Rational design of small modified peptides as ACE inhibitors

Silva, Daniel G.; Freitas, Matheus P.; Cunha, Elaine F. F. da; Ramalho, Teodorico C.; Nunes, Cleiton Antônio

doi:10.1039/c2md20214j

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…After validation and robustness tests, Model 1 was considered reliable to predict pEC 50 values of new structures for synthesis and activity tests. A successful strategy to propose new drug‐like compounds is based on the modification of substructures of the main activities of compound series . This procedure can maximize the activity of the new structure in comparison with its parent compounds and minimize its side‐effects or problems related to pharmacokinetics.…”

Section: Resultsmentioning

confidence: 99%

QSAR Models Guided by Molecular Dynamics Applied to Human Glucokinase Activators

et al. 2015

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In this study, quantitative structure-activity relationship studies which make use of molecular dynamics trajectories were performed on a set of 54 glucokinase protein activators. The conformations obtained by molecular dynamics simulation were superimposed according to the twelve alignments tested in a virtual three-dimensional box comprised of 2 Å cells. The models were generated by the technique that combines genetic algorithms and partial least squares. The best alignment models generated with a determination coefficient (r(2)) between 0.674 and 0.743 and cross-validation (q(2)) between 0.509 and 0.610, indicating good predictive capacity. The 4D-QSAR models developed in this study suggest novel molecular regions to be explored in the search for better glucokinase activators.

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Section: Resultsmentioning

confidence: 99%

QSAR Models Guided by Molecular Dynamics Applied to Human Glucokinase Activators

et al. 2015

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“…A variety of small peptide compounds have been developed to inhibit ACE [ 141 , 142 ]. Another promising method is to select and combine the substructures of highly active compounds to form new peptide analogs [ 143–146 ].…”

Section: Drug Design Strategies For Covid-19mentioning

confidence: 99%

Computational anti-COVID-19 drug design: progress and challenges

Wang

Zhang

Nie

et al. 2021

Briefings in Bioinformatics

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Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

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“…Hypertension is a worldwide health problem, the prevalence of which have affected up to 30% of the adult population according to the World Health Organization, and hypertension carries a high‐risk factor for arteriosclerosis, stoke, myocardial, infarction, and end‐stage renal disease (Silva and others ). It is predicted that by 2025, 60% of the world population will be suffered from hypertension (Kearney and others ).…”

Section: Introductionmentioning

confidence: 99%

A Virtual Screening Method for Inhibitory Peptides of Angiotensin I–Converting Enzyme

Liu

Guo

et al. 2014

Journal of Food Science

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Natural small peptides from foods have been proven to be efficient inhibitors of Angiotensin I-converting enzyme (ACE) for the regulation of blood pressure. The traditional ACE inhibitory peptides screening method is both time consuming and money costing, to the contrary, virtual screening method by computation can break these limitations. We establish a virtual screening method to obtain ACE inhibitory peptides with the help of Libdock module of Discovery Studio 3.5 software. A significant relationship between Libdock score and experimental IC(50) was found, Libdock score = 10.063 log(1/IC(50)) + 68.08 (R(2) = 0.62). The credibility of the relationship was confirmed by testing the coincidence of the estimated log(1/IC(50)) and measured log(1/IC(50)) (IC(50) is 50% inhibitory concentration toward ACE, in μmol/L) of 5 synthetic ACE inhibitory peptides, which was virtual hydrolyzed and screened from a kind of seafood, Phascolosoma esculenta. Accordingly, Libdock method is a valid IC(50) estimation tool and virtual screening method for small ACE inhibitory peptides.

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Rational design of small modified peptides as ACE inhibitors

Cited by 10 publications

References 25 publications

QSAR Models Guided by Molecular Dynamics Applied to Human Glucokinase Activators

QSAR Models Guided by Molecular Dynamics Applied to Human Glucokinase Activators

Computational anti-COVID-19 drug design: progress and challenges

A Virtual Screening Method for Inhibitory Peptides of Angiotensin I–Converting Enzyme

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