Structural and energetic analysis of NS5 protein inhibition by small molecules in Japanese encephalitis virus using machine learning and steered molecular dynamics approach.

One of the viral diseases that affect millions of people around the world, particularly in developing countries, is Japanese encephalitis (JE). In this study, the conserved protein of this virus, that is, non-structural protein 5 (NS5), was used as a target protein for this study, and a compound library of 749 antiviral molecules was screened against NS5. The current study employed machine learning-based virtual screening combined with molecular docking. Here, three hits (24360, 123519051 and 213039) had lower binding energies (< -8 kcal/mol) than the control, S-Adenosyl-L-homocysteine (SAH). All the compounds showed significant H-bond interactions with functional residues, which were also observed by the control. Molecular dynamics simulation, MM/GBSA for binding free energy analysis, principal component analysis and free energy landscape were also performed to study the stability of the complex formation. All three compounds had similar root mean square deviation trends, which were comparable to the control, SAH. Post-MD, the 123519051-receptor complex had the highest number of H-bonds (4 to 5) after the control, out of which three exhibited the highest percentage occupancy (50%, 24% and 79%). Both docking and MD, 123519051 showed an H-bond with the residue Gly111, which was also found for the control-protein complex. 123519051 showed the lowest binding free energy with ΔGbind of -89 kJ/mol. Steered molecular dynamics depicted that 123519051 had the maximum magnitude of dissociation (1436.43 kJ/mol/nm), which was more than the control, validating its stable complex formation. This study concluded that 123519051 is a binder and could inhibit the protein NS5 of JE.Communicated by Ramaswamy H. Sarma.

In silico assessment of a natural small molecule as an inhibitor of programmed death ligand 1 for cancer immunotherapy: a computational approach.

Programmed cell death ligand 1 (PD-L1) is a crucial target for cancer therapy. Here, an in silico study investigates PD-L1 to inhibit its interaction with PD1, thereby promoting an immune response to eliminate cancer cells. The study employed machine learning (ML) -based QSAR to detect PDL1 inhibitors. Morgan's fingerprint with docking score showed a 0.83 correlation with the experimental IC50, enabling the screening of 3200 natural compounds. The top three compounds, considered 2819, 2821 and 3188, were selected from the ML-based QSAR and subjected to molecular docking and simulation. The binding scores for 2819, 2821 and 3188 were -7.0, -9.0 and -8.9 kcal/mol, respectively. The stability of the ligands during a 100 ns simulation was assessed using RMSD, showing that 2819 and 2821 maintained stable patterns comparable to the control inhibitor. Notably, 2819 exhibited a consistent stable pattern throughout the simulation, while 2821 showed stability in the last 40 ns. The control compound showed the highest number of hydrogen bonds with proteins, whereas compounds 2819 and 2821 formed continuous H-bonds. 3188 was separated from the protein in later phases and is not regarded as a potential PD-L1-binding molecule. MMGBSA binding free energy for complexes was computed. Control had the lowest binding free energy, while 2819 and 2821 also had lower binding energies. In contrast, 3188 showed poor binding free energy, causing protein separation. Principal component analysis showed a loss of entropy and reduced protein conformational variation. Overall, 2819 and 2821 are potential binders for PD-L1 inhibition and immune response triggering.Communicated by Ramaswamy H. Sarma.

Integrated virtual screening and molecular dynamics simulation approaches revealed potential natural inhibitors for DNMT1 as therapeutic solution for triple negative breast cancer.

Triple negative breast cancers (TNBC) are clinically heterogeneous but mostly aggressive malignancies devoid of expression of the estrogen, progesterone, and HER2 (ERBB2 or NEU) receptors. It accounts for 15-20% of all cases. Altered epigenetic regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. The antitumor effect of DNMT1 has also been explored in TNBC that currently lacks targeted therapies. However, the actual treatment for TNBC is yet to be discovered. This study is attributed to the identification of novel drug targets against TNBC. A comprehensive docking and simulation analysis was performed to optimize promising new compounds by estimating their binding affinity to the target protein. Molecular dynamics simulation of 500 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between compound and binding pockets of DNMT1. In a nutshell, our study uncovered that Beta-Mangostin, Gancaonin Z, 5-hydroxysophoranone, Sophoraflavanone L, and Dorsmanin H showed maximum binding affinity with the active sites of DNMT1. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with TNBC, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

Structural insights into the mechanism of resistance to bicalutamide by the clinical mutations in androgen receptor in chemo-treatment resistant prostate cancer.

Despite advanced diagnosis and detection technologies, prostate cancer (PCa) is the most prevalent neoplasms in males. Dysregulation of the androgen receptor (AR) is centrally involved in the tumorigenesis of PCa cells. Acquisition of drug resistance due to modifications in AR leads to therapeutic failure and relapse in PCa. An overhaul of comprehensive catalogues of cancer-causing mutations and their juxta positioning on 3D protein can help in guiding the exploration of small drug molecules. Among several well-studied PCa-specific mutations, T877A, T877S and H874Y are the most common substitutions in the ligand-binding domain (LBD) of the AR. In this study, we combined structure as well as dynamics-based in silico approaches to infer the mechanistic effect of amino acid substitutions on the structural stability of LBD. Molecular dynamics simulations allowed us to unveil a possible drug resistance mechanism that acts through structural alteration and changes in the molecular motions of LBD. Our findings suggest that the resistance to bicalutamide is partially due to increased flexibility in the H12 helix, which disturbs the compactness, thereby reducing the affinity for bicalutamide. In conclusion, the current study helps in understanding the structural changes caused by mutations and could assist in the drug development process.Communicated by Ramaswamy H. Sarma.

Integrated virtual screening, molecular modeling and machine learning approaches revealed potential natural inhibitors for epilepsy.

Epilepsy, a prevalent chronic disorder of the central nervous system, is typified by recurrent seizures. Present treatments predominantly offer symptomatic relief by managing seizures, yet fall short of influencing epileptogenesis. This study endeavored to identify novel phytochemicals with potential therapeutic efficacy against S100B, an influential protein in epileptogenesis, through an innovative application of machine learning-enabled virtual screening. Our study incorporated the use of multiple machine learning algorithms, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), and Random Forest (RF). These algorithms were employed not only for virtual screening but also for essential feature extraction and selection, enhancing our ability to distinguish between active and inactive compounds. Among the tested machine learning algorithms, the RF model outshone the rest, delivering an impressive 93.43 % accuracy on both training and test datasets. This robust RF model was leveraged to sift through the library of 9,000 phytochemicals, culminating in the identification of 180 potential inhibitors of S100B. These 180 active compounds were than docked with the active site of S100B proteins. The results of our study highlighted that the 6-(3,12-dihydroxy-4,10,13-trimethyl-7,11-dioxo-2,3,4,5,6,12,14,15,16,17-decahydro-1H cyclopenta[a] phenanthren -17-yl)-2-methyl-3-methylideneheptanoic acid, rhinacanthin K, thiobinupharidine, scopadulcic acid, and maslinic acid form significant interactions within the binding pocket of S100B, resulting in stable complexes. This underscores their potential role as S100B antagonists, thereby presenting novel therapeutic possibilities for epilepsy management. To sum up, this study's deployment of machine learning in conjunction with virtual screening not only has the potential to unearth new epilepsy therapeutics but also underscores the transformative potential of these advanced computational techniques in streamlining and enhancing drug discovery processes.

Understanding the prevalence, progression, and management of metabolic syndrome in Saudi Arabia.

Metabolic syndrome (MetS) is characterized by the coexistence of several disorders comprising hypertension, abdominal obesity, insulin sensitivity, and dyslipidemia. In recent times, MetS has gained increased attention due to the global prevalence of obesity. Adipose tissue plays a crucial role in this syndrome by releasing various molecules significantly affecting lipid/insulin regulation, oxidative stress, and cardiovascular function. Tumor necrosis factor-α (p-α), an inflammatory cytokine, and adiponectin, an adipose tissue-specific protein, are considered vital adipokines that play a significant role in the pathogenesis of MetS. The impact of dietary ingredients on MetS management has been extensively studied over the past few decades. These plant-derived natural chemicals have demonstrated beneficial impacts on obesity, diabetes, and cardiovascular disease (CVD) due to their diverse properties. Saudi Arabia has a high prevalence of overweight and diabetes, but there has been limited research on the incidence of MetS in the country. As a result, in this review, we evaluated the prevalence of MetS in Saudi Arabia and its associated risk factors, as well as explored the mechanisms of progression of MetS and the role of natural phytochemicals in the prevention of MetS.

Network pharmacology-based screening of active constituents of Avicennia marina and their clinical biochemistry related mechanism against breast cancer.

Breast cancer is the second major cause of cancer death in women globally. Avicennia marina is a medicinal plant that belongs to the family Acanthaceae and is known as grey or white mangrove. It has antioxidant, antiviral, anticancer, anti-inflammatory, and antibacterial activity in the treatment of various diseases including cancer. The goal of the study is to use a network pharmacology method to identify the potential phenomena of bioactive compounds of A. marina in the treatment of breast cancer and explore clinical biochemistry related aspects. A total of 74 active compounds of A. marina were retrieved from various databases as well as a literature review and collectively 429 targets of these compounds were identified by STITCH and Swiss Target Prediction databases. Breast cancer related 15606 potential targets were retrieved from the GeneCards database. A Venn diagram was drawn to find common key targets. To check the biological functions, the GO enrichment and KEGG pathways analysis of 171 key targets were performed through the DAVID database. To understand the interactions among key targets, Protein-protein interaction (PPI) studies were completed using the STRING database, and the Protein-Protein Interaction (PPI) network, as well as the compound-target-pathway network, was constructed using Cytoscape 3.9.0. Finally, molecular docking analysis of 5 hub genes named tumor protein 53 (TP53), catenin beta 1 (CTNNB1), interleukin 6 (IL6), tumor necrosis factor (TNF), and RAC-alpha serine/threonine protein kinases 1 (AKT1) with the active constituent of A. marina against breast cancer were performed. Additionally, a molecular docking study demonstrates that active drugs have a higher affinity for the target that may be used to decrease breast cancer. The molecular dynamic simulation analysis predicted the very stable behavior of docked complexes with no global structure deviations seen. The MMGBSA further supported strong intermolecular interactions with net energy values as; AKT1_Betulinic_acid (-20.97 kcal/mol), AKT1_Stigmasterol (-44.56 kcal/mol), TNF_Betulinic_acid (-28.68 kcal/mol) and TNF_Stigmastero (-29.47 kcal/mol).Communicated by Ramaswamy H. Sarma.

Integrated molecular modeling and dynamics approaches revealed potential natural inhibitors of NF-κB transcription factor as breast cancer therapeutics.

Breast cancer is a silent killer malady among women and a serious economic burden in health care management. A case of breast cancer is diagnosed among women every 19 s, and every 74 s, a woman dies of breast cancer somewhere in the world. Despite the pop-up of progressive research, advanced treatment approaches, and preventive measures, breast cancer remains amplifying ailment. The nuclear factor kappa B (NF-κB) is a key transcription factor that links inflammation with cancer and is demonstrated as being involved in the tumorigenesis of breast cancer. The NF-κB transcription factor family in mammals consists of five proteins; c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52). The antitumor effect of NF-κB has also been explored in breast cancer, however, the actual treatment for breast cancer is yet to be discovered. This study is attributed to the identification of novel drug targets against breast cancer by targeting c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52) proteins. To identify the putative active compounds, a structure-based 3D pharmacophore model to the protein active site cavity was generated followed by virtual screening, molecular docking, and molecular dynamics (MD) simulation. Initially, a library of 45000 compounds were docked against the target protein and five compounds namely Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 were selected for further analysis. The relative binding affinity of Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 with NF-κB1 (p50), NF-κB2 (p52), RelA (p65), RelB, and c-Rel proteins were -6.8, -8, -7.0, -6.9, and -7.2 kcal/mol, respectively which remained stable throughout the simulations of 200 ns. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with breast cancer, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.