Synthesis of the Most Potent Isomer of μ-Conotoxin KIIIA Using Different Strategies

In the chemical synthesis of conotoxins with multiple disulfide bonds, the oxidative folding process can result in diverse disulfide bond connectivities, which presents a challenge for determining the natural disulfide bond connectivities and leads to significant structural differences in the synthesized toxins. Here, we focus on KIIIA, a μ-conotoxin that has high potency in inhibiting Nav1.2 and Nav1.4. The non-natural connectivity pattern (C1—C9, C2—C15, C4—C16) of KIIIA exhibits the highest activity. In this study, we report an optimized Fmoc solid-phase synthesis of KIIIA using various strategies. Our results indicate that free random oxidation is the simplest method for peptides containing triple disulfide bonds, resulting in high yields and a simplified process. Alternatively, the semi-selective strategy utilizing Trt/Acm groups can also produce the ideal isomer, albeit with a lower yield. Furthermore, we performed distributed oxidation using three different protecting groups, optimizing their positions and cleavage order. Our results showed that prioritizing the cleavage of the Mob group over Acm may result in disulfide bond scrambling and the formation of new isomers. We also tested the activity of synthesized isomers on Nav1.4. These findings provide valuable guidance for the synthesis of multi-disulfide-bonded peptides in future studies

RS-Dseg: semantic segmentation of high-resolution remote sensing images based on a diffusion model component with unsupervised pretraining

Abstract Semantic segmentation plays a crucial role in interpreting remote sensing images, especially in high-resolution scenarios where finer object details, complex spatial information and texture structures exist. To address the challenge of better extracting semantic information and ad-dressing class imbalance in multiclass segmentation, we propose utilizing diffusion models for remote sensing image semantic segmentation, along with a lightweight classification module based on a spatial-channel attention mechanism. Our approach incorporates unsupervised pretrained components with a classification module to accelerate model convergence. The diffusion model component, built on the UNet architecture, effectively captures multiscale features with rich contextual and edge information from images. The lightweight classification module, which leverages spatial-channel attention, focuses more efficiently on spatial-channel regions with significant feature information. We evaluated our approach using three publicly available datasets: Postdam, GID, and Five Billion Pixels. In the test of three datasets, our method achieved the best results. On the GID dataset, the overall accuracy was 96.99%, the mean IoU was 92.17%, and the mean F1 score was 95.83%. In the training phase, our model achieved good performance after only 30 training cycles. Compared with other models, our method reduces the number of parameters, improves the training speed, and has obvious performance advantages

De novo design of an intercellular signaling toolbox for multi-channel cell–cell communication and biological computation

Intercellular signalling is fundamental for the formation of complex structures from single cells. Here the authors design six orthogonal cell–cell signalling channels for cell consortia communication and bio-computation

Reproducibility of fluorescent expression from engineered biological constructs in E. coli

We present results of the first large-scale interlaboratory study carried out in synthetic biology, as part of the 2014 and 2015 International Genetically Engineered Machine (iGEM) competitions. Participants at 88 institutions around the world measured fluorescence from three engineered constitutive constructs in E. coli. Few participants were able to measure absolute fluorescence, so data was analyzed in terms of ratios. Precision was strongly related to fluorescent strength, ranging from 1.54-fold standard deviation for the ratio between strong promoters to 5.75-fold for the ratio between the strongest and weakest promoter, and while host strain did not affect expression ratios, choice of instrument did. This result shows that high quantitative precision and reproducibility of results is possible, while at the same time indicating areas needing improved laboratory practices.Peer reviewe