Computer Science
Computer-aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review
Natural plant sources are essential in the development of several anticancer drugs such as vincristine vinblastine vinorelbine docetaxel paclitaxel camptothecin etoposide and teniposide. However various chemotherapies fail due to adverse reactions drug resistance and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets have reduced adverse effects and are effective against several cancer types. Developing a new drug is a highly complex expensive and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources including Science Direct Elsevier NCBI and Web of Science are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD in-silico tools virtual screening in drug discovery and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.
Comprehensive In Silico Analysis of Uncaria Tomentosa Extract: Chemical Profiling, Antioxidant Assessment, and CLASP Protein Interaction for Drug Design in Neurodegenerative Diseases
Uncaria tomentosa is a traditional medicinal herb renowned for its anti-inflammatory antioxidant and immune-enhancing properties. In the realm of neurodegenerative diseases (NDDS) CLASP proteins responsible for regulating microtubule dynamics in neurons have emerged as critical players. Dysregulation of CLASP proteins is associated with NDDS such as Alzheimer's Parkinson's and Huntington's diseases. Consequently comprehending the role of CLASP proteins in NDDS holds promise for the development of innovative therapeutic interventions.
The objectives of the research were to identify phytoconstituents in the hydroalcoholic extract of Uncaria tomentosa (HEUT) to evaluate its antioxidant potential through in vitro free radical scavenging assays and to explore its potential interaction with CLASP using in silico molecular docking studies.
HPLC and LC-MS techniques were used to identify and quantify phytochemicals in HEUT. The antioxidant potential was assessed through DPPH ferric reducing antioxidant power (FRAP) nitric oxide (NO) and superoxide (SO) free radical scavenging methods. Interactions between conventional quinovic acid chlorogenic acid epicatechin corynoxeine rhynchophylline and syringic acid and CLASP were studied through in silico molecular docking using Auto Dock 4.2.
The HEUT extract demonstrated the highest concentration of quinovic acid derivatives. HEUT exhibited strong free radical-scavenging activity with IC50 values of 0.113 µg/ml (DPPH) and 9.51 µM (FRAP). It also suppressed NO production by 47.1 ± 0.37% at 40 µg/ml and inhibited 77.3 ± 0.69% of SO generation. Additionally molecular docking revealed the potential interaction of quinovic acid with CLASP for NDDS.
The strong antioxidant potential of HEUT and the interaction of quinovic acid with CLASP protein suggest a promising role in treating NDDS linked to CLASP protein dysregulation.
Discovery of Novel Pyrimidine Based Small Molecule Inhibitors as VEGFR-2 Inhibitors: Design, Synthesis, and Anti-cancer Studies
Receptor tyrosine kinases (RTKs) are potent oncoproteins in cancer that when mutated or overexpressed can cause uncontrolled growth of cells angiogenesis and metastasis making them significant targets for cancer treatment. Vascular endothelial growth factor receptor 2 (VEGFR2) is a tyrosine kinase receptor that is produced in endothelial cells and is the most crucial regulator of angiogenic factors involved in tumor angiogenesis. So a series of new substituted N-(4-((2-aminopyrimidin-5-yl)oxy)phenyl)-N-phenyl cyclopropane-11-dicarboxamide derivatives as VEGFR-2 inhibitors have been designed and synthesized.
Utilizing H-NMR C13-NMR and mass spectroscopy the proposed derivatives were produced and assessed. HT-29 and COLO-205 cell lines were used for the cytotoxicity tests. The effective compound was investigated further for the Vegfr-2 kinase inhibition assay cell cycle arrest and apoptosis. A molecular docking examination was also carried out with the Maestro-12.5v of Schrodinger.
In comparison to the reference drug Cabozantinib (IC50 = 9.10 and 10.66 μM) compound SP2 revealed promising cytotoxic activity (IC50 = 4.07 and 4.98 μM) against HT-29 and COLO-205 respectively. The synthesized compound SP2 showed VEGFR-2 kinase inhibition activity with (IC50 = 6.82 μM) against the reference drug Cabozantinib (IC50 = 0.045 μM). Moreover compound SP2 strongly induced apoptosis by arresting the cell cycle in the G1 phase. The new compounds' potent VEGFR-2 inhibitory effect was noted with key amino acids Asp1044 and Glu883 and the hydrophobic interaction was also observed in the pocket of the VEGFR-2 active site by using a docking study.
The results demonstrate that at the cellular and enzyme levels the synthetic compounds SP2 are similarly effective as cabozantinib. The cell cycle and apoptosis data demonstrate the effectiveness of the suggested compounds. Based on the findings of docking studies cytotoxic effects in vitro VEGFR-2 inhibition apoptosis and cell cycle arrest this research has given us identical or more effective VEGFR-2 inhibitors.
Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology
Sanguinarine (SAN) has been reported to have antioxidant anti-inflammatory and antimicrobial activities with potential for the treatment of osteoporosis (OP).
This work purposed to unravel the molecular mechanisms of SAN in the treatment of OP.
OP-related genes and SAN-related targets were predicted from public databases. Differential expression analysis and VennDiagram were adopted to detect SAN-related targets against OP. Protein-protein interaction (PPI) network was served for core target identification. Molecular docking and DeepPurpose algorithm were further adopted to investigate the binding ability between core targets and SAN. Gene pathway scoring of these targets was calculated utilizing gene set variation analysis (GSVA). Finally we explored the effect of SAN on the expressions of core targets in preosteoblastic MC3T3-E1 cells.
A total of 21 candidate targets of SAN against OP were acquired. Furthermore six core targets were identified among which CASP3 CTNNB1 and ERBB2 were remarkably differentially expressed in OP and healthy individuals. The binding energies of SAN with CASP3 CTNNB1 and ERBB2 were -6 -6.731 and -7.162 kcal/mol respectively. Moreover the GSVA scores of the Wnt/calcium signaling pathway were significantly lower in OP cases than in healthy individuals. In addition the expression of CASP3 was positively associated with Wnt/calcium signaling pathway. CASP3 and ERBB2 were significantly lower expressed in SAN group than in DMSO group whereas the expression of CTNNB1 was in contrast.
CASP3 CTNNB1 and ERBB2 emerge as potential targets of SAN in OP prevention and treatment.
Prescription Data Mining and Network Pharmacology Study of 1152 Patients with Rectal Prolapse using Traditional Chinese Medicine
In recent years the incidence of rectal prolapse has increased significantly due to the sedentary lifestyle and irregular eating habits of modern life. However there is a lack of clinical studies on the treatment of rectal prolapse with traditional Chinese medicine (TCM) with a large sample size. Therefore this study investigated the characteristics of rectal prolapse treatment formulas and then studied the network pharmacology of their core therapeutic drugs which can help to provide a reference for the treatment and postoperative care of rectal prolapse patients.
This study aimed to explore the prescription characteristics and the mechanism of action of core drugs in the treatment of rectal prolapse in Chinese medicine through data mining and bioinformatics techniques.
We collected the diagnosis and treatment information of patients with rectal prolapse from January 2014 to September 2021 in the electronic case database of Nanjing Hospital of TCM mined the patient information and prescription features using R screened the active ingredients of the core pairs of drugs and disease drug intersection targets using TCMSP and GnenCard databases and constructed a Protein-protein interaction (PPI) network using STRING and Cytoscape and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the intersecting targets were performed using Metascape and R.
We found that prolapse is easy to occur in people over 50 years old preferably in autumn and winter. Commonly used therapeutic Chinese medicines include Glycyrrhiza glabra Radix angelicae sinensis Radix astragali Atractylodes macrocephala and Pericarpium citri reticulatae which are mostly deficiency tonic medicines warm in nature and belong to spleen meridian. The core therapeutic medicinal pair was “Bupleuri radix-Cimicifugae rhizoma”. There were 190 common targets of Bupleuri radix and Cimicifugae rhizoma and 71 intersection targets of the drug pair and prolapse. The main components of the core drugs for the treatment of prolapse may be quercetin kaempferol Stigmasterol etc and the core targets may be CASP3 AKT1 HIF1A etc. The total number of GO entries for the intersection targets of “Bupleuri radix-Cimicifugae rhizoma” and diseases was 3495 among which the molecular functions accounted for the largest proportion mainly Pathways in cancer IL-18 signaling pathway etc. KEGG enriched pathway analysis yielded 168 results and the major pathways were pathways in cancer lipid and atherosclerosis IL-17 signaling pathway etc.
This study adopted real-world research methodology and used data mining and bioinformatics technology to mine the medication law of rectal prolapse and its core drug action mechanism from the clinical information of Chinese medicine.
DeepTransformer: Node Classification Research of a Deep Graph Network on an Osteoporosis Graph based on GraphTransformer
Osteoporosis (OP) is one of the most common diseases in the elderly population. It is mostly treated with medication but drug research and development have the disadvantage of taking a long time and having a high cost.
Therefore we developed a graph neural network with the help of artificial intelligence to provide new ideas for drug research and development for OP.
In this study we built a new osteoporosis graph (called OPGraph) and proposed a deep graph neural network (called DeepTransformer) to predict new drugs for OP. OPGraph is a graph data model established by gathering features and their interrelationships from a vast amount of OP data. DeepTransformer uses GraphTransformer as its foundational network and applies residual connections for deep layering.
The analysis and results showed that DeepTransformer outperformed numerous models on OPGraph with area under the curve (AUC) and area under the precision-recall curve (AUPR) reaching 0.9916 and 0.9911 respectively. In addition we conducted an in vitro validation experiment on two of the seven predicted compounds (Puerarin and Aucubin) and the results corroborated the predictions of our model.
The model we developed with the help of artificial intelligence can effectively reduce the time and cost of OP drug development and reduce the heavy economic burden brought to patient's family by complications caused by osteoporosis.
Insights into the Molecular Mechanisms of Bushen Huoxue Decoction in Breast Cancer via Network Pharmacology and In vitro experiments
Breast cancer (BC) is by far seen as the most common malignancy globally with 2.261 million patients newly diagnosed accounting for 11.7% of all cancer patients according to the Global Cancer Statistics Report (2020). The luminal A subtype accounts for at least half of all BC diagnoses. According to TCM theory Bushen Huoxue Decoction (BSHXD) is a prescription used for cancer treatment that may influence luminal A subtype breast cancer (LASBC).
To analyze the clinical efficacy and underlying mechanisms of BSHXD in LASBC.
Network pharmacology and in vitro experiments were utilized to foresee the underlying mechanism of BSHXD for LASBC.
According to the bioinformatics analysis BSHXD induced several proliferation and apoptosis processes against LASBC and the presumed targets of active components in BSHXD were mainly enriched in the HIF-1 and PI3K/AKT pathways. Flow cytometry assay and western blotting results revealed that the rate of apoptosis enhanced in a dose-dependent manner with BSHXD concentration increasing respectively. BSHXD notably downregulated the expressions of HIF-1α P-PI3K PI3K P-AKT and AKT proteins. However adding an HIF-1α agonist restored those protein levels.
The study proved that the mechanism of BSHXD in LASBC may be connected to suppressing proliferation by inhibiting the activity of the HIF-1α/PI3K/AKT signaling pathway and promoting apoptosis via the Caspase cascade in LASBC cells.
Insights to Design New Drugs against Human African Trypanosomiasis Targeting Rhodesain using Covalent Docking, Molecular Dynamics Simulations, and MM-PBSA Calculations
Neglected tropical diseases (NTDs) are parasitic and bacterial diseases that affect approximately 149 countries mainly the poor population without basic sanitation. Among these Human African Trypanosomiasis (HAT) known as sleeping sickness shows alarming data with treatment based on suramin and pentamidine in the initial phase and melarsoprol and eflornithine in the chronic phase. Thus to discover new drugs several studies point to rhodesain as a promising drug target due to the function of protein degradation and intracellular transport of proteins between the insect and host cells and is present in all cycle phases of the parasite.
Here based on the previous studies by Nascimento et al. (2021) [5] that show the main rhodesain inhibitors development in the last decade molecular docking and dynamics were applied in these inhibitors datasets to reveal crucial information that can be into drug design.
Also our findings using MD simulations and MM-PBSA calculations confirmed Gly19 Gly23 Gly65 Asp161 and Trp184 showing high binding energy (ΔGbind between -72.782 to -124.477 kJ.mol-1). In addition Van der Waals interactions have a better contribution (-140930 to -96988 kJ.mol-1) than electrostatic forces (-43270 to -6854 kJ.mol-1) indicating Van der Waals interactions are the leading forces in forming and maintaining ligand-rhodesain complexes. Thus conventional and covalent docking was employed and highlighted the presence of Michael acceptors in the ligands in a peptidomimetics scaffold and interaction with Gly19 Gly23 Gly65 Asp161 and Trp184 is essential to the inhibiting activity. Furthermore the Dynamic Cross-Correlation Maps (DCCM) show more correlated movements for all complexes than the free rhodesain and strong interactions in the regions of the aforementioned residues. Principal Component Analysis (PCA) demonstrates complex stability corroborating with RMSF and RMSD.
This study can provide valuable insights that can guide researchers worldwide to discover a new promising drug against HAT.
Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning
Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However there is high variability in the shape size and structure of the tumor making automatic segmentation difficult. The automatic and accurate detection and segmentation methods for brain tumors are proposed in this paper.
A modified ResNet50 model was used for tumor detection and a ResUNetmodel-based convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast FLAIR and post-contrast MRI images of 110 patients collected from the cancer imaging archive. Due to the use of residual networks the authors observed improvement in evaluation parameters such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation.
The accuracy of tumor detection and dice similarity coefficient achieved by the segmentation model were 96.77% and 0.893 respectively for the TCIA dataset. The results were compared based on manual segmentation and existing segmentation techniques. The tumor mask was also individually compared to the ground truth using the SSIM value. The proposed detection and segmentation models were validated on BraTS2015 and BraTS2017 datasets and the results were consensus.
The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the UNet model and the accuracy of the model was increased from 92% to 96.77% for the test set.
Hibiscus sabdariffa Linn. Extract Increases the mRNA Expression of the Arcuate Nucleus Leptin Receptor and is Predicted in silico as an Anti-obesity Agent
Background: Leptin is predominant in regulating body weight by stimulating energy expenditure through its neuronal action in the brain. Moreover it is projected to adipose tissue and induces adipocyte browning by activating the β3-adrenergic receptor (β3AR). However the expression of leptin receptor (Lep-R) and β3AR in people with obesity is downregulated. Aim: We hypothesized that Hibiscus sabdariffa Linn. extract (HSE) would increase hypothalamus arcuate nucleus (ARC) Lep-R and white adipose tissue (WAT) β3AR mRNA expression in DIO rats. This study also analyzed the potency of H. sabdariffa bioactive compounds as activators of Lep-R and β3AR by an in-silico experiment. Methods: Twenty-four male Sprague-Dawley rats were divided into four groups: Control (standard food) DIO (high-fat diet) DIO-Hib200 (HFD+HSE 200 mg/kg BW) and DIO-Hib400 (HFD+HSE400 mg/kg BW). HSE was administered orally for five weeks once a day. Results: HSE administration significantly (p <005) increased the ARC Lep-R expression. The Lee index significantly decreased to the normal range (≤ 310) with p <0001 for DIO-Hib200 and p <001 for DIO-Hib400. Among 39 bioactive compounds 5-O-caffeoyl shikimic acid exhibited high free binding scores (-863) for Lep-R and myricetin_3_arabinogalactoside had high free binding scores (-939) for β3AR. These binding predictions could activate Lep-R and β3AR. Conclusion: This study highlights that HSE could be a potential therapeutic target for obesity by increasing LepR mRNA and leptin sensitivity enhancing energy expenditure and reducing obesity.
A Computational Approach for Designing and Validating Small Interfering RNA against SARS-CoV-2 Variants
Aims: The aim of this study is to develop a novel antiviral strategy capable of efficiently targeting a broad set of SARS-CoV-2 variants. Background: Since the first emergence of SARS-CoV-2 it has rapidly transformed into a global pandemic posing an unprecedented threat to public health. SARS-CoV-2 is prone to mutation and continues to evolve leading to the emergence of new variants capable of escaping immune protection achieved due to previous SARS-CoV-2 infections or by vaccination. Objective: RNA interference (RNAi) is a remarkable biological mechanism that can induce gene silencing by targeting complementary mRNA and inhibiting its translation. Methods: In this study using the computational approach we predicted the most efficient siRNA capable of inhibiting SARS-CoV-2 variants of concern (VoCs). Results: The presented siRNA was characterized and evaluated for its thermodynamic properties offsite-target hits and in silico validation by molecular docking and molecular dynamics simulations (MD) with Human AGO2 protein. Conclusion: The study contributes to the possibility of designing and developing an effective response strategy against existing variants of concerns and preventing further.
Network Pharmacology, Molecular Docking and Experimental Verification Revealing the Mechanism of Fule Cream against Childhood Atopic Dermatitis
Background: The Fule Cream (FLC) is an herbal formula widely used for the treatment of pediatric atopic dermatitis (AD) however the main active components and functional mechanisms of FLC remain unclear. This study performed an initial exploration of the potential acting mechanisms of FLC in childhood AD treatment through analyses of an AD mouse model using network pharmacology molecular docking technology and RNA-seq analysis. Materials and Methods: The main bioactive ingredients and potential targets of FLC were collected from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and SwissTargetPrediction databases. An herb-compound-target network was built using Cytoscape 3.7.2. The disease targets of pediatric AD were searched in the DisGeNET Therapeutic Target Database (TTD) OMIM DrugBank and GeneCards databases. The overlapping targets between the active compounds and the disease were imported into the STRING database for the construction of the protein-protein interaction (PPI) network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the intersection targets were performed and molecular docking verification of the core compounds and targets was then performed using AutoDock Vina 1.1.2. The AD mouse model for experimental verification was induced by MC903. Results: The herb-compound-target network included 415 nodes and 1990 edges. Quercetin luteolin beta-sitosterol wogonin ursolic acid apigenin stigmasterol kaempferol sitogluside and myricetin were key nodes. The targets with higher degree values were IL-4 IL-10 IL-1α IL-1β TNFα CXCL8 CCL2 CXCL10 CSF2 and IL-6. GO enrichment and KEGG analyses illustrated that important biological functions involved response to extracellular stimulus regulation of cell adhesion and migration inflammatory response cellular response to cytokine stimulus and cytokine receptor binding. The signaling pathways in the FLC treatment of pediatric AD mainly involve the PI3K-Akt signaling pathway cytokine128;’cytokine receptor interaction chemokine signaling pathway TNF signaling pathway and NF-ΚB signaling pathway. The binding energy scores of the compounds and targets indicate a good binding activity. Luteolin quercetin and kaempferol showed a strong binding activity with TNFα and IL-4. Conclusion: This study illustrates the main bioactive components and potential mechanisms of FLC in the treatment of childhood AD and provides a basis and reference for subsequent exploration.
Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma
Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease. Objective: The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL. Methods: Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms. Results: A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL five final core target genes including CTSL NR1H2 PDPK1 MDM2 and JAK3 were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway. Conclusion: Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.
Deciphering the Underlying Mechanisms of Sanleng-Ezhu for the Treatment of Idiopathic Pulmonary Fibrosis Based on Network Pharmacology and Single-cell RNA Sequencing Data
Aims: To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data. Background: Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF its underlying mechanisms remain unknown. Methods: Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA) respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO) GO and KEGG enrichment analyses. Next we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis. Results: We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source we obtained 1907 DEGs of IPF. Next we performed a WGCNA analysis on the dataset and got 11 modules. Notably we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs respectively. The different IPF subgroups were compared with drugcandidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO GO and KEGG enrichment analyses were applied to drug targets IPF subgroup genes and MCODE component signature genes. In addition we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecular docking revealed the mode of interaction between targets and their corresponding components. Animal experiments confirmed the efficacy of SL-EZ. Conclusion: We found SL-EZ acted on epithelial cells mainly through the calcium signaling pathway in the lowly-expressed IPF subtype while in the highly-expressed IPF subtype SL-EZ acted on smooth muscle cells mainly through the viral infection apoptosis and p53 signaling pathway.
Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding
Background: In this study we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI) which integrates various types of information in the heterogeneous network data and to explore automatic learning of the topology-maintaining representations of drugs and targets thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0 protein–protein interaction (PPI) from the human protein reference database Release 9 drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit and protein sequence similarity from Smith-Waterman score. Methods: Our study consists of three major components. First various drugs and target proteins were integrated and a heterogeneous network was established based on a series of data sets. Second the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally potential DTI prediction was made and the obtained samples were sent to the classifier for secondary classification. Results: The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results. Conclusion: Compared with other baseline DTI prediction methods the results showed that Graph-DTI had better prediction performance. Additionally in this study we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research development and repositioning. Graph- DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.
Molecular Modelling of Resveratrol Derivatives with SIRT1 for the Stimulation of Deacetylase Activity
Background: Resveratrol is a polyphenol that is found in plants and has been proposed to have a potential therapeutic effect through the activation of SIRT1 which is a crucial member of the mammalian NAD+ -dependent deacetylases. However how its activity is enhanced toward specific substrates by resveratrol derivatives has not been studied. This study aimed to evaluate the types of interaction of resveratrol and its derivatives with SIRT1 as the target protein as well as to find out the best ligand with the strangest interaction with SIRT1. Materials and Methods: In this study we employed the extensive molecular docking analysis using AutoDock Vina to comparatively evaluate the interactions of resveratrol derivatives (22 molecules from the ZINC database) as ligands with SIRT1 (PDB ID: 5BTR) as a receptor. The ChemDraw and Chem3D tools were used to prepare 3D structures of all ligands and energetically minimize them by the MM2 force field. Results: The molecular docking and visualizations showed that conformational change in resveratrol derivatives significantly influenced the parameter for docking results. Several types of interactions including conventional hydrogen bonds carbon-hydrogen bonds Pi-donor hydrogen bonds and Pi-Alkyl were found via docking analysis of resveratrol derivatives and SIRT1 receptors. The possible activation effect of resveratrol 4'-(6-galloylglucoside) with ZINC ID: ZINC230079516 with higher binding energy score (-46.8608 kJ/mol) to the catalytic domain (CD) of SIRT1 was achieved at the maximum value for SIRT1 as compared to resveratrol and its other derivatives. Conclusion: Finally resveratrol 4'-(6-galloylglucoside) as a derivative for resveratrol has stably interacted with the CD of SIRT1 and might be a potential effective activator for SIRT1.
Discovery of a Potential Allosteric Site in the SARS-CoV-2 Spike Protein and Targeting Allosteric Inhibitor to Stabilize the RBD Down State using a Computational Approach
Background: The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide public health crisis. At present the development of effective drugs and/or related therapeutics is still the most urgent and important task for combating the virus. The viral entry and associated infectivity mainly rely on its envelope spike protein to recognize and bind to the host cell receptor angiotensin-converting enzyme 2 (ACE2) through a conformational switch of the spike receptor binding domain (RBD) from inactive to active state. Thus it is of great significance to design an allosteric inhibitor targeting spike to lock it in the inactive and ACE2-inaccessible state. Objective: This study aims to discover the potential broad-spectrum allosteric inhibitors capable of binding and stabilizing the diverse spike variants including the wild type Delta and Omicron in the inactive RBD down state. Methods: In this work we first detected a potential allosteric pocket within the SARS-CoV-2 spike protein. Then we performed large-scale structure-based virtual screening by targeting the putative allosteric pocket to identify allosteric inhibitors that could stabilize the spike inactive state. Molecular dynamics simulations were further carried out to evaluate the effects of compound binding on the stability of spike RBD. Results: Finally we identified three potential allosteric inhibitors CPD3 CPD5 and CPD6 against diverse SARS-CoV-2 variants including Wild-type Delta and Omicron variants. Our simulation results showed that the three compounds could stably bind the predicted allosteric site and effectively stabilize the spike in the inactive state. Conclusion: The three compounds provide novel chemical structures for rational drug design targeting spike protein which is expected to greatly assist in the development of new drugs against SARS-CoV-2.
Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope
The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation QSAR research molecular docking bioactivity prediction and toxicity testing all involve impurity profiling. Before committing to DD RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.
Fluorinated Diaryl Sulfonamides: Molecular Modeling, Synthesis, and In Vitro Validation as New CETP Inhibitors
Background: Hyperlipidemia a cardiovascular disease risk factor is characterized by a rise in low-density lipoprotein (LDL) triglycerides and total cholesterol and a decrease in high-density lipoprotein (HDL). Cholesteryl ester transfer protein (CETP) enables the transfer of cholesteryl ester from HDL to LDL and very low-density lipoprotein. Objectives: CETP inhibition is a promising approach to prevent and treat cardiovascular diseases. By inhibiting lipid transport activity it increases HDL levels and decreases LDL levels. Materials and Method: Herein diaryl sulfonamides 6a-6g and 7a-7g were prepared and the structure of these compounds was fully determined using different spectroscopic techniques. Results: These compounds underwent biological evaluation in vitro and showed different inhibitory activities against CETP; 100% inhibitory activity was observed for compounds 7a-7g while activities of compounds 6a-6g ranged up to 42.6% at 10 μM concentration. Pharmacophore mapping agreed with the bioassay results where the four aromatic ring compounds 7a-7g possessed higher fit values against Hypo4/8 and the shape-complemented Hypo4/8 in comparison to compounds 6a-6g. Conclusion: Docking of the synthesized compounds using libdock and ligandfit engines revealed that compounds 7a-7g formed п-п stacking and hydrophobic interactions with the binding pocket while compounds 6a-6g missed these hydrophobic interactions with amino acids Leu206 Phe265 and Phe263.
The Potential Roles of Ficus carica Extract in the Management of COVID-19 Viral Infections: A Computer-aided Drug Design Study
Introduction: The conventional processes of drug discovery are too expensive timeconsuming and the success rate is limited. Searching for alternatives that have evident safety and potential efficacy could save money time and improve the current therapeutic regimen outcomes. Methods: Clinical phytotherapy implies the use of extracts of natural origin for prophylaxis treatment or management of human disorders. In this work the potential role of common Fig (Ficus carica) in the management of COVID-19 infections has been explored. The antiviral effects of Cyanidin 3-rhamnoglucoside which is abundant in common Figs have been illustrated on COVID-19 targets. The immunomodulatory effect and the ability to ameliorate the cytokine storm associated with coronavirus infections have also been highlighted. This work involves various computational studies to investigate the potential roles of common figs in the management of COVID-19 viral infections. Results: Two molecular docking studies of all active ingredients in common Figs were conducted starting with MOE to provide initial insights followed by Autodock Vina for further confirmation of the results of the top five compounds with the best docking score. Conclusion: Finally Molecular dynamic simulation alongside MMPBSA calculations were conducted using GROMACS to endorse and validate the entire work.