Computational Prediction for the Slopes of AL-type Phase Solubility Curves of Organic Compounds Complexed With α-, β-, or γ-cyclodextrins Based on Monte Carlo Docking Simulations

2006 ◽  
Vol 55 (1-2) ◽  
pp. 103-108 ◽  
Author(s):  
Youngjin Choi ◽  
Kum Won Cho ◽  
Karpjoo Jeong ◽  
Seung R. Paik ◽  
Seunho Jung
Keyword(s):  
Monte Carlo ◽  
Organic Compounds ◽  
Computational Prediction ◽  
Phase Solubility ◽  
Docking Simulations ◽  
Type Phase

The Monte Carlo method to build up models of the hydrolysis half-lives of organic compounds

2021 ◽  
pp. 1-9
Author(s):  
A.A. Toropov ◽  
A.P. Toropova ◽  
A. Lombardo ◽  
A. Roncaglioni ◽  
G.J. Lavado ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Organic Compounds ◽  
The Monte Carlo Method

scholarly journals Molecular Dynamics Scoring of Protein–Peptide Models Derived from Coarse-Grained Docking

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3293
Author(s):  
Mateusz Zalewski ◽  
Sebastian Kmiecik ◽  
Michał Koliński
Keyword(s):  
Molecular Dynamics ◽  
Geometry Optimization ◽  
Computational Prediction ◽  
Md Simulations ◽  
Coarse Grained ◽  
Ligand Interaction ◽  
Vast Number ◽  
Docking Simulations ◽  
Peptide Models ◽  
Peptide Complexes

One of the major challenges in the computational prediction of protein–peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein–peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor–ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein–peptide complexes show that the proposed MD-based scoring approach can be used to identify protein–peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.

scholarly journals Binding of Androgen- and Estrogen-Like Flavonoids to Their Cognate (Non)Nuclear Receptors: A Comparison by Computational Prediction

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1613
Author(s):  
Giulia D’Arrigo ◽  
Eleonora Gianquinto ◽  
Giulia Rossetti ◽  
Gabriele Cruciani ◽  
Stefano Lorenzetti ◽  
...  
Keyword(s):  
Nuclear Receptors ◽  
Fruits And Vegetables ◽  
Computational Prediction ◽  
Membrane Receptors ◽  
Computational Docking ◽  
Docking Simulations ◽  
Mediated Effects ◽  
17Β Estradiol ◽  
G Protein Coupled ◽  
Relevant Degree

Flavonoids are plant bioactives that are recognized as hormone-like polyphenols because of their similarity to the endogenous sex steroids 17β-estradiol and testosterone, and to their estrogen- and androgen-like activity. Most efforts to verify flavonoid binding to nuclear receptors (NRs) and explain their action have been focused on ERα, while less attention has been paid to other nuclear and non-nuclear membrane androgen and estrogen receptors. Here, we investigate six flavonoids (apigenin, genistein, luteolin, naringenin, quercetin, and resveratrol) that are widely present in fruits and vegetables, and often used as replacement therapy in menopause. We performed comparative computational docking simulations to predict their capability of binding nuclear receptors ERα, ERβ, ERRβ, ERRγ, androgen receptor (AR), and its variant ART877A and membrane receptors for androgens, i.e., ZIP9, GPRC6A, OXER1, TRPM8, and estrogens, i.e., G Protein-Coupled Estrogen Receptor (GPER). In agreement with data reported in literature, our results suggest that these flavonoids show a relevant degree of complementarity with both estrogen and androgen NR binding sites, likely triggering genomic-mediated effects. It is noteworthy that reliable protein–ligand complexes and estimated interaction energies were also obtained for some suggested estrogen and androgen membrane receptors, indicating that flavonoids could also exert non-genomic actions. Further investigations are needed to clarify flavonoid multiple genomic and non-genomic effects. Caution in their administration could be necessary, until the safe assumption of these natural molecules that are largely present in food is assured.

Corrosion inhibition performance of newly synthesized 5-alkoxymethyl-8-hydroxyquinoline derivatives for carbon steel in 1 M HCl solution: experimental, DFT and Monte Carlo simulation studies

2018 ◽  
Vol 20 (30) ◽  
pp. 20167-20187 ◽  
Author(s):  
M. El Faydy ◽  
R. Touir ◽  
M. Ebn Touhami ◽  
A. Zarrouk ◽  
C. Jama ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Carbon Steel ◽  
Organic Compounds ◽  
Corrosion Inhibition ◽  
Spectroscopic Methods ◽  
Simulation Studies ◽  
Inhibition Performance ◽  
Hcl Solution ◽  
C Nmr

Three new organic compounds primarily based on 8-hydroxyquinoline have been successfully synthesized and characterized via different spectroscopic methods (FTIR, 1H, and 13C NMR).

Monte Carlo docking simulations of cyclomaltoheptaose and dimethyl cyclomaltoheptaose with paclitaxel

2002 ◽  
Vol 337 (6) ◽  
pp. 549-555 ◽  
Author(s):  
Hyunmyung Kim ◽  
Jungwon Choi ◽  
Hyun-Won Kim ◽  
Seunho Jung
Keyword(s):  
Monte Carlo ◽  
Docking Simulations

scholarly journals Towards Al3+-Induced Manganese-Containing Superoxide Dismutase Inactivation and Conformational Changes: An Integrating Study with Docking Simulations

2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
Jiang-Liu Yang ◽  
Shang-Jun Yin ◽  
Yue-Xiu Si ◽  
Zhi-Rong Lü ◽  
Xiangrong Shao ◽  
...  
Keyword(s):  
Superoxide Dismutase ◽  
Conformational Changes ◽  
Tertiary Structure ◽  
Kinetic Studies ◽  
Computational Prediction ◽  
Enzyme Inactivation ◽  
Dependent Manner ◽  
Inhibitory Effects ◽  
Docking Simulations ◽  
Dose Dependent

Superoxide dismutase (SOD, EC 1.15.1.1) plays an important antioxidant defense role in skins exposed to oxygen. We studied the inhibitory effects of Al3+ on the activity and conformation of manganese-containing SOD (Mn-SOD). Mn-SOD was significantly inactivated by Al3+ in a dose-dependent manner. The kinetic studies showed that Al3+ inactivated Mn-SOD follows the first-order reaction. Al3+ increased the degree of secondary structure of Mn-SOD and also disrupted the tertiary structure of Mn-SOD, which directly resulted in enzyme inactivation. We further simulated the docking between Mn-SOD and Al3+ (binding energy for Dock 6.3: −14.07 kcal/mol) and suggested that ASP152 and GLU157 residues were predicted to interact with Al3+, which are not located in the Mn-contained active site. Our results provide insight into the inactivation of Mn-SOD during unfolding in the presence of Al3+ and allow us to describe a ligand binding via inhibition kinetics combined with the computational prediction.

scholarly journals A comparison of bounding approach with isotopic correction factors and Monte Carlo sampling in burnup credit method

2021 ◽  
Vol 253 ◽  
pp. 07011
Author(s):  
Pavlína Haroková ◽  
Martin Lovecký
Keyword(s):  
Monte Carlo ◽  
Computational Prediction ◽  
Monte Carlo Sampling ◽  
Fuel Composition ◽  
Correction Factors ◽  
Conservative Approach ◽  
Multiplication Factor ◽  
Spent Fuel ◽  
Realistic Result ◽  
Fuel Storage

One of the methodologies used in criticality safety analysis is burnup credit method, which allows considering fuel burnup in models with spent fuel. This removes excessive conservatism from the analysis, but it also brings new uncertainties originating from computational prediction of spent fuel composition. The burnup credit method offer several possibilities of how to deal with this problem, e.g. using bounding approach with correction factors on nuclide concentrations, which is simple, but still very conservative approach. Another option is Monte Carlo sampling, which aims at receiving the most realistic result as possible, but is very computationally demanding. In this work, we have analyzed correction factors for selected nuclides and compared the results of both methods on model of spent fuel storage pool. The results show how much conservative the bounding approach is – in this case, the multiplication factor was higher by almost 0.03 than in Monte Carlo sampling, exceeding the standard deviation by more than 5.4 times.

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