ANALYSIS OF PURINIC ALKALOIDS BY XRD AND MOLECULAR MODELING METHODS

2021 ◽  
Vol 8 (2021) (1) ◽  
pp. 1-8
Author(s):  
José REGO ◽  
◽  
Jorddy CRUZ ◽  
Marcondes COSTA ◽  
Fabrine ALVES ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Molecular Modeling ◽  
Cross Validation ◽  
Linear Models ◽  
Monoclinic System ◽  
Purine Base ◽  
Crystal Sample ◽  
Modeling Methods ◽  
Statistical Results ◽  
Good Linear Correlation

Theophylline, theobromine and caffeine, are purine-based alkaloids in which the main differentiation in the molecular structure is the presence of methyls, one, two and three, respectively in these substances. This study presents an analysis by XRD and molecular modeling methods of the alkaloid’s caffeine and theobromine. The crystalline structure of caffeine was characterized as a monoclinic system, and the diffractogram of the caffeine crystals showed peaks with regions of greater intensity at 2θ = 11.7616 ° (d = 7.51 Å; I% = 80.13) and 2θ = 11.9416 ° (d = 7.40 Å; I% = 98.14). In the diffractogram of the theobromine crystal sample, peaks of greater intensity occurred in the regions 2θ = 13.4616 ° (d = 6.57 Å; I% = 98.92) and 2θ = 27.0816 ° (d = 3, 28 Å; I% = 67.23). Results obtained by XRD for caffeine and theobromine were compatible with standard cards of the X’Pert High Score Plus® program. The presence of an extra methyl in the structure of the caffeine purine base, suggests, a shift in the values ​​of the angle 2 θ for the main peaks of theobromine, as well as an increase in intensity, mainly in 27.016, theobromine also presents a peak in the region 10.6 which does not occur in caffeine. Statistical results reveal that the linear models for data of peaks of specific angles in 2θ of the samples, presented good linear correlation (R2> 98%) and satisfactory results after the procedure of cross validation. caffeine and theobromine also showed important differences in interactions with adenosine A2AR, particularly in hydrophobic and hydrogen interactions.

Investigation of Isoindolo[2,1-a]quinoxaline-6-imines as Topoisomerase I Inhibitors with Molecular Modeling Methods

2017 ◽  
Vol 13 (3) ◽  
Author(s):  
Balazs Balogh ◽  
Anna Carbone ◽  
Virginia Spanò ◽  
Alessandra Montalbano ◽  
Paola Barraja ◽  
...  
Keyword(s):  
Molecular Modeling ◽  
Topoisomerase I ◽  
Modeling Methods ◽  
Topoisomerase I Inhibitors

Interaction of donepezil with tau protein: Insights from surface plasmon resonance and molecular modeling methods

2021 ◽  
pp. 115924
Author(s):  
Sepideh Najar-Ahmadi ◽  
Hossein Haghaei ◽  
Safar Farajnia ◽  
Reza Yekta ◽  
Jafar Ezzati Nazhad Dolatabadi ◽  
...  
Keyword(s):  
Surface Plasmon Resonance ◽  
Molecular Modeling ◽  
Surface Plasmon ◽  
Tau Protein ◽  
Plasmon Resonance ◽  
Modeling Methods

Studies on the interaction mechanism between CXS and papain by spectroscopic and molecular modeling methods

2012 ◽  
Vol 39 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Xiaoli Xi ◽  
Manman Yang ◽  
Tinggui Cheng ◽  
Liwei Zhang ◽  
Pin Yang
Keyword(s):  
Molecular Modeling ◽  
Interaction Mechanism ◽  
Modeling Methods

scholarly journals Effect of Heldanamycin Binding on the Thr90 Phosphorylation Site of Hsp90

2021 ◽  
Vol 4 (3) ◽  
pp. e00145
Author(s):  
K.A. Shcherbakov ◽  
D.S. Shcherbinin ◽  
A.V. Veselovsky
Keyword(s):  
Prostate Cancer ◽  
Transcription Factor ◽  
Androgen Receptor ◽  
Molecular Modeling ◽  
Phosphorylation Site ◽  
Heat Shock Protein 90 ◽  
Hsp90 Inhibitor ◽  
Inhibitor Binding ◽  
Atp Binding Site ◽  
Modeling Methods

Prostate cancer is hormone-dependent and the androgen receptor (AR) is involved in its development. AR is a transcription factor that is activated by ligand binding, result in its translocation into the nucleus, where it initiates gene transcription. In an inactive state in cytoplasm AR exists as a complex with heat shock protein 90 (HSP90) and some other proteins. When the agonist binds, a conformational change in AR occurs, resulting in HSP90 and other chaperones dissociating. Recently it has been shown that for the dissociation of the HSP90-AR complex and the translocation of the latter into the nucleus, phosphorylation of the Thr-90 residue of the N-terminal domain of HSP90 is necessary. In this work, the effect of the HSP90 inhibitor, heldanamycin, interacting with the ATP-binding site, on the Thr90 phosphorylation site was investigated by molecular modeling methods. It has been shown that inhibitor binding slightly affects the size and mobility of cavity around Thr90. It is suggested that inhibitor binding to HSP90 does not result in changing the protein structure and does not influence on protein phosphorylation, and partially explains low effectiveness of such type of drugs in the therapy of prostate cancer.

scholarly journals modelBuildR: an R package for model building and feature selection with erroneous classifications

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10849
Author(s):  
Maximilian Knoll ◽  
Jennifer Furkel ◽  
Juergen Debus ◽  
Amir Abdollahi
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Model Building ◽  
Linear Models ◽  
Binary Classification ◽  
Ground Truth ◽  
R Package ◽  
Methylation Array ◽  
Survival Difference ◽  
Error Probabilities

Background Model building is a crucial part of omics based biomedical research to transfer classifications and obtain insights into underlying mechanisms. Feature selection is often based on minimizing error between model predictions and given classification (maximizing accuracy). Human ratings/classifications, however, might be error prone, with discordance rates between experts of 5–15%. We therefore evaluate if a feature pre-filtering step might improve identification of features associated with true underlying groups. Methods Data was simulated for up to 100 samples and up to 10,000 features, 10% of which were associated with the ground truth comprising 2–10 normally distributed populations. Binary and semi-quantitative ratings with varying error probabilities were used as classification. For feature preselection standard cross-validation (V2) was compared to a novel heuristic (V1) applying univariate testing, multiplicity adjustment and cross-validation on switched dependent (classification) and independent (features) variables. Preselected features were used to train logistic regression/linear models (backward selection, AIC). Predictions were compared against the ground truth (ROC, multiclass-ROC). As use case, multiple feature selection/classification methods were benchmarked against the novel heuristic to identify prognostically different G-CIMP negative glioblastoma tumors from the TCGA-GBM 450 k methylation array data cohort, starting from a fuzzy umap based rough and erroneous separation. Results V1 yielded higher median AUC ranks for two true groups (ground truth), with smaller differences for true graduated differences (3–10 groups). Lower fractions of models were successfully fit with V1. Median AUCs for binary classification and two true groups were 0.91 (range: 0.54–1.00) for V1 (Benjamini-Hochberg) and 0.70 (0.28–1.00) for V2, 13% (n = 616) of V2 models showed AUCs < = 50% for 25 samples and 100 features. For larger numbers of features and samples, median AUCs were 0.75 (range 0.59–1.00) for V1 and 0.54 (range 0.32–0.75) for V2. In the TCGA-GBM data, modelBuildR allowed best prognostic separation of patients with highest median overall survival difference (7.51 months) followed a difference of 6.04 months for a random forest based method. Conclusions The proposed heuristic is beneficial for the retrieval of features associated with two true groups classified with errors. We provide the R package modelBuildR to simplify (comparative) evaluation/application of the proposed heuristic (http://github.com/mknoll/modelBuildR).

scholarly journals The study of complexation of 5,17-bis-(n-tolyliminomethyl)- 25,27-dipropoxycalix[4]arene with benzoic acids by rp hplc and molecular modeling methods

2013 ◽  
Vol 11 (3(43)) ◽  
pp. 3-8
Author(s):  
O. I. Kalchenko ◽  
S. O. Cherenok ◽  
V. I. Kalchenko ◽  
A. V. Solovyov ◽  
V. V. Gorbatchuk
Keyword(s):  
Molecular Modeling ◽  
Benzoic Acids ◽  
Modeling Methods ◽  
Rp Hplc
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