A New Descriptor of Amino Acids‐SVGER and its Applications in Peptide QSAR

Tong, Jianbo; Li, Lingxiao; Bai, Min; Li, Kangnan

doi:10.1002/minf.201501023

Cited by 8 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors results provide a theoretical basis for the design of ACE inhibitors as well as insight into the structural and molecular properties of ACE and the mechanisms of inhibition of the different peptide inhibitors [60]. In other work, Tong et al [61] calculated amino acid descriptors and performed QSAR models applyed to 55 ACE tri-peptides inhibitors. Finally, Deng et al [62] constructed a benchmark data set containing 141 unique ACE inhibitory dipeptides through database mining, and carried out a QSAR study to predict activities of potential novel peptides.…”

Section: Molecular Modeling Applied To the Study Of Ace Inhibitors Inmentioning

confidence: 96%

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: Molecular Modeling Applied To the Study Of Ace Inhibitors Inmentioning

confidence: 96%

Considerations for Docking of Selective Angiotensin-Converting Enzyme Inhibitors

Caballero

2020

Molecules

View full text Add to dashboard Cite

show abstract

“…In Equation 3, S is the solvent accessible surface area of the atom (Table S4); a is the hydrophobic constant expressed with atomic solvation parameter (ASP) ( Table S5); T is the sign function, indicating the entropy changes resulting from different types of atomic interactions [23][24][25] (Table S6).…”

Section: Atomic Types and Interactionsmentioning

confidence: 99%

Molecular modeling studies of human immunodeficiency virus type 1 protease inhibitors using three‐dimensional quantitative structure‐activity relationship, virtual screening, and docking simulations

Tong

Zhan

Bai

et al. 2016

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

In this paper, two 3‐dimensional quantitative structure‐activity relationship models for 60 human immunodeficiency virus (HIV)‐1 protease inhibitors were established using random sampling analysis on molecular surface and translocation comparative molecular field vector analysis (Topomer CoMFA). The non–cross‐validation (r2), cross‐validation (q2), correlation coefficient of external validation (Q2ext), and F of 2 models were 0.94, 0.80, 0.79, and 198.84 and 0.94, 0.72, 0.75, and 208.53, respectively. The results indicated that 2 models were reasonable and had good prediction ability. Topomer Search was used to search R groups in the ZINC database, 20 new compounds were designed, and the Topomer CoMFA model was used to predicate the biological activity. The results showed that 18 new compounds were more active than the template molecule. So the Topomer Search is effective in screening and can guide the design of new HIV/AIDS drugs. The mechanism of action was studied by molecular docking, and it showed that the protease inhibitors and Ile50, Asp25, and Arg8 sites of HIV‐1 protease have interactions. These results have provided an insight for the design of new potent inhibitors of HIV‐1 protease.

show abstract

Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results

Toropov

Toropova

Roncaglioni

et al. 2018

Methods in Molecular Biology

View full text Add to dashboard Cite

show abstract

A New Descriptor of Amino Acids‐SVGER and its Applications in Peptide QSAR

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

Molecular modeling studies of human immunodeficiency virus type 1 protease inhibitors using three‐dimensional quantitative structure‐activity relationship, virtual screening, and docking simulations

Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results

Contact Info

Product

Resources

About