Taraxasterol presente en hojas de Jatropha gaumeri Greenm, como teórico antagonista parcial del receptor muscarínico Acho-M;, del tracto gastrointestinal

Jatropha gaumeri es un árbol endémico de la Península de Yucatán, México, y cuenta con estudios etnomédicos,fitoquimicos y farmacológicos. En el presente trabajo se plantea generar información teórica que relacione las propiedadesfisicoquímicas y biológicas con los efectos atribuidos en la medicina tradicional y certidumbre en el desarrollo de estudiosexperimentales. J. gaumeri se analizó por gravimetria y espectroscopia (UV-Vis, FTIR) y taraxasterol (6) medianteMolinspiration, Osiris y AutoDock Vina para el acoplamiento con el receptor muscarínico tipo 3 (Acho-M3r). Se registraun rendimiento del 14.66 ± 0.75%, por UV-Vis se identifican bandas relacionadas a compuestos fenólicos y flavonoides;por FTIR se identifican bandas relacionadas con 6. Por otro lado, 6 presenta una violación a la Regla de Lipinski,interacción con receptores nucleares, enzimas y con receptores acoplados a proteinas G, no se reportan riesgos demutagenicidad, tumorogenicidad, irritabilidad y sobre reproducción. 6 presenta alta probabilidad de absorción en tractogastrointestinal (TGD, inhibición de OATP1B1, BSEP y hERG, sustrato del CYP3A4 así como de receptores aestrógenos. El sitio activo del Acho-M3r se optimizó con escopolamina (RMSD: 2.48 Á) y 6 registro una alta afinidad conel Acho-M3r, pero menor a la de escopolamina (-10.18 vs.-5.9 Kcal/mol); los valores de Ki (0.03 vs. 0.48 uM) presentanel mismo comportamiento, hechos que sugieren interacción parcial mediante fuerzas de Van der Waals. Bajo estecontexto, 6 presenta buena absorción, baja permeabilidad, potencial acumulación en la superficie apical del enterocito,interacción con los Acho-M3r favoreciendo la inducción de efectos a nivel del TGI, modificación de la biodisponibilidady la farmacocinética de fármacos empleados de manera simultánea con las preparaciones tradicionales de J. gaumeri,entre otras especies vegetales medicinales. Se requieres evaluaciones experimentales (in vitro, ex vivo, in vivo) quecorroboren el planteamiento.

PENDEKATAN IN SILICO DALAM MENYINGKAP POTENSI ANTIKANKER MECIADANOL

Meciadanol merupakan flavanol katekin termetilasi pada posisi C3 yang mampu menghambat pembentukan histamin oleh histidin dekarboksilase. Senyawa ini merupakan target menarik dalam pengembangan agen antikanker karena histamin diketahui terlibat dalam perkembangan kanker. Histamin juga dilaporkan dapat berkaitan dengan death associated protein kinase 1 (DAPK1) yang berhubungan dengan apoptosis. Penelitian ini mempelajari potensi aktivitas antikanker meciadanol terhadap DAPK1 secara in silico. Penambatan molekul terhadap protein DAPK1 (kode 5AUX dan 5AV3) dilakukan dengan Autodock Vina yang dilanjutkan dengan evaluasi sifat fisikokimia dan profil ADMET menggunakan SwissADME dan pkCSM. Nilai afinitas ikatan meciadanol terhadap 5AUX dan 5AV3 masing-masing sebesar -7,4 kkal/mol dan -7,0 kkal/mol. Meciadanol selanjutnya tidak melanggar aturan Lipinski, Ghose, Veber, Egan dan Muegge, dan memiliki profil ADMET yang baik berdasarkan deskriptor evaluasi.

Identificação do possível padrão de reconhecimento molecular de compostos com atividade conhecida frente ao SARS-CoV-2 através de estratégias in silico

A COVID-19 é uma doença provocada pelo vírus SARS-CoV-2 e é responsável por uma pandemia já ocasionou mais de 4,8 milhões de óbitos. Apesar de já existirem vacinas eficazes no controle à doença, ainda há a necessidade de desenvolver medicamentos específicos contra o SARS-CoV-2. Alguns estudos in vitro apontam moléculas promissoras para o combate ao vírus, no entanto, não esclarecem os padrões de reconhecimento molecular para a modulação da resposta biológica. Assim, o objetivo desse trabalho foi identificar o possível padrão de reconhecimento molecular de inibidores previamente estudados. Os estudos de acoplamento molecular foram realizados com o programa Autodock vina©, sendo escolhidas as moléculas com melhores resultados para a realização da análise das interações intermoleculares. As interações encontradas nesse trabalho para os complexos estudados (M-pro e hexaclorofeno, Pl-pro e osajin, proteína S e oxiclozanida e Nsp15 com a niclosamida) coincidiram com resultados apresentados na literatura e, portanto, os resultados obtidos contribuem para futuros estudos de dinâmica molecular e testes in vivo para o desenvolvimento de abordagens terapêuticas para controle da COVID-19.

Molecular Docking Studies on Phenolic Constituents of Anethum graveolens L. Seed Extracts

Background: There are studies indicating that aqueous or hydroalcoholic dill extracts showed higher antioxidant activity compared to other fractions. Molecular docking studies would be relevant to get information on the mechanism of action of the phenolic constituents of Anethum graveolens seed extracts as bioactive compounds. Methodology: In order to perform the docking studies of antioxidant activity of phenolic constituents of Anethum graveolens seed extracts, BIOVIA Discovery Studio and AutoDock Vina software were used. Results: The orientation of flavonoids within Hck and CYP2C9 binding sites has been shown to be the main reason for their inhibitory potency. Conclusion: Molecular docking studies indicate that the compounds identified interact with the target enzymes Hck and CYP2C9 at molecular level through their condensed ring systems and hydroxyl substituents and therefore support the antioxidant capacity of the studied phenolic compounds.

Virtual Screening with Gnina 1.0

Virtual screening—predicting which compounds within a specified compound library bind to a target molecule, typically a protein—is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

Synthesis of new thiazolo[3,2-a]pyrimidine derivatives and in silico analysis of their bioactivity

An effective method of synthesis thiazolo[3,2-a]pyrimidine derivatives was developed and the compounds with n-pentyl or β-acetoxycyclopropyl as well as fluorescent benzo[f]coumarin substituents were obtained with yields 60 % and more. Using computational (in silico) approaches we demonstrated the ability of the obtained compounds to permeate lipid bilayer as well as their affinity to some protein kinases (compounds 4 and 6 bind with a protein kinase AKT1 with PDB code 3о96; Autodock Vina-computed energy of binding (Ebind) values were -10.9 and -10.6 kcal/mol, respectively), acethylcholine esterase and some human cytochromes P450 (for P450 3A4, pdb 5vcd, Ebind -12.3 kcal/mol).

Interactions of Ozone-Functionalized Activated Charcoal with SARS-Cov-2 Proteases Using Molecular Docking and Dynamics

The high contamination by the SARS-Cov-2 virus has led to the search for ways to minimize contagion. Masks are used as part of a strategy of measures to suppress transmission and save lives. However, they are not sufficient to provide an adequate level of protection against COVID-19. Activated charcoal has an efficient antibacterial action, adsorption and low cost. Here, the interaction between two molecules of activated carbon was analyzed, interacting with two structures of the SARS-Cov-2, through docking and molecular dynamics using the platforms Autodock Vina 4.2.6, Gaussian 09 and Amber 16. As a result, the complexes from ozone-functionalized coal to viral structures happen mainly through hydrophobic interactions at the binding site of each receptor. The values of the mean square deviations of the two systems formed by ligands/receptors and showed better stability. The results of Gibbs free energy showed a better interaction between proteins and functionalized charcoal, with △Gtotal values of −48.530 and −38.882 kcal/mol. Thus, the set formed by combinations of proteins with functionalized activated carbon tends to more efficiently adsorb the protein components of the coronavirus to the pores of the activated carbon with ozone during filtration.

Targeting Epigenetic Regulators Using Machine Learning: Potential Sirtuin 2 Inhibitors

Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.

Virtual Screening with Gnina 1.0

Virtual screening - predicting which compounds within a specified compound library bind to a target molecule, typically a protein - is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

A SIMPLE ONE-POT SYNTHESIS OF PYRIMIDO[4,5-C]PYRIDAZINES AND IN SILICO STUDIES OF HUMAN AKT1 INHIBITORS

A simple one-pot and efficient synthetic method for the synthesis of pyrimido[4,5-c]pyridazine by a multicomponent reaction. The synthesis compounds are study for their inhibitory activities towards AKT1 pathways by using in silico method. It was found that all compounds have binding energy lower than -7.9 kcal/mol and compound 3a is the most active with binding energy -9.65 kcal/mol, which have been performed using autodock vina.