RESUMEN
Bioelectrochemical systems (BESs) are an innovative technology for the efficient degradation of antibiotics. Shewanella oneidensis (S. oneidensis) MR-1 plays a pivotal role in degrading sulfamethoxazole (SMX) in BESs. Our study investigated the effect of BES conditions on SMX degradation, focusing on microbial activity. The results revealed that BESs operating with a 0.05 M electrolyte concentration and 2 mA/cm2 current density outperformed electrolysis cells (ECs). Additionally, higher electrolyte concentrations and elevated current density reduced SMX degradation efficiency. The presence of nutrients had minimal effect on the growth of S. oneidensis MR-1 in BESs; it indicates that S. oneidensis MR-1 can degrade SMX without nutrients in a short period of time. We also highlighted the significance of mass transfer between the cathode and anode. Limiting mass transfer at a 10 cm electrode distance enhanced S. oneidensis MR-1 activity and BES performance. In summary, this study reveals the complex interaction of factors affecting the efficiency of BES degradation of antibiotics and provides support for environmental pollution control.
Asunto(s)
Fuentes de Energía Bioeléctrica , Shewanella , Sulfametoxazol , Sulfametoxazol/metabolismo , Shewanella/metabolismo , Electrodos , Biodegradación Ambiental , Antibacterianos/farmacología , Antibacterianos/química , Electrólisis , Técnicas ElectroquímicasRESUMEN
Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein-ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The graph neural network-multilayer perceptron (GNN-MLP) module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6 M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson's Rp = 0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein-ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://github.com/xianyuco/SS-GNN.