Discovering potent inhibitors against the Mpro of the SARS-CoV-2. A medicinal chemistry approach.

The global pandemic caused by a single-stranded RNA (ssRNA) virus known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still at its peak, with new cases being reported daily. Although the vaccines have been administered on a massive scale, the frequent mutations in the viral gene and resilience of the future strains could be more problematic. Therefore, new compounds are always needed to be available for therapeutic approaches. We carried out the present study to discover potential drug compounds against the SARS-CoV-2 main protease (Mpro). A total of 16,000 drug-like small molecules from the ChemBridge database were virtually screened to obtain the top hits. As a result, 1032 hits were selected based on their docking scores. Next, these structures were prepared for molecular docking, and each small molecule was docked into the active site of the Mpro. Only compounds with solid interactions with the active site residues and the highest docking score were subjected to molecular dynamics (MD) simulation. The post-simulation analyses were carried out using the in-built GROMACS tools to gauge the stability, flexibility, and compactness. Principal component analysis (PCA) and hydrogen bonding were also calculated to observe trends and affinity of the drugs towards the target. Among the five top compounds, C1, C3, and C6 revealed strong interaction with the target's active site and remained highly stable throughout the simulation. We believe the predicted compounds in this study could be potential inhibitors in the natural system and can be utilized in designing therapeutic strategies against the SARS-CoV-2.

Polyvinylidene Fluoride-Added Ceramic Powder Composite Near-Field Electrospinned Piezoelectric Fiber-Based Low-Frequency Dynamic Sensors.

In this study, near-field electrospinning (NFES) is used to fabricate Ba x Sr1-x TiO3 (BST)/poly(vinylidene fluoride) (PVDF) piezoelectric fiber composites with excellent mechanical properties and chemical properties. BST ceramic powder is blended with PVDF solution uniformly to prepare a solution of appropriate conductance. The parameter for BST/PVDF fiber processing is based on PVDF fibers. Scanning electron microscopy, differential scanning calorimetry, microtensile testing, Fourier transform infrared spectroscopy, and electricity test of the blends of BST/PVDF fibers are incorporated. Mechanical properties of the fibers are then measured by microtensile testing. Effects of distinct ratios of Ba/Sr and the content of Ba0.7Sr0.3TiO3 ceramic powder on BST/PVDF piezoelectric fibers are discussed. Finally, BST/PVDF piezoelectric fiber composites are patterned on a poly(ethylene terephthalate) (PET)-based structure with an interdigital electrode as a BST/PVDF flexible energy harvester to capture ambient energy. The results show that the BST ceramic powder is ∼58-93 nm, and the diameters of piezoelectric fiber composites are ∼6.8-13.7 µm. The tensile strength of piezoelectric fiber composites is ∼74.92 MPa, and the Young's coefficient tensile strength is ∼3.74 GPa. Mechanical properties are 2-3 times higher than those of pure PVDF piezoelectric fibers. The maximum open-circuit voltage and closed-loop current of BST/PVDF fibers reached ∼1025 mV and ∼391 nA, respectively. The electromechanical energy conversion efficiency of the BST/PVDF energy harvester is found to be 1-2 times higher than that of the PVDF energy harvester. It is confirmed and validated that the addition of BST ceramic powder could effectively increase the piezoelectric constant of PVDF piezoelectric fibers.

An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15.

G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan G protein-coupled receptors (GPCRs) involving human immunodeficiency virus (HIV) infection, colonic inflammation, and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability to inhibit cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. Using large-scale publicly available data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we found that GPR15 expression is significantly lower in colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) than in normal tissues. Among 33 cancer types, GPR15 expression was significantly positively correlated with the prognoses of COAD, neck squamous carcinoma (HNSC), and lung adenocarcinoma (LUAD) and significantly negatively correlated with stomach adenocarcinoma (STAD). This study also revealed that commonly upregulated gene sets in the high GPR15 expression group (stratified via median) of COAD, HNSC, LUAD, and STAD are enriched in immune systems, indicating that GPR15 might be considered as a potential target for cancer immunotherapy. Furthermore, we modelled the 3D structure of GPR15 and conducted structure-based virtual screening. The top eight hit compounds were screened and then subjected to molecular dynamics (MD) simulation for stability analysis. Our study provides novel insights into the role of GPR15 in a pan-cancer manner and discovered a potential hit compound for GPR15 antagonists.

Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks.

The epidermal growth factor receptor, also known as EGFR, is a tyrosine kinase receptor commonly found in epithelial tumors. As part of the first target for cancer treatment, EGFR has been the subject of intense research for more than 20 years; as a result, there are a number of anti-EGFR agents currently available. More recently, with our basic understanding of mechanisms related to receptor activation and function, both the secondary and primary forms of EGFR somatic mutations have led to the discovery of new anti-EGFR agents aimed at providing new insights into the clinical targeting of this receptor and possibly acting as an ideal model for developing strategies to target other types of receptors. In this study, we use genomic pattern to prove that EGFR is most frequently altered in GBM, glioma and astrocytoma; and analysed the prognostic potentiality of EGFR in glioma, which is a major type of brain tumor. Further we proposed a new screening technique for EGFR inhibitors by employing an in silico optimized deep neural network approach. This method was applied to screen a nanoparticle (NP) library, and it was concluded that gold NPs (AuNPs) induced significant inhibition of EGFR compared with other selected NPs. These findings were further analyzed by molecular docking, systems biology, time course simulations and synthetic biology (biological circuits), revealing that anti-EGFR-iRGD and AuNP showed potential inhibition against tumors caused by EGFR.

Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease.

Herein, a two-step de novo approach was developed for the prediction of piperine targets and another prediction of similar (piperine) compounds from a small molecule library using a deep-learning method. Deep-learning and neural-network approaches were used for target prediction, similarity searches, and validation. The present approach was trained on records containing the data. The model attained an overall accuracy of around 87.5%, where the training and test set was kept as 70% and 30% (17 226/40 197), respectively. This method predicted two targets (MAO-A and MAO-B) and 101 compounds as piperine derivatives. MAO-A and MAO-B are important drug targets in Parkinson's disease. Validation of this method was also performed by considering piperine and its targets (monoamine oxidase A and B) using molecular docking, dynamics simulation and post-simulation analysis of all the selected compounds. Rasagiline, lazabemide, and selegiline were selected as controls, which are already FDA-approved drugs against these targets. Molecular docking studies of the FDA-approved drugs and the compounds we predicted using DL and neural networks were carried out against MAO-A and MAO-B. Using the molecular docking's scoring function, molecular dynamics simulation and free energy calculations as extended validation methods, it was observed that the compounds predicted herein possessed excellent inhibitory effects against the selected targets. Thus, deep learning may play a very effective role in predicting the potential compounds, their targets and can play an expanded role in computer-aided drug approaches.

The systematic modeling studies and free energy calculations of the phenazine compounds as anti-tuberculosis agents.

Phenazine compounds have good activity against Mycobacterium tuberculosis (MTB). Based on the reported activities that were obtained in MTB H37Rv, a three-dimensional quantitative structure-activity relationship (3D-QSAR) model was built to design novel compounds against MTB. A fivefold cross-validation method and external validation were used to analyze the accuracy of forecasting. The model has a cross-validation coefficient q2=0.7 and a non-cross-validation coefficient r2 = 0.903, indicating that the model has good predictive possibility. The design of anti-pneumococcus MTB compounds was guided by the obtained 3D-QSAR model, and several compounds with better activity were obtained. To test the activity of these compounds, molecular docking, molecular dynamics simulation, and post-simulation analysis of the already reported drug targets in MTB were carried out. Among the total 15 drug targets, only three targets (Rv2361c, Rv2965c, and Rv3048c) were selected based on the docking results. Initial results reported that these compounds possessed good inhibition activity for Rv2361c. The top nine complexes of Rv2361 ligands were only subjected to MD simulation which resulted in a stable dynamics of the structures and showed a residual fluctuation in inhibitors binding pocket. Free energy reported that overall, the derivatives hold strong energy against the protein target. Energetic contribution results showed that residues, Asp76, Arg80, Asn124, Arg127, Arg244, and Arg250, play a major role in total energy. Systems biology approach validates shortlisted drug effect on the entire system which might be useful to predict potential drug in wet lab as well. Communicated by Ramaswamy H. Sarma.

Allosteric ligands for the pharmacologically important Flavivirus target (NS5) from ZINC database based on pharmacophoric points, free energy calculations and dynamics correlation.

Dengue virus belongs to a group of human pathogens, which causes different diseases, dengue hemorrhagic fever and dengue shock syndrome in humans. It possesses RNA as a genetic material and is replicated with the aid of NS5 protein. RNA-dependent RNA polymerase (RdRp) is an important domain of NS5 in the replication of that virus. The catalytic process activity of RdRp is making it an important target for antiviral chemical therapy. To date, No FDA drug has been approved and marketed for the treatment of diseases caused by Dengue virus. So, there is a dire need to advance an area of active antiviral inhibitors that is safe, less expensive and widely available. An experimentally validated complex of Dengue NS5 and compound 27 (6LS) were used as pharmacophoric input and hits were identified. We also used Molecular dynamics (MD) simulations alongside free energy and dynamics of the internal residues of the apo and holo systems to understand the binding mechanism. Our analysis resulted that the three inhibitors (ZINC72070002, ZINC6551486, and ZINC39588257) greatly affected the interior dynamics and residual signaling to dysfunction the replicative role of NS5. The interaction of these inhibitors caused the loss of the correlated motion of NS5 near to the N terminus and helped the stability of the binding complex. This investigation provided a methodological route to discover allosteric inhibitors against the epidemics of this Flaviviruses. Allosteric inhibitors are important and major assets in considering the development of the competitive and robust antiviral agents such as against Dengue viral infection.