scholarly journals DOCKING MOLEKULER SENYAWA B-KAROTEN DALAM TANAMAN KELOR (Moringa Oleifera L.) SEBAGAI PENGHAMBAT ENZIM TIROSINASE DENGAN AUTODOCK – VINA

2020 ◽  
Vol 3 (2) ◽  
pp. 230-240
Author(s):  
Bayu Herdi Al Huda ◽  
◽  
Nining Sugihartini ◽  
Hari susanti ◽  
Dwi Utami
Keyword(s):  
Binding Affinity ◽  
Interaction Mechanism ◽  
Ligand Docking ◽  
Autodock Vina ◽  
Discovery Studio ◽  
Amino Acid Receptors ◽  
Know How ◽  
Tyrosinase Enzyme ◽  
Whitening Activity ◽  
Autodock 4.2

Hydroquinone has been used in cosmetics because of its whitening activity. In previous studies, B-carotene in Moringa plants was also known as an inhibitor of the tyrosinase enzyme. It is necessary to know how the interaction mechanism of B-carotene with tyrosinase (5M8N) and which compounds between hydroquinone and B-carotene provide computationally better activity as whitening. Tyrosinase was prepared using Discovery Studio Visualizer. Ligands were prepared using Autodock 4.2. Autodock-Vina is used for ligand docking between proteins. The result is the binding affinity (kcal/mol) of the ligand to protein. Visualization of docking between ligands and proteins using the Ligplot + Program with a 1 year license. Media used for the docking process is a computer with an Intel Core i7-3770 CPU with a speed of 3.40 GHz 8 cores, 1920x1080p resolution, VGA NVIDIA GeForce GTX 750, 8 GB RAM, Windows 8 64-bit. The docking results showed that the binding affinity of B-carotene to tyrosinase was -11.2 while hydroquinone with tyrosinase was -5.4 with RMSD 0. The results of visualization showed that B-carotene binds more amino acid receptors than hydroquinone. B-carotene in moringa has been shown to be active not only in wet laboratories, but also in dry laboratories.

scholarly journals Molecular Docking Senyawa Gingerol dan Zingiberol pada Tanaman Jahe sebagai Penanganan COVID-19

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Belinda D. P. M. Ratu ◽  
Widdhi Bodhi ◽  
Fona Budiarso ◽  
Billy J. Kepel ◽  
. Fatimawali ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
Amino Acid Residues ◽  
Autodock Vina ◽  
Discovery Studio ◽  
Main Protease ◽  
Docking Method ◽  
Pubmed Database ◽  
Almost All ◽  
The Impact

Abstract: COVID-19 is a new disease. Many people feel the impact of this disease. There is no definite cure for COVID-19, so many people use traditional medicine to ward off COVID-19, including ginger. This study aims to determine whether there is an interaction between compounds in ginger (gingerol and zingiberol) and the COVID-19’s main protease (6LU7). This study uses a molecular docking method using 4 main applications, namely Autodock Tools, Autodock Vina, Biovia Discovery Studio 2020, and Open Babel GUI. The samples used were gingerol and zingiberol compounds in ginger plants downloaded from Pubchem. The data used in this study used Mendeley, Clinical Key, and PubMed database. The study showed that almost all of the amino acid residues in the gingerol compound acted on the 6LU7 active site, whereas the zingiberol did not. The results of the binding affinity of ginger compounds, both gingerol and zingiberol, do not exceed the binding affinity of remdesivir, a drug that is widely researched as a COVID-19 handling drug. In conclusion, gingerol and zingiberol compounds in ginger can’t be considered as COVID-19’s treatment.Keywords: molecular docking, gingerol, zingiberol Abstrak: COVID-19 merupakan sebuah penyakit yang baru. Banyak masyarakat yang merasakan dampak dari penyakit ini. Belum ada pengobatan pasti untuk menyembuhkan COVID-19, sehingga banyak masyarakat yang menggunakan pengobatan tradisional untuk menangkal COVID-19, termasuk jahe. Penelitian ini bertujuan untuk mengetahui apakah ada interaksi antara senyawa pada jahe (gingerol dan zingiberol) dengan main protease COVID-19 (6LU7). Penelitian ini menggunakan metode molecular docking dengan menggunakan 4 aplikasi utama, yaitu Autodock Tools, Autodock Vina, Biovia Discovery Studio 2020, dan Open Babel GUI. Sampel yang digunakan yaitu senyawa gingerol dan zingiberol pada tanaman jahe yang diunduh di Pubchem. Data yang digunakan dalam penelitian ini menggunakan database Mendeley, Clinical Key, dan PubMed. Penelitian menunjukkan bahwa hampir semua residu asam amino pada senyawa gingerol bekerja pada sisi aktif 6LU7, sedangkan tidak demikian pada zingiberol. Hasil binding affinity senyawa jahe, baik gingerol maupun zingiberol tidak  melebihi binding affinity remdesivir, obat yang banyak diteliti sebagai obat penanganan COVID-19. Sebagai simpulan, senyawa gingerol dan zingiberol pada tanaman jahe tidak dapat dipertimbangkan sebagai penanganan COVID-19Kata Kunci: molecular docking, gingerol, zingiberol

scholarly journals Computational analysis of the interaction of limonene with the fat mass and obesity-associated protein

2021 ◽  
Vol 8 (1) ◽  
pp. 154-160
Author(s):  
Muhammad Zeeshan Ahmed ◽  
Shahzeb Hameed ◽  
Mazhar Ali ◽  
Ammad Zaheer
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
Fat Mass ◽  
Computational Analysis ◽  
Physical Interaction ◽  
Autodock Vina ◽  
Inhibitory Interaction ◽  
Protein Association ◽  
Discovery Studio ◽  
Bioinformatic Tool

This study aimed to predict the binding affinity, orientation, and physical interaction between limonene and fat mass and obesity-associated protein. The mechanism of limonene and protein association was explored by molecular docking, a bioinformatic tool. The results of association were compared with the reported results of the anti-obesity drug such as orlistat and with the flavonoids. AutoDock Vina tools were used for the molecular docking of limonene with fat mass and obesity-associated protein. PyMol and Discovery Studio Visualizer were used to visualize the results of this docking. The binding affinity of limonene was higher (Least negative G) than the orlistat and flavonoids such as Daidzein, Exemestane, Kaempherol, Letrozole, And Rutin. It is conducted in this study that the Limonene can alleviate obesity by making an interaction with the fat mass and obesity-associated protein. This inhibitory interaction was greater as compared to other reported phytochemicals and drugs.

scholarly journals Molekuler Docking tehadap Senyawa Eugenol dan Myricetin pada Tanaman Cengkeh (Syzygium aromaticum) sebagai Penghambat Pertumbuhan SARS-CoV-2

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yizreel Y. Gerungan ◽  
Billy J. Kepel ◽  
. Fatimawali ◽  
Aaltje Manampiring ◽  
Fona D. Budiarso ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
Growth Inhibitor ◽  
Syzygium Aromaticum ◽  
Amino Acid Residues ◽  
Autodock Vina ◽  
Discovery Studio ◽  
Main Protease ◽  
Almost All ◽  
The Impact

Abstract: Cloves contain many chemical compounds that can be used for health. COVID-19 is a disease that is shaking the world today. Many people feel the impact of this disease. Until now, there is no definite cure and vaccine for the handling of COVID-19.  Objective to determine the interaction between compounds in cloves (eugenol and myricetin) and the main protease COVID-19 (6LU7). This study use a molecular docking, method using 4 main applications: autodock tools, autodock vina, biovia discovery studio and open babel. This study showed that almost all amino acid residues in the eugenol and myricetin compounds worked on the 6LU7 active site. The binding affinity of eugenol compounds in clove plants does not exceed the binding affinity of remdesivir, a drug studied as a drug for handling COVID-19, while the binding affinity of myricetin compounds in cloves plant exceeds the binding affinity of remdesivir. In conclusion, myricetin compounds have better results for use as a growth inhibitor for COVID-19 than eugenol.Key words: Cloves, COVID-19, molecular docking.  Abstrak: Cengkeh memiliki banyak kandungan senyawa kimia yang dapat dimanfaatkan bagi kesehatan. COVID-19 merupakan penyakit yang mengguncang dunia saat ini. Banyak masyarakat yang merasakan dampak dari penyakit ini. Hingga saat ini belum ada obat dan vaksin yang pasti untuk penanganan COVID-19. Penelitian ini bertujuan untuk mengetahui interaksi antara senyawa pada cengkeh (eugenol dan myricetin) dengan main protease COVID-19 (6LU7). Jenis penelitian ini menggunakan metode molekuler docking dengan menggunakan 4 aplikasi utama: autodock tools, autodock vina, biovia discovery studio dan open babel. Hasil penelitian ini menunjukkan bahwa hampir semua residu asam amino pada senyawa eugenol dan myricetin bekerja pada sisi aktif 6LU7. Hasil binding affinity senyawa eugenol pada tumbuhan cengkeh tidak melebihi binding affinity dari remdesivir, obat yang diteliti sebagai obat penanganan COVID-19, sedangkan hasil binding affinity senyawa myricetin pada tumbuhan cengkeh melebihi binding affinity dari remdesivir. Simpulan penelitian ini ialah senyawa myricetin memiliki hasil yang lebih baik untuk digunakan sebagai penghambat pertumbuhan COVID-19 dari pada eugenol.Kata kunci: Cengkeh, COVID-19, molekuler docking.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

Application of Molecular Docking for the Development of Improved HIV-1 Reverse Transcriptase Inhibitors

2020 ◽  
Vol 16 ◽  
Author(s):  
Arash Soltani ◽  
Seyed Isaac Hashemy ◽  
Farnaz Zahedi Avval ◽  
Houshang Rafatpanah ◽  
Seyed Abdolrahim Rezaee ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
Binding Capacity ◽  
Binding Pocket ◽  
Ligand Docking ◽  
Binding Modes ◽  
Wild Type ◽  
Parent Molecule ◽  
Discovery Studio ◽  
Approved Drugs ◽  
Hiv 1

Introoduction: Inhibition of the reverse transcriptase (RT) enzyme of human immunodeficiency virus (HIV) by low molecular weight inhibitors is still an active area of research. Here, protein-ligand interactions and possible binding modes of novel compounds with the HIV-1 RT binding pocket (the wild-type as well as Y181C and K103N mutants) were obtained and discussed. Methods: A molecular fragment-based approach using FDA-approved drugs were followed to design novel chemical derivatives using delavirdine, efavirenz, etravirine and rilpivirine as the scaffolds. The drug-likeliness of the derivatives was evaluated using Swiss-ADME. Then the parent molecule and derivatives were docked into the binding pocket of related crystal structures (PDB ID: 4G1Q, 1IKW, 1KLM and 3MEC). Genetic Optimization for Ligand Docking (GOLD) Suite 5.2.2 software was used for docking and the results analyzed in the Discovery Studio Visualizer 4. A derivative was chosen for further analysis, if it passed drug-likeliness and the docked energy was more favorable than that of its parent molecule. Out of the fifty-seven derivatives, forty-eight failed in druglikeness screening by Swiss-ADME or in docking stage. Results: The final results showed that the selected compounds had higher predicted binding affinities than their parent scaffolds in both wild-type and the mutants. Binding energy improvement was higher for the structures designed based on second-generation NNRTIs (etravirine and rilpivirine) than the first-generation NNRTIs (delavirdine and efavirenz). For example, while the docked energy for rilpivirine was -51 KJ/mol, it was improved for its derivatives RPV01 and RPV15 up to -58.3 and -54.5 KJ/mol, respectively. Conclusion: In this study, we have identified and proposed some novel molecules with improved binding capacity for HIV RT using fragment-based approach.

scholarly journals Bioactive Antidiabetic Flavonoids from the Stem Bark of Cordia dichotoma Forst.: Identification, Docking and ADMET Studies

Molbank ◽  
10.3390/m1234 ◽  
2021 ◽  
Vol 2021 (2) ◽  
pp. M1234
Author(s):  
Nazim Hussain ◽  
Bibhuti Bhushan Kakoti ◽  
Mithun Rudrapal ◽  
Khomendra Kumar Sarwa ◽  
Ismail Celik ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
Mass Spectroscopy ◽  
Column Chromatography ◽  
Stem Bark ◽  
Antidiabetic Activity ◽  
Bark Extract ◽  
Computational Studies ◽  
Autodock Vina ◽  
Online Tool

Cordia dichotoma Forst. (F. Boraginaceae) has been traditionally used for the management of a variety of human ailments. In our earlier work, the antidiabetic activity of methanolic bark extract of C. dichotoma (MECD) has been reported. In this paper, two flavonoid molecules were isolated (by column chromatography) and identified (by IR, NMR and mass spectroscopy/spectrometry) from the MECD with an aim to investigate their antidiabetic effectiveness. Molecular docking and ADMET studies were carried out using AutoDock Vina software and Swiss ADME online tool, respectively. The isolated flavonoids were identified as 3,5,7,3′,4′-tetrahydroxy-4-methoxyflavone-3-O-L-rhamnopyranoside and 5,7,3′-trihydroxy-4-methoxyflavone-7-O-L-rhamnopyranoside (quercitrin). Docking and ADMET studies revealed the promising binding affinity of flavonoid molecules for human lysosomal α-glucosidase and human pancreatic α-amylase with acceptable ADMET properties. Based on computational studies, our study reports the antidiabetic potential of the isolated flavonoids with predictive pharmacokinetics profile.

Role of polarization in ligand docking and binding affinity prediction for inhibitors of dengue virus

2011 ◽  
Vol 21 (7) ◽  
pp. 1030-1038 ◽  
Author(s):  
Kshatresh Dutta Dubey ◽  
Amit Kumar Chaubey ◽  
Rajendra Prasad Ojha
Keyword(s):  
Dengue Virus ◽  
Binding Affinity ◽  
Ligand Docking ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

scholarly journals DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

2021 ◽  
Vol 15 ◽  
pp. 117793222110303
Author(s):  
Asad Ahmed ◽  
Bhavika Mam ◽  
Ramanathan Sowdhamini
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Chemical Space ◽  
Biological Significance ◽  
Protein Crystal ◽  
Ligand Docking ◽  
Complex Data ◽  
Binding Prediction ◽  
Data Set

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

scholarly journals Molecular Design of Anticancer Drug from Marine Fungi Derivatives

2021 ◽  
Author(s):  
Duc Tuan Cao ◽  
Thi Mai Huong DOAN ◽  
Van Cuong PHAM ◽  
Thi Hong Lien HOANG ◽  
Jung-Woo Chae ◽  
...  
Keyword(s):  
Cancer Therapy ◽  
Binding Affinity ◽  
Marine Fungi ◽  
Molecular Design ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Heat Shock Protein 90 ◽  
Autodock Vina ◽  
Docking Simulations ◽  
Potential Inhibitors

Heat shock protein 90 (HSP90) is known as one of the most potential target in cancer therapy. In this context, we have demonstrated that marine fungi derivatives can play as possible inhibitors for preventing the biological activity of HSP90 using a combination of molecular docking and fast pulling of ligand (FPL) simulations. In particular, the computational approaches were validated since compared with the respective experiments. Based on a benchmark on available inhibitors of HsP90, GOLD docking package using ChemPLP scoring function was found to be dominated over both Autodock Vina and Autodock4 in preliminary estimation the ligand binding affinity and binding pose with the Pearson correlation, R=-0.62. Moreover, FPL calculations were also indicated to be a suitable approach to refine docking simulations with a correlation coefficient with the respective experimental data of R=-0.81. Therefore, the binding affinity of marine fungi derivatives to Hsp90 was evaluated. Docking and FPL calculations suggested that five compounds including 23, 40, 46, 48, and 52 are as highly potential inhibitors for HSP90. The obtained results probably enhance the cancer therapy. <br>

scholarly journals PEMODELAN MOLEKUL SENYAWA TURUNAN KOJIC ACID SERTA INTERAKSINYA DENGAN ENZIM TIROSINASE

2020 ◽  
Vol 24 (2) ◽  
pp. 54-58
Author(s):  
Fazrul Permadi
Keyword(s):  
Kojic Acid ◽  
Autodock Vina ◽  
Discovery Studio

Studi HKSA dilakukan pada turunan kojyl thioether sebagai inhibitor tirosinase. Perhitungan prediktotr dilakukan menggunakan aplikasi hyperchem 8.0 dengan metode optimasi geometri semi-empirik PM3. Analisis regresi multilinier menggunakan SPSS 25.0 untuk mencari hubungan antara prediktor dan aktivitas senyawa turunan kojyl thioether sebagai inhibitor tirosinase. Model persamaan HKSA terbaik adalah  :pIC50 = -922.517 + 15.872*ELUMO – 436.654*qC4 – 209.509*qC5 + 1.0008*Eintn = 14; m = 4; r = 0.949; R2 = 0.901; PRESS = 3.0469; q2 = 0.8246Berdasarkan persamaan HKSA diatas didapatkan 4 senyawa baru turunun kojyl thioether yang bisa dijadikan sebagai analog inhibitor tirosinase yang baru. Parameter pemilihan senyawa tersebut karena memiliki aktivitas yang lebih baik sebagai inhibitor tirosinase dibandingkan senyawa penuntun, tidak hepatotoksik, tidak menimbulkan AMES Toxicity, tidak menimbulkan skin sensititasion dan LD50 pada tikus masuk kategori relative tidak membahayakan. Untuk melihat interaksi antara senyawa turunun kojyl thioether dengan enzim tirosinase dilakukan docking menggunakan Autodock vina yang visualisasinya menggunakan discovery studio 2020 client. Hasil docking menunjukkan bahwa semua senyawa setidaknya memiliki satu interaksi pada residu asam amino yang sama dengan native ligan. yaitu Val283.

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