Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach

Stoičkov, V.; Sarić, S; Golubović, Mlađjan; Zlatanović, Dragan; Krtinić, Dane; Dinić, Ljubomir; Mladenović, Bojan; Sokolović, Dušan; Veselinović, Aleksandar M.

doi:10.1080/1062936x.2018.1485737

Cited by 8 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an example of theoretical reports on the use of molecular modeling methods to investigate complexes between ACE and synthetic compounds, the work of Stoičkov et al [75] can be mentioned. These authors developed QSAR models for a series of non-peptide compounds (63 derivatives of N-substituted (mercaptoalkanoyl)-and [(acylthio)alkanoyl] glycines) as ACE inhibitors by using descriptors based on Simplified Molecular Input-Line Entry System (SMILES) notation and local graph invariants.…”

Section: -Bis(dihydrocaffeoyl)-spermidine)mentioning

confidence: 99%

Considerations for Docking of Selective Angiotensin-Converting Enzyme Inhibitors

Caballero

2020

Molecules

View full text Add to dashboard Cite

The angiotensin-converting enzyme (ACE) is a two-domain dipeptidylcarboxypeptidase, which has a direct involvement in the control of blood pressure by performing the hydrolysis of angiotensin I to produce angiotensin II. At the same time, ACE hydrolyzes other substrates such as the vasodilator peptide bradykinin and the anti-inflammatory peptide N-acetyl-SDKP. In this sense, ACE inhibitors are bioactive substances with potential use as medicinal products for treatment or prevention of hypertension, heart failures, myocardial infarction, and other important diseases. This review examined the most recent literature reporting ACE inhibitors with the help of molecular modeling. The examples exposed here demonstrate that molecular modeling methods, including docking, molecular dynamics (MD) simulations, quantitative structure-activity relationship (QSAR), etc, are essential for a complete structural picture of the mode of action of ACE inhibitors, where molecular docking has a key role. Examples show that too many works identified ACE inhibitory activities of natural peptides and peptides obtained from hydrolysates. In addition, other works report non-peptide compounds extracted from natural sources and synthetic compounds. In all these cases, molecular docking was used to provide explanation of the chemical interactions between inhibitors and the ACE binding sites. For docking applications, most of the examples exposed here do not consider that: (i) ACE has two domains (nACE and cACE) with available X-ray structures, which are relevant for the design of selective inhibitors, and (ii) nACE and cACE binding sites have large dimensions, which leads to non-reliable solutions during docking calculations. In support of the solution of these problems, the structural information found in Protein Data Bank (PDB) was used to perform an interaction fingerprints (IFPs) analysis applied on both nACE and cACE domains. This analysis provides plots that identify the chemical interactions between ligands and both ACE binding sites, which can be used to guide docking experiments in the search of selective natural components or novel drugs. In addition, the use of hydrogen bond constraints in the S2 and S2′ subsites of nACE and cACE are suggested to guarantee that docking solutions are reliable.

show abstract

Section: -Bis(dihydrocaffeoyl)-spermidine)mentioning

confidence: 99%

Considerations for Docking of Selective Angiotensin-Converting Enzyme Inhibitors

Caballero

2020

Molecules

View full text Add to dashboard Cite

show abstract

“…Molecular graph-based descriptors were derived from local graph invariants, and the most elementary theoretical graph concepts, such as paths and walks, were used in this research. Literature contains detailed mathematical definitions and applications in QSAR model development (Stoičkov et al 2018 ). The following local graph invariants were applied for the purpose of defining the optimal descriptors used in conformation-independent QSAR models: Morgan extended connectivity indices of increasing orders (EC0), path numbers with the length of 2 and 3 (p 2 , p 3 ), valence shells in the range of 2 and 3 (s 2 , s 3 ), as well as the number of carbon atom neighbors ( NumberOfCarbon ), and the non-carbon atom neighbors ( NumberofNonCarbon ).…”

Section: Methodsmentioning

confidence: 99%

“…The methodology from the published paper was used entirely for this purpose (Veselinović et al 2015 ; Ojha et al 2011 ; Roy et al 2008 ), which involved the calculation of various statistical parameter values, such as the regular and cross-validated correlation coefficient, standard estimation error, mean absolute error (MAE), the Fischer ratio, root-mean-square error, R m 2 , and MAE-based metrics. What is more, the validation of the developed QSAR models in this research was achieved through data randomization (Y-scrambling test) and with the determination of concordance correlation coefficient (CCC), as well as the novel parameter, dubbed the index of ideality of correlation (IIC) (Stoičkov et al 2018 ; Toropov and Toropova 2017 ; Veselinović et al 2018 ).…”

Section: Methodsmentioning

confidence: 99%

In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition

Ničkčović¹,

Nikolić²,

Nedeljković³

et al. 2022

Chem. Pap.

View full text Add to dashboard Cite

The COVID-19 pandemic emerged in 2019, bringing with it the need for greater stores of effective antiviral drugs. This paper deals with the conformation-independent, QSAR model, developed by employing the Monte Carlo optimization method, as well as molecular graphs and the SMILES notation-based descriptors for the purpose of modeling the SARS-CoV-3CLpro enzyme inhibition. The main purpose was developing a reproducible model involving easy interpretation, utilized for a quick prediction of the inhibitory activity of SAR-CoV-3CLpro. The following statistical parameters were present in the best-developed QSAR model: (training set) R 2 = 0.9314, Q 2 = 0.9271; (test set) R 2 = 0.9243, Q 2 = 0.8986. Molecular fragments, defined as SMILES notation descriptors, that have a positive and negative impact on 3CLpro inhibition were identified on the basis of the results obtained for structural indicators, and were applied to the computer-aided design of five new compounds with (4-methoxyphenyl)[2-(methylsulfanyl)-6,7-dihydro-1H-[1,4]dioxino[2,3-f]benzimidazol-1-yl]methanone as a template molecule. Molecular docking studies were used to examine the potential inhibition effect of designed molecules on SARS-CoV-3CLpro enzyme inhibition and obtained results have high correlation with the QSAR modeling results. In addition, the interactions between the designed molecules and amino acids from the 3CLpro active site were determined, and the energies they yield were calculated. Supplementary Information The online version contains supplementary material available at 10.1007/s11696-022-02170-8.

show abstract

“…Threshold value classifies the molecular features into active and rare structural attributes while the epoch is a sequence of variation for correlation weight of each molecular attribute which leads to increase of target function. A model can be developed with CORAL software by following methods named as: classic scheme, the balance of correlation, the balance of correlation with ideal slopes and a new move towards advancement is achieved by introduction of parameter the index of ideality of correlation [29]. In this work, the balance of correlation with ideal slopes method was used for QSAR model development [30].…”

Section: Model Developmentmentioning

confidence: 99%

Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

et al. 2020

View full text Add to dashboard Cite

Fructose-1,6-bisphosphatase performs a significant function in regulating the blood glucose level in type 2 diabetes by controlling the process gluconeogenesis. In this research work optimal descriptor (graph) based quantitative structural activity relationship studies of a set of 203 fructose-1,6-bisphosphatase has been performed with the help of Monte Carlo optimization. Distribution of compounds into different sets such as training set, invisible training set, calibration set and validation sets resulted in formation of splits. Statistical parameters obtained from quantitative structural activity relationship modeling were good for various designed splits. The statistical parameters such as R2 and Q2 for calibration and validation sets of best split developed were found to be 0.8338, 0.7908 & 0.7920 and 0.7036 respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoters and demoters of the endpoint. Further these structural attributes were used in designing of new fructose-1,6-bisphosphatase inhibitors and molecular docking study was accomplished for the determination of interactions of designed molecules with the enzyme.

show abstract

Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach

Cited by 8 publications

References 23 publications

Considerations for Docking of Selective Angiotensin-Converting Enzyme Inhibitors

Considerations for Docking of Selective Angiotensin-Converting Enzyme Inhibitors

In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition

Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

Contact Info

Product

Resources

About