MLASM: Machine learning based prediction of anticancer small molecules.

Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared to traditional methods. However, the process of identifying ACPs through experimental means is often time-intensive and expensive. To overcome this issue, we employed a machine learning-based approach for the first time to develop an anticancer model using small molecules. Anticancer small molecules (ACSMs) are compounds that have been developed to target and inhibit cancer cells. In this study, we used 10,000 compounds to develop the machine learning models using five algorithms such as, Random Forest (RF), Light gradient boosting machine (LightGBM), K-nearest neighbors (KNN), Decision tree (DT) and Extreme Gradient Boosting (XGB). The developed models were evaluated using the test set and top three models were identified (RF, LightGBM and XGB). Furthermore, to validate the predictive performance of our models, we have performed external validation using an FDA approved anticancer compounds/drugs. Following this analysis, we found that our LightGBM model correctly predicted 9 compounds as active. However, RF and XGB exhibited some limitations by predicting 8 and 7 compounds as active out of 10, respectively. These results demonstrate that, when compared to RF and XGB, the LightGBM model showcase robust prediction capabilities, achieving a superior accuracy of 79% with an AUC of 0.88. These findings provide promising insights into the potential of our approach for predicting anticancer small molecules, highlighting the role of machine learning in advancing cancer treatment research.

Computational drug repositioning for IL6 triggered JAK3 in rheumatoid arthritis using FDA database.

Rheumatoid Arthritis (RA) is a persistent autoimmune disease affecting approximately 0.5-1 percent of the world population. RA prevalence is higher in woman aged between 35 and 50 years than in age matched men, though this difference is less evident among elderly patients. The profound immune specific effects of disrupted JAK 3 (Janus kinase 3) signaling highlight the possibility of therapeutic targeting of JAK3 as a highly specific mode of immune system suppression. To address the above problem which is unendurable to patients and in the hope to cater some respite to such suffering we have targeted JAK 3 protein and JAK/STAT signaling pathway with compounds downloaded from FDA database, and performed screening of all available compounds docked against JAK3 protein. The difference between the target protein and other proteins of the same family was studied using cross docking and the compounds having higher binding affinity to JAK3 protein also showed more selectivity towards the particular protein. Density functional theory and molecular dynamics simulation study was done to study the compounds at their atomic level to know more about their drug likeliness. At the end of the study and based on our analysis we have come up with three FDA approved drugs that can be proposed as a treatment option for Rheumatoid Arthritis.

Association of Laccase from Bacillus cereus O2-B and Pseudomonas aeruginosa O1-P with the bio-degradation of polymers: an in vitro to in silico approach.

Plastic accumulation has become a serious environmental threat. Mitigation of plastic is important to save the ecosystem of our planet. With current research being focused on microbial degradation of plastics, microbes with the potential to degrade polyethylene were isolated in this study. In vitro studies were performed to define the correlation between the degrading capability of the isolates and laccase, a common oxidase enzyme. Instrumental analyses were used to evaluate morphological and chemical modifications in polyethylene, which demonstrated a steady onset of the degradation process in case of both isolates, Pseudomonas aeruginosa O1-P and Bacillus cereus O2-B. To understand the efficiency of laccase in degrading other common polymers, in silico approach was employed, for which 3D structures of laccase in both the isolates were constructed via homology modeling and molecular docking was performed, revealing that the enzyme laccase can be exploited to degrade a wide range of polymers.

Structure and dynamics of the somatostatin receptor 3-ligand binding in the presence of lipids examined using computational structural biology methods.

In the past two decades, the structural biology studies on G-protein coupled receptors (GPCRs) are on the rise. Understanding the relation between the structure and function of GPCRs is important as they play a huge role in various signaling mechanisms in a eukaryotic cell. Somatostatin receptor 3 (SSTR3), one of the GPCRs, is one such important receptor which oversees different cellular processes including cell-to-cell signaling. However, the information available regarding the structural features of SSTR3 responsible for their bioactivity is scarce. In this study, we report a structural understanding of SSTR3-ligand binding that could be helpful in demystifying the structural complexities related to functioning of the receptor. An integrated protocol consisting of different computational structural biology tools including protein structure prediction via comparative modeling, binding site characterization, three-dimensional quantitative structure-activity relationship based on comparative molecular field analysis and comparative molecular similarity indices analysis, density functional theory, and molecular dynamics simulations were performed. Different understandings from the simulation of SSTR3-ligand complexes, mainly the conditions that are favorable for the formation of lowest bioactive state of SSTR3 ligands are reported. In addition to that, we report the important physicochemical descriptors of SSTR3 ligands that could significantly influence their bioactivity. The results of the study could be helpful in developing novel SSTR3 ligands (both agonists and antagonists) with high potency and receptor selectivity.

Anti-inflammatory role demonstrated both in vitro and in vivo models using nonsteroidal tetranortriterpenoid, Nimbin (N1) and its analogs (N2 and N3) that alleviate the domestication of alternative medicine.

Human health may benefit from the study of natural compounds and phytoconstituents that can protect from inflammation. We investigated Nimbin (N1), a member of the ring C Seco-tetranortriterpenoids family, and its semi-natural analog deacetyl Nimbin namely N2 and N3 for their anti-inflammatory properties. As key findings, N1, N2, and N3 were able to improve wound healing by cell proliferation in a period of 24 h and were able to reduce the reactive oxygen species (ROS) production in Madin-Darby Canine Kidney cells which were screened using dichloro-dihydro fluorescein diacetate (DCF-DA) staining. When the zebrafish larvae were subjected to DCF-DA assay N1, N2, and N3 were able to substantially reduce the ROS levels in a dose-dependent manner. In zebrafish larvae, the cell death indicates the fluorescent intensity due to acridine orange staining that was found to be dramatically decreasing upon the treatment of N1, N2, and N3. The cell membrane lipid peroxidation levels were also reduced in a dose-dependent manner upon the treatment of Nimbin and its analogs indicating lesser blue fluorescent levels. Among the Nimbin and its analogs, N2 was subjected to have better activity. To confirm the activity of N1, N2, and N3, in silico characterization was performed using Density functional theory and molecular docking. As a result, N2 exhibited the lowest electronegative value and highest binding energy when docked with anti-inflammatory and antioxidant proteins CAT, COX, GP, IL-1, and MPO. Furthermore, the therapeutic potential of N2 must be explored at the molecular level as well as in clinical studies for the treatment of inflammation-associated diseases.

Computational evaluation of major components from plant essential oils as potent inhibitors of SARS-CoV-2 spike protein.

COVID-19, caused by SARS-CoV-2 has recently emerged as a global pandemic. Intense efforts are ongoing to find a vaccine or a drug to control the disease across the globe. Meanwhile, alternative therapies are also being explored to manage the disease. Phytochemicals present in essential oils are promising candidates which have been known to possess wide range of therapeutic activities. In this study, major components of several essential oils which are known for their antimicrobial properties have been docked against the S1 receptor binding domain of the spike (S) glycoprotein, which is the key target for novel antiviral drugs, to ascertain their inhibitory effects based on their binding affinities. It has been found that some monoterpenes, terpenoid phenols and phenyl propanoids such as anethole, cinnamaldehyde, carvacrol, geraniol, cinnamyl acetate, L-4-terpineol, thymol and pulegone from essential oils extracted from plants belonging to families such as Lamiaceae, Lauraceae, Myrtaceae, Apiaceae, Geraniaceae and Fabaceae are effective antiviral agents that have potential to inhibit the viral spikeprotein.

Understanding the structural features of JAK2 inhibitors: a combined 3D-QSAR, DFT and molecular dynamics study.

JAK2 plays a critical role in JAK/STAT signaling pathway and in patho-mechanism of myeloproliferative disorders and autoimmune diseases. Thus, effective JAK2 inhibitors provide a promising opportunity for the pharmaceutical intervention of many diseases. In this work, 3D-QSAR study was performed on a series of 1-amino-5H-pyrido-indole-4-carboxamide derivatives as JAK2 inhibitors to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) models with three different alignment methods. Among the different alignment methods, ligand-based (CoMFA: q2 = 0.676, r2 = 0.979; CoMSIA: q2 = 0.700, r2 = 0.953) and pharmacophore-based alignment (CoMFA: q2 = 0.710, r2 = 0.982; CoMSIA: q2 = 0.686, r2 = 0.960) has produced better statistical results when compared to receptor-based alignment (CoMFA: q2 = 0.507, r2 = 0.979; CoMSIA: q2 = 0.544, r2 = 0.917). Statistical parameters indicated that data are well fitted and have high predictive ability. The presence of electrostatic and hydrophobic field is highly desirable for potent inhibitory activity, and the steric field plays a minor role in modulating the activity. The contour analysis indicates ARG980, ASN981, ASP939 and LEU937 have more possibility of interacting with bulky, hydrophobic groups in pyrido and positive and negative groups in pyrazole ring. Based on our findings, we have designed sixteen molecules and predicted its activity and drug-like properties. Subsequently, molecular docking, molecular dynamics and DFT calculations were performed to evaluate its potency.

Theoretical analysis of somatostatin receptor 5 with antagonists and agonists for the treatment of neuroendocrine tumors.

We report on SSTR5 receptor modeling and its interaction with reported antagonist and agonist molecules. Modeling of the SSTR5 receptor was carried out using multiple templates with the aim of improving the precision of the generated models. The selective SSTR5 antagonists, agonists and native somatostatin SRIF-14 were employed to propose the binding site of SSTR5 and to identify the critical residues involved in the interaction of the receptor with other molecules. Residues Q2.63, D3.32, Q3.36, C186, Y7.34 and Y7.42 were found to be highly significant for their strong interaction with the receptor. SSTR5 antagonists were utilized to perform a 3D quantitative structure-activity relationship study. A comparative molecular field analysis (CoMFA) was conducted using two different alignment schemes, namely the ligand-based and receptor-based alignment methods. The best statistical results were obtained for ligand-based ([Formula: see text], [Formula: see text] = 0.988, noc = 4) and receptor-guided methods (docked mode 1:[Formula: see text], [Formula: see text], noc = 5), (docked mode 2:[Formula: see text] = 0.555, [Formula: see text], noc = 5). Based on CoMFA contour maps, an electropositive substitution at [Formula: see text], [Formula: see text] and [Formula: see text] position and bulky group at [Formula: see text] position are important in enhancing molecular activity.

ANN multi-layer perceptron for prediction of blood-brain barrier permeable compounds for central nervous system therapeutics.

Endothelial cells produce a semipermeable barrier known as the blood-brain barrier (BBB) to keep undesired chemicals out of the central nervous system (CNS). However, this barrier also restricts the exploration of potential new medications due to insufficient exposure. To address this challenge, machine learning (ML) algorithms can be useful to predict the BBB permeability of chemical compounds. Support vector machines, continuous neural networks, and deep learning approaches have been used to identify compounds that can penetrate the BBB. However, predicting BBB permeability based solely on chemical structure can be difficult. In the current research, we developed an ML model using a large dataset to predict BBB permeability, which could be used for early-stage drug screening of potential CNS medications. Our artificial neural network ANN algorithm exhibited an accuracy of 0.94, specificity of 0.83, sensitivity of 0.97, AUC of 0.96, and MCC of 0.83. These metrics suggest that our model has a high accuracy rate in predicting BBB permeability and therefore has the potential to advance drug discovery efforts in the CNS. This study's outcomes demonstrate the potential for ML models to predict BBB permeability accurately, aiding in the identification of new CNS therapeutic options.Communicated by Ramaswamy H. Sarma.

In-silico binding affinity of a phage display library screened novel peptide against various FABPs.

In accordance to the American Heart Association (AHA), cardiovascular diseases (CVDs) are the leading cause of death around the globe, causing more than 19.1 million deaths in 2020. Heart-type fatty acid binding protein (H-FABP) is required for the metabolism of fatty acids (FA) inside cardiomyocytes is reported as a biomarker for myocardial damage. As early as one hour after an Acute myocardial infarction (AMI), H-FABP can be used to detect myocardial ischemia. Thus, H-FABP based detection can reduce the burden on the emergency department. A peptide-based detection system can provide point-of-care diagnostics for CVDs. There is a lot of research being done on peptide-based detection, and it has a lot of potential to help with unmet medical diagnostic needs. A twelve (12) amino acid peptide has been discovered using Phage Display Library Screening. The affinity of peptide with H-FABP and other FABPs has been done using molecular docking and ADMET profile has been done. Molecular docking of small peptides against the target protein can play a crucial role in recognizing peptide binding sites and poses. The docking study was done using the HDOCK server and the visualization of the docked complex was done using Pymol and UCSF chimera. The molecular simulation study of three protein-peptide complexes were done which also validated the binding affinity of peptide with the proteins. The RMSD, RMSF and radius of gyration are also analyzed. The results indicate that H-FABP shows higher level of binding interaction with the peptide having bond length ranging from 2.3 to 3.4 Å. The screened peptide is suitable for H-FABP binding and can be used for prognosis purposes in the heart ischemic conditions.

Repurposing FDA-approved compounds to target JAK2 for colon cancer treatment.

Colorectal cancer is one of the common cancers worldwide and the second leading cause of cancer-related death. The current treatment has the inherent drawbacks and there is a need of developing a new treatment. Interleukin-6 a pleiotropic cytokine involved in immune regulation and activation of JAK2/STAT3 pathway in colorectal cancer. JAK2/STAT3 signaling pathway functions as a critical regulator of cell growth, differentiation, and immune expression. The abnormality in the JAK2/STAT3 pathway is involved in the tumorigenesis of colon cancer including apoptosis. In this study, we identified novel inhibitors for JAK2 protein by performing virtual screening against FDA-approved compounds. To address the selectivity issue, we implemented cross-docking method followed by DFT calculations to understand the chemical reactivity of the identified compounds. Additionally, molecular dynamics (MD) simulations were performed for the top FDA compounds against JAK2 to understand the molecular interactions and structural stability of the complex over a period of 200 ns. Our results indicated that ergotamine, entrectinib, exatecan, dihydroergotamine, and paritaprevir can be used as alternative drugs for colon cancer. In addition, ergotamine was found to efficiently lower the cell viability with IC50 values of 100 µM on colon cancer cell lines. The long-term inhibitory effect of the ergotamine led to a decrease in colony size, and the toxicity properties were studied using hemolysis assay. Our study shows the potential of targeting JAK2 as a novel approach to colon cancer treatment, and demonstrate that ergotamine as a promising effects as an anti-cancer drug.

Inducing Cytotoxicity in Colon Cancer Cells and Suppressing Cancer Stem Cells by Dolasetron and Ketoprofen through Inhibition of RNA Binding Protein PUM1.

Clinical trials of new drugs often face a high failure rate of approximately 45 percent due to safety and toxicity concerns. Repurposing drugs with well-established safety profiles becomes crucial in addressing this challenge. Colon cancer ranks as the third most prevalent cancer and the second leading cause of cancer related mortality worldwide. This study focuses on the RNA-binding protein pumilio1 (PUM1), a member of the PUF family involved in post-transcriptional gene expression regulation. By utilizing molecular docking techniques and FDA-approved drugs, potential inhibitors against PUM1 were identified. Notably, dolasetron and ketoprofen demonstrated promising results, exhibiting strong binding affinity, hydrophobic interactions, and favorable chemical reactivity according to Conceptual-DFT calculations. Both compounds effectively reduced cell viability, with IC50 values of 150 µM and 175 µM, respectively and shows long term inhibitory effects as seen by reduced in number of colonies. Moreover, they exhibited inhibitory effects on colon cancer stem cells, as indicated by reduced colonospheroid size and numbers. Apoptosis is induced by these compounds and has triggered activation of executioner caspase 3/7 in HCT116 cells which is evident through a caspase 3/7 assay and AO/EB staining, while the non-toxic effect of these compounds was evident from viability against non-cancerous cell line and hemolysis assay. Additionally, the treatment group showed a significant decrease in PUM1 and cancer stem cell markers expression compared to the control group. In conclusion, this study highlights the potential of targeting PUM1 as a novel approach to colon cancer treatment. Dolasetron and ketoprofen demonstrate promise as effective anti-cancer and anti-cancer stem cell drugs, inducing apoptosis in colon cancer cells through inhibition of PUM1.

Computational study of the motor neuron protein KIF5A to identify nsSNPs, bioactive compounds, and its key regulators.

Introduction: Kinesin family member 5A (KIF5A) is a motor neuron protein expressed in neurons and involved in anterograde transportation of organelles, proteins, and RNA. Variations in the KIF5A gene that interfere with axonal transport have emerged as a distinguishing feature in several neurodegenerative disorders, including hereditary spastic paraplegia (HSP10), Charcot-Marie-Tooth disease type 2 (CMT2), and Amyotrophic Lateral Sclerosis (ALS). Methods: In this study, we implemented a computational structural and systems biology approach to uncover the role of KIF5A in ALS. Using the computational structural biology method, we explored the role of non-synonymous Single Nucleotide Polymorphism (nsSNPs) in KIF5A. Further, to identify the potential inhibitory molecule against the highly destabilizing structure variant, we docked 24 plant-derived phytochemicals involved in ALS. Results: We found KIF5AS291F variant showed the most structure destabilizing behavior and the phytocompound "epigallocatechin gallate" showed the highest binding affinity (-9.0 Kcal/mol) as compared to wild KIF5A (-8.4 Kcal/mol). Further, with the systems biology approach, we constructed the KIF5A protein-protein interaction (PPI) network to identify the associated Kinesin Families (KIFs) proteins, modules, and their function. We also constructed a transcriptional and post-transcriptional regulatory network of KIF5A. With the network topological parameters of PPIN (Degree, Bottleneck, Closeness, and MNC) using CytoHubba and computational knock-out experiment using Network Analyzer, we found KIF1A, 5B, and 5C were the significant proteins. The functional modules were highly enriched with microtubule motor activity, chemical synaptic transmission in neurons, GTP binding, and GABA receptor activity. In regulatory network analysis, we found KIF5A post-transcriptionally down-regulated by miR-107 which is further transcriptionally up-regulated by four TFs (HIF1A, PPARA, SREBF1, and TP53) and down-regulated by three TFs (ZEB1, ZEB2, and LIN28A). Discussion: We concluded our study by finding a crucial variant of KIF5A and its potential therapeutic target (epigallocatechin gallate) and KIF5A associated significant genes with important regulators which could decrypt the novel therapeutics in ALS and other neurodegenerative diseases.

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches.

JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein-ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction.

Anticancer peptides are emerging anticancer drug that offers fewer side effects and is more effective than chemotherapy and targeted therapy. Predicting anticancer peptides from sequence information is one of the most challenging tasks in immunoinformatics. In the past ten years, machine learning-based approaches have been proposed for identifying ACP activity from peptide sequences. These methods include our previous method MLACP (developed in 2017) which made a significant impact on anticancer research. MLACP tool has been widely used by the research community, however, its robustness must be improved significantly for its continued practical application. In this study, the first large non-redundant training and independent datasets were constructed for ACP research. Using the training dataset, the study explored a wide range of feature encodings and developed their respective models using seven different conventional classifiers. Subsequently, a subset of encoding-based models was selected for each classifier based on their performance, whose predicted scores were concatenated and trained through a convolutional neural network (CNN), whose corresponding predictor is named MLACP 2.0. The evaluation of MLACP 2.0 with a very diverse independent dataset showed excellent performance and significantly outperformed the recent ACP prediction tools. Additionally, MLACP 2.0 exhibits superior performance during cross-validation and independent assessment when compared to CNN-based embedding models and conventional single models. Consequently, we anticipate that our proposed MLACP 2.0 will facilitate the design of hypothesis-driven experiments by making it easier to discover novel ACPs. The MLACP 2.0 is freely available at https://balalab-skku.org/mlacp2.

Morin inhibits colon cancer stem cells by inhibiting PUM1 expression in vitro.

Over the last few decades, the number of people diagnosed with cancer has increased dramatically every year, making it a major cause of mortality today. Colon cancer is the third most common cancer worldwide, and the second in mortality rate. Current cancer treatment fails to treat colon cancer completely due to the remains of Cancer Stem Cells (CSCs). Morin flavonoid present in figs (Ficus carica) and other plant sources, was found to have an anti-proliferative effect on the colon cancer model and cell line, but it is not studied for its effect on the colon CSCs. In this study, we have tested the potency of morin to inhibit CSCs. We found that morin has significantly reduced colon cancer cell proliferation, colony formation, migration, and colonospheroid formation in a dose-dependent manner. Pumilio-1 (PUM1) has been shown to play an important role in colon CSCs maintenance. We found that morin has a good binding affinity with PUM1 protein with one hydrophobic and two hydrogen bond interactions. Further, the immunofluorescence results have also shown a reduction in PUM1 expression in colon cancer cell lines after morin treatment. CD133 is overexpressed in colon CSCs and morin treatment has reduced the CD133 expression in HCT116 and CT26 colon cancer cell lines. Our research outcome has explored the anti-cancer stem cell potency of morin via targeting the PUM1 protein and further reducing the colon spheroids formation and reducing the CD133 expression in colon cancer cells.

Radio-protective efficacy of Gymnema sylvestre on Pangasius sutchi against gamma (60Co) irradiation.

PURPOSE: Freshwater fish Pangasius sutchi was used in this study as a vertebrate model. We evaluated the induction of certain antioxidant enzymes in various vital organs. The radioprotective efficacy of Gymnema sylvestre leaves extract (GS) [25 mg/kg Body Weight (B.W)] and its bioactive compound Gymnemagenin (GG) [0.3 mg/kg B.W] was compared with Amifostine (Ami), the only radioprotector clinically approved by the US-FDA [Ami- 83.3 mg/kg B.W] against different doses of gamma radiation - 60Co (Lethal Dose: LD30-9.2 Gy, LD50-10.2 Gy and LD70-11.4 Gy). MATERIALS AND METHODS: This study was done via stress marker enzymes, cell cycle analysis (CCA) and DNA damage assay prediction with molecular docking, which are reported here for the first time. The results indicate an elevated LPO level and decreased level of CAT, SOD and GSH due to oxidative stress initiation by 60Co Ionizing Radiation (IR) on 4th day and slightly reduced on 32nd day while the reverse observed when the fishes were pretreated with Ami, GS and GG. Similarly, CCA and dead/live cells counts were conducted with pretreatment of Ami, GS and GG against 60Co IR dose (LD50-10.2 Gy). RESULTS: In CCA, G0/G1 phase was observed to be the highest in Ami and lowest in GG, against 60Co IR doses 10.2 Gy which was 51.76 ± 7.55. The dead cells range observed in pretreated group of Ami, GS and GG was lowest in Ami and highest in GG and live cells (highest in Ami and lowest in GG) as compared to 60Co IR group (86.43 ± 3.42 and 8.77 ± 5.95). Thus, antioxidant profile improvement by oxidative stress reduction and gradual progression of different phases of cell cycle except the apoptotic phase along with the live cells counts indicates that the radio-protective efficacy of GS is similar to Ami. CONCLUSION: Predictive assessment was carried out by docking of Ami, various components of GS with p53, NF-κß cells and Rad51 proteins structures responsible for CCA, apoptosis and repair mechanism. These structural proteins were docked with other structural proteins like USP7, TNF-α and partner and localizer of BRCA2 associated (PALB2/BRCA2) complex which made us perform these systemic efforts to find the functional activity of these known radio-protectants.

Current Update of Phytotherapeutic Agents in the Treatment of COVID-19: In-Silico Based Virtual Screening Approach for the Development of Antiviral Drug.

COVID-19, caused by the severe acquired respiratory syndrome coronavirus-2 (SARS-CoV-2), is a highly contagious disease that has emerged as a pandemic. Researchers and the medical fraternity are working towards the identification of anti-viral drug candidates. Meanwhile, several alternative treatment approaches are being explored to manage the disease effectively. Various phyto-drugs and essential oils have been reported to have antiviral activity, but this has not been well studied in the context of SARS-CoV-2. The main focus of this review is on the biology of infection and the different therapeutic strategies involved, including drug repurposing and phytopharmaceuticals. The role of phytochemicals in treating COVID-19 and various other diseases has also been emphasized.

In silico quantitative structure-activity relationship studies on P-gp modulators of tetrahydroisoquinoline-ethyl-phenylamine series.

BACKGROUND: Multidrug resistance (MDR) is a major obstacle in cancer chemotherapy. The drug efflux by a transport protein is the main reason for MDR. In humans, MDR mainly occurs when the ATP-binding cassette (ABC) family of proteins is overexpressed simultaneously. P-glycoprotein (P-gp) is most commonly associated with human MDR; it utilizes energy from adenosine triphosphate (ATP) to transport a number of substrates out of cells against concentration gradients. By the active transport of substrates against concentration gradients, intracellular concentrations of substrates are decreased. This leads to the cause of failure in cancer chemotherapy. RESULTS: Herein, we report Topomer CoMFA (Comparative Molecular Field Analysis) and HQSAR (Hologram Quantitative Structure Activity Relationship) models for third generation MDR modulators. The Topomer CoMFA model showed good correlation between the actual and predicted values for training set molecules. The developed model showed cross validated correlation coefficient (q2) = 0.536 and non-cross validated correlation coefficient (r2) = 0.975 with eight components. The best HQSAR model (q2 = 0.777, r2 = 0.956) with 5-8 atom counts was used to predict the activity of test set compounds. Both models were validated using test set compounds, and gave a good predictive values of 0.604 and 0.730. CONCLUSIONS: The contour map near R1 indicates that substitution of a bulkier and polar group to the ortho position of the benzene ring enhances the inhibitory effect. This explains why compounds with a nitro group have good inhibitory potency. Molecular fragment analyses shed light on some essential structural and topological features of third generation MDR modulators. Fragments analysis showed that the presence of tertiary nitrogen, a central phenyl ring and an aromatic dimethoxy group contributed to the inhibitory effect. Based on contour map information and fragment information, five new molecules with variable R1 substituents were designed. The activity of these designed molecules was predicted by the Topomer CoMFA and HQSAR models. The novel compounds showed higher potency than existing compounds.