Xiu, P. | Search bioRxiv

bioRxiv2026

Asymmetric Hydration and Protonation Switching of Dual Aspartates Drive Flagellar Rotation

The bacterial flagellar motor is an intricate nanomachine that transforms chemical energy from ion gradients into mechanical rotation, enabling bacterial movement. While stator unit architectures are conserved across species, the molecular link connecting ion translocation to rotational force generation remains elusive. In this study, we refined the cryo-EM structures of MotAB from Campylobacter jejuni (CjMotAB) and integrated a suite of approaches--including single-structure based pKa predictors and free energy perturbation (FEP) calculations, as well as standard and constant-pH molecular dynamics (CpHMD) simulations of various structural models representing the plugged, unplugged, and plug-removed states with different protonation states of D22--to dissect its rotational mechanism. Based on pKa calculations, the D22 residues in chains F and G of MotB were identified as proton carriers supporting the previous hypotheses. Importantly, we observed asymmetric hydration patterns of the two D22 residues in the MotB dimer, along with their hydrogen bonding interactions with MotA T189, which contribute to functional specialization. Our findings reveal that MotA rotation requires two essential prerequisites: plug removal and alternating D22 protonation switching, coupled with dynamic gauche-trans conformational changes in the sidechain of D22. This work clarifies how protonation dynamics and structural asymmetry synergistically regulate CjMotAB rotation, advancing our understanding of bacterial flagellar motor function and providing a foundational framework for investigating diverse ion-driven biological motors.

biophysics↗

bioRxiv2024

BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors

Identifying molecules that bind strongly to target proteins in rational drug design is crucial. Machine learning techniques, such as generative adversarial networks (GAN), are now essential tools for generating such molecules. In this study, we present an enhanced method for molecule generation using objective-reinforced GANs. Specifically, we introduce BEGAN (Boltzmann-Enhanced GAN), a novel approach that adjusts molecule occurrence frequencies during training based on the Boltzmann distribution exp(-{Delta}U/{tau}), where {Delta}U represents the estimated binding free energy derived from docking algorithms and{tau} is a temperature-related scaling hyperparameter. This Boltzmann reweighting process shifts the generation process towards molecules with higher binding affinities, allowing the GAN to explore molecular spaces with superior binding properties. The reweighting process can also be refined through multiple iterations without altering the overall distribution shape. To validate our approach, we apply it to the design of sex pheromone analogs targeting Spodoptera frugiperda pheromone receptor SfruOR16, illustrating that the Boltzmann reweighting significantly increases the likelihood of generating promising sex pheromone analogs with improved binding affinities to SfruOR16, further supported by atomistic molecular dynamics simulations. Furthermore, we conduct a comprehensive investigation into parameter dependencies and propose a reasonable range for the hyperparameter{tau} . Our method offers a promising approach for optimizing molecular generation for enhanced protein binding, potentially increasing the efficiency of drug discovery pipelines. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/611826v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@120c4d3org.highwire.dtl.DTLVardef@5a2034org.highwire.dtl.DTLVardef@f8478aorg.highwire.dtl.DTLVardef@20db79_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗