3D pharmacophore-based virtual screening, docking and density functional theory approach towards the discovery of novel human epidermal growth factor receptor-2 (HER2) inhibitors

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein's crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

In Silico Comparison of Bioactive Compounds Characterized from Azadirachta indica with an FDA-Approved Drug against Schistosomal Agents: New Insight into Schistosomiasis Treatment

The burden of human schistosomiasis, a known but neglected tropical disease in Sub-Saharan Africa, has been worrisome in recent years. It is becoming increasingly difficult to tackle schistosomiasis with praziquantel, a drug known to be effective against all Schistosoma species, due to reports of reduced efficacy and resistance. Therefore, this study seeks to investigate the antischistosomal potential of phytochemicals from Azadirachta indica against proteins that have been implicated as druggable targets for the treatment of schistosomiasis using computational techniques. In this study, sixty-three (63) previously isolated and characterized phytochemicals from A. indica were identified from the literature and retrieved from the PubChem database. In silico screening was conducted to assess the inhibitory potential of these phytochemicals against three receptors (Schistosoma mansoni Thioredoxin glutathione reductase, dihydroorotate dehydrogenase, and Arginase) that may serve as therapeutic targets for schistosomiasis treatment. Molecular docking, ADMET prediction, ligand interaction, MMGBSA, and molecular dynamics simulation of the hit compounds were conducted using the Schrodinger molecular drug discovery suite. The results show that Andrographolide possesses a satisfactory pharmacokinetic profile, does not violate the Lipinski rule of five, binds with favourable affinity with the receptors, and interacts with key amino acids at the active site. Importantly, its interaction with dihydroorotate dehydrogenase, an enzyme responsible for the catalysis of the de novo pyrimidine nucleotide biosynthetic pathway rate-limiting step, shows a glide score and MMGBSA of -10.19 and -45.75 Kcal/mol, respectively. In addition, the MD simulation shows its stability at the active site of the receptor. Overall, this study revealed that Andrographolide from Azadirachta indica could serve as a potential lead compound for the development of an anti-schistosomal drug.

An Integrative Approach to Study the Inhibition of Providencia vermicola FabD Using C2-Quaternary Indolinones

Background

The present study investigates the potential bioactivity of twelve experimentally designed C-2 quaternary indolinones against Providencia spp., a bacterial group of the Enterobacteriaceae family known to cause urinary tract infections. The study aims to provide insights into the bioactive properties of the investigated compounds and their potential use in developing novel treatments against Providencia spp. The experimental design of indolinones, combined with their unique chemical structure, makes them attractive candidates for further investigation. The results of this research may contribute to the development of novel therapeutic agents to combat Providencia spp. infections.

Methods

The synthesized indolinones (moL1-moL12) are evaluated to identify any superior activity, particularly focusing on moL12, which possesses aza functionality. The antimicrobial activities of all twelve compounds are tested in triplicates against six different Gram-positive and Gram-negative organisms, including P. vermicola (P<0.05). Computational methods have been employed to assess the pharmacokinetic properties of the compounds.

Results

Among the synthesized indolinones, moL12 exhibits superior activity compared to the other compounds with similar skeleton but different functional moieties. All six strains tested, including P. vermicola, demonstrated sensitivity to moL12. Computational studies support the pharmacokinetic properties of moL12, indicating acceptable absorption, distribution, metabolism, excretion, and toxicity characteristics.

Conclusion

Utilizing the PPI approach, we have identified a promising target, FabD, in Gram-negative bacteria. Our analysis has shown that moL12 exhibits significant potential in binding with FabD, thereby, might inhibit cell wall formation, and display superior antimicrobial activity compared to other compounds. Consequently, moL12 may be a potential therapeutic agent that could be used to combat urinary tract infections caused by Providencia spp. The findings of this research hold significant promise for the development of new and effective treatments for bacterial infections.

Disruption of DYRK1A-induced hyperphosphorylation of amyloid-beta and tau protein in Alzheimer's disease: An integrative molecular modeling approach

Alzheimer's disease (AD) is a neurological disorder caused by the abnormal accumulation of hyperphosphorylated proteins. Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a dual phosphorylation enzyme which phosphorylates the amyloid-β (Aβ) and neurofibrillary tangles (NFTs). A high throughput virtual screening approach was applied to screen a library of 98,071 compounds against DYRK1A using different programs including AutoDock Vina, Smina, and idock. Based on the binding affinities, we selected 330 compounds for absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. Various pharmacokinetics parameters were predicted using the admetSAR server, and based on the pharmacokinetics results, 14 compounds were selected for cross-docking analysis using AutoDock. Cross-docking analysis revealed four compounds, namely, ZINC3843365 (-11.07 kcal/mol^-1), ZINC2123081 (-10.93 kcal/mol^-1), ZINC5220992 (-10.63 kcal/mol^-1), and ZINC68569602 (-10.35 kcal/mol^-1), which had the highest negative affinity scores compared to the 10 other molecules analyzed. Density functional theory (DFT) analysis was conducted for all the four top-ranked compounds. The molecular interaction stability of these four compounds with DYRK1A has been evaluated using molecular dynamics (MD) simulations on 100 nanoseconds followed by principal component analysis (PCA) and binding free energy calculations. The Gibbs free energy landscape analysis suggested the metastable state and folding pattern of selected docking complexes. Based on the present study outcome, we propose four antagonists, viz., ZINC3843365, ZINC2123081, ZINC5220992, and ZINC68569602 as potential inhibitors against DYRK1A and to reduce the amyloid-β and neurofibrillary tangle burden. These screened molecules can be further investigated using a number of in vitro and in vivo experiments.

EGFRisopred: a machine learning-based classification model for identifying isoform-specific inhibitors against EGFR and HER2

The EGFR kinase pathway is one of the most frequently activated signaling pathways in human cancers. EGFR and HER2 are the two significant members of this pathway, which are attractive drug targets of clinical relevance in lung and breast cancer. Therefore, identifying EGFR- and HER2-specific inhibitors is one of the important challenges in cancer drug discovery. To address this issue, a dataset of 519 compounds having inhibitory activity against both the isoforms, i.e., EGFR and HER2, was collected from the literature and developed a knowledge-based computational classification model for predicting the specificity of a molecule for an isoform (EGFR/HER2) with precision. A total of seventy-two classification models using nine fingerprint types, four classifiers (IBK, NB, SMO and RF) and two different datasets (EGFR and HER2 isoform specific) were developed. It was observed that the models developed using random forest and IBK performed better for EGFR- and HER2-specific datasets, respectively. Scaffold and functional group analysis led to the identification of prevalent core and fragments in each of the datasets. The accuracy of the selected best performing models was also evaluated using the decoy dataset. We have also developed an application EGFRisopred, which integrates the best performing models and permits the user to predict the specificity of a compound as an EGFR-/HER2-specific anticancer agent. It is expected that the tool's availability as a free utility will allow researchers to identify new inhibitors against these targets important in cancer.

Network pharmacology based high throughput screening for identification of multi targeted anti-diabetic compound from traditionally used plants

The incurable Type 2 diabetes mellitus (T2DM) has now been considered a pandemic with only supportive care in existence. Due to the adverse effects of available anti-diabetic drugs, there arises a great urgency to develop new drug molecules. One of the alternatives that can be considered for the treatment of T2DM are natural compounds from traditionally used herbal medicine. The present study undertakes, an integrated multidisciplinary concept of Network Pharmacology to evaluate the efficacy of potent anti-diabetic compound from traditionally used anti-diabetic plants of north east India and followed by DFT analysis. In the course of the study, 22 plant species were selected on the basis of their use in traditional medicine for the treatment of T2DM by various ethnic groups of the north eastern region of India. Initially, a library of 1053 compounds derived from these plants was generated. This was followed by network preparation between compounds and targets based on the docking result. The compounds having the best network property were considered for DFT analysis. We have identified that auraptene, a monoterpene coumarin for its activity in the management of Type 2 diabetes mellitus and deciphered its unexplored probable mechanisms. Molecular dynamics simulation of the ligand-protein complexes also reveals the stable binding of auraptene with the target proteins namely, Protein Kinase C θ, Glucocorticoid receptor, 11-β hydroxysteroid dehydrogenase 1 and Aldose Reductase, all of which form uniform interactions throughout the MD simulation trajectory. Therefore, this finding could provide new insights for the development of a new anti-diabetic drug.Communicated by Ramaswamy H. Sarma.

Design, synthesis, and computational validation of novel compounds selectively targeting HER2-expressing breast cancer

Human epidermal growth factor receptor (HER) is a family of multidomain proteins that plays important role in the regulation of several biological functions. HER2 is a member of HER that is highly presented in breast cancer cells. Here, we designed and synthesized a series of diaryl urea/thiourea compounds. The compounds were tested on HER2⁺ breast cancer cells including MCF-7 and SkBr3, compared to HER2^- breast cancer cells including MDA-MB-231 and BT-549. Only compounds 12-14 at 10 µM showed selective anti-proliferative activity against MCF-7 and SkBr3 by 65-79%. Compounds 12-14 showed >80% inhibition of the intracellular kinase domain of HER2. The results obtained indicated that compounds 12-14 are selectively targeting HER2⁺ cells. The IC₅₀ of compound 13 against MCF-7 and SkBR3 were 1.3 ± 0.009 and 0.73 ± 0.03 µM, respectively. Molecular docking and MD simulations (50 ns) were carried out, and their binding free energies were calculated. Compounds 12-14 formed strong hydrogen bond and pi-pi stacking interactions with the key residues Thr862 and Phe864. 3DQSAR model confirmed the role of 3-bromo substituent of pyridine ring and 4-chloro substituent of phenyl ring in the activity of the compounds. In conclusion, novel compounds, particularly 13 were developed selectively against HER2-expressing/overexpressing breast cancer cells including MCF7 and SkBr3.

Discovery of Potential Anti-infective Therapy Targeting Glutamine Synthetase in Staphylococcus xylosus

Glutamine synthetase (GS), which catalyzes the production of glutamine, plays essential roles in most biological growth and biofilm formation, suggesting that GS may be used as a promising target for antibacterial therapy. We asked whether a GS inhibitor could be found as an anti-infective agent of Staphylococcus xylosus (S. xylosus). Here, computational prediction followed by experimental testing was used to characterize GS. Sorafenib was finally determined through computational prediction. In vitro experiments showed that sorafenib has an inhibitory effect on the growth of S. xylosus by competitively occupying the active site of GS, and the minimum inhibitory concentration was 4 mg/L. In vivo experiments also proved that treatment with sorafenib significantly reduced the levels of TNF-α and IL-6 in breast tissue from mice mastitis, which was further confirmed by histopathology examination. These findings indicated that sorafenib could be utilized as an anti-infective agent for the treatment of infections caused by S. xylosus.

Discovery of novel indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors by virtual screening

In this study, a combination of virtual screening methods were utilized to identify novel potential indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors. A series of IDO1 potential inhibitors were identified by a combination of following steps: Lipinski's Rule of Five, Veber rules filter, molecular docking, HipHop pharmacophores, 3D-Quantitative structure activity relationship (3D-QSAR) studies and Pan-assay Interference Compounds (PAINS) filter. Three known categories of IDO1 inhibitors were used to constructed pharmacophores and 3D-QSAR models. Four point pharmacophores (RHDA) of IDO1 inhibitors were generated from the training set. The 3D-QSAR models were obtained using partial least squares (PLS) analyze based on the docking conformation alignment from the training set. The leave-one-out correlation (q²) and non-cross-validated correlation coefficient (r²_pred) of the best CoMFA model were 0.601 and 0.546, and the ones from the best CoMSIA model were 0.506 and 0.541, respectively. Six hits from Specs database were identified and analyzed to confirm their binding modes and key interactions to the amino acid residues in the protein. This work may provide novel backbones for new generation of inhibitors of IDO1.

Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins

Targeting ErbB family of receptors is an important therapeutic option, because of its essential role in the broad spectrum of human cancers, including non-small cell lung cancer (NSCLC). Therefore, in the present work, considerable effort has been made to develop an inhibitor against HER family proteins, by combining the use of pharmacophore modelling, docking scoring functions, and ADME property analysis. Initially, a five-point pharmacophore model was developed using known HER family inhibitors. The generated model was then used as a query to screen a total of 468,880 compounds of three databases namely ZINC, ASINEX, and DrugBank. Subsequently, docking analysis was carried out to obtain hit molecules that could inhibit the HER receptors. Further, analysis of GLIDE scores and ADME properties resulted in one hit namely BAS01025917 with higher glide scores, increased CNS involvement, and good pharmaceutically relevant properties than reference ligand, afatinib. Furthermore, the inhibitory activity of the lead compounds was validated by performing molecular dynamic simulations. Of note, BAS01025917 was found to possess scaffolds with a broad spectrum of antitumor activity. We believe that this novel hit molecule can be further exploited for the development of a pan-HER inhibitor with low toxicity and greater potential.

Combination of Virtual Screening Protocol by in Silico toward the Discovery of Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) is a potent new bleaching herbicide target. Therefore, in silico structure-based virtual screening was performed in order to speed up the identification of promising HPPD inhibitors. In this study, an integrated virtual screening protocol by combining 3D-pharmacophore model, molecular docking and molecular dynamics (MD) simulation was established to find novel HPPD inhibitors from four commercial databases. 3D-pharmacophore Hypo1 model was applied to efficiently narrow potential hits. The hit compounds were subsequently submitted to molecular docking studies, showing four compounds as potent inhibitor with the mechanism of the Fe(II) coordination and interaction with Phe360, Phe403, and Phe398. MD result demonstrated that nonpolar term of compound 3881 made great contributions to binding affinities. It showed an IC₅₀ being 2.49 μM against AtHPPD in vitro. The results provided useful information for developing novel HPPD inhibitors, leading to further understanding of the interaction mechanism of HPPD inhibitors.

Novel HDAC8 inhibitors: A multi-computational approach

Abnormal HDAC function triggers irregular gene transcription that hampers the essential cellular activities leading to tumour activation and progression. HDAC inhibition has, therefore, been reported as a potential target for cancer treatment. In the present study, a sequential computational framework was carried out to discover newer lead compounds, namely HDAC8 inhibitors for cancer therapy. Pharmacophoric hypotheses were generated based on hydroxamic acid derivatives reported earlier for HDAC inhibition. The model AAADR.122, demonstrated statistical significance (r² = 0.93, Q² = 0.81) and proved robust on validation with a cross-validated correlation coefficient of 0.89. It was utilized to arrive at novel hits through a virtual screening workflow. The specificity of the process was enhanced further by analysing the crucial interactions of the ligands with key catalytic residues, achieved by induced fit docking (PDB ID: 1T64). On assessment, the filtered leads displayed optimal drug like features. Investigations using density functional theory (DFT) also facilitated the recognition of molecular spots in the leads beneficial for HDAC8 interaction. Overall, two leads were proposed for HDAC8 inhibition with potential anti-cancer activity.

3D Pharmacophore-Based Virtual Screening and Docking Approaches toward the Discovery of Novel HPPD Inhibitors

p-Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking-based virtual screening were performed to identify novel potential HPPD inhibitors. The complex-based pharmacophore model (CBP) with 0.721 of ROC used for screening compounds showed remarkable ability to retrieve known active ligands from among decoy molecules. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5 (DS 2.5) to discern interactions with key residues at the active site of HPPD. Four compounds with top rankings in the HipHop model and well-known binding model were finally chosen as lead compounds with potential inhibitory effects on the active site of target. The results provided powerful insight into the development of novel HPPD inhibitors herbicides using computational techniques.