Ora Schueler‐Furman

The paper validates and analyzes 12,000 AI-designed CAR-T binders from a global competition, identifying successful designs and factors improving their performance.

The paper maps over 20,000 human protein-protein interactions mediated by short linear motifs using a peptide library screening approach.

This study identifies dihydropyrimidines (DHPs) as signaling metabolites that sustain aggressive cancer states by stabilizing DPYSL2, linking metabolic activity to oncogenic signaling.

PatchMAN2 improves peptide-protein docking efficiency by filtering fragments and focusing sampling on relevant regions, reducing computational cost while maintaining accuracy.

CLIPepPI predicts domain-peptide specificity using contrastive learning, achieving competitive performance across benchmarks and enabling proteome-wide analysis.

The paper systematically identifies SLiM-binding interactions of USP family auxiliary domains using proteomic and biochemical methods, revealing functional redundancy and motif specificity.

The paper investigates how the SCFDas1 ubiquitin ligase recognizes C-terminal degrons, identifying a conserved binding site and key residues involved in substrate recognition.

This paper identifies a chaperonin complex in malaria parasites that regulates apicoplast proteostasis and shows its potential as an antimalarial drug target.

This study identifies Gid11 as a substrate receptor for the GID ubiquitin ligase in budding yeast, revealing its role in regulating metabolic transitions by targeting glycolytic factors during ethanol metabolism.

The paper investigates how signal peptides (SPs) can modulate the structure and function of human splice isoforms by stabilizing truncated domains through complementation.

This study shows that SARS-CoV-2 nucleocapsid protein binds to uninfected cells, triggering antibody-mediated complement activation in COVID-19 patients.

The paper identifies conserved mechanisms by which PP2A-B55 substrates bind B55 through α-helices and develops a competitive inhibitor to study its function.

This study computationally designs and validates peptides that inhibit key inflammatory cytokines in IBD, showing reduced cytokine release in vitro.

The paper evaluates AlphaFold-Multimer for predicting protein interaction interfaces using short linear motifs and validates some predictions experimentally.

The paper trains a protein language model to predict protein quaternary states from sequences, showing it can distinguish multimers from monomers but performs worse than structure-based methods.

PatchMAN models peptide-protein interactions by aligning structural motifs on the receptor surface, achieving high accuracy in docking without using sequence information.

The paper predicts HDAC6 substrates using a structure-based method calibrated by enzymatic assays, identifies new substrates, and highlights structural determinants of HDAC6 promiscuity.

The paper applies AlphaFold2 to model peptide-protein interactions using a poly glycine linker, showing accurate predictions without sequence alignment.

The paper presents a framework for ensuring reproducibility in bio-macromolecular modeling through automated benchmarks and a test server, demonstrating its utility for Rosetta and other tools.

The paper introduces Sylites, fluorogenic probes for visualizing inhibitory synapses with high contrast and selectivity, enabling precise nanoscopic measurements and 3D distribution analysis in brain tissue.

The paper describes a workflow to refine protein-peptide complex structures from C-alpha models to high-resolution all-atom models using MODELLER and RosettaFlexPepDock.

The paper develops a high-resolution peptide-protein docking method called PIPER-FlexPepDock, validated on known complexes to show accuracy and efficiency.