Ratmir Derda

This paper develops a two-step synthesis method for covalent genetically-encoded libraries of peptide-derived macrocycles (cGELs) that enables the use of electrophiles with diverse reactivity by controlling reaction conditions.

This paper develops PD-L1-targeting macrocycle imaging agents using phage-display technology without non-canonical amino acids, achieving single-digit-nanomolar potency and structural characterization.

This paper discovers non-carbohydrate inhibitors of Siglec-7:GD3 interactions using BiGEL libraries and validates their potency through SPR, ELISA, and NMR.

The paper evaluates how glycan-binding proteins and immune cells recognize mirror-image glycans, finding widespread binding and donor-specific recognition of L-glycans.

The paper develops deep-learning models to predict glycan-protein binding strengths using hybrid datasets and tail-aware losses, finding that glycan interactions are harder than small-molecule interactions due to chiral feature limitations.

The paper uses LiGA to profile glycan binding of gut bacteria, revealing cross-chiral recognition between natural bacteria and mirror-image glycans.

The paper introduces two methods to profile functional glycan-lectin interactions in tissues using single-cell and spatial multiomic approaches, revealing stimulus-specific glycan remodeling and spatial glycan programs in immune cells and follicular lymphoma.

The paper introduces a universal baseline method to improve reproducibility in in vitro selection of genetically encoded libraries using NGS data.

This paper develops MCNet, a machine learning model that predicts protein-glycan binding strength using atom-level representations, enabling cross-chiral recognition and unifying diverse glycobiology datasets.

The paper develops Siglec-liposomes to detect Siglec ligands and modulate immune cell function, showing they enhance T cell proliferation.

This paper evaluates the multiplexed liquid glycan array (LiGA) for detecting ABO blood group antibodies in human serum, identifying its strengths and limitations.

GlyNet predicts protein-glycan interactions with continuous relative binding strengths, outperforming classifiers and serving as a benchmark for quantitative glycobiology.

This paper develops a DNA-encoded method to display multivalent proteins on phage and demonstrates its use to study in vivo clearance rates of L-asparaginase.