Ciambur, C. B. | Search bioRxiv

bioRxiv2026

Structure-guided generative design of peptides targeting the FtsQBL divisome complex inhibit Escherichia coli cell division.

The discovery of antibiotics targeting Gram-negative bacteria remains limited in part by the difficulty of pharmacologically modulating protein-protein interactions essential for bacterial physiology. The divisome complex formed by FtsQ, FtsB and FtsL represents an attractive but challenging target, as its assembly relies on {beta}-strand-mediated interface interactions within the bacterial periplasm. Here, we combined interpretable interface mapping using InDeep with hotspot-constrained RFdiffusion design to generate peptides targeting the FtsB-binding site of Escherichia coli FtsQ. The designed peptides mimic the native {beta}-augmentation interaction and selectively engage the FtsQ interface both in bacterial cells and in vitro. X-ray crystallography of one of these peptides in complex with FtsQ reveals that it accurately adopts the native binding geometry while introducing additional stabilizing interactions within a hydrophobic pocket. Complementary NMR analyses further show that optimized peptides adopt pre-organized {beta}-hairpin conformations in solution consistent with the bound state. Several of these peptides disrupt bacterial cell division and inhibit growth in an E. coli strain exhibiting increased outer membrane permeability. Together, these results establish a structurally validated framework in which predictive interface analysis and generative design can be combined to target cooperative protein-protein interfaces in bacteria and provide a foundation for the development of divisome-targeting antibacterial strategies.

microbiology↗

bioRxiv2026

A pocket-centric framework for selective targeting of amyloid fibril polymorphs

The rapid expansion of high-resolution cryo-EM structures of amyloid fibrils has transformed our understanding of fibril polymorphism, yet it has not been matched by comparable progress in the rational development of protein-selective or polymorph-specific amyloid ligands. One possible explanation is that ligand selectivity is governed not only by global fibril folds, but also by local surface pockets accessible to small molecules. Here, we present a systematic analysis of 400 cryo-EM structures of amyloid-{beta}, tau, and -synuclein fibrils. Using a unified pocket similarity index and minimum spanning tree representations, we construct global and protein-specific graph representations of the amyloid binding pocket space, and examine how surface cavities are distributed across proteins, polymorphs, and structural contexts. We find that many detectable pockets are shared across multiple fibrillar folds and, in several cases, across distinct amyloid-forming proteins, suggesting that pocket-level convergence may contribute to the limited selectivity of amyloid-directed ligands. Conversely, only a restricted subset of pockets occupies isolated regions of pocket similarity space, defining rare structural opportunities for protein-selective or polymorph-restricted targeting. Analysis of structures of extracted fibrils further shows that disease-derived fibril pockets do not form a completely isolated pocketome subset, but can resemble pockets observed in selected in vitro polymorphs. Together, these results reframe amyloid ligand development as a problem of pocket-level discriminability within a constrained fibril landscape, and provide a structural framework for identifying promising binding sites while avoiding intrinsically non-discriminatory pockets. Significance StatementDespite major advances in cryo-EM structure determination of amyloid fibrils, the development of selective ligands for amyloid assemblies remains challenging. By systematically comparing surface binding pockets across 400 amyloid-{beta}, tau, and -synuclein fibrillar structures, we show that many ligand-accessible cavities exhibit similar geometric and physicochemical properties across fibrillar polymorphs made of distinct proteins. This pocket-level convergence provides a structural basis for understanding why many amyloid ligands exhibit broad binding profiles, while also identifying rare pockets that are sufficiently isolated to support more selective targeting strategies. Our work establishes a pocket-centric framework for interpreting amyloid ligand selectivity and for prioritizing fibril binding sites in imaging and therapeutic ligand development.

bioinformatics↗