Covalent Allosteric Inhibitors of Akt Generated Using a Click Fragment Approach

Akt is a protein kinase that has been implicated in the progression of cancerous tumours. A number of covalent allosteric Akt inhibitors are known, and based on these scaffolds, a small library of novel potential covalent allosteric imidazopyridine-based inhibitors was designed. The envisaged compounds were synthesised, with click chemistry enabling a modular approach to a number of the target compounds. The binding modes, potencies and antiproliferative activities of these synthesised compounds were explored, thereby furthering the structure activity relationship knowledge of this class of Akt inhibitors. Three novel covalent inhibitors were identified, exhibiting moderate activity against Akt1 and various cancer cell lines, potentially paving the way for future covalent allosteric inhibitors with improved properties.     

Polyether Cyclization Cascade Alterations in Response to Monensin Polyketide Synthase Mutations

The targeted manipulation of polyketide synthases has in recent years led to numerous new-to-nature polyketides. For type I polyketide synthases the response of post-polyketide synthases (PKS) processing enzymes onto the most frequently polyketide backbone manipulations is so far insufficiently studied. In particular, complex processes such as the polyether cyclisation in the biosynthesis of ionophores such as monensin pose interesting objects of research. We present here a study of the substrate promiscuity of the polyether cyclisation cascade enzymes in monensin biosynthesis in the conversion of redox derivatives of the nascent polyketide chain. LC-HRMS/MS²-based studies revealed a remarkable flexibility of the post-PKS enzymes. They acted on derivatized polyketide backbones based on the three possible polyketide redox states within two different modules and gave rise to an altered polyether structure. One of these monensin derivatives was isolated and characterized by 2D-NMR spectroscopy, crystallography, and bioactivity studies.     

From zero-shot machine learning to zero-day attack detection

Machine learning (ML) models have proved efficient in classifying data samples into their respective categories. The standard ML evaluation methodology assumes that test data samples are derived from pre-observed classes used in the training phase. However, in applications such as Network Intrusion Detection Systems (NIDSs), obtaining data samples of all attack classes to be observed is challenging. ML-based NIDSs face new attack traffic known as zero-day attacks that are not used in training due to their non-existence at the time. Therefore, this paper proposes a novel zero-shot learning methodology to evaluate the performance of ML-based NIDSs in recognising zero-day attack scenarios. In the attribute learning stage, the learning models map network data features to semantic attributes that distinguish between known attacks and benign behaviour. In the inference stage, the models construct the relationships between known and zero-day attacks to detect them as malicious. A new evaluation metric is defined as Zero-day Detection Rate (Z-DR) to measure the effectiveness of the learning model in detecting unknown attacks. The proposed framework is evaluated using two key ML models and two modern NIDS data sets. The results demonstrate that for certain zero-day attack groups discovered in this paper, ML-based NIDSs are ineffective in detecting them as malicious. Further analysis shows that attacks with a low Z-DR have a significantly distinct feature distribution and a higher Wasserstein Distance range than the other attack classes.

     

Trichoderma-Derived Pentapeptides from the Infected Nest Mycobiome of the Subterranean Termite Coptotermes testaceus

Termites live in a dynamic environment where colony health is strongly influenced by surrounding microbes. However, little is known about the mycobiomes of lower termites and their nests, and how these change in response to disease. Here we compared the individual and nest mycobiomes of a healthy subterranean termite colony (Coptotermes testaceus) to one infected and ultimately eradicated by a fungal pathogen. We identified Trichoderma species in the materials of both nests, but they were also abundant in the infected termites. Methanolic extracts of Trichoderma sp. FHG000531, isolated from the infected nest, were screened for secondary metabolites by UHPLC-HR MS/MS-guided molecular networking. We identified many bioactive compounds with potential roles in the eradication of the infected colony, as well as a cluster of six unknown peptides. The novel peptide FE011 was isolated and characterized by NMR spectroscopy. The function of this novel peptide family as well as the role of Trichoderma species in dying termite colonies therefore requires further investigation.     

Development and Characterization of a 96-Well Exposure System for Safety Assessment of Nanomaterials

In this study, a 96-well exposure system for safety assessment of nanomaterials is developed and characterized using an air–liquid interface lung epithelial model. This system is designed for sequential nebulization. Distribution studies verify the reproducible distribution over all 96 wells, with lower insert-to-insert variability compared to non-sequential application. With a first set of chemicals (TritonX), drugs (Bortezomib), and nanomaterials (silver nanoparticles and (non-)fluorescent crystalline nanocellulose), sequential exposure studies are performed with human lung epithelial cells followed by quantification of the deposited mass and of cell viability. The developed exposure system offers for the first time the possibility of exposing an air–liquid interface model in a 96-well format, resulting in high-throughput rates, combined with the feature for sequential dosing. This exposure system allows the possibility of creating dose-response curves resulting in the generation of more reliable cell-based assay data for many types of applications, such as safety analysis. In addition to chemicals and drugs, nanomaterials with spherical shapes, but also morphologically more complex nanostructures can be exposed sequentially with high efficiency. This allows new perspectives on in vivo-like and animal-free approaches for chemical and pharmaceutical safety assessment, in line with the 3R principle of replacing and reducing animal experiments.