Biology subjects

Shor, B.

Publications and source records attributed to Shor, B..

4 recordsLinked to original sources

bioRxiv2026

Inter-domain flexibility in F-Box proteins revealed by integrative modeling

F-box proteins, the substrate-recognition modules of the SCF (Skp1-Cul1-F-box) ubiquitin ligase complex, play a central role in targeted protein degradation by enabling the selective recruitment of diverse substrates. F-box proteins share a common architecture with a conserved anchor domain linked to the substrate-binding domain. Despite extensive structural characterization of F-box proteins, their potential conformational dynamics remain poorly understood. Here, we employ an integrative modeling approach combining cryo-EM structures, cross-linking mass spectrometry (XL-MS), and deep learning-based conformational ensemble prediction to profile the accessible conformations of F-box proteins. Our analyses reveal a significant inter-domain flexibility in FBXL2 and FBXL17 that enables sampling of a broad range of conformations within assembled SCF and CSN-SCF complexes. By contrast, other F-box proteins, including FBXL1 and FBXO22, exhibit intrinsic flexibility but are restricted to a considerably narrower conformational landscape. Together, our work highlights inter-domain plasticity as a tunable architectural feature of F-box proteins, which may shape their functional versatility within SCF complexes.

molecular biology↗

bioRxiv2025

Orthosteric Molecular Glue Inhibits COP9 Signalosome with Substrate-Dependent Potency

Orthosteric inhibitors block enzyme active sites and prevent substrates from binding. Enhancing their specificity through substrate dependence seems inherently unlikely, as their mechanism hinges on direct competition rather than selective recognition. Here, we show that a molecular glue mechanism unexpectedly imparts substrate-dependent potency to CSN5i-3, an orthosteric inhibitor of the COP9 signalosome (CSN). We first confirm that CSN5i-3 inhibits CSN, which catalyzes NEDD8 deconjugation from the cullin-RING ubiquitin ligases (CRLs), by occupying the active site of its catalytic subunit, CSN5, and directly competing with the iso-peptide bond substrate. Curiously, the orthosteric inhibitor binds free CSN with only micromolar affinity, yet achieves nanomolar potency in blocking its deneddylase activity. Cryo-EM structures of the enzyme-substrate-inhibitor complex reveal that active site-engaged CSN5i-3 occludes the substrate iso-peptide linkage while simultaneously extending an NEDD8-binding exosite of CSN5, acting as a molecular glue to cement the NEDD8-CSN5 interaction. The cooperativity of this tri-molecular CSN5i-3-NEDD8-CSN5 assembly, in turn, sequesters CSN5i-3 at its binding site, conferring high potency to the orthosteric inhibitor despite its low affinity for the free enzyme. Together, our findings highlight the modest affinity requirements of molecule glues for individual target proteins and establish "orthosteric molecular glue inhibitors" as a new class of substrate-dependent enzyme antagonists.

molecular biology↗

bioRxiv2024

DockFormer: Efficient Multi-Modal Receptor-Ligand Interaction Prediction using Pair Transformer

Protein-small molecule interactions, or receptor-ligand interactions, are essential for understanding biological processes and advancing drug design. Despite advancements, existing prediction models of these interactions still lack capabilities and accuracy needed to replace traditional screening. In this paper, we introduce DockFormer, a method that leverages multi-modal learning to predict both the binding affinity and structure of these interactions. DockFormer employs fully flexible docking, where no part of the receptor remains rigid, by adapting the AlphaFold2 architecture. Instead of relying on protein sequences and Multiple Sequence Alignments, DockFormer uses predicted receptor structures as input. This modification enables the model to concentrate on ligand docking prediction rather than protein folding, while preserving full receptor flexibility. The stream-lined design also reduces the model size to just 8 layers, compared to AlphaFold2s 48 layers, greatly accelerating the inference process and making it more efficient for large-scale screening. When evaluated on affinity benchmarks such as CASF-2016, PLINDER, and the recently released CASP16 ligand screening benchmark, DockFormer performs comparably to or better than state-of-the-art methods, which typically rely on templates or bound structures as input. On structural benchmarks such as Posebusters and PLINDER, DockFormer demonstrated success rate of 20% and 15%, respectively.

bioinformatics↗

bioRxiv2023

Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFolds high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

bioinformatics↗