Peptide toxins secreted by venomous animals bind to mammalian ion channel proteins and modulate their function. The high specificity of these toxins for their target ion channels enables them to serve as powerful tools for ion channel biology. Toxins labeled with fluorescent dyes are employed for the cellular imaging of channels and also for studying toxin-channel and toxin-membrane interactions. Several of these toxins are cysteine-rich, rendering the production of properly folded fluorescently labeled toxins technically challenging. Herein, we evaluate a variety of site-specific protein bioconjugation approaches for producing fluorescently labeled double-knot toxin (DkTx), a potent TRPV1 ion channel agonist that contains an uncommonly large number of cysteines (12 out of a total of 75 amino acids present in the protein). We find that popular cysteine-mediated bioconjugation approaches are unsuccessful as the introduction of a non-native cysteine residue for thiol modification leads to the formation of misfolded toxin species. Moreover, N-terminal aldehyde-mediated bioconjugation approaches are also not suitable as the resultant labeled toxin lacks activity. In contrast to these approaches, C-terminal bioconjugation of DkTx via the sortase bioconjugation technology yields functionally active fluorescently labeled DkTx. We employ this labeled toxin for imaging rat TRPV1 heterologously expressed in Xenopus laevis oocytes, as well as for performing membrane binding studies on giant unilamellar vesicles composed of different lipid compositions. Our studies set the stage for using fluorescent DkTx as a tool for TRPV1 biology and provide an informative blueprint for labeling cysteine-rich proteins.

Lightweight cryptography (LWC) is an area of cryptographic techniques with low computational complexity and resource requirements. There must be a reason for using it in Internet of Things (IoT) network with a strict resource constraints environment. The key features of a 5G network are low latency, high throughput, heterogeneous network architecture, and massive connectivity. A new area of network architecture called SDN-IoT comes into the picture to control and manage IoT devices in a network with low latency and high throughput. SDN helps to reprogram the network according to the application’s requirements. Also, higher mobile applications lead to higher data growth. SDN helps to secure, manage, and control the huge data in the network. SDN-IoT architecture divides the network into three layers: The infrastructure layer, the control layer, and the service or application layer. In this chapter, we are focusing on the LWC algorithms from different perspectives so that they will fit into different layers of SDN-IoT network. We will discuss all the pros and cons of implementing LWC algorithms in hardware and software environments and also, the different layers of the SDN-IoT network. We also discuss SDN security architecture and different performance metrics for LWC algorithms.