Dataset Information

Pharmacophore modeling, atom based 3D-QSAR and Molecular docking approaches to screen C-X-C chemokine receptor type 4 antagonists as microbicides for human immunodeficiency virus-1.

ABSTRACT: The C-X-C chemokine receptor type 4 receptor CXCR4 which acts as a co-receptor for human immunodeficiency virus-1, expressed in the later stages of infection is considered as an attractive and new target for drug design. Microbicides acting as co-receptor blockers are highly significant as these drugs block HIV lifecycle at early stage itself. The urgent need for a safe and effective microbicide urges to explore new CXCR4 antagonists which could be developed as microbicides. The pharmacophore based 3D-QSAR models and docking models were developed using PHASE and GLIDE modules of Schrodinger software. Three-dimensional quantitative structure-activity relationships (3D-QSAR) studies and pharmacophore modelling was carried out on a dataset of 114 CXCR4 antagonists with the intention of exploring entry inhibitors with better therapeutic potential. A training set of 43 compounds was used to create 3D-QSAR models and they were validated using a test set of 28 compounds. CXCR4 antagonists with good inhibitory activity could be designed and structurally modified based upon the QSAR model developed with necessary pharmacophore features. The results revealed that the common pharmacophore hypothesis ADHPR.1 was used for 3D-QSAR model development and the most active compound, CXCR4 antagonist no.44 which is a imidazopyridine-tetrahydro-8-quinolinamine derivative interacted with the CXCR4 receptor residue ASP 97 by the formation of a hydrogen bond. Also, the docking studies were carried out for the dataset for analyzing the binding conformation of CXCR4 and 114 antagonists. The results obtained from the 3D-QSAR studies and docking simulation can be used for designing new and potent CXCR4 antagonists. The compound identified from this study can be taken up further for validation by in vitro/in vivo studies.

SUBMITTER: Ramachandran R

PROVIDER: S-EPMC5684990 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Pharmacophore modeling, atom based 3D-QSAR and Molecular docking approaches to screen C-X-C chemokine receptor type 4 antagonists as microbicides for human immunodeficiency virus-1.

Ramachandran Radhika R Piramanyagam Shanmughavel S

Virusdisease 20170926 3

The C-X-C chemokine receptor type 4 receptor CXCR4 which acts as a co-receptor for human immunodeficiency virus-1, expressed in the later stages of infection is considered as an attractive and new target for drug design. Microbicides acting as co-receptor blockers are highly significant as these drugs block HIV lifecycle at early stage itself. The urgent need for a safe and effective microbicide urges to explore new CXCR4 antagonists which could be developed as microbicides. The pharmacophore ba ...[more]

PMID: 29291213

Similar Datasets

Project description:Neurodegenerative diseases, for example Alzheimer's, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds' pIC50 values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R2 = 0.86 and Q2 = 0.73), pls_38 (R2 = 0.77 and Q2 = 0.72), kpls_desc_44 (R2 = 0.81 and Q2 = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of -9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of -9.60 and -9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (-6.30 kcal/mol). The docking scores of our standards were -10.40 and -7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.

Dataset Information

Pharmacophore modeling, atom based 3D-QSAR and Molecular docking approaches to screen C-X-C chemokine receptor type 4 antagonists as microbicides for human immunodeficiency virus-1.

Publications

Pharmacophore modeling, atom based 3D-QSAR and Molecular docking approaches to screen C-X-C chemokine receptor type 4 antagonists as microbicides for human immunodeficiency virus-1.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets