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 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 Structural investigations of CXCR2 receptor antagonists by CoMFA, CoMSIA and flexible docking studies

2012 ◽  
Vol 62 (3) ◽  
pp. 287-304 ◽  
Author(s):  
Shravan Kumar Gunda ◽  
Rohith Kumar Anugolu ◽  
Sri Ramya Tata ◽  
Saikh Mahmood
Keyword(s):  
Standard Error ◽  
Three Dimensional ◽  
3D Qsar ◽  
Homology Model ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Structural Investigations ◽  
Qsar Analysis ◽  
Qsar Studies

= Three-dimensional quantitative structure activity relationship (3D QSAR) analysis was carried out on a et of 56 N,N’-diarylsquaramides, N,N’-diarylureas and diaminocyclobutenediones in order to understand their antagonistic activities against CXCR2. The studies included comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Models with good predictive abilities were generated with CoMFA q2 0.709, r2 (non-cross-validated square of correlation coefficient) = 0.951, F value = 139.903, r2 bs = 0.978 with five components, standard error of estimate = 0.144 and the CoMSIA q2 = 0.592, r2 = 0.955, F value = 122.399, r2 bs = 0.973 with six components, standard error of estimate = 0.141. In addition, a homology model of CXCR2 was used for docking based alignment of the compounds. The most active compound then served as a template for alignment of the remaining structures. Further, mapping of contours onto the active site validated each other in terms of residues involved with reference to the respective contours. This integrated molecular docking based alignment followed by 3D QSAR studies provided a further insight to support the structure-based design of CXCR2 antagonistic agents with improved activity profiles. Furthermore, in silico screening was adapted to the QSAR model in order to predict the structures of new, potentially active compounds.

scholarly journals Probing Allosteric Hsp70 Inhibitors by Molecular Modelling Studies to Expedite the Development of Novel Combined F508del CFTR Modulators

2021 ◽  
Vol 14 (12) ◽  
pp. 1296
Author(s):  
Roberto Sabbadini ◽  
Emanuela Pesce ◽  
Alice Parodi ◽  
Eleonora Mustorgi ◽  
Santina Bruzzone ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Rational Design ◽  
Binding Mode ◽  
Docking Studies ◽  
Computational Method ◽  
Crystallographic Structure ◽  
Qsar Analysis ◽  
Modelling Studies ◽  
Hsp70 Inhibitors ◽  
Cftr Modulators

Cystic fibrosis (CF) is caused by different mutations related to the cystic fibrosis transmembrane regulator protein (CFTR), with F508del being the most common. Pioneering the development of CFTR modulators, thanks to the development of effective correctors or potentiators, more recent studies deeply encouraged the administration of triple combination therapeutics. However, combinations of molecules interacting with other proteins involved in functionality of the CFTR channel recently arose as a promising approach to address a large rescue of F508del-CFTR. In this context, the design of compounds properly targeting the molecular chaperone Hsp70, such as the allosteric inhibitor MKT-077, proved to be effective for the development of indirect CFTR modulators, endowed with ability to amplify the accumulation of the rescued protein. Herein we performed structure-based studies of a number of allosteric HSP70 inhibitors, considering the recent X-ray crystallographic structure of the human enzyme. This allowed us to point out the main interaction supporting the binding mode of MKT-077, as well as of the related analogues. In particular, cation-π and π–π stacking with the conserve residue Tyr175 deeply stabilized inhibitor binding at the HSP70 cavity. Molecular docking studies had been followed by QSAR analysis and then by virtual screening of aminoaryl thiazoles (I–IIIa) as putative HSP70 inhibitors. Their effectiveness as CFTR modulators has been verified by biological assays, in combination with VX-809, whose positive results confirmed the reliability of the whole applied computational method. Along with this, the “in-silico” prediction of absorption, distribution, metabolism, and excretion (ADME) properties highlighted, once more, that AATs may represent a chemical class to be further investigated for the rational design of novel combination of compounds for CF treatment.

scholarly journals INTEGRATING STRUCTURE AND LIGAND-BASED APPROACHES FOR MODELLING THE HISTONE DEACETYLASE INHIBITION ACTIVITY OF HYDROXAMIC ACID DERIVATIVES

2018 ◽  
Vol 11 (2) ◽  
pp. 198 ◽  
Author(s):  
Hai Pham-the ◽  
Huong Le-thi-thu
Keyword(s):  
Histone Deacetylase ◽  
Hydroxamic Acid ◽  
Rational Design ◽  
Binding Mode ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Inhibition Activity ◽  
Histone Deacetylase Inhibition ◽  
Histone Deacetylase 2 ◽  
Design And Synthesis

  Objective: Structure and ligand-based drug design approaches have be been integrated to accurately predict the inhibition activity of hydroxamic acid (HA) derivatives against the histone deacetylase-2 enzyme (HDAC2).Methods: The “active conformations” of the ligands in the binding site of the enzyme were determined by docking assays. More than 1000 0–3 dimensional molecular descriptors included in Dragon package were calculated and utilized for developing quantitative structure-activity relationship (QSAR) models through a multiple linear regression approach coupled with the genetic algorithm (GA-MLR).Results: The final model obtained showed suitable robustness and stability, with low correlation between descriptors and good predictive power. QSAR model was then used for screening bioactivity from a series of 36 novel HAs and found five candidates with very good bioactivity (half maximal inhibitory concentration<0.1 μM). Docking experiment revealed the binding mode of these compounds into the active site of HDAC2. Drug-likeness and toxicity profiles of the compounds were checked through chemoinformatics tools.Conclusion: The results from this study can lead to rational design and synthesis of highly selective and potent HDAC2 inhibitors.

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

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.

QSAR Analysis of Saquinavir Analogues (Peptidomimetics) as Potent HIV‐Protease Inhibitors

2012 ◽  
Vol 2 (3) ◽  
pp. 118-127
Author(s):  
Vandana Saini ◽  
Ajit Kumar
Keyword(s):  
Biological Activity ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Partial Least Square ◽  
Least Square ◽  
Activity Prediction ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Physicochemical Descriptors ◽  
Qsar Models

The correlation of structural features with the biological activity has always played an important role in drug designing process. The present paper discussesthe 2D‐ and 3D‐ Quantitative structure activity relationship (QSAR) studies, performed on a series of compounds related to saquinavir, an established HIV‐protease inhibitor (PI). The analysis was done on structure based calculations using various methods of QSAR like multiple linear regression (MLR), k‐nearest neighbour (k‐NN) and partial least square (PLS), to establish QSAR models for biological activity prediction of unknown compounds. A total of 27 peptidomimetics (Saquinavir analogues) were used for the study and models were developed using a training set of 22 compounds and test set of 5 compounds. The r2 value of 0.959 and crossvalidated r2 (q2) of 0.926 was obtained when models were generated using physicochemical descriptors during 2D‐QSAR analysis. In case of 3D‐QSAR analysis, database alignment of all compounds was done by field fit of steric and electrostatic molecular fields. 3D‐QSAR models generated showed r2 of 0.81 when steric and electrostatic fields were considered as basis of model generation. The meaningful information obtained from the study can be used for the design of saquinavir analogues having better inhibitory activity for HIV‐protease. Also, the QSAR models generated can be very useful to predict the HIV‐PIs and also for virtual screening for identification of new lead molecules.

scholarly journals 2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: A Comparative Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Prasanna A. Datar
Keyword(s):  
Staphylococcus Aureus ◽  
Pseudomonas Aeruginosa ◽  
Qsar Model ◽  
Antibacterial Activities ◽  
Quantitative Structure Activity Relationship ◽  
Comparative Approach ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Leave One Out ◽  
And Staphylococcus Aureus

A set of 15 indolylpyrimidine derivatives with their antibacterial activities in terms of minimum inhibitory concentration against the gram-negative bacteria Pseudomonas aeruginosa and gram-positive Staphylococcus aureus were selected for 2D quantitative structure activity relationship (QSAR) analysis. QSAR was performed using a combination of various descriptors such as steric, electronic and topological. Stepwise regression method was used to derive the most significant QSAR equation for predicting the inhibitory activity of this class of molecules. The best QSAR model was further validated by a leave one out technique as well as by the random trials. A high correlation between experimental and predicted inhibitory values was observed. A comparative picture of behavior of indolylpyrimidines against both of the microorganisms is discussed.

scholarly journals Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity

2021 ◽  
Vol 22 (15) ◽  
pp. 8352
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Manoj K. Sabnani ◽  
Abdul Samad
Keyword(s):  
External Validation ◽  
Factor Xa ◽  
Predictive Ability ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Data Set ◽  
Qsar Analysis ◽  
Lead Compounds ◽  
Charged Ring ◽  
Life Threatening

Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.

scholarly journals QSAR Analysis of 5-Substituted-2-Benzoyl-aminobenzoic acids as PPAR Modulator

2004 ◽  
Vol 1 (5) ◽  
pp. 243-250 ◽  
Author(s):  
R. Hemalatha ◽  
L. K. Soni ◽  
A. K. Gupta ◽  
S. G. Kaskhedikar
Keyword(s):  
Linear Regression Analysis ◽  
Qsar Model ◽  
Multiple Linear Regression Analysis ◽  
Quantitative Structure Activity Relationship ◽  
Statistical Regression ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Aminobenzoic Acids ◽  
Leave One Out ◽  
Α Activity

A quantitative structure activity relationship (QSAR) study on a series of analogs of 5-aryl thiazolidine-2, 4-diones with activity on PPAR-α and PPAR-γwas made using combination of various thermodynamic, electronic and spatial descriptors. Several statistical regression expressions were obtained using multiple linear regression analysis. The best QSAR model was further validated by leave one out cross validation method. The studied revealed that for dual PPAR-α/γactivity dipole-dipole energy and PMI-Z play significant role and contributed positively for PPAR-γand PPAR-α activity respectively. Thus, QSAR brings important structural insight to aid the design of dual PPAR-α /γreceptor agonist.

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