scholarly journals New Insights into the Binding Features of F508del CFTR Potentiators: A Molecular Docking, Pharmacophore Mapping and QSAR Analysis Approach

2020 ◽  
Vol 13 (12) ◽  
pp. 445
Author(s):  
Giada Righetti ◽  
Monica Casale ◽  
Michele Tonelli ◽  
Nara Liessi ◽  
Paola Fossa ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Molecular Docking ◽  
Computational Study ◽  
Autosomal Recessive Disorder ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Van Der Waals Interactions ◽  
Transmembrane Conductance Regulator ◽  
Qsar Analysis

Cystic fibrosis (CF) is the autosomal recessive disorder most recurrent in Caucasian populations. To combat this disease, many life-prolonging therapies are required and deeply investigated, including the development of the so-called cystic fibrosis transmembrane conductance regulator (CFTR) modulators, such as correctors and potentiators. Combination therapy with the two series of drugs led to the approval of several multi-drug effective treatments, such as Orkambi, and to the recent promising evaluation of the triple-combination Elexacaftor-Tezacaftor-Ivacaftor. This scenario enlightened the effectiveness of the multi-drug approach to pave the way for the discovery of novel therapeutic agents to contrast CF. The recent X-crystallographic data about the human CFTR in complex with the well-known potentiator Ivacaftor (VX-770) opened the possibility to apply a computational study aimed to explore the key features involved in the potentiator binding. Herein, we discussed molecular docking studies performed onto the chemotypes so far discussed in the literature as CFTR potentiator, reporting the most relevant interactions responsible for their mechanism of action, involving Van der Waals interactions and π–π stacking with F236, Y304, F305 and F312, as well as H-bonding F931, Y304, S308 and R933. This kind of positioning will stabilize the effective potentiator at the CFTR channel. These data have been accompanied by pharmacophore analyses, which promoted the design of novel derivatives endowed with a main (hetero)aromatic core connected to proper substituents, featuring H-bonding moieties. A highly predictive quantitative-structure activity relationship (QSAR) model has been developed, giving a cross-validated r2 (r2cv) = 0.74, a non-cross validated r2 (r2ncv) = 0.90, root mean square error (RMSE) = 0.347, and a test set r2 (r2pred) = 0.86. On the whole, the results are expected to gain useful information to guide the further development and optimization of new CFTR potentiators.

scholarly journals Molecular Docking and QSAR Studies as Computational Tools Exploring the Rescue Ability of F508del CFTR Correctors

2020 ◽  
Vol 21 (21) ◽  
pp. 8084
Author(s):  
Giada Righetti ◽  
Monica Casale ◽  
Nara Liessi ◽  
Bruno Tasso ◽  
Annalisa Salis ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Rational Design ◽  
Autosomal Recessive Disorder ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Beneficial Effects ◽  
Design And Synthesis ◽  
Cftr Modulators

Cystic fibrosis (CF) is the autosomal recessive disorder most recurrent in Caucasian populations. Different mutations involving the cystic fibrosis transmembrane regulator protein (CFTR) gene, which encodes the CFTR channel, are involved in CF. A number of life-prolonging therapies have been conceived and deeply investigated to combat this disease. Among them, the administration of the so-called CFTR modulators, such as correctors and potentiators, have led to quite beneficial effects. Recently, based on QSAR (quantitative structure activity relationship) studies, we reported the rational design and synthesis of compound 2, an aminoarylthiazole-VX-809 hybrid derivative exhibiting promising F508del-CFTR corrector ability. Herein, we explored the docking mode of the prototype VX-809 as well as of the aforementioned correctors in order to derive useful guidelines for the rational design of further analogues. In addition, we refined our previous QSAR analysis taking into account our first series of in-house hybrids. This allowed us to optimize the QSAR model based on the chemical structure and the potency profile of hybrids as F508del-CFTR correctors, identifying novel molecular descriptors explaining the SAR of the dataset. This study is expected to speed up the discovery process of novel potent CFTR modulators.

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Design of Dual COX-2 and 5-LOX Inhibitors with Iron-Chelating Properties Using Structure-Based and Ligand-Based Methods

2021 ◽  
Vol 18 ◽  
Author(s):  
Jelena Bošković ◽  
Dušan Ružić ◽  
Olivera Čudina ◽  
Katarina Nikolic ◽  
Vladimir Dobričić
Keyword(s):  
Molecular Docking ◽  
External Validation ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Qsar Analysis ◽  
In Silico Admet ◽  
Cox 2 ◽  
Lox Inhibitors

Background: Inflammation is common pathogenesis of many diseases progression, such as malignancy, cardiovascular and rheumatic diseases. The inhibition of the synthesis of inflammatory mediators by modulation of cyclooxygenase (COX) and lipoxygenase (LOX) pathways provides a challenging strategy for the development of more effective drugs. Objective: The aim of this study was to design dual COX-2 and 5-LOX inhibitors with iron-chelating properties using a combination of ligand-based (three-dimensional quantitative structure-activity relationship (3D-QSAR)) and structure-based (molecular docking) methods. Methods: The 3D-QSAR analysis was applied on a literature dataset consisting of 28 dual COX-2 and 5-LOX inhibitors in Pentacle software. The quality of developed COX-2 and 5-LOX 3D-QSAR models were evaluated by internal and external validation methods. The molecular docking analysis was performed in GOLD software, while selected ADMET properties were predicted in ADMET predictor software. Results: According to the molecular docking studies, the class of sulfohydroxamic acid analogues, previously designed by 3D-QSAR, was clustered as potential dual COX-2 and 5-LOX inhibitors with iron-chelating properties. Based on the 3D-QSAR and molecular docking, 1j, 1g, and 1l were selected as the most promising dual COX-2 and 5-LOX inhibitors. According to the in silico ADMET predictions, all compounds had an ADMET_Risk score less than 7 and a CYP_Risk score lower than 2.5. Designed compounds were not estimated as hERG inhibitors, and 1j had improved intrinsic solubility (8.704) in comparison to the dataset compounds (0.411-7.946). Conclusion: By combining 3D-QSAR and molecular docking, three compounds (1j, 1g, and 1l) are selected as the most promising designed dual COX-2 and 5-LOX inhibitors, for which good activity, as well as favourable ADMET properties and toxicity, are expected.

scholarly journals 3D-QSAR and Docking Studies of a Series ofβ-Carboline Derivatives as Antitumor Agents of PLK1

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jahan B. Ghasemi ◽  
Valentin Davoudian
Keyword(s):  
Antitumor Agents ◽  
Three Dimensional ◽  
Human Tumor ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Human Tumor Cell Lines ◽  
Qsar Analysis ◽  
Leave One Out

An alignment-free, three dimensional quantitative structure-activity relationship (3D-QSAR) analysis has been performed on a series ofβ-carboline derivatives as potent antitumor agents toward HepG2 human tumor cell lines. A highly descriptive and predictive 3D-QSAR model was obtained through the calculation of alignment-independent descriptors (GRIND descriptors) using ALMOND software. For a training set of 30 compounds, PLS analyses result in a three-component model which displays a squared correlation coefficient (r2) of 0.957 and a standard deviation of the error of calculation (SDEC) of 0.116. Validation of this model was performed using leave-one-out,q2looof 0.85, and leave-multiple-out. This model gives a remarkably highr2pred(0.66) for a test set of 10 compounds. Docking studies were performed to investigate the mode of interaction betweenβ-carboline derivatives and the active site of the most probable anticancer receptor, polo-like kinase protein.

scholarly journals QSAR and Molecular Docking Studies on Non-Imidazole-Based Histamine H3 Receptor Antagonists

2020 ◽  
Vol 26 (2) ◽  
pp. 165-174
Author(s):  
Maryam Hamzeh-Mivehroud ◽  
Zoha Khoshravan-Azar ◽  
Siavoush Dastmalchi
Keyword(s):  
Molecular Docking ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Antagonistic Activity ◽  
Partial Least Square ◽  
Least Square ◽  
Structural Modifications ◽  
Regression Methods ◽  
Mechanism Of Interaction

Background: In the recent years, histamine H3 receptor (H3R) has been receiving increasing attention in pharmacotherapy of neurological disorders. The aim of the current study was to investigate structural requirements for the prediction of H3 antagonistic activity using quantitative structure-activity relationship (QSAR) and molecular docking techniques. Methods: To this end, genetic algorithm coupled partial least square and stepwise multiple linear regression methods were employed for developing a QSAR model. The obtained QSAR model was stringently assessed using different validation criteria. Results: The generated model indicated that connectivity information and mean absolute charge are two important descriptors for the prediction of H3 antagonistic activity of the studied compounds. To gain insight into the mechanism of interaction between studied molecules and H3R, molecular docking was performed. The most important residues involved in the ligand-receptor interactions were identified. Conclusion: The result of current study can be used for designing of new H3 antagonist and proposing structural modifications to improve H3 inhibitory potency.

scholarly journals Theoretical investigation of a few selected compounds as potent anti-tubercular agents and molecular docking evaluation: A multi-linear regression approach

2020 ◽  
Vol 11 (1) ◽  
pp. 60-67
Author(s):  
Shola Elijah Adeniji ◽  
Abdulwahab Isiaka ◽  
Kalen Ephraim Audu ◽  
Olajumoke Bosede Adalumo
Keyword(s):  
Molecular Docking ◽  
Correlation Coefficient ◽  
Drug Targets ◽  
Biological Activities ◽  
External Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Multi Drug Resistance ◽  
Validation Test

Emergence of multi-drug resistant strains of Mycobacterium tuberculosis to the available drugs has demanded for the development of more potent anti-tubercular agents with efficient pharmacological activities. Time consumed and expenses in discovering and synthesizing new drug targets with improved biological activity have been a major challenge toward the treatment of multi-drug resistance strain M. tuberculosis. To solve the above problem, Quantitative Structure Activity Relationship (QSAR) is a recent approach developed to discover a novel drug with a better biological against M. Tuberculosis. A validated QSAR model developed in this study to predict the biological activities of some anti-tubercular compounds and to design new hypothetical drugs is influenced with the molecular descriptors; AATS7s, VR1-Dzi, VR1-Dzs, SpMin7-Bhe and RDF110i. The internal validation test for the derived model was found to have correlation coefficient (R2) of 0.8875, adjusted correlation coefficient (R2adj) value of 0.8234 and leave one out cross validation coefficient (Qcv2) value of 0.8012 while the external validation test was found to have (R2test) of 0.7961 and Y-randomization Coefficient (cRp2) of 0.6832. Molecular docking shows that ligand 13 of 2,4-disubstituted quinoline derivatives have promising higher binding score of -18.8 kcal/mol compared to the recommended drugs; isoniazid -14.6 kcal/mol. The proposed QSAR model and molecular docking studies will provides valuable approach for the modification of the lead compound, designing and synthesis more potent anti-tubercular agents.

GA-SMLR-Based QSAR Modeling and Molecular Docking Studies of Bisamidine Derivatives as NMT Inhibitors

2020 ◽  
Vol 5 (4) ◽  
pp. 1-31
Author(s):  
Sapna Jain Dabade ◽  
Dheeraj Mandloi ◽  
Amritlal V. Bajaj ◽  
Naveen Dhingra
Keyword(s):  
Molecular Docking ◽  
External Validation ◽  
Qsar Model ◽  
Docking Studies ◽  
Qsar Modeling ◽  
Statistical Parameters ◽  
Van Der Waals Volume ◽  
Qsar Analysis ◽  
Hydrogen Bond Interactions

The present investigation deals with a combination of genetic algorithm-stepwise multiple linear regression (GA-SMLR)-based QSAR modeling and molecular docking applied to bisamidine analogues in an attempt to explore their role as novel NMT inhibitors of Candida albicans. In this regard, 43 bisamidine analogues were investigated for the development of mathematical models. The robustness of the proposed QSAR model was not only ascertained through traditionally used internal and external validation statistical parameters (Q2= 0.740, R2 = 0.819, R_Pred^2 = 0.636) but also through various R_(m)^2 metrics proposed by Roy and Mitra. The descriptors recognized in the QSAR analysis have culminated a significant role of atomic van der Waals volume, topology, nature of bond and dipole moment to modulate the antifungal activity of compounds under investigation. The most active compound revealed enhanced binding potency with MolDock score of -183.451 kcal/mol and displayed hydrogen bond interactions with active amino acids Leu177, Thr211, Tyr225, and IIe111 of NMT.

scholarly journals Lipoxygenase Inhibition Activity of Coumarin Derivatives—QSAR and Molecular Docking Study

2020 ◽  
Vol 13 (7) ◽  
pp. 154 ◽  
Author(s):  
Melita Lončarić ◽  
Ivica Strelec ◽  
Valentina Pavić ◽  
Domagoj Šubarić ◽  
Vesna Rastija ◽  
...  
Keyword(s):  
Lipid Peroxidation ◽  
Molecular Docking ◽  
Unsaturated Fatty Acids ◽  
Docking Study ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Docking Studies ◽  
Molecular Docking Study ◽  
Lipoxygenase Inhibitors

Lipoxygenases (LOXs) are a family of enzymes found in plants, mammals, and microorganisms. In animals and plants, the enzyme has the capability for the peroxidation of unsaturated fatty acids. Although LOXs participate in the plant defense system, the enzyme’s metabolites can have numerous negative effects on human health. Therefore, many types of research are searching for compounds that can inhibit LOXs. The best quantitative structure–activity relationship (QSAR) model was obtained using a Genetic Algorithm (GA). Molecular docking was performed with iGEMDOCK. The inhibition of lipoxygenase was in the range of 7.1 to 96.6%, and the inhibition of lipid peroxidation was 7.0–91.0%. Among the synthesized compounds, the strongest inhibitor of soybean LOX-3 (96.6%) was found to be 3-benzoyl-7-(benzyloxy)-2H-chromen-2-one. A lipid peroxidation inhibition of 91.0% was achieved with ethyl 7-methoxy-2-oxo-2H-chromene-3-carboxylate. The docking scores for the soybean LOX-3 and human 5-LOX also indicated that this compound has the best affinity for these LOX enzymes. The best multiple linear QSAR model contains the atom-centered fragment descriptors C-06, RDF035p, and HATS8p. QSAR and molecular docking studies elucidated the structural features important for the enhanced inhibitory activity of the most active compounds, such as the presence of the benzoyl ring at the 3-position of coumarin’s core. Compounds with benzoyl substituents are promising candidates as potent lipoxygenase inhibitors.

scholarly journals QSAR and molecular docking studies on 4-quinoline carboxylic acid derivatives as inhibition of vesicular stomatitis virus replication

2019 ◽  
Vol 10 (1) ◽  
pp. 45-51
Author(s):  
Tawassl Tajelsir Hassan Hajalsiddig ◽  
Ahmed Elsadig Mohammed Saeed
Keyword(s):  
Carboxylic Acid ◽  
Vesicular Stomatitis Virus ◽  
Virus Replication ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Qsar Analysis ◽  
Quinoline Carboxylic Acid ◽  
Vesicular Stomatitis ◽  
In Silico Models

The current study describes the development of in silico models based on quantitative structure-activity relationship (QSAR) analysis has been performed on 4-quinoline carboxylic acid derivatives as inhibition capacity of vesicular stomatitis virus replication in Madin Darby canine kidney epithelial cells. A highly descriptive and predictive QSAR model was obtained through the calculation of alignment-independent descriptors using MOE 2009.10 software. For a training set of 20 compounds, the partial least squares analyses result in a model which displays a squared correlation coefficient (r2) of 0.913. Validation of this model was performed using leave-one-out (q2) of 0.842. This model gives (r2pre) of 0.889 for a test set of five compounds. Docking studies were performed for 25 compounds to investigate the mode of interaction between 4-quinoline carboxylic acid derivatives and the active site of the human dihydroorotate dehydrogenase.

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