired by how nature uses multivalency, we developed Siglec-liposomes as a highly multivalent and versatile platform for detecting Siglec glycan ligands in which recombinant Siglecs were conjugated to liposomes using the SpyCatcher-SpyTag system. Siglec-liposomes offer tunable multivalency and a modular assembly, enabling presentation of different Siglecs on the same liposome. Using Siglec-liposomes, we profiled Siglec ligands on human leukocytes, revealing new insights into Siglec ligands. Moreover, Siglec-liposomes are in vivo compatible, where we demonstrated that Siglec-7-liposomes bind to the brain vasculature in a mucin-dependent manner. Given the abundance of Siglec ligands on T cells, we investigated whether Siglec-liposomes modulate T cell function and find that Siglec-7-liposomes increase T cell proliferation in a ST3Gal1-dependent and CD43-independent manner. Taken together, Siglec-liposomes are a versatile and sensitive tool for detecting Siglec ligands and immunomodulation.

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↗

bioRxiv2023

Phage display uncovers a sequence motif that drives polypeptide binding to a conserved regulatory exosite of O-GlcNAc transferase

The modification of nucleocytoplasmic proteins by O-linked N-acetylglucosamine (O-GlcNAc) is an important regulator of cell physiology. O-GlcNAc is installed on over a thousand proteins by just one enzyme, O-GlcNAc transferase (OGT). How OGT is therefore regulated is therefore a topic of interest. To gain insight into these questions, we used OGT to perform phage display selection from an unbiased library of [~]108 peptides of 15 amino acids in length. Following rounds of selection and deep mutational panning we identified a high-fidelity peptide consensus sequence, [Y/F]-x-P-x-Y-x-[I/M/F], that drives peptide binding to OGT. Peptides containing this sequence bind to OGT in the high nanomolar to low micromolar range and inhibit OGT in a non-competitive manner with low micromolar potencies. X-ray structural analyses of OGT in complex with a peptide containing this motif surprisingly revealed binding to an exosite proximal to the active site of OGT. This structure defines the detailed molecular basis driving peptide binding and explains the need for specific residues within the sequence motif. Analysis of the human proteome revealed this motif within 52 nuclear and cytoplasmic proteins. Collectively, these data suggest an unprecedented mode of regulation of OGT by which polypeptides can bind to this exosite to cause allosteric inhibition of OGT through steric occlusion of its active site. We expect these insights will drive improved understanding of the regulation of OGT within cells and enable the development of new chemical tools to exert fine control over OGT activity. SIGNIFICANCE STATEMENTThousands of proteins within humans are modified by the monosaccharide N-acetylglucosamine (O-GlcNAc). O-GlcNAc regulates cellular physiology and is being pursued to create therapeutics. Remarkably, only one enzyme, O-GlcNAc transferase (OGT), installs O-GlcNAc and its regulation is poorly understood. By affinity selection using a vast peptide library, we uncover an amino acid sequence motif that drives binding of polypeptides to OGT. An OGT-peptide complex shows how this motif binds to an allosteric site proximal to the active site and inhibits OGT in an unprecedented manner. Given the distribution of this sequence motif within the human proteome proteins containing this motif likely regulate the activity of OGT, outlining a new mode by which OGT is controlled and opening new avenues for research.

biochemistry↗

bioRxiv2022

Genetically-Encoded Discovery of Perfluoroaryl-Macrocycles that Bind to Albumin and Exhibit Extended Circulation in-vivo

In this paper, we report selection of albumin-binding macrocyclic peptides from genetically encoded libraries of peptides modified by perfluoroaryl-cysteine SNAr chemistry. Modification of phage-displayed libraries SXCXnC-phage, n=3-5, where X is any amino acid except for cysteine by decafluoro-diphenylsulfone (DFS), yields genetically-encoded library of octafluoro-diphen-ylsulfone-crosslinked macrocycles (OFS-SXCXnC-phage). Selection from these libraries using albumin as a bait identified a family of significantly enriched perfluoroaryl-macrocycles. Synthesis of perfluoroaryl-macrocycles predicted by phage display and testing their binding properties by 19F NMR and fluorescent polarization identified OFS-macrocycle with SICRFFC sequence as the most potent albumin binder. We observed that OFS-macrocycles slowly react with biological nucleophiles such as glutathione. Replacing decafluoro-diphenylsulfone by nearly isosteric pentafluorophenyl sulfide yielded perfluorophenylsulfide (PFS)-crosslinked macrocycles devoid of undesired reactivity. The augmented lead PFS-macrocycle with SICRFFC sequence exhibited KD = 4-6 M towards human serum albumin and similar affinities towards rat and mouse albumins. When injected in mouse, the PFS-SICRFFCGGG compound was significantly retained in circulation in vivo when compared to control PFS-macrocyclic peptide. The perfluoroaryl-macrocycles with SICRFFC motif are the smallest known peptide macrocycle with significant affinity for human albumin and they are a productive starting point for future development of compact macrocycles with predictable circulation half-life in vivo. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/504611v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@cd54d2org.highwire.dtl.DTLVardef@7bd9aborg.highwire.dtl.DTLVardef@10f7515org.highwire.dtl.DTLVardef@1cfc49a_HPS_FORMAT_FIGEXP M_FIG C_FIG

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

Reproducible Discovery of Cell-Binding Peptides "Lost" in Bulk Amplification via Emulsion Amplification in Phage Display Panning

Many pharmaceutically-relevant cell surface receptors are functional only in the context of intact cells. Phage display, while being a powerful method for the discovery of ligands for purified proteins often fails to identify a diverse set of ligands to receptors on a cell membrane mosaic. To understand this deficiency, we examined growth bias in naive phage display libraries and observed that it fundamentally changes selection outcomes: The presence of growth-biased (parasite) phage clones in a phage library is detrimental to selection and cell-based panning of such biased libraries is poised to yield ligands from within a small parasite population. Importantly, amplification of phage libraries in water-oil emulsions suppressed the amplification of parasites and steered the selection of biased phage libraries away from parasite population. Attenuation of the growth bias through the use of emulsion amplification reproducibly discovers the ligands for cell-surface receptors that cannot be identified in screen that use conventional bulk amplification. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/466683v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1ed14c5org.highwire.dtl.DTLVardef@1eec8deorg.highwire.dtl.DTLVardef@1c5741forg.highwire.dtl.DTLVardef@4aa542_HPS_FORMAT_FIGEXP M_FIG Find Ligands in Droplets Canonical phage display selection of ligands for breast cancer cells, which uses bulk amplification (BA) of phage library, reproducibly identified peptide ligands from a ~0.0001% sub-population of the library, which harbors fast-growing phage. Replacing BA by emulsion-amplification (EmA) altered the selection landscape and yielded cell-binding ligands not accessible to conventional phage-display select C_FIG

bioengineering↗

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↗