scholarly journals DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina

2020 ◽  
Vol 21 (24) ◽  
pp. 9548
Author(s):  
Gabriele Macari ◽  
Daniele Toti ◽  
Andrea Pasquadibisceglie ◽  
Fabio Polticelli
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
State Of The Art ◽  
Scoring Function ◽  
Conformational Space ◽  
Solvent Accessible Surface Area ◽  
Autodock Vina ◽  
Wide Range ◽  
User Friendly ◽  
New Scoring

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.

A New Scoring Function for Molecular Docking Based on AutoDock and AutoDock Vina

2015 ◽  
Vol 12 (3) ◽  
pp. 170-178 ◽  
Author(s):  
Vsevolod Tanchuk ◽  
Volodymyr Tanin ◽  
Andriy Vovk ◽  
Gennady Poda
Keyword(s):  
Molecular Docking ◽  
Scoring Function ◽  
Autodock Vina ◽  
New Scoring

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 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.

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 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 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 DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7362 ◽  
Author(s):  
Haiping Zhang ◽  
Linbu Liao ◽  
Konda Mani Saravanan ◽  
Peng Yin ◽  
Yanjie Wei
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Mean Squared Error ◽  
Scoring Function ◽  
Binding Mode ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Cellular Functions ◽  
Ligand Complex

Proteins interact with small molecules to modulate several important cellular functions. Many acute diseases were cured by small molecule binding in the active site of protein either by inhibition or activation. Currently, there are several docking programs to estimate the binding position and the binding orientation of protein–ligand complex. Many scoring functions were developed to estimate the binding strength and predict the effective protein–ligand binding. While the accuracy of current scoring function is limited by several aspects, the solvent effect, entropy effect, and multibody effect are largely ignored in traditional machine learning methods. In this paper, we proposed a new deep neural network-based model named DeepBindRG to predict the binding affinity of protein–ligand complex, which learns all the effects, binding mode, and specificity implicitly by learning protein–ligand interface contact information from a large protein–ligand dataset. During the initial data processing step, the critical interface information was preserved to make sure the input is suitable for the proposed deep learning model. While validating our model on three independent datasets, DeepBindRG achieves root mean squared error (RMSE) value of pKa (−logKd or −logKi) about 1.6–1.8 and R value around 0.5–0.6, which is better than the autodock vina whose RMSE value is about 2.2–2.4 and R value is 0.42–0.57. We also explored the detailed reasons for the performance of DeepBindRG, especially for several failed cases by vina. Furthermore, DeepBindRG performed better for four challenging datasets from DUD.E database with no experimental protein–ligand complexes. The better performance of DeepBindRG than autodock vina in predicting protein–ligand binding affinity indicates that deep learning approach can greatly help with the drug discovery process. We also compare the performance of DeepBindRG with a 4D based deep learning method “pafnucy”, the advantage and limitation of both methods have provided clues for improving the deep learning based protein–ligand prediction model in the future.

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

2021 ◽  
Author(s):  
Nazim Hussain ◽  
Bibhuti Bhushan Kakoti ◽  
Mithun Rudrapal ◽  
Khomendra Kumar Sarwa ◽  
Ismail Celik ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
Mass Spectroscopy ◽  
Stem Bark ◽  
Antidiabetic Activity ◽  
Bark Extract ◽  
Computational Studies ◽  
Autodock Vina ◽  
Online Tool ◽  
Alpha Glucosidase

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&rsquo;,4&rsquo;-tetrahydroxy-4-methoxyflavanone-3-O-L-rhamnopyranoside and 5,7,3&rsquo;-trihydroxy-4-methoxyflavanone-7-O-L-rhamnopyranoside (quercitrin). Docking and ADMET studies revealed the promising binding affinity of flavonoid molecules for human lysosomal alpha-glucosidase and human pancreatic alpha-amylase with acceptable ADMET properties. Based on computational studies, our study reports the antidiabetic potential of the isolated flavonoids with predictive pharmacokinetics profile.

scholarly journals AMDock: a versatile graphical tool for assisting molecular docking with Autodock Vina and Autodock4

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Mario S. Valdés-Tresanco ◽  
Mario E. Valdés-Tresanco ◽  
Pedro A. Valiente ◽  
Ernesto Moreno
Keyword(s):  
Molecular Docking ◽  
Search Space ◽  
Docking Studies ◽  
Molecular Structures ◽  
Autodock Vina ◽  
Ligand Selectivity ◽  
Input Structure ◽  
Graphical Tool ◽  
Selectivity Studies ◽  
User Friendly

Abstract AMDock (Assisted Molecular Docking) is a user-friendly graphical tool to assist in the docking of protein-ligand complexes using Autodock Vina and AutoDock4, including the option of using the Autodock4Zn force field for metalloproteins. AMDock integrates several external programs (Open Babel, PDB2PQR, AutoLigand, ADT scripts) to accurately prepare the input structure files and to optimally define the search space, offering several alternatives and different degrees of user supervision. For visualization of molecular structures, AMDock uses PyMOL, starting it automatically with several predefined visualization schemes to aid in setting up the box defining the search space and to visualize and analyze the docking results. One particularly useful feature implemented in AMDock is the off-target docking procedure that allows to conduct ligand selectivity studies easily. In summary, AMDock’s functional versatility makes it a very useful tool to conduct different docking studies, especially for beginners. The program is available, either for Windows or Linux, at https://github.com/Valdes-Tresanco-MS. Reviewers This article was reviewed by Alexander Krah and Thomas Gaillard.

scholarly journals A COMPARATIVE STUDY OF STEREOCHEMICAL EFFECTS OF ANTI-PROSTATE AGENTS BY MOLECULAR DOCKING

2018 ◽  
Vol 11 (14) ◽  
pp. 76 ◽  
Author(s):  
Oluwaseun S Osanyinpeju ◽  
Roqia Bashary ◽  
Amit Mittal ◽  
Manish Vyas ◽  
Surendra Kumar Nayak ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Molecular Docking ◽  
Comparative Study ◽  
Binding Affinity ◽  
Energy Minimization ◽  
Receptor Protein ◽  
Autodock Vina ◽  
Androgen Receptor Protein ◽  
The Right ◽  
3D Software

Objective: A comparative study of anti-prostate agents to investigate the stereochemical influences on binding affinity by molecular docking.Methods: Structures of enantiomers (R and S stereoisomers) for known anti-prostate cancer (PCa) agents were drawn using ChemBioDraw 2D software. Thereafter, they were converted to 3D structures using the ChemBioDraw 3D software in which they were subjected to energy minimization using the MM2 method and then saved as PDB extension files which can be accessed using the ADT interface. AutoDock Vina (ADT) 1.5.6 software version was used for molecular docking study.Results: A total of 12 different anti-PCa agents were selected and drawn including well-known drug R-bicalutamide. All molecules showed the binding affinity with respect to the nature of stereochemistry. R-stereoisomers showed better interaction as well as binding affinity toward 1z95 (mutated androgen receptor protein involved in the progression of PCa) whereas their S-stereoisomers were found inferior in comparison.Conclusion: This study showed that CB1-R and R-bicalutamide (with R-stereochemistry) were better in binding affinity comparative to their counterpart CB1-S and S-Bicalutamide (with S-stereochemistry). All the selected anti-PCa agents were showing the effect of stereochemical center; therefore, we must choose the right kind of stereochemistry while planning to develop the newer anti-PCa agents.

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