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Journal articles on the topic 'Autodock'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Tanisa, Asti Anna, and Rezi Riadhi. "VIRTUAL SCREENING OF BETA-SECRETASE 1 (BACE1) INHIBITORS IN THE INDONESIAN HERBAL DATABASE AS USING AUTODOCK AND AUTODOCK VINA." Asian Journal of Pharmaceutical and Clinical Research 10, no. 17 (October 1, 2017): 148. http://dx.doi.org/10.22159/ajpcr.2017.v10s5.23119.

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Objective: Alzheimer’s is a neurodegenerative disease caused by the accumulation of senile plaque in the brain that affects neuronal system leading to a less sensitive cellular response from neurons. Previous research has found that beta-secretase 1 (BACE1) plays an important role in the senile plaque formation, become a target in Alzheimer’s medication.Methods: In this study, virtual screening of BACE1 inhibitors on the Indonesian Herbal Database was done using AutoDock and AutoDock Vina. The screening was validated using the directory of useful decoys: Enhanced database. Parameters for validation process of AutoDock and AutoDock Vina are enrichment factor (EF), receiver operating characteristics, and area under the curve (AUC).Results: The dimensions of grid boxes were 30×30×30 (AutoDock) and 11.25×11.25×11.25 (AutoDock Vina). The EF 1% and AUC values obtained from the AutoDock are 7.74 and 0.73, respectively, and in the AutoDock Vina are 4.6 and 0.77, respectively. Based on the virtual screening results, the top six compounds obtained using AutoDock (binding energy ranging from −7.84 kcal/mol to −8.79 kcal/mol) include: Azadiradione, cylindrin, lanosterol, sapogenin, simiarenol, and taraxerol. The top seven compounds (binding energy ranging from −8.8 kcal/mol to −9.4 kcal/mol) obtained using AutoDeck Vina include: Bryophyllin A, diosgenin, azadiradione, sojagol, beta-amyrin, epifriedelinol, and jasmolactone C.Conclusions: Only azadiradione was obtained from the virtual screening conducted using both types of software; it interacts with the active region in BACE1 at residue Trp 76 (AutoDock result) and Thr 232 (AutoDock Vina result).

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2

Arcon, Juan Pablo, Carlos P. Modenutti, Demian Avendaño, Elias D. Lopez, Lucas A. Defelipe, Francesca Alessandra Ambrosio, Adrian G. Turjanski, Stefano Forli, and Marcelo A. Marti. "AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions." Bioinformatics 35, no. 19 (March 2, 2019): 3836–38. http://dx.doi.org/10.1093/bioinformatics/btz152.

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Abstract Summary The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein–ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations. Availability and implementation AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at http://ccsb.scripps.edu/mgltools/ or http://autodockbias.wordpress.com. Supplementary information Supplementary data are available at Bioinformatics online.

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3

Nguyen, Nguyen Thanh, Trung Hai Nguyen, T. Ngoc Han Pham, Nguyen Truong Huy, Mai Van Bay, Minh Quan Pham, Pham Cam Nam, Van V. Vu, and Son Tung Ngo. "Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity." Journal of Chemical Information and Modeling 60, no. 1 (December 30, 2019): 204–11. http://dx.doi.org/10.1021/acs.jcim.9b00778.

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4

Gaillard, Thomas. "Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark." Journal of Chemical Information and Modeling 58, no. 8 (July 10, 2018): 1697–706. http://dx.doi.org/10.1021/acs.jcim.8b00312.

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5

Tanchuk, Vsevolod, Volodymyr Tanin, Andriy Vovk, and Gennady Poda. "A New Scoring Function for Molecular Docking Based on AutoDock and AutoDock Vina." Current Drug Discovery Technologies 12, no. 3 (September 16, 2015): 170–78. http://dx.doi.org/10.2174/1570163812666150825110208.

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6

Tang, Shidi, Ruiqi Chen, Mengru Lin, Qingde Lin, Yanxiang Zhu, Ji Ding, Haifeng Hu, Ming Ling, and Jiansheng Wu. "Accelerating AutoDock Vina with GPUs." Molecules 27, no. 9 (May 9, 2022): 3041. http://dx.doi.org/10.3390/molecules27093041.

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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.

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7

Goodsell, David S., Michel F. Sanner, Arthur J. Olson, and Stefano Forli. "The AutoDock suite at 30." Protein Science 30, no. 1 (September 12, 2020): 31–43. http://dx.doi.org/10.1002/pro.3934.

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8

Salamah, Nabilah Nurtika, Widya Dwi Aryati, and Arry Yanuar. "Virtual Screening of Indonesian Herbal Database as Adenosine A2A Antagonist using AutoDock and AutoDock Vina." Pharmacognosy Journal 11, no. 6 (October 15, 2019): 1219–24. http://dx.doi.org/10.5530/pj.2019.11.189.

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9

Ivonie, Ulfa, Arry Yanuar, and Firdayani . "VIRTUAL SCREENING OF INDONESIAN HERBAL DATABASE FOR CP ALLOSTERIC MODULATOR OF HEPATITIS B VIRUS." International Journal of Applied Pharmaceutics 10, no. 1 (December 20, 2018): 190. http://dx.doi.org/10.22159/ijap.2018.v10s1.42.

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Objective: This study performed a virtual screening of the Indonesian Herbal Database for the core protein allosteric modulator of the hepatitis Bvirus (HBV) using AutoDock and AutoDock Vina software, to discover novel safe drugs for patients.Methods: The method was validated using the parameters enrichment factor (EF), receiver operating characteristics, and area under the curve (AUC).The grid box size used in virtual screening with AutoDock was 55 × 55 × 55 with EF10% of 0.7652 and AUC of 0.6709, whereas that used in virtualscreening with AutoDock Vina was 20.625 × 20.625 × 20.625 with EF5% of 0.5075 and AUC of 0.7832.Results: The top 10 compounds from virtual screening with AutoDock at G levels −11.74–−10.31 kcal/mol were yuehchukene, lansionic acid, stigmast-4-en-3-one, myrtillin, sanggenol O, lanosterol, erycrista-gallin, alpha-spinasterol, cyanidin 3-arabinoside, and cathasterone and with AutoDock Vinaat G levels −12.1 to −10.7 kcal/mol were sanggenol O, cucumerin A, yuehchukene, palmarumycin CP1, dehydrocycloguanandin, myrtillin, liriodenine,myricetin 3-alpha-L-arabinopyranoside, myricetin 3-galactoside, and cassameridine.Conclusion: Three compounds were in top list of both virtual screening methods against Cp allosteric modulator of HBV are myrtillin, sanggenol O,and yuehchukene have a prospect to be investigated futher for anti HBV.

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10

Tanchuk, Vsevolod Yu, Volodymyr O. Tanin, Andriy I. Vovk, and Gennady Poda. "A New, Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina." Chemical Biology & Drug Design 87, no. 4 (December 29, 2015): 618–25. http://dx.doi.org/10.1111/cbdd.12697.

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11

Vieira, Tatiana F., and Sérgio F. Sousa. "Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening." Applied Sciences 9, no. 21 (October 25, 2019): 4538. http://dx.doi.org/10.3390/app9214538.

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AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.

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12

Istyastono, Enade Perdana. "DOCKING STUDIES OF CURCUMIN AS A POTENTIAL LEAD COMPOUND TO DEVELOP NOVEL DIPEPTYDYL PEPTIDASE-4 INHIBITORS." Indonesian Journal of Chemistry 9, no. 1 (June 20, 2010): 132–36. http://dx.doi.org/10.22146/ijc.21574.

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Interaction of curcumin to dipeptydyl peptidase-4 (DPP-4) has been studied by employing docking method using Molecular Operating Environment (MOE) and AutoDock as the docking software applications. Although MOE can sample more conformational spaces that represent the original interaction poses than AutoDock, both softwares serve as valid and acceptable docking applications to study the interactions of small compound to DPP-4. The calculated free energy of binding (DGbinding) results from MOE and AutoDock shows that curcumin is needed to be optimized to reach similar or better DGbinding compare to the reference compound. Curcumin can be considered as a good lead compound in the development of new DPP-4 inhibitor. The results of these studies can serve as an initial effort of the further study. Keywords: curcumin, docking, molecular operating environment (MOE), AutoDock, dipeptydyl peptidase-4 (DPP-4)

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13

Lestari, Ayu Rahmania, Irmanida Batubara, Setyanto Tri Wahyudi, and Auliya Ilmiawati. "Phenolic Compound in Garlic (Allium sativum) and Black Garlic Potency as Antigout Using Molecular Docking Approach." Jurnal Kimia Sains dan Aplikasi 25, no. 7 (July 27, 2022): 253–63. http://dx.doi.org/10.14710/jksa.25.7.253-263.

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Phenolics, including flavonoids, are bioactive components in garlic in relatively abundant amounts and are present 2–4 times more in black garlic. Both of these compounds are reported to have biological activity, with one of them acting as an antioxidant. However, its ability as an antigout is still not widely reported. Xanthine oxidase, adenine deaminase, guanine deaminase, purine nucleoside phosphorylase, and 5-Nucleotidase II are involved during the production of uric acid and causes gout. This study predicted the potential of the phenolic and flavonoid compounds in garlic and black garlic as antigout in inhibiting five target receptors through a molecular docking approach. Utilizing AutoDock Tools v.1.5.7 for receptor and ligand preparation, AutoDock Vina and AutoDock4 for molecular docking, and LigPlot+ and PyMOL for visualization. About 21 compounds from the phenolic and flavonoid groups were used as test ligands and 16 reference ligands (substrate and commercial). SwissADME predicted the pharmacokinetic parameters. The results showed that apigenin, morin, resveratrol, kaempferol, (+)-catechin, isorhamnetin, and (-)-epicatechin were predicted to have good interactions at each target receptor and had the potential to be developed as candidates for multi-target antigout. Based on the pharmacokinetic parameters, all these compounds had good scores in each, making them feasible to continue in vitro or in vivo trials.

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14

Cosconati, Sandro, Stefano Forli, Alex L. Perryman, Rodney Harris, David S. Goodsell, and Arthur J. Olson. "Virtual screening with AutoDock: theory and practice." Expert Opinion on Drug Discovery 5, no. 6 (April 23, 2010): 597–607. http://dx.doi.org/10.1517/17460441.2010.484460.

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15

Zhang, Yuqi, and Michel F. Sanner. "Docking Flexible Cyclic Peptides with AutoDock CrankPep." Journal of Chemical Theory and Computation 15, no. 10 (September 11, 2019): 5161–68. http://dx.doi.org/10.1021/acs.jctc.9b00557.

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16

Goodsell, D. S. "Computational Docking of Biomolecular Complexes with AutoDock." Cold Spring Harbor Protocols 2009, no. 5 (May 1, 2009): pdb.prot5200. http://dx.doi.org/10.1101/pdb.prot5200.

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17

Xue, Qiao, Xian Liu, Paul Russell, Jin Li, Wenxiao Pan, Jianjie Fu, and Aiqian Zhang. "Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock." Ecotoxicology and Environmental Safety 233 (March 2022): 113323. http://dx.doi.org/10.1016/j.ecoenv.2022.113323.

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18

Pratama, M. R. F., and S. Siswandono. "NUMBER OF RUNS VARIATIONS ON AUTODOCK 4 DO NOT HAVE A SIGNIFICANT EFFECT ON RMSD FROM DOCKING RESULTS." Pharmacy & Pharmacology 8, no. 6 (May 17, 2021): 476–80. http://dx.doi.org/10.19163/2307-9266-2020-8-6-476-480.

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The aim. The number of runs in the docking process with AutoDock 4 is known to play an important role in the validity of the results obtained. The greater the number of runs it is often associated with the more valid docking results. However, it is not known exactly how the most ideal runs in the docking process with AutoDock 4. This study aims to determine the effect of the number of runs docking processes with AutoDock 4 on the validity of the docking results.Materials and methods. The method used is the redocking process with AutoDock 4.2.6. The receptor used is an estrogen receptor with ligand reference estradiol (PDB ID 1GWR). Variations were made on the number of runs from 10 to 100 in multiples of 10. The parameters observed were RMSD, free energy of binding, inhibition constants, amino acid residues, and the number of hydrogen bonds.Results. All experiments produce identical bond free energy, where the maximum difference in inhibition constant is only 0.06 nM. The lowest RMSD is indicated by the number of runs of 60, with a RMSD value of 0.942. There is no linear relationship between the number of runs and RMSD, with R in the linear equation of 0.4607.Conclusion. Overall, the number of runs does not show a significant contribution to the validity of the results of docking with AutoDock 4. However, these results have only been proven with the receptors used.

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19

Lu, Qiangna, Lian-Wen Qi, and Jinfeng Liu. "Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock." Journal of Theoretical and Computational Chemistry 18, no. 05 (August 2019): 1950027. http://dx.doi.org/10.1142/s0219633619500275.

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Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.

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20

H.N., Thao,, De, T.Q., Hue, B.T.B., Tuan, N.T., Bach, L.T., Quoc, N.C., Si, N.T., Toan, N.H., and Quy, H.T.K. "Docking belinostat into HDAC 8 using autodock tool." Can Tho University Journal of Science Vol.12(2) (2020): 1. http://dx.doi.org/10.22144/ctu.jen.2020.009.

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21

Wojciechowski, Marek. "Simplified AutoDock force field for hydrated binding sites." Journal of Molecular Graphics and Modelling 78 (November 2017): 74–80. http://dx.doi.org/10.1016/j.jmgm.2017.09.016.

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22

Goodsell, David S., Garrett M. Morris, and Arthur J. Olson. "Automated docking of flexible ligands: Applications of autodock." Journal of Molecular Recognition 9, no. 1 (January 1996): 1–5. http://dx.doi.org/10.1002/(sici)1099-1352(199601)9:1<1::aid-jmr241>3.0.co;2-6.

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23

Reznichenko, Liliia V. "ПРОГРАМА AUTODOCK VINA ЯК ЗАСІБ НАВЧАННЯ МАЙБУТНІХ УЧИТЕЛІВ ПРИРОДНИЧИХ ДИСЦИПЛІН." Information Technologies and Learning Tools 38, no. 6 (December 14, 2013): 149–61. http://dx.doi.org/10.33407/itlt.v38i6.928.

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Стаття присвячена проблемі впровадження засобів комп’ютерних технологій у процес навчання майбутніх учителів природничих дисциплін, зокрема учителів хімії. Обґрунтовано важливість комп’ютерного моделювання під час дослідження хімічних процесів і явищ. Висвітлено особливості процесу молекулярного докінгу, як одного з методів комп’ютерного моделювання. Запропонована програма для молекулярного докінгу AutoDock Vina розглядається як засіб підвищення ефективності навчання майбутніх учителів хімії. Окреслено теоретичні положення і запропоновано практичні рекомендації щодо формування у студентів навичок роботи з програмним продуктом AutoDock Vina.

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24

Santoso, Broto. "DOCKING ANALOG KURKUMIN TURUNAN PIPERAZINDION DENGAN TUBULIN (1TUB) RANTAI  MENGGUNAKAN VINA DAN AUTODOCK1." Pharmacon: Jurnal Farmasi Indonesia 12, no. 1 (January 31, 2015): 14–18. http://dx.doi.org/10.23917/pharmacon.v12i1.43.

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Program Autodock mampu memprediksi energi bebas dan konformasi ikatan antara fleksibel ligan dan makromolekul target yang telah diketahui. Senyawa turunan dan analog kurkumin adalah ligan yang telah banyak dihasilkan dan diuji aktivitasnya. Beberapa diantaranya memiliki khasiat yang lebih baik dari kurkumin. Enam senyawa turunan piperazindion, kurkumin, PGV-0, dan PGV-1 dihitung energi optimasi geometrinya menggunakan density functional theory (DFT) – Gaussian. Ligan hasil optimasi dicari energi ikatan ligan dengan reseptor 1TUB rantai b melalui docking menggunakan Vina dan Autodock dengan metode Lamarckian Genetic Algorithm (LGA), traditional Genetic Algorithm (tGA), dan Simulated Annealing (SA) Monte Carlo. Data energi ikatan (affinitas) terbaik yang diperoleh dianalisis dengan Anova: Two-Factor Without Replication (P=0,01). Hasil docking dengan semua metode menunjukkan bahwa senyawa analog kurkumin turunan piperazindion mempunyai potensi ikatan lebih baik dibanding senyawa induknya Kata Kunci: 1TUB, Autodock, docking, kurkumin, piperazindionage:IN'Kata kunci: Citrus reticulata, antiproliferatif, DMBA, AgNOR, c-Myc.

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MA, RUIXIN, XIUJUAN XU, LEI ZHAO, REN CAO, and QIANG FANG. "MUTUAL ARTIFICIAL BEE COLONY ALGORITHM FOR MOLECULAR DOCKING." International Journal of Biomathematics 06, no. 06 (November 2013): 1350038. http://dx.doi.org/10.1142/s1793524513500381.

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Molecular docking method plays an important role on the quest of potential drug candidates, which has been proven to be a valuable tool for virtual screening. Molecular docking is commonly referred to as a parameter optimization problem. During the last decade, some optimization algorithms have been introduced, such as Lamarckian genetic algorithm (LGA) and SODOCK embedded in the AutoDock program. On the basis of the latest docking software AutoDock4.2, we present a novel docking program ABCDock, which incorporates mutual artificial bee colony (MutualABC) into AutoDock. Computer simulation results demonstrate that ABCDock takes precedence over AutoDock and SODOCK, in terms of convergence performance, accuracy, and the lowest energy, especially for highly flexible ligands. It is noteworthy that ABCDock yields a higher success rate. Also, in comparison with the other state-of-the-art docking methods, namely GOLD, DOCK and FlexX, ABCDock provides the smallest RMSD in 27 of 37 cases.

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Sharma, Tripti. "INSILICO DOCKING APPROACH TO STUDY THE BINDING AFFINITY OF ISOFLAVONES ON THE CRYSTAL STRUCTURE OF ESTROGEN RECEPTOR ALPHA." INDIAN DRUGS 54, no. 10 (October 28, 2017): 7–15. http://dx.doi.org/10.53879/id.54.10.11152.

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The objective of the study was to carry out docking studies of isoflavone derivatives and examine their binding efficiencies to the ligand binding domain of ERα using Autodock program. A series of isoflavone derivatives were computationally designed and optimized with the AutoDock Vina software to investigate the interactions between the target compounds and the amino acid residues of the ERα.. In silico docking studies were carried out using AutoDock Vina, based on the Lamarckian genetics algorithm principle. The results showed that all the selected isoflavones showed binding energy ranging between -7.44 kcal/mol to -10.1 kcal/mol, when compared with that of the standard compound tamoxifen (-10.0 kcal/mol). Among all the designed compounds, 3-[3-(naphthalen-2-yl) phenyl]-2, 3-dihydro-4Hchroman- 4-one (Compound 12) shows more binding energy values (-10.1 kcal/mol). The present findings provide valuable information on the binding process of Isoflavones compounds to the binding site of ERα and reveal the structural requirement needed for binding.

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Meneses, Lorena, María Fernanda Pilaquinga, and Sebastián Cuesta H. "Modelamiento molecular de la interacción de ibuprofeno con las enzimas Ciclooxigenasa 1, 2 y el Citocromo P450 2C9." Revista Ecuatoriana de Medicina y Ciencias Biológicas 35, no. 1-2 (August 14, 2017): 21–29. http://dx.doi.org/10.26807/remcb.v35i1-2.248.

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En esta investigación, se presenta el modelamiento computacional de la interacción de ibuprofeno con las enzimas Ciclooxigenasa 1, Ciclooxigenasa 2 y Citocromo P450 2C9. El objetivo fue comprobar la aplicabilidad de métodos de acoplamiento molecular en la determinación de nuevos ligandos y la localización de sitios activos en las enzimas, asàcomo también tener un mejor entendimiento del mecanismo de acción farmacológica de ibuprofeno. Para el estudio se aplicaron métodos de dinámica molecular, para modelar la interacción de la molécula de ibuprofeno con las enzimas, determinando el sitio activo de éstas. Se utilizaron los programas Autodock 4 y Autodock VINA. En el modelamiento molecular, los mejores resultados se lograron con el programa Autodock VINA, por lo que éstos fueron comparados con resultados experimentales obtenidos mediante cristalografía de rayos X. Los métodos computacionales de acoplamiento molecular son totalmente comparables con resultados obtenidos experimentalmente, demostrando ser bastante exactos. Esto comprueba la aplicabilidad de estos métodos en el proceso de síntesis y diseño de nuevos fármacos.

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Ostrikova, O. I. "COMPUTER SIMULATION OF GLICOPHORIN A AND 4-METHYL-2,6-DIISOBORNILFENOL INTERACTION BY AUTODOCK AND HEXSERVER PROGRAMS." Bulletin of Siberian Medicine 13, no. 5 (October 28, 2014): 62–66. http://dx.doi.org/10.20538/1682-0363-2014-5-62-66.

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4-methyl-2,6-diisobornilphenol (dibornol) – a promising drug with a hemorheological activity. Glycophorin A is one of erythrocyte membrane proteins involved in the aggregation and possibly mediating rheological effects of dibornol.Objective: to conduct a modeling of the interaction dibornol and glycophorin A by AutoDock and HexServer programs.Material and methods. We used three-dimensional models of molecules dibornol and glycophorin A. Information on the three-dimensional model of glycophorin A was received from the database RCSB Protein Data Bank – 1AFO. Modeling the three-dimensional model of a dibornol (4-methyl-2,6- diisobornilfenol) was modeling by PRODRG Server.Results. This paper presents the results of computer modeling of interaction dibornol and glycophorin A by HexServer and AutoDock programs. We used the electrostatic properties of the molecule glycophorin A, site of interaction is position chain A VAL83, chain B – ALA82, GLY83, GLY86, THR87. The energy of binding was –6.73 kcal/mol by AutoDock program, HexServer – –2.89 kcal/mol. The charge of the molecular complex dibornol-glycophorin A decreased to –4.126 (the charge of the native molecule glycophorin A – –4.003).Conclusion. Integrated use of the program AutoDock and HexServer helps significantly reduce the time and computational resources in the modeling. The study identified the amino acids that may play a key role in the interaction with dibornol glycophorin A. This study has given us reason to believe that as a result of such interaction dibornol may prevent adhesion of red blood cells.

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Satpute, Uday M., and Sachin H. Rohane. "Efficiency of AUTODOCK: Insilico study of Pharmaceutical Drug Molecules." Asian Journal Of Research in Chemistry 14, no. 1 (2021): 1–5. http://dx.doi.org/10.5958/0974-4150.2021.00016.x.

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McElfresh, GW, and Christos Deligkaris. "A vibrational entropy term for DNA docking with autodock." Computational Biology and Chemistry 74 (June 2018): 286–93. http://dx.doi.org/10.1016/j.compbiolchem.2018.03.027.

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Jaghoori, Mohammad Mahdi, Boris Bleijlevens, and Silvia D. Olabarriaga. "1001 Ways to run AutoDock Vina for virtual screening." Journal of Computer-Aided Molecular Design 30, no. 3 (February 20, 2016): 237–49. http://dx.doi.org/10.1007/s10822-016-9900-9.

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Isaac, Arnold Emerson, Faizy Khan, Shantanu Bafna, and Tanu Gupta. "Virtual Screening of potential inhibitors from Herbs for the treatment of Breast Cancer." Asian Journal of Pharmaceutical and Clinical Research 10, no. 4 (April 1, 2017): 62. http://dx.doi.org/10.22159/ajpcr.2017.v10i4.14959.

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Objectives: Cancer is a disease which results in uncontrollable abnormal cells division and destruction of body tissues. Breast cancer occurs when malignant tumors develop in the breast. Breast cancer is the second leading cause of death among women. To study the role of herbs used in the treatment for breast cancer. To investigate the anti-breast cancer activity of compounds present on most common herbs and to analyse their interaction with amino acids in the active sites. Methods: Complementary and alternative medicine is often used for curing cancer mainly the breast cancer. Also certain studies support the benefits of herbal medicines over others among Complementary and alternative medicine. Herbal treatments are more popular due to less complications and more safety. We selected a dataset of 38 compounds and performed virtual screening to identify the potential inhibitor against the known protein target BRCA1 involved in breast cancer using AutoDock4 as docking software. The binding site analyses were carried out using Discovery studio.Results: From our study, we deduced that cimigenol (black cohosh) and glycyrrhetinic acid (licorice) were found to have the highest affinity with the target protein. The amino acid interactions with the top five compounds were also analysed.Conclusion: During the course of our research we explored over common herbs used globally in treatment for breast cancer. Virtual screening was performed using AutoDock to search ligands to identify those structures which are most likely to bind to the protein. The high affinity compounds canbind more efficiently to the BRCA1 receptor and, hence, has potential to emerge as lead compound in the treatment of breast cancer.Keywords: Protein, Ligands, AutoDock, Virtual screening, Visualization, BRCA1.

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LIU, YU, WENTAO LI, and RUIXIN MA. "PARTICLE SWARM OPTIMIZATION ON FLEXIBLE DOCKING." International Journal of Biomathematics 05, no. 05 (June 17, 2012): 1250044. http://dx.doi.org/10.1142/s1793524511001866.

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Molecular docking is an important tool in screening large libraries of compounds to determine the interactions between potential drugs and the target proteins. The molecular docking problem is how to locate a good conformation to dock a ligand to the large molecule. It can be formulated as a parameter optimization problem consisting of a scoring function and a global optimization method. Many docking methods have been developed with primarily these two parts varying. In this paper, a variety of particle swarm optimization (PSO) variants were introduced to cooperate with the semiempirical free energy force field in AutoDock 4.05. The search ability and the docking accuracy of these methods were evaluated by multiple redocking experiments. The results demonstrate that PSOs were more suitable than Lamarckian genetic algorithm (LGA). Among all of the PSO variants, FIPS takes precedence over others. Compared with the four state-of-art docking methods-GOLD, DOCK, FlexX and AutoDock with LGA, AutoDock cooperated with FIPS is more accurate. Thus, FIPS is an efficient PSO variant which has promising prospects that can be expected in the application to virtual screening.

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Macari, Gabriele, Daniele Toti, Andrea Pasquadibisceglie, and Fabio Polticelli. "DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina." International Journal of Molecular Sciences 21, no. 24 (December 15, 2020): 9548. http://dx.doi.org/10.3390/ijms21249548.

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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.

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Yanuar, Arry, Rezi Riadhi Syahdi, and Widya Dwi Aryati. "PARAMETER OPTIMIZATION AND VIRTUAL SCREENING INDONESIAN HERBAL DATABASE AS HUMAN IMMUNODEFICIENCY VIRUS -1 INTEGRASE INHIBITOR USING AUTODOCK AND VINA." International Journal of Applied Pharmaceutics 9 (October 30, 2017): 90. http://dx.doi.org/10.22159/ijap.2017.v9s1.51_57.

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Objective: Human immunodeficiency virus (HIV-1) is a virus that causes acquired immunodeficiency syndrome, a disease considered to be one of themost dangerous because of its high mortality, morbidity, and infectivity. The emergence of mutant HIV strains has led treatment to target proteaseas reverse transcriptase and integrase enzyme become less effective. This study aims to provide knowledge about the potential of HIV-1 integraseinhibitors for use as guiding compounds in the development of new anti-HIV drugs.Methods: This study used AutoDock and AutoDock Vina for virtual screening of the Indonesian herbal database for inhibitors of HIV-1 integrase andis validated using a database of the directory of useful decoys. Optimization was accomplished by selecting the grid size, the number of calculations,and the addition of two water molecules and a magnesium atom as cofactor.Results: This study determined that the best grid box size is 21.1725×21.1725×21.1725 in unit space size (1 unit space equals to macromolecules 1Ǻ),using AutoDock Vina with EF and AUC values, 3.93 and 0.693, respectively. Three important water molecules have meaning in molecular dockingaround the binding pocket.Conclusions: This study obtained the top ten ranked compounds using AutoDock Vina. The compounds include: Casuarinin; Myricetin-3-O-(2’’,6’’-di-O-α-rhamnosyl)-β-glucoside; 5,7,2’,4’-tetrahydroxy-6,3’-diprenylisoflavone 5-O-(4’’-rhamnosylrhamnoside); myricetin 3-robinobioside; cyanidin3-[6-(6-ferulylglucosyl)-2-xylosylgalactoside]; mesuein, cyanidin 7-(3-glucosyl-6-malonylglucoside)-4’-glucoside; kaempferol 3-[glucosyl-(1→3)-rhamnosyl-(1→6)-galactoside]; 3-O-galloylepicatechin-(4-β→8)-epicatechin-3-O-gallate; and quercetin 4’-glucuronide.

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Marvaniya, Vanita, Hirak V. Joshi, Ujashkumar A. Shah, and Jayvadan K. Patel. "Synthesis, Anticancer Evaluation and Molecular Docking Studies of Isonicotinamide and Diaryl Urea Hybrid Motifs." Asian Journal of Organic & Medicinal Chemistry 7, no. 2 (2022): 179–86. http://dx.doi.org/10.14233/ajomc.2022.ajomc-p382.

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In search of new anticancer agents with improved efficacy, we designed and synthesized novel hybrid series of isonicotinamide and diaryl urea motifs (R1-R9). Design of series compounds carried out using docking study by Autodock vina tool. Binding energy (more than -9.7 kcal/mol) calculated using Autodock vina against Raf kinase (PDB: 4DBN). All the synthesized compounds were evaluated for them in vitro anticancer activity against MCF-7 cell line. The anticancer activities of the synthesized compounds were also carried. Some of the compounds (R1, R8, R9) showed better activities towards MCF-7 cell line by MTT assay.

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Butt, Sania Safdar, Yasmin Badshah, Maria Shabbir, and Mehak Rafiq. "Molecular Docking Using Chimera and Autodock Vina Software for Nonbioinformaticians." JMIR Bioinformatics and Biotechnology 1, no. 1 (June 19, 2020): e14232. http://dx.doi.org/10.2196/14232.

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In the field of drug discovery, many methods of molecular modeling have been employed to study complex biological and chemical systems. Experimental strategies are integrated with computational approaches for the identification, characterization, and development of novel drugs and compounds. In modern drug designing, molecular docking is an approach that explores the confirmation of a ligand within the binding site of a macromolecule. To date, many software and tools for docking have been employed. AutoDock Vina (in UCSF [University of California, San Francisco] Chimera) is one of the computationally fastest and most accurate software employed in docking. In this paper, a sequential demonstration of molecular docking of the ligand fisetin with the target protein Akt has been provided, using AutoDock Vina in UCSF Chimera 1.12. The first step involves target protein ID retrieval from the protein database, the second step involves visualization of the protein structure in UCSF Chimera, the third step involves preparation of the target protein for docking, the fourth step involves preparation of the ligand for docking, the fifth step involves docking of the ligand and the target protein as Mol.2 files in Chimera by using AutoDock Vina, and the final step involves interpretation and analysis of the docking results. By following the guidelines and steps outlined in this paper, researchers with no previous background in bioinformatics research can perform computational docking in an easier and more user-friendly manner.

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Reddy, K. Kumar, R. S. Rathore, P. Srujana, R. R. Burri, C. Ravikumar Reddy, M. Sumakanth, Pallu Reddanna, and M. Rami Reddy. "Performance Evaluation of Docking Programs- Glide, GOLD, AutoDock & SurflexDock, Using Free Energy Perturbation Reference Data: A Case Study of Fructose-1, 6-bisphosphatase-AMP Analogs." Mini-Reviews in Medicinal Chemistry 20, no. 12 (July 23, 2020): 1179–87. http://dx.doi.org/10.2174/1389557520666200526183353.

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Background: The accurate ranking of analogs of lead molecules with respect to their estimated binding free energies to drug targets remains highly challenging in molecular docking due to small relative differences in their free energy values. Methods: Free energy perturbation (FEP) method, which provides the most accurate relative binding free energy values were earlier used to calculate free energies of many ligands for several important drug targets including Fructose-1,6-BisphosPhatase (FBPase). The availability of abundant structural and experimental binding affinity data for FBPase inhibitors provided an ideal system to evaluate four widely used docking programs, AutoDock, Glide, GOLD and SurflexDock, distinct from earlier comparative evaluation studies. Results: The analyses suggested that, considering various parameters such as docking pose, scoring and ranking accuracy, sensitivity analysis and newly introduced relative ranking score, Glide provided reasonably consistent results in all respects for the system studied in the present work. Whereas GOLD and AutoDock also demonstrated better performance, AutoDock results were found to be significantly superior in terms of scoring accuracy compared to the rest. Conclusion: Present analysis serves as a useful guide for researchers working in the field of lead optimization and for developers in upgradation of the docking programs.

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Aldisa, Olivia, Azminah Azminah, Linda Erlina, Hayun Hayun, and Arry Yanuar. "VIRTUAL SCREENING OF INDONESIAN HERBAL DATABASE TO FIND SIRTUIN 1 ACTIVATORS USING THE DOCKING METHOD." Asian Journal of Pharmaceutical and Clinical Research 10, no. 17 (October 1, 2017): 158. http://dx.doi.org/10.22159/ajpcr.2017.v10s5.23121.

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Objective: Chemical compounds in plants often have benefits and efficacy that can be useful for medicine. Biochemistry and biomedicine research aims to develop new drugs for degenerative human diseases such as cancer, cardiovascular diseases, and diabetes mellitus. Humans have a protein that is the key for metabolic sensors in a variety of metabolic pathways, Sirtuin 1 (SIRT1). Currently, only resveratrol, fisetin, and quercetin, which are compounds from natural ingredients, have been tested as activators of SIRT1 even though there are many chemical compounds in plants that could potentially be SIRT1 activators. Four crystal forms act as SIRT1 activators: 4ZZH, 4ZZI, 4ZZJ, and 5BTR.Methods: In this study, we employed the docking of new molecular compounds from an Indonesian herbal database as SIRT1 activators. Virtual screening was done using AutoDock Vina. AutoDock Vina was validated beforehand to obtain the best grid box; based on this research, the best grid box for AutoDock Vina is 60 × 60 × 60.Results: The top 10 ranked compounds were obtained for each crystal form and for the same compounds of the four crystal forms, which are alpha-carotene, Cassiamin C, casuarinin, and lutein.

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Shamsara, Jamal. "Correlation between Virtual Screening Performance and Binding Site Descriptors of Protein Targets." International Journal of Medicinal Chemistry 2018 (January 11, 2018): 1–10. http://dx.doi.org/10.1155/2018/3829307.

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Rescoring is a simple approach that theoretically could improve the original docking results. In this study AutoDock Vina was used as a docked engine and three other scoring functions besides the original scoring function, Vina, as well as their combinations as consensus scoring functions were employed to explore the effect of rescoring on virtual screenings that had been done on diverse targets. Rescoring by DrugScore produces the most number of cases with significant changes in screening power. Thus, the DrugScore results were used to build a simple model based on two binding site descriptors that could predict possible improvement by DrugScore rescoring. Furthermore, generally the screening power of all rescoring approach as well as original AutoDock Vina docking results correlated with the Maximum Theoretical Shape Complementarity (MTSC) and Maximum Distance from Center of Mass and all Alpha spheres (MDCMA). Therefore, it was suggested that, with a more complete set of binding site descriptors, it could be possible to find robust relationship between binding site descriptors and response to certain molecular docking programs and scoring functions. The results could be helpful for future researches aiming to do a virtual screening using AutoDock Vina and/or rescoring using DrugScore.

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Fu, Yi, Xiaojun Wu, Zhiguo Chen, Jun Sun, Ji Zhao, and Wenbo Xu. "A New Approach for Flexible Molecular Docking Based on Swarm Intelligence." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/540186.

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Molecular docking methods play an important role in the field of computer-aided drug design. In the work, on the basis of the molecular docking program AutoDock, we present QLDock as a tool for flexible molecular docking. For the energy evaluation, the algorithm uses the binding free energy function that is provided by the AutoDock 4.2 tool. The new search algorithm combines the features of a quantum-behaved particle swarm optimization (QPSO) algorithm and local search method of Solis and Wets for solving the highly flexible protein-ligand docking problem. We compute the interaction of 23 protein-ligand complexes and compare the results with those of the QDock and AutoDock programs. The experimental results show that our approach leads to substantially lower docking energy and higher docking precision in comparison to Lamarckian genetic algorithm and QPSO algorithm alone. QPSO-ls algorithm was able to identify the correct binding mode of 74% of the complexes. In comparison, the accuracy of QPSO and LGA is 52% and 61%, respectively. This difference in performance rises with increasing complexity of the ligand. Thus, the novel algorithm QPSO-ls may be used to dock ligand with many rotatable bonds with high accuracy.

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Kannadasan, R., I. Arnold Emerson, and M. S. Saleem Basha. "Docking of HIV-1 with Neem using Autodock in Bioinformatics." Research Journal of Pharmacy and Technology 10, no. 11 (2017): 3877. http://dx.doi.org/10.5958/0974-360x.2017.00704.1.

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Chawsheen, Mahmoud. "Predicting the efficacy of Akt inhibitors using AutoDock Vina software." Journal of Garmian University 5, no. 4 (August 1, 2018): 1–10. http://dx.doi.org/10.24271/garmian.610.

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Zhang, Shuxing, Kamal Kumar, Xiaohui Jiang, Anders Wallqvist, and Jaques Reifman. "DOVIS: an implementation for high-throughput virtual screening using AutoDock." BMC Bioinformatics 9, no. 1 (2008): 126. http://dx.doi.org/10.1186/1471-2105-9-126.

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Kumar, V. "In Silico Analysis of Indoles Against 1KE8 Inhibitors Using Autodock." British Journal of Pharmaceutical Research 3, no. 3 (January 10, 2013): 446–53. http://dx.doi.org/10.9734/bjpr/2013/3310.

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Sandeep, Gaddam, Kurre Purna Nagasree, Muppaneni Hanisha, and Muthyala Murali Krishna Kumar. "AUDocker LE: A GUI for virtual screening with AUTODOCK Vina." BMC Research Notes 4, no. 1 (2011): 445. http://dx.doi.org/10.1186/1756-0500-4-445.

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Helgren, Travis R., and Timothy J. Hagen. "Demonstration of AutoDock as an Educational Tool for Drug Discovery." Journal of Chemical Education 94, no. 3 (February 13, 2017): 345–49. http://dx.doi.org/10.1021/acs.jchemed.6b00555.

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Dhanik, Ankur, John S. McMurray, and Lydia E. Kavraki. "DINC: A new AutoDock-based protocol for docking large ligands." BMC Structural Biology 13, Suppl 1 (2013): S11. http://dx.doi.org/10.1186/1472-6807-13-s1-s11.

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Zhang, Yuqi, Stefano Forli, Anna Omelchenko, and Michel F. Sanner. "AutoGridFR: Improvements on AutoDock Affinity Maps and Associated Software Tools." Journal of Computational Chemistry 40, no. 32 (August 22, 2019): 2882–86. http://dx.doi.org/10.1002/jcc.26054.

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Salih, Twana Mohsin. "A Comparative Study for the Accuracy of Three Molecular Docking Programs Using HIV-1 Protease Inhibitors as a Model." Iraqi Journal of Pharmaceutical Sciences ( P-ISSN 1683 - 3597 E-ISSN 2521 - 3512) 31, no. 2 (December 24, 2022): 160–68. http://dx.doi.org/10.31351/vol31iss2pp160-168.

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Flexible molecular docking is a computational method of structure-based drug design to evaluate binding interactions between receptor and ligand and identify the ligand conformation within the receptor pocket. Currently, various molecular docking programs are extensively applied; therefore, realizing accuracy and performance of the various docking programs could have a significant value. In this comparative study, the performance and accuracy of three widely used non-commercial docking software (AutoDock Vina, 1-Click Docking, and UCSF DOCK) was evaluated through investigations of the predicted binding affinity and binding conformation of the same set of small molecules (HIV-1 protease inhibitors) and a protein target HIV-1 protease enzyme. The tested sets are composed of eight receptor-ligand complexes with high resolution crystal structures downloaded from Protein Data Bank website. Molecular dockings were applied between approved HIV-1 protease inhibitors and the HIV-1 protease using AutoDock Vina, 1-Click Docking, and DOCK6. Then, docking poses of the top-ranked solution was realized using UCSF Chimera. Furthermore, Pearson correlation coefficient (r) and coefficient of determination (r2) between the experimental results and the top scored docking results of each program were calculated using Graphpad prism V9.2. After comparing saquinavir top scored binding poses of each docking program with the crystal structure, various conformational changes were observed. Moreover, according to the relative comparison between the top ranked calculated ?Gbinding values against the experimental results, r2 value of AutoDock Vina, 1-Click Docking, and DOCK6 were 0.65, 0.41, and 0.005, respectively. The outcome of this study shows that the top scored binding free energy could not produce the best pose prediction. In addition, AutoDock Vina results have the highest correlation with the experimental results.

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