Hybrid Imidazole-Pyridine Derivatives: An Approach to Novel Anticancer DNA Intercalators

2020 ◽  
Vol 27 (1) ◽  
pp. 154-169 ◽  
Author(s):  
Claudiu N. Lungu ◽  
Bogdan Ionel Bratanovici ◽  
Maria Mirabela Grigore ◽  
Vasilichia Antoci ◽  
Ionel I. Mangalagiu
Keyword(s):  
Experimental Data ◽  
Antimicrobial Properties ◽  
Qsar Model ◽  
Therapeutic Effectiveness ◽  
Computational Results ◽  
Pyridine Derivatives ◽  
Quadruplex Dna ◽  
Dna Intercalators ◽  
Structure Activity ◽  
G Quadruplex

Lack of specificity and subsequent therapeutic effectiveness of antimicrobial and antitumoral drugs is a common difficulty in therapy. The aim of this study is to investigate, both by experimental and computational methods, the antitumoral and antimicrobial properties of a series of synthesized imidazole-pyridine derivatives. Interaction with three targets was discussed: Dickerson-Drew dodecamer (PDB id 2ADU), G-quadruplex DNA string (PDB id 2F8U) and DNA strain in complex with dioxygenase (PDB id 3S5A). Docking energies were computed and represented graphically. On them, a QSAR model was developed in order to further investigate the structure-activity relationship. Results showed that synthesized compounds have antitumoral and antimicrobial properties. Computational results agreed with the experimental data.

Antimicrobial Screening, in Silico Studies and QSAR of Chalcone-based 1,4-disubstituted 1,2,3-triazole Hybrids

Drug Research ◽  
2020 ◽  
Author(s):  
Pinki Yadav ◽  
Kashmiri Lal ◽  
Ashwani Kumar
Keyword(s):  
Topoisomerase Ii ◽  
Antimicrobial Properties ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Binding Modes ◽  
E Coli ◽  
Structure Activity ◽  
In Silico Studies ◽  
Microbial Strains

AbstractThe in vitro antimicrobial properties of some chalcones (1a–1c ) and chalcone tethred 1,4-disubstituted 1,2,3-triazoles (2a–2u) towards different microbial strains viz. Staphylococcus aureus, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Aspergillus niger and Candida albicans are reported. Compounds 2g and 2u exhibited better potency than the standard Fluconazole with MIC values of 0.0063 µmol/mL and 0.0068 µmol/mL, respectively. Furthermore, molecular docking was performed to investigate the binding modes of two potent compounds 2q and 2g with E. coli topoisomerase II DNA gyrase B and C. albicans lanosterol 14α-demethylase, respectively. Based on these results, a statistically significant quantitative structure activity relationship (QSAR) model was successfully summarized for antibacterial activity against B. subtilis.

scholarly journals QSAR, pharmacophore modeling and molecular docking studies to identify structural alerts for some nitrogen heterocycles as dual inhibitor of telomerase reverse transcriptase and human telomeric G-quadruplex DNA

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
R. D. Jawarkar ◽  
R. L. Bakal ◽  
P. N. Khatale ◽  
Israa Lewaa ◽  
Chetan M. Jain ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Carbon Atom ◽  
Reverse Transcriptase ◽  
Qsar Model ◽  
Pharmacophore Modeling ◽  
Telomerase Reverse Transcriptase ◽  
Quadruplex Dna ◽  
Pharmacophore Modelling ◽  
G Quadruplex ◽  
Dual Inhibition

Abstract Background Telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA are amongst the favorable target for researchers to discover novel and more effective anticancer agents. To understand and elucidate structure activity relationship and mechanism of inhibition of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA, a QSAR modeling and molecular docking were conducted. Results Two robust QSAR model were obtained which consist of full set QSAR model (R2: 0.8174, CCCtr: 0.8995, Q2loo: 0.7881, Q2LMO: 0.7814) and divided set QSAR model (R2: 0.8217, CCCtr: 0.9021, Q2loo: 0.7886, Q2LMO: 0.7783, Q2-F1: 0.7078, Q2-F2: 0.6865, Q2-F3: 0.7346) for envisaging the inhibitory activity of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA. The analysis reveals that carbon atom exactly at 3 bonds from aromatic carbon atom, nitrogen atom exactly at six bonds from planer nitrogen atom, aromatic carbon atom within 2 A0 from the center of mass of molecule and occurrence of element hydrogen within 2 A0 from donar atom are the key pharmacophoric features important for dual inhibition of TERT and human telomeric G-quadruplex DNA. To validate this analysis, pharmacophore modeling and the molecular docking is performed. Molecular docking analysis support QSAR analysis and revealed that, dual inhibition of TERT and human telomeric DNA is mainly contributed from hydrophobic and hydrogen bonding interactions. Conclusion The findings of molecular docking, pharmacophore modelling, and QSAR are all consistent and in strong agreement. The validated QSAR analyses can detect structural alerts, pharmacophore modelling can classify a molecule's consensus pharmacophore involving hydrophobic and acceptor regions, whereas docking analysis can reveal the mechanism of dual inhibition of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA. The combination of QSAR, pharmacophore modeling and molecular docking may be useful for the future drug design of dual inhibitors to combat the devastating issue of resistance. Graphical abstract

scholarly journals HUBUNGAN KUANTITATIF ANTARA STRUKTUR DAN TOKSISITAS SENYAWA KLOROFENOL

2015 ◽  
Vol 5 (1) ◽  
pp. 12
Author(s):  
Emdeniz
Keyword(s):  
Experimental Data ◽  
Regression Analysis ◽  
Systematic Approach ◽  
Structural Parameters ◽  
Structure Optimization ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Ab Initio Methods ◽  
Quantitative Structure ◽  
Structure Activity

Vol 5 No 1ABSTRACT Quantitative Structure-Activity Relationship (QSAR) for the toxicity of chlorophenols (CPs) from mono to pentachlorine substituted compounds has been done. The structural parameters are obtained from geometry structure optimization by computational chemistry using ab initio methods and the experimental data of acute toxicity (-log EC50 ) of chlorophenols to Daphnia magna were taken from the literature. The best QSAR model obtained by multilinier regression analysis, using the systematic approach for variable selection and the result showed that QSAR equations i.e: -log EC50 pre = -99.545 – 83.402 rOH + 145.879 rCO – 0.053 qO + 26.198 qH + 0.568 EH – 2.599 EL – 0.067 HE – 0.134 Rm + 0.133 μ  (n = 18, R = 0.971, R2  = 0.943, SD = 0.447794, Fcal/Ftab  = 3.956) Keywords: Chlorophenol, QSAR, toxicity

Emerging Role of G-quadruplex DNA as Target in Anticancer Therapy

2017 ◽  
Vol 22 (44) ◽  
pp. 6612-6624 ◽  
Author(s):  
Graziella Cimino-Reale ◽  
Nadia Zaffaroni ◽  
Marco Folini
Keyword(s):  
Anticancer Therapy ◽  
Quadruplex Dna ◽  
G Quadruplex

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

The quantitative structure-activity relationships between GABAA receptor and ligands based on binding interface characteristic

2020 ◽  
Vol 16 ◽  
Author(s):  
Shu Cheng ◽  
Yanrui Ding
Keyword(s):  
Gabaa Receptor ◽  
Molecular Descriptors ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Receptor Interaction ◽  
Gamma Aminobutyric Acid ◽  
Quantitative Structure ◽  
Binding Interface ◽  
Structure Activity

Background: Quantitative Structure Activity Relationship (QSAR) methods based on machine learning play a vital role in predicting biological effect. Objective: Considering the characteristics of the binding interface between ligands and the inhibitory neurotransmitter Gamma Aminobutyric Acid A(GABAA) receptor, we built a QSAR model of ligands that bind to the human GABAA receptor. Method: After feature selection with Mean Decrease Impurity, we selected 53 from 1,286 docked ligand molecular descriptors. Three QSAR models are built using gradient boosting regression tree algorithm based on the different combinations of docked ligand molecular descriptors and ligand-receptor interaction characteristics. Results: The features of the optimal QSAR model contain both the docked ligand molecular descriptors and ligand-receptor interaction characteristics. The Leave-One-Out-Cross-Validation (Q2 LOO) of the optimal QSAR model is 0.8974, the Coefficient of Determination (R2) for the testing set is 0.9261, the Mean Square Error (MSE) is 0.1862. We also used this model to predict the pIC50 of two new ligands, the differences between the predicted and experimental pIC50 are -0.02 and 0.03 respectively. Conclusion : We found the BELm2, BELe2, MATS1m, X5v, Mor08v, and Mor29m are crucial features, which can help to build the QSAR model more accurately.

scholarly journals The Functional Role of Loops and Flanking Sequences of G-Quadruplex Aptamer to the Hemagglutinin of Influenza a Virus

2021 ◽  
Vol 22 (5) ◽  
pp. 2409
Author(s):  
Anastasia A. Bizyaeva ◽  
Dmitry A. Bunin ◽  
Valeria L. Moiseenko ◽  
Alexandra S. Gambaryan ◽  
Sonja Balk ◽  
...  
Keyword(s):  
Influenza A Virus ◽  
Influenza A ◽  
Core Structure ◽  
Dna Aptamer ◽  
Tertiary Structures ◽  
Quadruplex Dna ◽  
G Quadruplex ◽  
Flanking Sequences ◽  
Related Proteins

Nucleic acid aptamers are generally accepted as promising elements for the specific and high-affinity binding of various biomolecules. It has been shown for a number of aptamers that the complexes with several related proteins may possess a similar affinity. An outstanding example is the G-quadruplex DNA aptamer RHA0385, which binds to the hemagglutinins of various influenza A virus strains. These hemagglutinins have homologous tertiary structures but moderate-to-low amino acid sequence identities. Here, the experiment was inverted, targeting the same protein using a set of related, parallel G-quadruplexes. The 5′- and 3′-flanking sequences of RHA0385 were truncated to yield parallel G-quadruplex with three propeller loops that were 7, 1, and 1 nucleotides in length. Next, a set of minimal, parallel G-quadruplexes with three single-nucleotide loops was tested. These G-quadruplexes were characterized both structurally and functionally. All parallel G-quadruplexes had affinities for both recombinant hemagglutinin and influenza virions. In summary, the parallel G-quadruplex represents a minimal core structure with functional activity that binds influenza A hemagglutinin. The flanking sequences and loops represent additional features that can be used to modulate the affinity. Thus, the RHA0385–hemagglutinin complex serves as an excellent example of the hypothesis of a core structure that is decorated with additional recognizing elements capable of improving the binding properties of the aptamer.

scholarly journals DNA G-quadruplexes for native mass spectrometry in potassium: a database of validated structures in electrospray-compatible conditions

2021 ◽  
Author(s):  
Anirban Ghosh ◽  
Eric Largy ◽  
Valérie Gabelica
Keyword(s):  
Mass Spectrometry ◽  
Drug Targets ◽  
Native Mass Spectrometry ◽  
Drug Binding ◽  
Quadruplex Dna ◽  
Dna Structures ◽  
Nmr Structures ◽  
G Quadruplex ◽  
Screening Assays

Abstract G-quadruplex DNA structures have become attractive drug targets, and native mass spectrometry can provide detailed characterization of drug binding stoichiometry and affinity, potentially at high throughput. However, the G-quadruplex DNA polymorphism poses problems for interpreting ligand screening assays. In order to establish standardized MS-based screening assays, we studied 28 sequences with documented NMR structures in (usually ∼100 mM) potassium, and report here their circular dichroism (CD), melting temperature (Tm), NMR spectra and electrospray mass spectra in 1 mM KCl/100 mM trimethylammonium acetate. Based on these results, we make a short-list of sequences that adopt the same structure in the MS assay as reported by NMR, and provide recommendations on using them for MS-based assays. We also built an R-based open-source application to build and consult a database, wherein further sequences can be incorporated in the future. The application handles automatically most of the data processing, and allows generating custom figures and reports. The database is included in the g4dbr package (https://github.com/EricLarG4/g4dbr) and can be explored online (https://ericlarg4.github.io/G4_database.html).

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