AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all ...the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.
AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an ...obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking‐success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (Rset1=0.617±0.017) than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand‐binding affinity using Autodock Vina.
A new set of empirical parameters of AutoDock Vina was proposed. The accuracy of affinity prediction was significantly increased from RDefault=0.493±0.028 to Rset1=0.556±0.025 over 800 testing complexes. Over 1036 validating complexes, the proposed parameter formed Rset1=0.617±0.017, which is rigidly larger than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020).
•Luteolin and chloroquine bind to the main protease of SARS-CoV-2•The binding sites of luteolin are highly consistent with the main protease inhibitors•Luteolin can be used as a potent compound ...against SARS-CoV-2•Binding of luteolin is conducive to the study of antiviral mechanism of traditional Chinese medicine
In the current spread of novel coronavirus (SARS-CoV-2), antiviral drug discovery is of great importance. AutoDock Vina was used to screen potential drugs by molecular docking with the structural protein and non-structural protein sites of new coronavirus. Ribavirin, a common antiviral drug, remdesivir, chloroquine and luteolin were studied. Honeysuckle is generally believed to have antiviral effects in traditional Chinese medicine. In this study, luteolin (the main flavonoid in honeysuckle) was found to bind with a high affinity to the same sites of the main protease of SARS-CoV-2 as the control molecule. Chloroquine has been proved clinically effective and can bind to the main protease; this may be the antiviral mechanism of this drug. The study was restricted to molecular docking without validation by molecular dynamics simulations. Interactions with the main protease may play a key role in fighting against viruses. Luteolin is a potential antiviral molecule worthy of attention.
Virtual molecular screening is used to dock small-molecule libraries to a macromolecule in order to find lead compounds with desired biological function. This in silico method is well known for its ...application in computer-aided drug design. This chapter describes how to perform small-molecule virtual screening by docking with PyRx, which is open-source software with an intuitive user interface that runs on all major operating systems (Linux, Windows, and Mac OS). Specific steps for using PyRx, as well as considerations for data preparation, docking, and data analysis, are also described.
AutoDock Vina (Vina) stands out among numerous molecular docking tools due to its precision and comparatively high speed, playing a key role in the drug discovery process. Hardware acceleration of ...Vina on FPGA platforms offers a high energy-efficiency approach to speed up the docking process. However, previous FPGA-based Vina accelerators exhibit several shortcomings: 1) Simple uniform quantization results in inevitable accuracy drop; 2) Due to Vina's complex computing process, the evaluation and optimization phase for hardware design becomes extended; 3) The iterative computations in Vina constrain the potential for further parallelization. 4) The system's scalability is limited by its unwieldy architecture. To address the above challenges, we propose Vina-FPGA-cluster, a multi-FPGA-based molecular docking tool enabling high-accuracy and multi-level parallel Vina acceleration. Standing upon the shoulders of Vina-FPGA, we first adapt hybrid fixed-point quantization to minimize accuracy loss. We then propose a SystemC-based model, accelerating the hardware accelerator architecture design evaluation. Next, we propose a novel bidirectional AG module for data-level parallelism. Finally, we optimize the system architecture for scalable deployment on multiple Xilinx ZCU104 boards, achieving task-level parallelism. Vina-FPGA-cluster is tested on three representative molecular docking datasets. The experiment results indicate that in the context of RMSD (for successful docking outcomes with metrics below 2Å), Vina-FPGA-cluster shows a mere 0.2% lose. Relative to CPU and Vina-FPGA, Vina-FPGA-cluster achieves 27.33× and 7.26× speedup, respectively. Notably, Vina-FPGA-cluster is able to deliver the 1.38× speedup as GPU implementation (Vina-GPU), with just the 28.99% power consumption.
Since ancient Greek-Roman times, the use of plants to cure many human diseases is still common. The present ethnobotanical survey was conducted to contribute to the knowledge of medicinal plants used ...for the treatment of typhoid fever in three sub divisions of Vina division, Adamawa Cameroon. After having explained the importance of this study to interviewees, 41 traditional healers have agreed and delivered information regarding the medicinal plants they use as well as the different preparation and administration through a well- structured questionnaire that was given to them on this matter. Among 41 traditional healers whose attended this study, 32 were men and 09 were women. The ethnobotanical survey allowed the identification of 70 plants belonging to 38 families. With a frequency of 11/70, the Fabaceae family was the most represented followed by that of Rubiaceae and Asteraceae (04/70 each). The leaves are the most used parts (34.28%) followed by leaves + roots (14.28%) and the whole plant (12.86%). The majority of the recipes consisted of four to six plants (34.66), and were prepared by decoction (50%), with water as the main solvent (87.80%). 41.56% of typhoid preparations are administered twice daily for a duration of 14 days (46.77%). This is the first report on antityphoid herbal remedies in Vina division-Adamawa Cameroon. It would therefore be judicious for our government and research institution to investigate on their therapeutic properties in order to develop ameliorated and efficient phytomedicines.
Abstract
Exploring protein–ligand interactions is a subject of immense interest, as it provides deeper insights into molecular recognition, mechanism of interaction and subsequent functions. ...Predicting an accurate model for a protein–ligand interaction is a challenging task. Molecular docking is a computational method used for predicting the preferred orientation, binding conformations and the binding affinity of a ligand to a macromolecular target, especially protein. It has been applied in ‘virtual high-throughput screening’ of chemical libraries containing millions of compounds to find potential leads in drug design and discovery. Here, we have developed InstaDock, a free and open access Graphical User Interface (GUI) program that performs molecular docking and high-throughput virtual screening efficiently. InstaDock is a single-click GUI that uses QuickVina-W, a modified version of AutoDock Vina for docking calculations, made especially for the convenience of non-bioinformaticians and for people who are not experts in using computers. InstaDock facilitates onboard analysis of docking and visual results in just a single click. To sum up, InstaDock is the easiest and more interactive interface than ever existing GUIs for molecular docking and high-throughput virtual screening. InstaDock is freely available for academic and industrial research purposes via https://hassanlab.org/instadock.
Molecular docking is a widely used method to predict the binding modes of small-molecule ligands to the target binding site. However, it remains a challenge to identify the correct binding ...conformation and the corresponding binding affinity for a series of structurally similar ligands, especially those with weak binding. An understanding of the various relative attributes of popular docking programs is required to ensure a successful docking outcome. In this study, we systematically compared the performance of three popular docking programs, Autodock, Autodock Vina, and Surflex-Dock for a series of structurally similar weekly binding flavonoids (22) binding to the estrogen receptor alpha (ERα). For these flavonoids-ERα interactions, Surflex-Dock showed higher accuracy than Autodock and Autodock Vina. The hydrogen bond overweighting by Autodock and Autodock Vina led to incorrect binding results, while Surflex-Dock effectively balanced both hydrogen bond and hydrophobic interactions. Moreover, the selection of initial receptor structure is critical as it influences the docking conformations of flavonoids-ERα complexes. The flexible docking method failed to further improve the docking accuracy of the semi-flexible docking method for such chemicals. In addition, binding interaction analysis revealed that 8 residues, including Ala350, Glu353, Leu387, Arg394, Phe404, Gly521, His524, and Leu525, are the key residues in ERα-flavonoids complexes. This work provides reference for assessing molecular interactions between ERα and flavonoid-like chemicals and provides instructive information for other environmental chemicals.
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•The predicted binding performance of flavonoids to ERα is evaluated.•Surflex-Dock shows higher docking accuracy than Autodock and Vina for flavonoids.•The prediction by flexible docking method is not improved for flavonoids with ERα.•The binding mechanism of flavonoids with ERα is indicated.
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.
A new strain of coronavirus (CoV) has been identified as SARS-CoV-2, which is responsible for the recent COVID-19 pandemic. Currently, there is no approved vaccine or drug available to combat the ...pandemic. COVID-19 main protease (M
pro
) is a key CoV enzyme, which plays an important role in triggering viral replication and transcription, turns it into an attractive target. Therefore, we aim to screen natural products library to find out potential COVID-19 M
pro
inhibitors. Plant-based natural compounds from Sigma-Aldrich plant profiler chemical library have been screened through virtual molecular docking and molecular dynamics simulation to identify potential inhibitors of COVID M
pro
. Our virtual molecular docking results have shown that there are twenty-eight natural compounds with a greater binding affinity toward the COVID-19 M
pro
inhibition site as compared to the co-crystal native ligand Inhibitor N3 (-7.9 kcal/mol). Also, molecular dynamics simulation results have confirmed that Peonidin 3-O-glucoside, Kaempferol 3-O-β-rutinoside, 4-(3,4-Dihydroxyphenyl)-7-methoxy-5-(6-O-β-D-xylopyranosyl-β-D-glucopyranosyl)oxy-2H-1-benzopyran-2-one, Quercetin-3-D-xyloside, and Quercetin 3-O-α-L-arabinopyranoside (selected based on the docking score) possess a significant amount of dynamic properties such as stability, flexibility and binding energy. Our In silco results suggests that all the above mention natural compounds have the potential to be developed as a COVID-19 M
pro
inhibitor. But before that, it must go through under the proper preclinical and clinical trials for further scientific validation.
Communicated by Ramaswamy H. Sarma
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