Novel inhibitors of the tRNA-dependent amidotransferase of "Helicobacter pylori" : Peptides generated by phage display and dipeptide-like compounds

Cette thèse présente la découverte de nouveaux inhibiteurs de l’amidotranférase ARNt-dépendante (AdT), et résume les connaissances récentes sur la biosynthèse du Gln-ARNtGln et de l’Asn-ARNtAsn par la voie indirecte chez la bactérie Helicobacter pylori. Dans le cytoplasme des eucaryotes, vingt acides aminés sont liés à leur ARNt correspondant par vingt aminoacyl-ARNt synthétases (aaRSs). Ces enzymes sont très spécifiques, et leur fonction est importante pour le décodage correct du code génétique. Cependant, la plupart des bactéries, dont H. pylori, sont dépourvues d’asparaginyl-ARNt synthétase et/ou de glutaminyl-ARNt synthétase. Pour former le Gln-ARNtGln, H. pylori utilise une GluRS noncanonique nommée GluRS2 qui glutamyle spécifiquement l’ARNtGln ; ensuite, une AdT trimérique, la GatCAB corrige le Glu-ARNtGln mésapparié en le transamidant pour former le Gln-ARNtGln, qui lira correctement les codons glutamine pendant la biosynthèse des protéines sur les ribosomes. La formation de l’Asn-ARNtAsn est similaire à celle du Gln-ARNtGln, et utilise la même GatCAB et une AspRS non-discriminatrice. Depuis des années 2000, la GatCAB est considérée comme une cible prometteuse pour le développement de nouveaux antibiotiques, puisqu’elle est absente du cytoplasme de l’être humain, et qu’elle est encodée dans le génome de plusieurs bactéries pathogènes. Dans le chapitre 3, nous présentons la découverte par la technique du « phage display » de peptides cycliques riches en tryptophane et en proline, et qui inhibent l’activité de la GatCAB de H. pylori. Les peptides P10 (CMPVWKPDC) et P9 (CSAHNWPNC) inhibent cette enzyme de façon compétitive par rapport au substrat Glu-ARNtGln. Leur constante d’inhibition (Ki) est 126 μM pour P10, et 392 μM pour P9. Des modèles moléculaires ont montré qu’ils lient le site actif de la réaction de transmidation catalysée par la GatCAB, grâce à la formation d’une interaction π-π entre le résidu Trp de ces peptides et le résidu Tyr81 de la sous-unité GatB, comme fait le A76 3’-terminal de l’ARNt. Dans une autre étude concernant des petits composés contenant un groupe sulfone, et qui mimiquent l’intermédiaire de la réaction de transamidation, nous avons identifié des composés qui inhibent la GatCAB de H. pylori de façon compétitive par rapport au substrat Glu-ARNtGln. Cinq fois plus petits que les peptides cycliques mentionnés plus haut, ces composés inhibent l’activité de la GatCAB avec des Ki de 139 μM pour le composé 7, et de 214 μM pour le composé 4. Ces inhibiteurs de GatCAB pourraient être utiles pour des études mécanistiques, et pourraient être des molécules de base pour le développement de nouvelles classes d’antibiotiques contre des infections causées par H. pylori.This thesis describes the discovery of inhibitors of a tRNA-dependent amidotransferase (AdT) and summarizes the present state of our knowledge about the two-step biosynthesis of Gln-tRNAGln and Asn-tRNAAsn in Helicobacter pylori. In eukaryotic cytoplasm, twenty amino acids (aa) are generally attached to their cognate tRNAs by twenty corresponding aminoacyl-tRNA synthetases (aaRSs). These enzymes have a high specificity, and their function is important to the proper decoding of mRNA. However, in a number of bacteria including H. pylori, GlnRS and/or AsnRS are absent. To synthesize Gln-tRNAGln, H. pylori first uses a noncanonical GluRS2 which is specific for tRNAGln to form Glu-tRNAGln; then the trimeric AdT (GatCAB) transforms Glu-tRNAGln into Gln-tRNAGln which is proper for protein biosynthesis. In a similar manner, the biosynthesis of Asn-tRNAAsn also takes place in H. pylori by using the same GatCAB and a canonical nondiscriminating AspRS. The widespread use of these indirect pathways among prominent human pathogens, and their absence in the mammalian cytoplasm, identify AdT as a promising target for the development of new and highly specific antimicrobial agents. By using phage display, we discovered several cyclic peptides rich in tryptophan and proline that inhibit H. pylori GatCAB. Peptides P10 (CMPVWKPDC) and P9 (CSAHNWPNC) are competitive inhibitors of GatCAB with respect to its substrate Glu-tRNAGln. The inhibition constants (Ki) of P10 and P9 are 126 and 392 μM, respectively. Their docking models revealed that they bind to the transamidation active site of GatB via π-π stacking interactions with Tyr81, as does the 3’-terminal A76 of tRNA. We also discovered two small dipeptide-like sulfone-containing inhibitors of H. pylori GatCAB by mimicking the intermediate of its transamidation reaction. Although they are much smaller than the cyclic peptides mentioned above, they are competitive inhibitors of GatCAB with respect to GlutRNAGln, with Ki values of 139 μM for compound 7 and 214 μM for compound 4. These inhibitors could be useful not only to study the reaction mechanisms of GatCAB, but also could be lead compounds for the development of a new class of antibiotics to treat infections caused by H. pylori

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

An experimental study and a proposed theoretical solution for the prediction of the ductile/brittle failure modes of reinforced concrete beams strengthened with external steel plates

An experimental study and a proposed theoretical solution are conducted in the present study to investigate the ductile/brittle failure mode of reinforced concrete beams strengthened with an external steel plate. The present experimental study has fabricated and tested six steel plate-strengthened RC beams and one non-strengthened RC beam under 4-point bending loads. The proposed theoretical model is then developed based on the observed experimental results to analyze the crack formation, to determine the distance between vertical cracks and to quantitatively predict the ductile/brittle failure mode of plate-strengthened RC beams. The experimental study shows that the failure mode is based on the sliding of concrete along with the external plate. This slip is limited between two vertical cracks, from which the maximum stress in the external steel is determined. Based on comparisons conducted in the present study, excellent agreements of the stresses/strains in soffit steel plates, crack distances, and system failure modes between the current theoretical solution and the previous and present experimental results are observed.&nbsp

Fish distribution in the Ba Che and Tien Yen rivers

The ichthyo-fauna in the Ba Che and Tien Yen rivers, northern Vietnam is highly diverse, with a total of 245 species determined. However, data on the distribution of fish species are not sufficiently provided for the whole area. This study was conducted from 2008 to 2011 at 27 stations from the Ba Che and Tien Yen river basins to determine the distribution of fish species according to different sections of the rivers, water bodies, seasons, and salinity levels. The results show that fish species are distributed mainly in the river (with 210 species), concentrated in the downstream area (with 213 species). Fishes are mainly collected in the dry season when the salinity level of river is high, due to seawater intrusion which also brings in 160 species of marine fish. This shows that fish distribution in the research areas is mainly dominated by the presence of marine species. While for freshwater fish, it is clearly affected by the mountainous features. Apart from the common characteristics sharing between the two river basins, but the number of both freshwater and marine fish species in the Tien Yen river are larger than those of Ba Che river, which is related to fresh water surface, river morphology, the width and location of the estuary to the sea. The downstream area is home to the fish species recorded in the Red Data Book of Vietnam and the complementary species for Vietnam, while the middle and upstream of the rivers may offer a high potential of biodiversity, with many possibly new species for science. These are important data for the conservation and sustainable development of fish resources in the Ba Che and Tien Yen river basins.

XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advantages of large-scale pre-training, harnessing vast raw data, and transfer learning by learning representations for training data through graph convolution. The research results demonstrate that the XGV-BERT method significantly improves vulnerability detection accuracy compared to two existing methods such as VulDeePecker and SySeVR. For the VulDeePecker dataset, XGV-BERT achieves an impressive F1-score of 97.5%, significantly outperforming VulDeePecker, which achieved an F1-score of 78.3%. Again, with the SySeVR dataset, XGV-BERT achieves an F1-score of 95.5%, surpassing the results of SySeVR with an F1-score of 83.5%

Extracting inter-arrival time based behaviour from honeypot traffic using cliques

The Leurre.com project is a worldwide network of honeypot environments that collect traces of malicious Internet traffic every day. Clustering techniques have been utilized to categorize and classify honeypot activities based on several traffic features. While such clusters of traffic provide useful information about different activities that are happening in the Internet, a new correlation approach is needed to automate the discovery of refined types of activities that share common features. This paper proposes the use of packet inter-arrival time (IAT) as a main feature in grouping clusters that exhibit commonalities in their IAT distributions. Our approach utilizes the cliquing algorithm for the automatic discovery of cliques of clusters. We demonstrate the usefulness of our methodology by providing several examples of IAT cliques and a discussion of the types of activity they represent. We also give some insight into the causes of these activities. In addition, we address the limitation of our approach, through the manual extraction of what we term supercliques, and discuss ideas for further improvement

Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspection

The significant rise of security concerns in conventional centralized learning has promoted federated learning (FL) adoption in building intelligent applications without privacy breaches. In cybersecurity, the sensitive data along with the contextual information and high-quality labeling in each enterprise organization play an essential role in constructing high-performance machine learning (ML) models for detecting cyber threats. Nonetheless, the risks coming from poisoning internal adversaries against FL systems have raised discussions about designing robust anti-poisoning frameworks. Whereas defensive mechanisms in the past were based on outlier detection, recent approaches tend to be more concerned with latent space representation. In this paper, we investigate a novel robust aggregation method for FL, namely Fed-LSAE, which takes advantage of latent space representation via the penultimate layer and Autoencoder to exclude malicious clients from the training process. The experimental results on the CIC-ToN-IoT and N-BaIoT datasets confirm the feasibility of our defensive mechanism against cutting-edge poisoning attacks for developing a robust FL-based threat detector in the context of IoT. More specifically, the FL evaluation witnesses an upward trend of approximately 98% across all metrics when integrating with our Fed-LSAE defense

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors

Recently, there has been a growing focus and interest in applying machine learning (ML) to the field of cybersecurity, particularly in malware detection and prevention. Several research works on malware analysis have been proposed, offering promising results for both academic and practical applications. In these works, the use of Generative Adversarial Networks (GANs) or Reinforcement Learning (RL) can aid malware creators in crafting metamorphic malware that evades antivirus software. In this study, we propose a mutation system to counteract ensemble learning-based detectors by combining GANs and an RL model, overcoming the limitations of the MalGAN model. Our proposed FeaGAN model is built based on MalGAN by incorporating an RL model called the Deep Q-network anti-malware Engines Attacking Framework (DQEAF). The RL model addresses three key challenges in performing adversarial attacks on Windows Portable Executable malware, including format preservation, executability preservation, and maliciousness preservation. In the FeaGAN model, ensemble learning is utilized to enhance the malware detector's evasion ability, with the generated adversarial patterns. The experimental results demonstrate that 100\% of the selected mutant samples preserve the format of executable files, while certain successes in both executability preservation and maliciousness preservation are achieved, reaching a stable success rate