1
|
Moore GJ, Ridway H, Gadanec LK, Apostolopoulos V, Zulli A, Swiderski J, Kelaidonis K, Vidali VP, Matsoukas MT, Chasapis CT, Matsoukas JM. Structural Features Influencing the Bioactive Conformation of Angiotensin II and Angiotensin A: Relationship between Receptor Desensitization, Addiction, and the Blood-Brain Barrier. Int J Mol Sci 2024; 25:5779. [PMID: 38891966 PMCID: PMC11171751 DOI: 10.3390/ijms25115779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/04/2024] [Accepted: 05/08/2024] [Indexed: 06/21/2024] Open
Abstract
The N-terminal portion of the octapeptide angiotensin II (DRVYIHPF; AngII), a vasopressor peptide that favorably binds to, and activates, AngII type 1 receptor (AT1R), has an important role in maintaining bioactive conformation. It involves all three charged groups, namely (i) the N-terminal amino group cation, (ii) the Asp sidechain anion and (iii) the Arg guanidino cation. Neutralization of any one of these three charged groups results in a substantial reduction (<5%) in bioactivity, implicating a specialized function for this cluster. In contrast, angiotensin A (ARVYIHPF; AngA) has reduced bioactivity at AT1R; however, replacement of Asp in AngII with sarcosine (N-methyl-glycine) not only restores bioactivity but increases the activity of agonist, antagonist, and inverse agonist analogues. A bend produced at the N-terminus by the introduction of the secondary amino acid sarcosine is thought to realign the functional groups that chaperone the C-terminal portion of AngII, allowing transfer of the negative charge originating at the C-terminus to be transferred to the Tyr hydroxyl-forming tyrosinate anion, which is required to activate the receptor and desensitizes the receptor (tachyphylaxis). Peptide (sarilesin) and nonpeptide (sartans) moieties, which are long-acting inverse agonists, appear to desensitize the receptor by a mechanism analogous to tachyphylaxis. Sartans/bisartans were found to bind to alpha adrenergic receptors resulting in structure-dependent desensitization or resensitization. These considerations have provided information on the mechanisms of receptor desensitization/tolerance and insights into possible avenues for treating addiction. In this regard sartans, which appear to cross the blood-brain barrier more readily than bisartans, are the preferred drug candidates.
Collapse
Affiliation(s)
- Graham J. Moore
- Pepmetics Inc., 772 Murphy Place, Victoria, BC V8Y 3H4, Canada;
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Harry Ridway
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 8001, Australia;
| | - Laura Kate Gadanec
- Institute for Health and Sport, Immunology and Translational Research, Victoria University, Melbourne, VIC 3030, Australia; (L.K.G.); (V.A.); (A.Z.); (J.S.)
| | - Vasso Apostolopoulos
- Institute for Health and Sport, Immunology and Translational Research, Victoria University, Melbourne, VIC 3030, Australia; (L.K.G.); (V.A.); (A.Z.); (J.S.)
- Immunology Program, Australian Institute for Musculoskeletal Science (AIMSS), Melbourne, VIC 3021, Australia
| | - Anthony Zulli
- Institute for Health and Sport, Immunology and Translational Research, Victoria University, Melbourne, VIC 3030, Australia; (L.K.G.); (V.A.); (A.Z.); (J.S.)
| | - Jordan Swiderski
- Institute for Health and Sport, Immunology and Translational Research, Victoria University, Melbourne, VIC 3030, Australia; (L.K.G.); (V.A.); (A.Z.); (J.S.)
| | | | - Veroniki P. Vidali
- Institute of Nanoscience and Nanotechnology, National Centre for Scientific Research “Demokritos”, 15341 Athens, Greece;
| | | | - Christos T. Chasapis
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece;
| | - John M. Matsoukas
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
- Institute for Health and Sport, Immunology and Translational Research, Victoria University, Melbourne, VIC 3030, Australia; (L.K.G.); (V.A.); (A.Z.); (J.S.)
- NewDrug/NeoFar PC, Patras Science Park, 26504 Patras, Greece;
- Department of Chemistry, University of Patras, 26504 Patras, Greece
| |
Collapse
|
2
|
Bhawna, Kumar S, Kumar P, Kumar A. Correlation intensity index-index of ideality of correlation: A hyphenated target function for furtherance of MAO-B inhibitory activity assessment. Comput Biol Chem 2024; 108:107975. [PMID: 37950961 DOI: 10.1016/j.compbiolchem.2023.107975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/13/2023]
Abstract
Monoamine oxidases are the enzymes involved in the management of brain homeostasis through oxidative deamination of monoamines such as neurotransmitters, tyramine etc. The excessive production of monoamine oxidase-B specifically results in numerous neurodegenerative disorders like Alzheimer's and Parkinson's diseases. Inhibitors of monoamine oxidase-B are applied in the management of these disorders. Here in this article we have developed robust hybrid descriptor based QSAR models related to 123 monoamine oxidase-B inhibitors through CORAL software by means of Monte Carlo optimization method. Three target functions were applied to prepare QSAR models and three splits were made for each target function. The most reliable, robust and better predictive QSAR models were developed with TF3 (correlation intensity index -index of ideality of correlation). Correlation intensity index showed positive effect on QSAR models. The structural features obtained from the QSAR modeling were incorporated in newly designed molecules and exhibited positive effect on their endpoint. Significant binding interactions were represented by these molecules in docking studies. Molecule B5 displayed prominent pIC50 (8.3) and binding affinity (-11.5 kcal mol-1) towards monoamine oxidase-B.
Collapse
Affiliation(s)
- Bhawna
- Department of Pharmaceutical Sciences,Guru Jambheshwar University of Science and Technology, Hisar, Haryana 125001, India
| | - Sunil Kumar
- Department of Pharmaceutical Sciences,Guru Jambheshwar University of Science and Technology, Hisar, Haryana 125001, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences,Guru Jambheshwar University of Science and Technology, Hisar, Haryana 125001, India.
| |
Collapse
|
3
|
Toropova AP, Toropov AA. Quasi-SMILES as a basis to build up models of endpoints for nanomaterials. ENVIRONMENTAL TECHNOLOGY 2023; 44:4460-4467. [PMID: 35748421 DOI: 10.1080/09593330.2022.2093655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Simplified molecular input-line entry system (SMILES) is a format for representing of the molecular structure. Quasi-SMILES is an extended format for representing molecular structure data and some eclectic data, which in principle could be applied to improve a model's predictive potential. Nano-quantitative structure-property relationships (nano-QSPRs) for energy gap (Eg, eV) of the metals oxide nanoparticles based on the quasi-SMILES give a predictive model for Eg, characterized by the following statistical quality for external validation set n = 22, R2 = 0.83, RMSE = 0.267.
Collapse
Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| |
Collapse
|
4
|
CORAL: Quantitative Structure Retention Relationship (QSRR) of flavors and fragrances compounds studied on the stationary phase methyl silicone OV-101 column in gas chromatography using correlation intensity index and consensus modelling. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.133437] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
|
5
|
Kumar P, Kumar A, Singh D. CORAL: Development of a hybrid descriptor based QSTR model to predict the toxicity of dioxins and dioxin-like compounds with correlation intensity index and consensus modelling. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2022; 93:103893. [PMID: 35654373 DOI: 10.1016/j.etap.2022.103893] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/21/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
In the present study, ninety-five halogenated dioxins and related chemicals (dibenzo-p-dioxins, dibenzofurans, biphenyls, and naphthalene) with endpoint pEC50 were used to develop twelve quantitative structure toxicity relationship (QSTR) models using inbuilt Monte Carlo algorithm of CORAL software. The hybrid optimal descriptor of correlation weights (DCW) using a combination of SMILES and HSG (hydrogen suppressed graph) was employed to generate QSTR models. Three target functions i.e. TF1 (WIIC=WCII=0), TF2 (WIIC= 0.3 & WCII=0) and TF3 (WIIC= 0.0 &WCII=0.3) were employed to develop robust QSTR models and the statistical outcomes of each target function were compared with each other. The correlation intensity index (CII) was found a reliable benchmark of the predictive potential for QSTR models. The numerical value of the determination coefficient of the validation set of split 1 computed by TF3 was found highest (RValid2=0.8438). The fragments responsible for the toxicity of dioxins and related chemicals were also identified in terms of the promoter of increase/decrease for pEC50. Three random splits (Split 1, Split 2 and Split 4) were selected for the extraction of the promoter of increase/decrease for pEC50. In the last, consensus modelling was performed using the intelligent consensus tool of DTC lab (https://dtclab.webs.com/software-tools). The original consensus model, which was created by combining four distinct models employing the split 4 arrangement, was more predictive for the validation set and the numerical value of the determination coefficient of the test set (validation set) was increased from 0.8133 to 0.9725. For the validation set of split 4, the mean absolute error (MAE 100%) was also lowered from 0.513 to 0.2739.
Collapse
Affiliation(s)
- Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana 136119, India.
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, Haryana 125001, India.
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, Haryana 124001, India
| |
Collapse
|
6
|
Toropova AP, Toropov AA, Lombardo A, Lavado G, Benfenati E. Paradox of 'ideal correlations': improved model for air half-life of persistent organic pollutants. ENVIRONMENTAL TECHNOLOGY 2022; 43:2510-2515. [PMID: 33502960 DOI: 10.1080/09593330.2021.1882588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 01/22/2021] [Indexed: 06/12/2023]
Abstract
The persistence of organic pollutants is an important environmental property due to the extended possibility to have an impact of corresponding substances. In many cases, the experimental values of the thousands of contaminants are missing. The object of the study is novel computational modelling for air pollutions. Quantitative structure-property relationship (QSPR) for air half-life has been built using the Monte Carlo method with applying the index of ideality of correlation (IIC). The basis of the predictive model of air half-life is the representation of the molecular structure by simplifying molecular input-line entry system (SMILES) and numerical data on the above endpoint (expressed by hours) converted to a decimal logarithm. The statistical quality of the model has been checked up with different validation metrics and is quite good. Paradoxically, the improvement of the statistical quality via the IIC for the validation set is done in detriment to the training set. The new model has performed better than those obtained previously on the same set of compounds, for the prediction of new compounds in the validation set. Some semi-quantitative indicators for the mechanistic interpretation of the model are suggested.
Collapse
Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Anna Lombardo
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Giovanna Lavado
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| |
Collapse
|
7
|
Singh R, Kumar P, Devi M, Lal S, Kumar A, Sindhu J, Toropova AP, Toropov AA, Singh D. Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors. NEW J CHEM 2022. [DOI: 10.1039/d2nj03515d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Monte Carlo optimization based QSGFEAR model development using CII results in the formation of more reliable, robust and predictive models.
Collapse
Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Alla P. Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Andrey A. Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India
| |
Collapse
|
8
|
Kumar P, Kumar A. Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling. Nanotoxicology 2021; 15:1199-1214. [PMID: 34961428 DOI: 10.1080/17435390.2021.2008039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.
Collapse
Affiliation(s)
- Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| |
Collapse
|
9
|
Kumar P, Kumar A. Correlation intensity index (CII) as a benchmark of predictive potential: Construction of quantitative structure activity relationship models for anti-influenza single-stranded DNA aptamers using Monte Carlo optimization. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.131205] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
|
10
|
Lotfi S, Ahmadi S, Kumar P. The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors. RSC Adv 2021; 11:33849-33857. [PMID: 35497322 PMCID: PMC9042335 DOI: 10.1039/d1ra06861j] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/11/2021] [Indexed: 12/17/2022] Open
Abstract
Ionic liquids (ILs) have captured intensive attention owing to their unique properties such as high thermal stability, negligible vapour pressure, high dissolution capacity and high ionic conductivity as well as their wide applications in various scientific fields including organic synthesis, catalysis, and industrial extraction processes. Many applications of ionic liquids (ILs) rely on the melting point (Tm). Therefore, in the present manuscript, the melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs. The Monte Carlo algorithm of CORAL software is applied to build up a robust QSPR model to calculate the values Tm of 353 imidazolium ILs. Using a combination of SMILES and hydrogen-suppressed molecular graphs (HSGs), the hybrid optimal descriptor is computed and used to generate the QSPR models. Internal and external validation parameters are also employed to evaluate the predictability and reliability of the QSPR model. Four splits are prepared from the dataset and each split is randomly distributed into four sets i.e. training set (≈33%), invisible training set (≈31%), calibration set (≈16%) and validation set (≈20%). In QSPR modelling, the numerical values of various statistical features of the validation sets such as RValidation2, QValidation2, and IICValidation are found to be in the range of 0.7846–0.8535, 0.7687–0.8423 and 0.7424–0.8982, respectively. For mechanistic interpretation, the structural attributes which are responsible for the increase/decrease of Tm are also extracted. The melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs.![]()
Collapse
Affiliation(s)
- Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU) 19395-4697 Tehran Iran
| | - Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University Tehran Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University Kurukshetra Haryana 136119 India
| |
Collapse
|
11
|
Exploring biological efficacy of novel benzothiazole linked 2,5-disubstituted-1,3,4-oxadiazole hybrids as efficient α-amylase inhibitors: Synthesis, characterization, inhibition, molecular docking, molecular dynamics and Monte Carlo based QSAR studies. Comput Biol Med 2021; 138:104876. [PMID: 34598068 DOI: 10.1016/j.compbiomed.2021.104876] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/14/2021] [Accepted: 09/14/2021] [Indexed: 12/29/2022]
Abstract
In an effort to explore a class of novel antidiabetic agents, we have made an effort to synergize the α-amylase inhibitory potential of 1,3-benzothiazole and 1,3,4-oxadiazole scaffolds by combining the two into a single structure via an ether linkage. The structure of synthesized benzothiazole clubbed oxadiazole derivatives are established by different spectral techniques. The synthesized hybrids are evaluated for their in vitro inhibitory potential against α-amylase. Compound 8f is found to be the most potent with a significant inhibition (87.5 ± 0.74% at 50 μg/mL, 82.27 ± 1.85% at 25 μg/mL and 79.94 ± 1.88% at 12.5 μg/mL) when compared to positive control acarbose (77.96 ± 2.06%, 71.17 ± 0.60%, 67.24 ± 1.16% at 50 μg/mL, 25 μg/mL and 12.5 μg/mL concentration). Molecular docking of the most potent enzyme inhibitor, 8f, shows promising interaction with the binding site of biological macromolecule Aspergillus oryzae α-amylase (PDB ID: 7TAA) and human pancreatic α-amylase (PDB ID: 3BAJ). To a step further, in-depth QSAR studies show a significant correlation between the experimental and the predicted inhibitory activities with the best Rvalidation2= 0.8701. The developed QSAR model can provide ample information about the structural features responsible for the increase and decrease of inhibitory activity. The mechanistic interpretation of the structure-activity relationship (SAR) is done with the help of combined computational calculations i.e. molecular docking and QSAR. Finally, molecular dynamic simulations are performed to get an insight into the binding mode of the most potent derivative with α-amylase from A. oryzae (PDB ID: 7TAA) and human pancreas (PDB ID: 3BAJ).
Collapse
|
12
|
Kumar A, Kumar P. Prediction of power conversion efficiency of phenothiazine-based dye-sensitized solar cells using Monte Carlo method with index of ideality of correlation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:817-834. [PMID: 34530657 DOI: 10.1080/1062936x.2021.1973095] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Simplified molecular-input line-entry system (SMILES) notation and inbuilt Monte Carlo algorithm of CORAL software were employed to construct generative and prediction QSPR models for the analysis of the power conversion efficiency (PCE) of 215 phenothiazine derivatives. The dataset was divided into four splits and each split was further divided into four sets. A hybrid descriptor, a combination of SMILES and hydrogen suppressed graph (HSG), was employed to build reliable and robust QSPR models. The role of the index of ideality of correlation (IIC) was also studied in depth. We performed a comparative study to predict PCE using two target functions (TF1 without IIC and TF2 with IIC). Eight QSPR models were developed and the models developed with TF2 was shown robust and reliable. The QSPR model generated from split 4 was considered a leading model. The different statistical benchmarks were computed for the lead model and these were rtraining set2=0.7784; rinvisible training set2=0.7955; rcalibration set2=0.7738; rvalidation set2=0.7506; Qtraining set2=0.7691; Qinvisible training set2=0.7850; Qcalibration set2=0.7501; Qvalidation set2=0.7085; IICtraining set = 0.8590; IICinvisible training set = 0.8297; IICcalibration set = 0.8796; IICvalidation set = 0.8293, etc. The promoters of increase and decrease of endpoint PCE were also extracted.
Collapse
Affiliation(s)
- A Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| |
Collapse
|
13
|
Lotfi S, Ahmadi S, Kumar P. A hybrid descriptor based QSPR model to predict the thermal decomposition temperature of imidazolium ionic liquids using Monte Carlo approach. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116465] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
14
|
Ahmadi S, Moradi Z, Kumar A, Almasirad A. SMILES-based QSAR and molecular docking study of xanthone derivatives as α-glucosidase inhibitors. J Recept Signal Transduct Res 2021; 42:361-372. [PMID: 34384326 DOI: 10.1080/10799893.2021.1957932] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Increasing diabetic population is one of the major health concerns all over the world. Inhibition of α-glucosidase is a clinically proved and attractive strategy to manage diabetes. In this study, robust and reliable QSAR models to predict α-glucosidase inhibitory potential of xanthone derivatives are developed by the Monte Carlo technique. The chemical structures are represented by SMILES notation without any 3D-optimization. The significance of the index of ideality correlation (IIC) with applicability domain (AD) is also studied in depth. The models developed using CORAL software by considering IIC criteria are found to be statistically more significant and robust than simple balance of correlation. The QSAR models are validated by both internal and external validation methods. The promoters of increase and decrease of activity are also extracted and interpreted in detail. The interpretation of developed models explains the role of different structural attributes in predicting the pIC50 of xanthone derivatives as α-glucosidase inhibitors. Based on the results of model interpretation, modifications are done on some xanthone derivatives and 15 new molecules are designed. The α-glucosidase inhibitory activity of novel molecules is further supported by docking studies.
Collapse
Affiliation(s)
- Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Zohreh Moradi
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Ali Almasirad
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| |
Collapse
|
15
|
Ghiasi T, Ahmadi S, Ahmadi E, Talei Bavil Olyai MR, Khodadadi Z. The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:495-520. [PMID: 34074200 DOI: 10.1080/1062936x.2021.1925344] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 04/29/2021] [Indexed: 06/12/2023]
Abstract
Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC50) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target function 1 (TF1) without applying the index of ideality of correlation (IIC) and target function 2 (TF2) that uses IIC. The constructed models based on TF2 were statistically more significant and robust than the models based on TF1. The determination coefficients (r2) of training and test sets were 0.86 and 0.88 for the best split based on TF2. The promoters of the increase/decrease of activity were also extracted and interpreted in detail. The model interpretation results explain the role of different structural attributes in predicting the pIC50 values of hepatitis C virus NS3/4A protease inhibitors. Based on the mechanistic model interpretation results, eight new compounds were designed and their pIC50 values were predicted based on the average prediction of ten models.
Collapse
Affiliation(s)
- T Ghiasi
- Department of Chemistry, Faculty of Science, Islamic Azad University, South Tehran Branch, Tehran, Iran
| | - S Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - E Ahmadi
- Department of Chemistry, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
| | - M R Talei Bavil Olyai
- Department of Chemistry, Faculty of Science, Islamic Azad University, South Tehran Branch, Tehran, Iran
| | - Z Khodadadi
- Department of Chemistry, Faculty of Science, Islamic Azad University, South Tehran Branch, Tehran, Iran
| |
Collapse
|
16
|
Kumar A, Kumar P. Cytotoxicity of quantum dots: Use of quasiSMILES in development of reliable models with index of ideality of correlation and the consensus modelling. JOURNAL OF HAZARDOUS MATERIALS 2021; 402:123777. [PMID: 33254788 DOI: 10.1016/j.jhazmat.2020.123777] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/30/2020] [Accepted: 08/15/2020] [Indexed: 05/23/2023]
Abstract
The assessment of cytotoxicity of quantum dots is very essential for environmental and health risk analysis. In the present work we have modelled HeLa cell cytotoxicity of sixty one CdSe quantum dots with ZnS shell as a function of its experimental conditions and molecular construction using quasiSMILES representations. The index of ideality of correlation helps in the building of ten statistically significant models having good fitting ability with value of R2 ranging from 0.8414 to 0.9609 for the training set. The split 5 model is rated as the best model with values of R2, Q2F1, Q2F2 and Q2F3 as 0.8964, 0.8267, 0.8264 and 0.8777 respectively for the calibration set. The extraction of features causing increase and decrease of cytotoxicity of quantum dots indicates importance of neutral surface charge, surface modified with protein, 72 h exposure time, combination of MTT assay with surface protein in decreasing the cytotoxicity. Amphiphilic polymer, polyol ligand with neutral charge, 0.5 - 0.6 nm quantum dot diameter with lipid ligand and unmodified positively charged surface are grouped in toxicity enhancer features. Further, consensus modelling using split 5 and 8 patterns enhances the prediction quality by increasing the R2val to 0.9361 and 0.9656 respectively.
Collapse
Affiliation(s)
- Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, 125001, India.
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
| |
Collapse
|
17
|
Quantitative structure toxicity analysis of ionic liquids toward acetylcholinesterase enzyme using novel QSTR models with index of ideality of correlation and correlation contradiction index. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.114055] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
|
18
|
Kumar P, Kumar A. In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:697-715. [PMID: 32878494 DOI: 10.1080/1062936x.2020.1806105] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/03/2020] [Indexed: 06/11/2023]
Abstract
Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because of poor affinity for the fibre and discharge into the water stream. The chemical aspects of 72 azo dyes towards cellulose fibre in terms of their affinity by QSPR have been explored in the present work. We have employed two approaches, namely balance of correlation without IIC (TF1) and balance of correlation with IIC (TF2), to generate 16 QSAR models from 8 splits. The determination coefficient of calibration and validation set was found higher when the QSPR models were developed using the index of ideality correlation (IIC) parameter (TF2). The model developed with TF2 for split 3 was considered as a prominent model because the determination coefficient of the validation set was maximum (r 2 = 0.9468). The applicability domain (AD) was also analysed based on 'statistical defect', d(A) for a SMILES attribute. The mechanistic interpretation was done by identifying the SMILES attributes responsible for the promoter of endpoint increase and promoter of endpoint decrease. These SMILES attributes were applied to design 15 new dyes with higher affinity for cellulose fibre.
Collapse
Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University , Kurukshetra, India
| | - A Kumar
- Department of Pharmaceutical Sciences, Guru Jambeshwar University of Science and Technology , Hisar, India
| |
Collapse
|
19
|
Kumar A, Kumar P. Identification of good and bad fragments of tricyclic triazinone analogues as potential PKC-θ inhibitors through SMILES–based QSAR and molecular docking. Struct Chem 2020. [DOI: 10.1007/s11224-020-01629-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|