In this study, we examined the anti-Helicobactor pylori effects of the main protoberberine-type alkaloids in Rhizoma Coptidis. Coptisine exerted varying antibacterial and bactericidal effects against three standard H. pylori strains and eleven clinical isolates, including four drug-resistant strains, with minimum inhibitory concentrations ranging from 25 to 50 μg/mL and minimal bactericidal concentrations ranging from 37.5 to 125 μg/mL. Coptisine’s anti-H. pylori effects derived from specific inhibition of urease in vivo. In vitro, coptisine inactivated urease in a concentration-dependent manner through slow-binding inhibition and involved binding to the urease active site sulfhydryl group. Coptisine inhibition of H. pylori urease (HPU) was mixed type, while inhibition of jack bean urease was non-competitive. Importantly, coptisine also inhibited HPU by binding to its nickel metallocentre. Besides, coptisine interfered with urease maturation by inhibiting activity of prototypical urease accessory protein UreG and formation of UreG dimers and by promoting dissociation of nickel from UreG dimers. These findings demonstrate that coptisine inhibits urease activity by targeting its active site and inhibiting its maturation, thereby effectively inhibiting H. pylori. Coptisine may thus be an effective anti-H. pylori agent.

Modeling the binding pose of an antibody is a prerequisite to structure-based affinity maturation and design. Without knowing a reliable binding pose, the subsequent structural simulation is largely futile. In this study, we have developed a method of machine learning-guided re-ranking of antigen binding poses of nanobodies, the single-domain antibody which has drawn much interest recently in antibody drug development. We performed a large-scale self-docking experiment of nanobody–antigen complexes. By training a decision tree classifier through mapping a feature set consisting of energy, contact and interface property descriptors to a measure of their docking quality of the refined poses, significant improvement in the median ranking of native-like nanobody poses by was achieved eightfold compared with ClusPro and an established deep 3D CNN classifier of native protein–protein interaction. We further interpreted our model by identifying features that showed relatively important contributions to the prediction performance. This study demonstrated a useful method in improving our current ability in pose prediction of nanobodies.

No abstract

The control of cell movement through manipulation of cytoskeletal structure has therapeutic prospects notably in the development of novel anti-metastatic drugs. In this study, we determine the structure of Ras-binding domain (RBD) of ELMO1, a protein involved in cytoskeletal regulation, both alone and in complex with the activator RhoG and verify its targetability through computational nanobody design. Using our dock-and-design approach optimized with native-like initial pose selection, we obtain Nb01, a detectable binder from scratch in the first-round design. An affinity maturation step guided by structure-activity relationship at the interface generates 23 Nb01 sequence variants and 17 of them show enhanced binding to ELMO1-RBD and are modeled to form major spatial overlaps with RhoG. The best binder, Nb29, inhibited ELMO1-RBD/RhoG interaction. Molecular dynamics simulation of the flexibility of CDR2 and CDR3 of Nb29 reveal the design of stabilizing mutations at the CDR-framework junctions potentially confers the affinity enhancement.