Browse
Submit Data
Databases
API
Help

Dataset Information

32 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Structure-Based Discovery of ABCG2 Inhibitors: A Homology Protein-Based Pharmacophore Modeling and Molecular Docking Approach.

ABSTRACT: ABCG2 is an ABC membrane protein reverse transport pump, which removes toxic substances such as medicines out of cells. As a result, drug bioavailability is an unexpected change and negatively influences the ADMET (absorption, distribution, metabolism, excretion, and toxicity), leading to multi-drug resistance (MDR). Currently, in spite of promising studies, screening for ABCG2 inhibitors showed modest results. The aim of this study was to search for small molecules that could inhibit the ABCG2 pump. We first used the WISS MODEL automatic server to build up ABCG2 homology protein from 655 amino acids. Pharmacophore models, which were con-structed based on strong ABCG2 inhibitors (IC₅₀ < 1 μM), consist of two hydrophobic (Hyd) groups, two hydrogen bonding acceptors (Acc2), and an aromatic or conjugated ring (Aro|PiR). Using molecular docking method, 714 substances from the DrugBank and 837 substances from the TCM with potential to inhibit the ABCG2 were obtained. These chemicals maybe favor synthesized or extracted and bioactivity testing.

SUBMITTER: Le MT

PROVIDER: S-EPMC8197086 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Combined pharmacophore modeling, 3D-QSAR, homology modeling and docking studies on CYP11B1 inhibitors.

Project description:The mitochondrial cytochrome P450 enzymes inhibitor steroid 11β-hydroxylase (CYP11B1) can decrease the production of cortisol. Therefore, these inhibitors have an effect in the treatment of Cushing's syndrome. A pharmacophore model generated by Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) was used to align the compounds and perform comparative molecular field analysis (CoMFA) with Q2 = 0.658, R2 = 0.959. The pharmacophore model contained six hydrophobic regions and one acceptor atom, and electropositive and bulky substituents would be tolerated at the A and B sites, respectively. A three-dimensional quantitative structure-activity relationship (3D-QSAR) study based on the alignment with the atom root mean square (RMS) was applied using comparative molecular field analysis (CoMFA) with Q2 = 0.666, R2 = 0.978, and comparative molecular similarity indices analysis (CoMSIA) with Q2 = 0.721, R2 = 0.972. These results proved that all the models have good predictability of the bioactivities of inhibitors. Furthermore, the QSAR models indicated that a hydrogen bond acceptor substituent would be disfavored at the A and B groups, while hydrophobic groups would be favored at the B site. The three-dimensional (3D) model of the CYP11B1 was generated based on the crystal structure of the CYP11B2 (PDB code 4DVQ). In order to probe the ligand-binding modes, Surflex-dock was employed to dock CYP11B1 inhibitory compounds into the active site of the receptor. The docking result showed that the imidazolidine ring of CYP11B1 inhibitors form H bonds with the amino group of residue Arg155 and Arg519, which suggested that an electronegative substituent at these positions could enhance the activities of compounds. All the models generated by GALAHAD QSAR and Docking methods provide guidance about how to design novel and potential drugs for Cushing's syndrome treatment.

| S-EPMC6272247 | biostudies-literature

Discovery of dual kinase inhibitors targeting VEGFR2 and FAK: structure-based pharmacophore modeling, virtual screening, and molecular docking studies.

Project description:VEGFR2 and FAK signaling pathways are interconnected and have synergistic effects on tumor angiogenesis, growth, and metastasis. Thus, instead of the conventional targeting of each of these proteins individually with a specific inhibitor, the present work aimed to discover novel dual inhibitors targeting both VEGFR2 and FAK exploiting their association. To this end, receptor-based pharmacophore modeling technique was opted to generate 3D pharmacophore models for VEGFR2 and FAK type II kinase inhibitors. The generated pharmacophore models were validated by assessing their ability to discriminate between active and decoy compounds in a pre-compiled test set of VEGFR2 and FAK active compounds and decoys. ZINCPharmer web tool was then used to screen the ZINC database purchasable subset using the validated pharmacophore models retrieving 42,616 hits for VEGFR2 and 28,475 hits for FAK. Subsequently, they were filtered using various filters leaving 13,023 and 6,832 survived compounds for VEGFR2 and FAK, respectively, with 124 common compounds. Based on molecular docking simulations, thirteen compounds were found to satisfy all necessary interactions with VEGFR2 and FAK kinase domains. Thus, they are predicted to have a possible dual VEGFR2/FAK inhibitory activity. Finally, SwissADME web tool showed that compound ZINC09875266 is not only promising in terms of binding pattern to our target kinases, but also in terms of pharmacokinetic properties.

| S-EPMC10863211 | biostudies-literature

Identification of Human Leukotriene A4 Hydrolase Inhibitors Using Structure-Based Pharmacophore Modeling and Molecular Docking.

Project description:Leukotriene B4 (LTB4) is a potent, proinflammatory lipid mediator implicated in the pathologies of an array of inflammatory diseases and cancer. The biosynthesis of LTB4 is regulated by the leukotriene A4 hydrolase (LTA4H). Compounds capable of limiting the formation of LTB4, through selective inhibition of LTA4H, are expected to provide potent anti-inflammatory and anti-cancer agents. The aim of the current study is to obtain potential LTA4H inhibitors using computer-aided drug design. A hybrid 3D structure-based pharmacophore model was generated based on the crystal structure of LTA4H in complex with bestatin. The generated pharmacophore was used in a virtual screen of the Maybridge database. The retrieved hits were extensively filtered, then docked into the active site of the enzyme. Finally, they were consensually scored to yield five hits as potential LTA4H inhibitors. Consequently, the selected hits were purchased and their biological activity assessed in vitro against the epoxide hydrolase activity of LTA4H. The results were very promising, with the most active compound showing 73.6% inhibition of the basal epoxide hydrolase activity of LTA4H. The results from this exploratory study provide valuable information for the design and development of more potent and selective inhibitors.

| S-EPMC7356593 | biostudies-literature

In silico Discovery of Novel FXa Inhibitors by Pharmacophore Modeling and Molecular Docking.

Project description:Coagulation Factor Xa (FXa) is the crucial enzyme at the convergent point of the intrinsic and extrinsic coagulation pathways. The inhibition of FXa is an effective approach against thrombotic diseases. In the present study, a specific strategy is reported to discover 10 novel FXa inhibitors based on ligand-based (pharmacophore) virtual screening and molecular docking analysis from a dataset of specs(containing 220000 molecules). The binding modes analysis provide insights into the contribution of particular structural moieties of the compounds towards their activity against FXa, and 10 novel structural compounds were discovered as potent candidate molecules. This work could be helpful in further design and development of FXa inhibitors.

| S-EPMC5481271 | biostudies-literature

Ligand-based Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Discovery of Potential Topoisomerase I Inhibitors.

Project description:Camptothecin (CPT), a natural product and its synthetic derivatives exert potent anticancer activity by selectively targeting DNA Topoisomerase I (Top1) enzyme. CPT and its clinically approved derivatives are used as Top1 poisons for cancer therapy suffer from many limitations related to stability and toxicity. In order to envisage structurally diverse novel chemical entity as Top1 poison with better efficacy, Ligand-based-pharmacophore model was developed using 3D QSAR pharmacophore generation (HypoGen algorithm) methodology in Discovery studio 4.1 clients. The chemical features of 29 CPT derivatives were taken as the training set. The selected pharmacophore model Hypo1 was further validated by 33 test set molecules and used as a query model for further screening of 1,087,724 drug-like molecules from ZINC databases. These molecules were subjected to several assessments such as Lipinski rule of 5, SMART filtration and activity filtration. The molecule obtained after filtration was further scrutinized by molecular docking analysis on the active site of Top1 crystal structure (PDB ID: 1T8I). Six potential inhibitory molecules have been selected by analyzing the binding interaction and Ligand-Pharmacophore mapping with the validated pharmacophore model. Toxicity assessment TOPKAT program provided three potential inhibitory 'hit molecules' ZINC68997780, ZINC15018994 and ZINC38550809. MD simulation of these three molecules proved that the ligand binding into the protein-DNA cleavage complex is stable and the protein-ligands conformation remains unchanged. These three hit molecules can be utilized for designing future class of potential topoisomerase I inhibitor.

| S-EPMC6396084 | biostudies-literature

Discovery of Potential RAGE inhibitors using Receptor-Based Pharmacophore Modeling, High Throughput Virtual Screening and Docking Studies.

Project description:Receptor for Advanced Glycation End products (RAGE) is a transmembrane receptor that can bind to various endogenous and exogenous ligands and initiate the inflammatory downstream signaling pathways. So far RAGE has been involved in various disorders including cardiovascular and neurodegenerative diseases, cancer, and diabetes. Blocking the interactions between RAGE and its ligands is a therapeutic approach to treat these conditions. In this context, we effectively utilized the receptor-based-pharmacophore modeling to discover structurally diverse molecular compounds having potential to effectively bind with RAGE. Two pharmacophore models were developed on V-domain of RAGE using Phase application of Schrodinger suite. The developed pharmacophoric features were used for screening of 1.8 million drug-like molecules downloaded from ChEMBL database. The molecules were scrutinized according to their molecular weight as well as clogP values. Phase screening was performed to find out the molecules that matched the developed pharmacophoric features that were further selected to analyze their binding modes using high-throughput virtual screening, extra precision docking studies and MM-GBSA ΔG binding calculations. These analyses provided ten hit RAGE inhibitory molecules that can bind to two different shallow binding sites on the V-domain of RAGE. Among the obtained compounds two compounds ChEMBL501494 and ChEMBL4081874 were found with best binding free energies that proved their receptor-ligand complex stability within their respective binding cavity on RAGE. Therefore, these molecules could be utilized for further designing and optimizing the future class of potential RAGE inhibitors.

| S-EPMC10695404 | biostudies-literature

Discovery of YopE Inhibitors by Pharmacophore-Based Virtual Screening and Docking.

Project description:Gram-negative bacteria Yersinia secrete virulence factors that invade eukaryotic cells via type III secretion system. One particular virulence member, Yersinia outer protein E (YopE), targets Rho family of small GTPases by mimicking regulator GAP protein activity, and its secretion mainly induces cytoskeletal disruption and depolymerization of actin stress fibers within the host cell. In this work, potent drug-like inhibitors of YopE are investigated with virtual screening approaches. More than 500,000 unique small molecules from ZINC database were screened with a five-point pharmacophore, comprising three hydrogen acceptors, one hydrogen donor, and one ring, and derived from different salicylidene acylhydrazides. Binding modes and features of these molecules were investigated with a multistep molecular docking approach using Glide software. Virtual screening hits were further analyzed based on their docking score, chemical similarity, pharmacokinetic properties, and the key Arg144 interaction along with other active site residue interactions with the receptor. As a final outcome, a diverse set of ligands with inhibitory potential were proposed.

| S-EPMC4393062 | biostudies-literature

Pharmacophore modeling, virtual screening, and molecular docking studies for discovery of novel Akt2 inhibitors.

Project description:Akt2 is considered as a potential target for cancer therapy. In order to find novel Akt2 inhibitors which have different scaffolds, structure-based pharmacophore model and 3D-QSAR pharmacophore model were built and validated by different methods. Then, they were used for chemical databases virtual screening. The selected compounds were further analyzed and refined using drug-like filters and ADMET analysis. Finally, seven hits with different scaffolds were picked out for docking studies. These seven hits were predicted to have high inhibitory activity and good ADMET properties, they may act as novel leads for Akt2 inhibitors designing.

| S-EPMC3558715 | biostudies-literature

Discovery of novel JAK1 inhibitors through combining machine learning, structure-based pharmacophore modeling and bio-evaluation.

Project description:BackgroundJanus kinase 1 (JAK1) plays a critical role in most cytokine-mediated inflammatory, autoimmune responses and various cancers via the JAK/STAT signaling pathway. Inhibition of JAK1 is therefore an attractive therapeutic strategy for several diseases. Recently, high-performance machine learning techniques have been increasingly applied in virtual screening to develop new kinase inhibitors. Our study aimed to develop a novel layered virtual screening method based on machine learning (ML) and pharmacophore models to identify the potential JAK1 inhibitors.MethodsFirstly, we constructed a high-quality dataset comprising 3834 JAK1 inhibitors and 12,230 decoys, followed by establishing a series of classification models based on a combination of three molecular descriptors and six ML algorithms. To further screen potential compounds, we constructed several pharmacophore models based on Hiphop and receptor-ligand algorithms. We then used molecular docking to filter the recognized compounds. Finally, the binding stability and enzyme inhibition activity of the identified compounds were assessed by molecular dynamics (MD) simulations and in vitro enzyme activity tests.ResultsThe best performance ML model DNN-ECFP4 and two pharmacophore models Hiphop3 and 6TPF 08 were utilized to screen the ZINC database. A total of 13 potentially active compounds were screened and the MD results demonstrated that all of the above molecules could bind with JAK1 stably in dynamic conditions. Among the shortlisted compounds, the four purchasable compounds demonstrated significant kinase inhibition activity, with Z-10 being the most active (IC50 = 194.9 nM).ConclusionThe current study provides an efficient and accurate integrated model. The hit compounds were promising candidates for the further development of novel JAK1 inhibitors.

| S-EPMC10464202 | biostudies-literature

Pharmacophore modeling and docking studies on some nonpeptide-based caspase-3 inhibitors.

Project description:Neurodegenerative disorders are major consequences of excessive apoptosis caused by a proteolytic enzyme known as caspase-3. Therefore, caspase-3 inhibition has become a validated therapeutic approach for neurodegenerative disorders. We performed pharmacophore modeling on some synthetic derivatives of caspase-3 inhibitors (pyrrolo[3,4-c]quinoline-1,3-diones) using PHASE 3.0. This resulted in the common pharmacophore hypothesis AAHRR.6 which might be responsible for the biological activity: two aromatic rings (R) mainly in the quinoline nucleus, one hydrophobic (H) group (CH₃), and two acceptor (A) groups (-C=O). After identifying a valid hypothesis, we also developed an atom-based 3D-QSAR model applying the PLS algorithm. The developed model was statistically robust (q² = 0.53; pred_r² = 0.80). Additionally, we have performed molecular docking studies, cross-validated our results, and gained a deeper insight into its molecular recognition process. Our developed model may serve as a query tool for future virtual screening and drug designing for this particular target.

| S-EPMC3780516 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data