In Silico Identification of Natural Food Compounds as Potential Quorum-Sensing Inhibitors Targeting the LasR Receptor of Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a major cause of nosocomial infections and is often associated with biofilm-mediated antibiotic resistance. The LasR protein is a key component of the quorum system in P. aeruginosa, allowing it to regulate its biofilm-induced pathogenicity. When the bacterial population reaches a sufficient density, the accumulation of N-(3-oxododecanoyl) acyl homoserine lactone (3O-C12-HSL) leads to the activation of the LasR receptor, which then acts as a transcriptional activator of target genes involved in biofilm formation and virulence, thereby increasing the bacteria's antibiotic resistance and enhancing its virulence. In this study, we performed a structure-based virtual screening of a natural food database of 10 997 compounds against the crystal structure of the ligand-binding domain of the LasR receptor (PDB ID: 3IX4). This allowed us to identify four molecules, namely ZINC000001580795, ZINC000014819517, ZINC000014708292, and ZINC000004098719, that exhibited a favorable binding mode and docking scores greater than -13 kcal/mol. Furthermore, the molecular dynamics simulation showed that these four molecules formed stable complexes with LasR during the 150-ns molecular dynamics (MD) simulation, indicating their potential for use as inhibitors of the LasR receptor in P. aeruginosa. However, further experimental validation is needed to confirm their activity.

Targeted Gene Panel Sequencing Unveiled New Pathogenic Mutations in Patients With Breast Cancer.

The increasing commercialization of new gene panels based on next-generation sequencing for clinical research has significantly improved our understanding of breast cancer genetics and has led to the discovery of new mutation variants. The study included 16 unselected Moroccan breast cancer patients tested with multi-gene panel (HEVA screen panel) using Illumina Miseq, followed by Sanger sequencing to validate the most relevant mutation. Mutational analysis revealed the presence of 13 mutations (11 single-nucleotide polymorphisms [SNPs] and 2 indels), and 6 of 11 identified SNPs were predicted as pathogenic. One of the 6 pathogenic mutations was c.7874G>C, a heterozygous SNP in HD-OB domain of BRCA2 gene, which led to the arginine to threonine change at codon 2625 of the protein. This work describes the first case of a patient with breast cancer harboring this pathogenic variant and analyzes its functional impact using molecular docking and molecular dynamics simulation. Further experimental investigations are needed to validate its pathogenicity and to verify its association with breast cancer.

In Silico Analyses of All STAT3 Missense Variants Leading to Explore Divergent AD-HIES Clinical Phenotypes.

Autosomal dominant hyper-IgE syndrome (AD-HIES) is linked to dominant negative mutations of the STAT3 protein whose molecular basis for dysfunction is unclear and presenting with a variety of clinical manifestations with only supportive treatment. To establish the relationship between the impact of STAT3 mutations in different domains and the severity of the clinical manifestations, 105 STAT3 mutations were analyzed for their impact on protein stability, flexibility, function, and binding affinity using in Silico approaches. Our results showed that 73% of the studied mutations have an impact on the physicochemical properties of the protein, altering the stability, flexibility and function to varying degrees. In particular, mutations affecting the DNA binding domain (DBD) and the Src Homology 2 (SH2) have a significant impact on the protein structure and disrupt its interaction either with DNA or other STAT3 to form a heterodomain complex, leading to severe clinical phenotypes. Collectively, this study suggests that there is a close relationship between the domain involving the mutation, the degree of variation in the properties of the protein and the degree of loss of function ranging from partial loss to complete loss, explaining the variability of clinical manifestations between mild and severe.

Facing Antitubercular Resistance: Identification of Potential Direct Inhibitors Targeting InhA Enzyme and Generation of 3D-pharmacophore Model by in silico Approach.

Purpose: The enoyl-acyl carrier protein reductase (InhA) is one of the important key enzymes employed in mycolic acids biosynthesis pathway and an important component of mycobacterial cell walls. This enzyme has also been identified as major target of isoniazid drug, except that isoniazid needs to be activated first by the catalase peroxidase (KatG) protein to form the isonicotinoyl-NAD (INH-NAD) adduct that inhibits the action of InhA enzyme. However, this activation becomes more difficult and unreachable with the problem of mutation-related resistance caused mainly by acquired mutations in KatG and InhA protein. Our main interest in this study is to identify direct InhA inhibitors using computer-aided drug design. Methods: Computer-aided drug design was used to solve this problem by applying three different approaches including mutation impact modelling, virtual screening and 3D-pharmacophore search. Results: A total of 15 mutations were collected from the literature, then a 3D model was generated for each of them and their impact was predicted. Of the 15 mutations, 10 were found to be deleterious and have a direct effect on flexibility, stability and SASA of the protein. In virtual screening, from 1,000 similar INH-NAD analogues obtained by the similarity search method, 823 compounds passed toxicity filter and drug likeness rules, which were then docked to the wild-type of InhA protein. Subsequently, 34 compounds with binding energy score better than that of INH-NAD were selected and docked against the 10 generated mutated models of InhA. Only three leads showed a lower binding affinity better than the reference. The 3D-pharmacophore model approach was used to identify the common features between those three compounds by generating a pharmacophoric map. Conclusion: The result of this study may pave the way to develop more potent mutant-specific inhibitors to overcome this resistance.

Metagenomics Analysis of Breast Microbiome Highlights the Abundance of Rothia Genus in Tumor Tissues.

Breast cancer is one of the main global priorities in terms of public health. It remains the most frequent cancer in women and is the leading cause of their death. The human microbiome plays various roles in maintaining health by ensuring a dynamic balance with the host or in the appearance of various pathologies including breast cancer. In this study, we performed an analysis of bacterial signature differences between tumor and adjacent tissues of breast cancer patients in Morocco. Using 16S rRNA gene sequencing, we observed that adjacent tissue contained a much higher percentage of the Gammaproteobacteria class (35.7%) while tumor tissue was characterized by a higher percentage of Bacilli and Actinobacteria classes, with about 18.8% and 17.2% average abundance, respectively. Analysis of tumor subtype revealed enrichment of genus Sphingomonodas in TNBC while Sphingomonodas was predominant in HER2. The LEfSe and the genus level heatmap analysis revealed a higher abundance of the Rothia genus in tumor tissues. The identified microbial communities can therefore serve as potential biomarkers for prognosis and diagnosis, while also helping to develop new strategies for the treatment of breast cancer patients.

Computational modeling and druggability assessment of Aggregatibacter actinomycetemcomitans leukotoxin.

The leukotoxin (LtxA) of Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) is a protein exotoxin belonging to the repeat-in-toxin family (RTX). Numerous studies have demonstrated that LtxA may play a critical role in the pathogenicity of A. actinomycetemcomitans since hyper-leukotoxic strains have been associated with severe disease. Accordingly, considerable effort has been made to elucidate the mechanisms by which LtxA interacts with host cells and induce their death. However, these attempts have been hampered by the unavailability of a tertiary structure of the toxin, which limits the understanding of its molecular properties and mechanisms. In this paper, we used homology and template free modeling algorithms to build the complete tertiary model of LtxA at atomic level in its calcium-bound Holo-state. The resulting model was refined by energy minimization, validated by Molprobity and ProSA tools, and subsequently subjected to a cumulative 600ns of all-atom classical molecular dynamics simulation to evaluate its structural aspects. The druggability of the proposed model was assessed using Fpocket and FTMap tools, resulting in the identification of four putative cavities and fifteen binding hotspots that could be targeted by rational drug design tools to find new ligands to inhibit LtxA activity.

In-silico identification of potential inhibitors targeting the DNA binding domain of estrogen receptor α for the treatment of hormone therapy-resistant breast cancer.

Estrogen receptor α (ERα) plays a critical role in breast cancer (BC) development. The standard therapeutic strategies for ERα- positive (ERα+) BC consist of impairing ERα signalling pathway by either estrogen competitors blocking its interaction with the ligand binding domain (LBD) or agents inhibiting the production of estrogen. These strategies are limited by many factors that lead to constitutive activation of ERα and consequently, resistance to treatment. Targeting the DNA binding domain (DBD) of ERα instead of its LBD with small-molecule inhibitors could be an alternative to impair ERα's signalling pathway. For this purpose, we conducted a structure based virtual screening of DrugBank against the crystal structure of ERα-DBD (PDB ID: 1HCQ) using the Glide module in standard precision (SP) and extra precision (XP) mode of docking. Molecules with XP Gscore less than -8 kcal/mol were selected and visually inspected to keep only the reasonable docking poses. Subsequently, these molecules were clustered using structural interaction fingerprints analysis and the complexes of the top ranked molecules of each cluster based on XP Gscore were subjected to 200 ns molecular dynamics simulations followed by MM-GBSA binding free energy calculation for the last 100 ns of each complex. In this study, we identified three molecules from DrugBank namely DB03450, DB02593 and DB08001 showing significant stability and strong interaction with the key amino acids during MD simulation suggesting a potential inhibition of the target. These molecules could be used as promising lead compounds to impair the ERα signalisation in hormone therapy-resistant breast cancer.Communicated by Ramaswamy H. Sarma.

Identifying epitopes for cluster of differentiation and design of new peptides inhibitors against human SARS-CoV-2 spike RBD by an in-silico approach.

The coronavirus disease 19 (COVID-19) is a highly contagious and rapidly spreading infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In some cases, the disease can be fatal which resulted in more than one million deaths worldwide according the WHO. Currently, there is no effective vaccine or treatment for COVID-19, however many small-molecule inhibitors have shown potent antiviral activity against SARS-CoV-2 and some of them are now under clinical trials. Despite their promising activities, the development of these small molecules for the clinical use can be limited by many factors like the off-target effect, the poor stability, and the low bioavailability. The clusters of differentiation CD147, CD209, CD299 have been identified as essential entry co-receptors for SARS-CoV-2 species specificity to humans, although the underlying mechanisms are yet to be fully elucidated. In this paper, protein-protein docking was utilized for identifying the critical epitopes in CD147, CD209 and CD299 which are involved in the binding with SARS-CoV-2 Spike receptor binding domain (RBD). The results of binding free energies showed a high affinity of SARS-CoV-2 RBD to CD299 receptor which was used as a reference to derive hypothetical peptide sequences with specific binding activities to SARS-CoV-2 RBD. Molecular docking and molecular dynamics simulations of the newly designed peptides showed favorable binding features and stability with SARS-CoV-2 RBD and therefore can be further considered as potential candidates in future anti-SARS CoV-2 drug discovery studies.

In silico analysis of ACE2 orthologues to predict animal host range with high susceptibility to SARS-CoV-2.

SARS-CoV-2, which causes severe pneumonia epidemics, probably originated from Chinese horseshoe bats, but the intermediate and host range is still unknown. ACE2 is the entry receptor for SARS-CoV-2. The binding capacity of SARS-CoV-2 spike protein to ACE2 is the critical determinant of viral host range and cross-species infection. Here, we used an in silico approach to predict the potential animals range with high susceptibility to SARS-CoV-2 by modelling and studying the Spike-ACE2 interaction of 22 domestic and wild animals. Our results showed that all studied animals are potentially susceptible to SARS-CoV-2 infection with a slight difference in the binding affinity and stability of their ACE2-RBD complexes. Furthermore, we identified a specific substitution of tyrosine to histidine at position 41 in ACE2 that likely reduces the affinity to SARS-CoV-2 in horses and greater horseshoe bats. These results may help to provide important insights into SARS-CoV-2 host range which will make it possible to control the spread of the virus and identify animal models that could be used for screening antiviral drugs or vaccine candidates against SARS-CoV-2.

Genomic Diversity and Hotspot Mutations in 30,983 SARS-CoV-2 Genomes: Moving Toward a Universal Vaccine for the "Confined Virus"?

The COVID-19 pandemic has been ongoing since its onset in late November 2019 in Wuhan, China. Understanding and monitoring the genetic evolution of the virus, its geographical characteristics, and its stability are particularly important for controlling the spread of the disease and especially for the development of a universal vaccine covering all circulating strains. From this perspective, we analyzed 30,983 complete SARS-CoV-2 genomes from 79 countries located in the six continents and collected from 24 December 2019, to 13 May 2020, according to the GISAID database. Our analysis revealed the presence of 3206 variant sites, with a uniform distribution of mutation types in different geographic areas. Remarkably, a low frequency of recurrent mutations has been observed; only 169 mutations (5.27%) had a prevalence greater than 1% of genomes. Nevertheless, fourteen non-synonymous hotspot mutations (>10%) have been identified at different locations along the viral genome; eight in ORF1ab polyprotein (in nsp2, nsp3, transmembrane domain, RdRp, helicase, exonuclease, and endoribonuclease), three in nucleocapsid protein, and one in each of three proteins: Spike, ORF3a, and ORF8. Moreover, 36 non-synonymous mutations were identified in the receptor-binding domain (RBD) of the spike protein with a low prevalence (<1%) across all genomes, of which only four could potentially enhance the binding of the SARS-CoV-2 spike protein to the human ACE2 receptor. These results along with intra-genomic divergence of SARS-CoV-2 could indicate that unlike the influenza virus or HIV viruses, SARS-CoV-2 has a low mutation rate which makes the development of an effective global vaccine very likely.

Repurposing of known anti-virals as potential inhibitors for SARS-CoV-2 main protease using molecular docking analysis.

The new SARS-CoV-2 coronavirus is the causative agent of the COVID-19 pandemic outbreak that affected more than 190 countries worldwide with more than 292,000 confirmed cases and over 12,700 deaths. There is at the moment no vaccine or effective treatment for this disease which constitutes a serious global health problem. It is of interest to use a structure based virtual screening approach for the identification of potential inhibitors of the main protease of SARS-CoV-2 (Mpro) from antiviral drugs used to treat other viral disease such as human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infections. The crystallographic structure with PDB ID: 6LU7 of Mpro in complex with the inhibitor N3 was used as a model in the virtual screening of 33 protease inhibitors collected from the ChEMBL chemical database. Molecular docking analysis was performed using the standard AutoDock vina protocol followed by ranking and selection of compounds based on their binding affinity. We report 10 candidates with optimal binding features to the active site of the protease for further consideration as potential drugs to treat patients infected with the emerging COVID-19 disease.