POTENSI DAUN ASAM JAWA (Tamarindus indica L.) SEBAGAI ALTERNATIF ANTIINFLAMASI: STUDI IN SILICO

2019 ◽  
pp. 42-50
Author(s):  
Erma Yunita ◽  
Siti Fatimah ◽  
Deni Yulianto ◽  
Vedy Trikuncahyo ◽  
Zihan Khodijah
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
In Silico ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Tamarindus Indica ◽  
Protein Target ◽  
Cox 2 ◽  
Cox 1

  Daun asam jawa (Tamarindus indica L.) merupakan tanaman yang memiliki banyak khasiat. Kandungan senyawa kimia yang terkandung salah satunya Kuersetin. Kuersetin merupakan senyawa flavonoid yang dapat digunakan sebagai anti inflamasi. Penelitian ini bertujuan untuk mengetahui potensi aktivitas Kuersetin dari daun asam jawa sebagai anti inflamasi terhadap protein COX-1 dan COX-2 secara in silico. Ekstrak daun asam jawa diperoleh dengan maserasi bertingkat menggunakan heksan dan etanol. Kadar Kuersetinnya dihitung secara spektrofotometri UVVis. Konfirmasi aktivitas antiinflamasi dilakukan secara in silico. Protein yang digunakan adalah 6COX, 3PGH, dan 1EQH. Kuersetin sebagai senyawa aktif sedangkan Aspirin digunakan sebagai zat pembanding. Preparasi ligan Kuersetin menggunakan MarvinSketch kemudian preparasi protein target 6COX, 1EQH, dan 3PGH menggunakan YASARA. Selanjutnya melakukan molecular docking menggunakan program PLANTS. Parameter evaluasi validasi dapat dilihat dari nilai Root Mean Square Deviation (RMSD), dimana nilai RMSD yang diterima adalah kurang dari 2Å. Kadar Kuersetin yang diperoleh dalam ekstrak dalam daun asam jawa sebesar 31,26 mg/g. Hasil docking menunjukkan bahwa Kuersetin mampu berinteraksi dengan 1EQH, 3PGH, dan 6COX dimana skor dockingnya masing-masing adalah -77,6195; -75,1344; dan -82,2454, sedangkan hasil docking Aspirin masing-masing adalah -69,8784; -75,2421; dan - 72,0884. Kuersetin memiliki potensi sebagai anti inflamasi yang lebih baik dibandingkan dengan Aspirin namun memiliki resiko lebih tinggi menyebabkan ulkus lambung dibanding Aspirin.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals GNINA 1.0: Molecular Docking with Deep Learning

2021 ◽  
Author(s):  
Andrew McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2A root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under and open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals Identification of Terpenoids From Abrus precatorius Against Parkinson’s Disease Proteins Using In Silico Approach

2021 ◽  
Vol 15 ◽  
pp. 117793222110507
Author(s):  
Damilola Alex Omoboyowa ◽  
Toheeb Adewale Balogun ◽  
Oluwaseun Motunrayo Omomule ◽  
Oluwatosin A Saibu
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
In Silico ◽  
High Performance ◽  
Md Simulation ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Abrus Precatorius

Parkinson’s disease (PD) is the second major neuro-degenrative disorder that causes morbidity and mortality among older populations. Terpenoids were reported as potential neuro-protective agents. Therefore, this study seeks to unlock the inhibitory potential of terpenoids from Abrus precatorius seeds against proteins involve in PD pathogenesis. In this study, in silico molecular docking of 5 terpenoids derived from high-performance liquid chromatography (HPLC) analysis of A. precatorius seeds against α-synuclein, catechol-o-methyltransferase, and monoamine oxidase B which are markers of PD was performed using Autodock vina. The absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) of the hits were done using Swiss ADME predictor and molecular dynamic (MD) simulation of the hit-protein complex was performed using Desmond Schrodinger software. Five out of 6 compounds satisfied the ADME/Tox parameters and showed varying degrees of binding affinities with selected proteins. Drimenin-α-synuclein complex showed the lowest binding energy of −9.1 kcal/mol followed by interaction with key amino acid residues necessary for α-synuclein inhibition. The selection of this complex was justified by its stability in MD simulation conducted for 10 ns and exhibited stable interaction in terms of root mean square deviation (RMSD) and root mean square deviation error fluctuation (RMSF) values.

scholarly journals GNINA 1.0: Molecular Docking with Deep Learning

2021 ◽  
Author(s):  
Andrew McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2A root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under and open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals Desain Turunan Senyawa Leonurine Sebagai Kandidat Obat Anti Inflamasi

2020 ◽  
Vol 6 (1) ◽  
pp. 181-191
Author(s):  
Ruslin Beny ◽  
Nindy Rachma Az Yana ◽  
Mesi Leorita
Keyword(s):  
Binding Energy ◽  
Protein Data Bank ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Inhibitory Activity ◽  
Data Bank ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Cox 2 ◽  
Autodock 4.2

In this research, there was docking process of leonurine compounds and their derivatives to Cyclooxygenase-2 (COX-2) enzyme as an anti-inflammatory. The receptor code used is 6COX which is downloaded from the protein data bank site (PDB). The aims of this study was to investigate the interaction of leonurine compounds and their derivatives against COX-2 receptors. All compounds are docked using AutoDock 4.2 software. The docking validation is performed by tethering the ligand-receptor with the parameter of the Root Mean Square Deviation (RMSD) value <2 Å. From the docking validation results, have been obtained RMSD value = 0.31 Å. Analysis of docking results indicates that leonurine and its derivatives are predicted to have good interaction with COX-2 receptors. From the results of this research, it can be concluded that leonurine compounds and their derivatives have inhibitory activity against COX-2 receptors. The docking results showed the lowest binding energy value ligand-receptor (ΔG) best matched in the 11th derived compound which is -7.95 kcal/mol.

scholarly journals Potensi Sinamaldehid sebagai Anti Hiperpigmentasi secara In Silico

2021 ◽  
Vol 7 (2) ◽  
pp. 95-101
Author(s):  
Ni Made Gani Pratiwi ◽  
Ni Made Atika Saraswati ◽  
Ni Made Irma Febby Prasasti Dewi ◽  
Luh Pande Putu Tirta
Keyword(s):  
Root Mean Square ◽  
In Silico ◽  
Mean Square ◽  
Inhibitor Protein ◽  
Protein Target ◽  
Root Mean Square Distance ◽  
Mean Square Distance

Permasalahan kulit yang sering ditemui yaitu hiperpigmentasi yang terjadi akibat adanya sintesis melanin berlebihan yang menyebabkan penggelapan warna kulit. Hiperpigmentasi dapat diatasi dengan agen anti hiperpigmentasi yang beraktivitas dalam menghambat proses sintesis melanin. Sintesis melanin dapat dihambat dengan berbagai cara salah satunya dengan menghambat aktivitas tyrosinase. Tyrosinase merupakan enzim yang berperan dalam mengkatalisis proses biosintesis melanin. Sinamaldehid merupakan senyawa bahan alam banyak ditemukan pada tanaman Cinnamomum burmanni mempunyai aktivitas sebagai antioksidan. Penelitian ini bertujuan untuk mengetahui potensi sinamaldehid dalam menghambat tyrosinase yang akan dibandingkan dengan native liganya secara in silico. Uji in silico dilakukan secara docking molecular dengan tahapan yaitu preparasi dan optimasi sinamaldehid, preparasi tyrosinase serta validasi dan docking. Metode docking molecular telah dinyatakan valid karena RMSD (root mean square distance) yang diperoleh tidak lebih dari 3 Å. Analisis data dilakukan dengan melihat energi ikatan yang dihasilkan dan ikatan yang terbentuk antara senyawa dengan residu asam amino pada protein. Nilai energi ikatan yang diperoleh antara ikatan sinamaldehid dengan tyrosinase adalah-6,21 kkal/mol. Sedangkan energi ikatan antara tyrosinase dengan native ligandnya -4,79 kkal/mol. Hal tersebut menunjukkan afinitas dari sinamaldehid pada protein tyrosinase lebih besar dibandingkan native ligandnya, sehingga sinamaldehid dikatakan memiliki potensi sebagai anti hiperpigmentasi dengan mekanisme molecular berupa inhibitor protein target tyrosinase sehingga dapat menghambat aktivitas enzim tyrosinase.

Fast collocation for Moho estimation from GOCE gravity data: the Iran case study

2020 ◽  
Vol 221 (1) ◽  
pp. 651-664
Author(s):  
H Heydarizadeh Shali ◽  
D Sampietro ◽  
A Safari ◽  
M Capponi ◽  
A Bahroudi
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Seismic Data ◽  
Active Faults ◽  
Earth’S Crust ◽  
Arabian Plate ◽  
Moho Depth ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Earth's Crust

SUMMARY The study of the discontinuity between crust and mantle beneath Iran is still an open issue in the geophysical community due to its various tectonic features created by the collision between the Iranian and Arabian Plate. For instance in regions such as Zagros, Alborz or Makran, despite the number of studies performed, both by exploiting gravity or seismic data, the depth of the Moho and also interior structure is still highly uncertain. This is due to the complexity of the crust and to the presence of large short wavelength signals in the Moho depth. GOCE observations are capable and useful products to describe the Earth’s crust structure either at the regional or global scale. Furthermore, it is plausible to retrieve important information regarding the structure of the Earth’s crust by combining the GOCE observations with seismic data and considering additional information. In the current study, we used as observation a grid of second radial derivative of the anomalous gravitational potential computed at an altitude of 221 km by means of the space-wise approach, to study the depth of the Moho. The observations have been reduced for the gravitational effects of topography, bathymetry and sediments. The residual gravity has been inverted accordingly to a simple two-layer model. In particular, this guarantees the uniqueness of the solution of the inverse problem which has been regularized by means of a collocation approach in the frequency domain. Although results of this study show a general good agreement with seismically derived depths with a root mean square deviation of 6 km, there are some discrepancies under the Alborz zone and also Oman sea with a root mean square deviation up 10 km for the former and an average difference of 3 km for the latter. Further comparisons with the natural feature of the study area, for instance, active faults, show that the resulting Moho features can be directly associated with geophysical and tectonic blocks.

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