Targeting decaprenylphosphoryl-ß-D-ribose 2'-epimerase for Innovative Drug Development Against Mycobacterium Tuberculosis Drug-Resistant Strains.

Tuberculosis (TB) remains a global health challenge with the emergence of drug-resistant Mycobacterium tuberculosis variants, necessitating innovative drug molecules. One potential target is the cell wall synthesis enzyme decaprenylphosphoryl-ß-D-ribose 2'-epimerase (DprE1), crucial for virulence and survival. This study employed virtual screening of 111 Protein Data Bank (PDB) database molecules known for their inhibitory biological activity against DprE1 with known IC50 values. Six compounds, PubChem ID: 390820, 86287492, 155294899, 155522922, 162651615, and 162665075, exhibited promising attributes as drug candidates and validated against clinical trial inhibitors BTZ043, TBA-7371, PBTZ169, and OPC-167832. Concurrently, this research focused on DprE1 mutation effects using molecular dynamic simulations. Among the 10 mutations tested, C387N significantly influenced protein behavior, leading to structural alterations observed through root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent-accessible surface area (SASA) analysis. Ligand 2 (ID: 390820) emerged as a promising candidate through ligand-based pharmacophore analysis, displaying enhanced binding compared with reference inhibitors. Molecular dynamic simulations highlighted ligand 2's interaction with the C387N mutation, reducing fluctuations, augmenting hydrogen bonding, and influencing solvent accessibility. These collective findings emphasize ligand 2's efficacy, particularly against severe mutations, in enhancing protein-ligand complex stability. Integrated computational and pharmacophore methodologies offer valuable insights into drug candidates and their interactions within intricate protein environments. This research lays a strategic foundation for targeted interventions against drug-resistant TB, highlighting ligand 2's potential for advanced drug development strategies.

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.

Selective Non-toxics Inhibitors Targeting DHFR for Tuberculosis and Cancer Therapy: Pharmacophore Generation and Molecular Dynamics Simulation.

Dihydrofolate reductase (DHFR) is a crucial enzyme that catalyzes the conversion of folic acid. Its reserved properties and significance in both human (h-DHFR) and mycobacterium (mt-DHFR) make it a challenging target for developing drugs against cancer and bacterial infections. Although methotrexate (MTX) is commonly used for cancer therapy and bacterial infections, it has a toxic profile. In this study, we aimed to identify selective and non-toxic inhibitors against h-DHFR and mt-DHFR using an in silico approach. From a data set of 8 412 inhibitors, 11 compounds passed the toxicity and drug-likeness tests, and their interaction with h-DHFR and mt-DHFR was studied by performing molecular docking. To evaluate the inhibitory activity of the compounds against mt-DHFR, five known reference ligands and the natural ligand (dihydrofolate) were used to generate a pharmacophoric map. Two potential selective inhibitors for mt-DHFR and h-DHFR were selected for further investigation using molecular dynamics for 100 ns. As a result, BDBM18226 was identified as the best compound selective for mt-DHFR, non-toxic, with five features listed in the map, with a binding energy of -9.6 kcal/mol. BDBM50145798 was identified as a non-toxic selective compound with a better affinity than MTX for h-DHFR. Molecular dynamics of the two best ligands suggest that they provide more stable, compact, and hydrogen bond interactions with the protein. Our findings could significantly expand the chemical space for new mt-DHFR inhibitors and provide a non-toxic alternative toward h-DHFR for the respective treatment of tuberculosis and cancer therapy.

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.

Virtual docking screening and quantitative structure-activity relationship studies to explore AKT and PI3K inhibitors acting on mTOR in cancers by theoretical biology and medical modeling.

Introduction: The mechanistic target of rapamycin (mTOR) coordinates the growth and metabolism of eukaryotic cells with a central role in the regulation of many fundamental cellular processes. It is strongly connected to phosphatidylinositol 3-kinase (PI3K) and AKT signaling. Activation of the PI3K/AKT/mTOR pathway leads to a profound disruption in the control of cell growth and survival, which ultimately leads to competitive growth advantage, metastatic competence, angiogenesis and therapeutic resistance. Material and methods: To explore the common competitive adenosine triphosphate (ATP) inhibitors PI3K/AKT and PI3K/mTOR, we built a 2D mTOR-SAR model that predicted the bioactivity of AKT and PI3K inhibitors towards mTOR. The interaction of the best inhibitors was evaluated by docking analysis and compared to that of the standard AZ8055 and XL388 inhibitors. Results: A mechanistic target of rapamycin-quantitative structure-activity relationship (mTOR-QSAR) model with a correlation coefficient (R2) of 0.80813 and a root mean square error of 0.17756 was obtained, validated and evaluated by a cross-validation leave-one-out method. The best predicted AKT and PI3K inhibitor pIC50 activities were 9.36-9.95 and 9.23-9.87 respectively. Conclusions: After docking and several comparisons, the inhibitors with better predictions showed better affinity and interaction with mTOR compared to AZ8055 and XL388, so we have found that 2 AKT inhibitors and 9 mTOR inhibitors met the Lipinski and Veber criteria and could be future drugs.

Phylogeography and genomic analysis of SARS-CoV-2 delta variant in Morocco.

INTRODUCTION: Since the COVID-19 pandemic began in December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously evolved with many variants of concern emerging across the world. METHODOLOGY: In order to monitor the evolution of these variants in Morocco, we analyzed a total of 2130 genomes of the delta variant circulating around the world. We also included 164 Moroccan delta variant sequences in our analysis. RESULTS: Our findings suggest at least four introductions from multiple international sources and a rise of a dominant delta sub-lineage AY.33 in Morocco. Moreover, we report three mutations in the N-terminal domain of the S protein specific to the Moroccan AY.33 isolates, T29A, T250I and T299I. The effect of these mutations on the secondary structure and the dynamic behavior of the S protein N-terminal domain was further determined. CONCLUSIONS: We conclude that these mutations might have functional consequences on the S protein of SARS-CoV-2.

AKT1 Polymorphism (rs10138227) and Risk of Colorectal Cancer in Moroccan Population: A Case Control Study.

BACKGROUND: LMTK3 and AKT1 each have a role in carcinogenesis and tumor progression. The analysis of single nucleotide polymorphisms of AKT1 and LMTK3 could lead to more complete and accurate risk estimates for colorectal cancer. AIM: We evaluated the association between single nucleotide polymorphisms (SNPs) of AKT1 and LMTK3 and the risk of colorectal cancer in a case-control study in Moroccan population. METHODS: Genomic DNA from 70 colorectal cancer patients and 50 healthy control subjects was extracted from whole blood. Genotyping was performed by direct sequencing after polymerase chain reactions for the 7 SNPs (AKT1rs1130214G/T, AKT1rs10138227C/T, AKT1rs3730358C/T, AKT1rs1000559097G/A, AKT1rs2494737A/T, LMTK3rs8108419G/A, and LMTK3rs9989661A/G.). Study subjects provided detailed information during the collection. All P values come from bilateral tests. RESULTS: In the logistic regression analysis, a significantly high risk of colorectal cancer was associated with TC/TT genotypes of rs10138227 with adjusted odds ratio [OR] equal to 2.82 and 95% confidence interval [CI] of 1.15 to 6.91. CONCLUSION: Our results suggest that the SNP AKT1rs10138227 could affect susceptibility to CRC, probably by modulating the transcriptional activity of AKT1. However, larger independent studies are needed to validate our results.

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.

Virtual docking screening and QSAR studies to explore AKT and mTOR inhibitors acting on PI3K in cancers.

The phosphoinositide 3-kinase (PI3K) pathway is an important regulator of cell proliferation and metabolism. PI3K activation initiates a signal transduction cascade, of which the major effectors are the kinases AKT and mTOR. Aberrant activation of the PI3K/AKT/mTOR pathway is frequently observed in many human malignancies and the combination of compounds simultaneously targeting different related molecules in the PI3K/AKT/mTOR pathway leads to synergistic activity. To explore the competing common ATP inhibitors PI3K/AKT and PI3K/mTOR we developed a model PI3K-SAR 2D which made it possible to predict the bioactivity of inhibitors of AKT and mTOR towards PI3K; the interaction of the best inhibitors was evaluated by docking analysis and compared to that of dactolisib and pictilisib. A PI3K-SAR model with a correlation coefficient (R2) of 0.81706 and an RMSE of 0.16029 was obtained, which was validated and evaluated by a cross-validation method, LOO. The most predicted AKT and mTOR inhibitors present respectively pIC50 activities between 9.26-9.93 and 9.59-9.87. After docking and several comparisons, inhibitors with better predictions showed better affinity and interaction with PI3K compared to pictilisib and dactolisib, so we found that 4 inhibitors of AKT and 14 mTOR inhibitors met the criteria of Lipinski and Veber and could be future drugs.

Docking analysis of verteporfin with YAP WW domain.

The YAP oncogene is a known cancer target. Therefore, it is of interest to understand the molecular docking interaction of verteporfin (a derivative of benzo-porphyrin) with the WW domain of YAP (clinically used for photo-dynamic therapy in macular degeneration) as a potential WW domain-ligand modulator by inhibition. A homology protein SWISS MODEL of the human YAP protein was constructed to dock (using AutoDock vina) with the PubChem verteporfin structure for interaction analysis. The docking result shows the possibilities of verteporfin interaction with the oncogenic transcription cofactor YAP having WW1 and WW2 domains. Thus, the ability of verteporfin to bind with the YAP WW domain having modulator activity is implied in this analysis.