Journal articles: 'Protein-ligand docking'

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Bottegoni, Giovanni. "Protein-ligand docking." Frontiers in Bioscience 16, no. 1 (2011): 2289. http://dx.doi.org/10.2741/3854.

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Velesinović, Aleksandar, and Goran Nikolić. "Protein-protein interaction networks and protein-ligand docking: Contemporary insights and future perspectives." Acta Facultatis Medicae Naissensis 38, no. 1 (2021): 5–17. http://dx.doi.org/10.5937/afmnai38-28322.

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Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry - protein-ligand docking and the prediction of protein-protein interaction networks.

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Pérez, Carlos, and Angel R. Ortiz. "Evaluation of Docking Functions for Protein−Ligand Docking." Journal of Medicinal Chemistry 44, no. 23 (2001): 3768–85. http://dx.doi.org/10.1021/jm010141r.

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Wang, Kai, Nan Lyu, Hongjuan Diao, et al. "GM-DockZn: a geometry matching-based docking algorithm for zinc proteins." Bioinformatics 36, no. 13 (2020): 4004–11. http://dx.doi.org/10.1093/bioinformatics/btaa292.

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Abstract Motivation Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino-acid residues, metal ions and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing docking methods developed specifically for zinc proteins, GM-DockZn samples ligand conformations directly using a geometric grid around the ideal zinc-coordination positions of seven discovered coordination motifs, which were found from the survey of known zinc proteins complexed with a single ligand. Results GM-DockZn has the best performance in sampling near-native poses with correct coordination atoms and numbers within the top 50 and top 10 predictions when compared to several state-of-the-art techniques. This is true not only for a non-redundant dataset of zinc proteins but also for a homolog set of different ligand and zinc-coordination systems for the same zinc proteins. Similar superior performance of GM-DockZn for near-native-pose sampling was also observed for docking to apo-structures and cross-docking between different ligand complex structures of the same protein. The highest success rate for sampling nearest near-native poses within top 5 and top 1 was achieved by combining GM-DockZn for conformational sampling with GOLD for ranking. The proposed geometry-based sampling technique will be useful for ligand docking to other metalloproteins. Availability and implementation GM-DockZn is freely available at www.qmclab.com/ for academic users. Supplementary information Supplementary data are available at Bioinformatics online.

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Bentham Science Publisher, Bentham Science Publisher. "Scoring Functions for Protein-Ligand Docking." Current Protein & Peptide Science 7, no. 5 (2006): 407–20. http://dx.doi.org/10.2174/138920306778559395.

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Roberts, Benjamin C., and Ricardo L. Mancera. "Ligand−Protein Docking with Water Molecules." Journal of Chemical Information and Modeling 48, no. 2 (2008): 397–408. http://dx.doi.org/10.1021/ci700285e.

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Goto, Junichi, Ryoichi Kataoka, and Noriaki Hirayama. "Ph4Dock: Pharmacophore-Based Protein−Ligand Docking." Journal of Medicinal Chemistry 47, no. 27 (2004): 6804–11. http://dx.doi.org/10.1021/jm0493818.

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Verdonk, Marcel L., Jason C. Cole, Michael J. Hartshorn, Christopher W. Murray, and Richard D. Taylor. "Improved protein-ligand docking using GOLD." Proteins: Structure, Function, and Bioinformatics 52, no. 4 (2003): 609–23. http://dx.doi.org/10.1002/prot.10465.

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Pippel, Martin, Michael Scharfe, René Meier, and Wolfgang Sippl. "Einfach und frei: Protein-Ligand-Docking." Nachrichten aus der Chemie 60, no. 6 (2012): 656–57. http://dx.doi.org/10.1002/nadc.201290238.

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Morris, Connor J., and Dennis Della Corte. "Using molecular docking and molecular dynamics to investigate protein-ligand interactions." Modern Physics Letters B 35, no. 08 (2021): 2130002. http://dx.doi.org/10.1142/s0217984921300027.

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Molecular docking and molecular dynamics (MD) are powerful tools used to investigate protein-ligand interactions. Molecular docking programs predict the binding pose and affinity of a protein-ligand complex, while MD can be used to incorporate flexibility into docking calculations and gain further information on the kinetics and stability of the protein-ligand bond. This review covers state-of-the-art methods of using molecular docking and MD to explore protein-ligand interactions, with emphasis on application to drug discovery. We also call for further research on combining common molecular docking and MD methods.

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Verdonk, Marcel L., Paul N. Mortenson, Richard J. Hall, Michael J. Hartshorn, and Christopher W. Murray. "Protein−Ligand Docking against Non-Native Protein Conformers." Journal of Chemical Information and Modeling 48, no. 11 (2008): 2214–25. http://dx.doi.org/10.1021/ci8002254.

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Sulimov, Vladimir B., Danil C. Kutov, and Alexey V. Sulimov. "Advances in Docking." Current Medicinal Chemistry 26, no. 42 (2020): 7555–80. http://dx.doi.org/10.2174/0929867325666180904115000.

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Background: Design of small molecules which are able to bind to the protein responsible for a disease is the key step of the entire process of the new medicine discovery. Atomistic computer modeling can significantly improve effectiveness of such design. The accurate calculation of the free energy of binding a small molecule (a ligand) to the target protein is the most important problem of such modeling. Docking is one of the most popular molecular modeling methods for finding ligand binding poses in the target protein and calculating the protein-ligand binding energy. This energy is used for finding the most active compounds for the given target protein. This short review aims to give a concise description of distinctive features of docking programs focusing on computation methods and approximations influencing their accuracy. Methods: This review is based on the peer-reviewed research literature including author’s own publications. The main features of several representative docking programs are briefly described focusing on their characteristics influencing docking accuracy: force fields, energy calculations, solvent models, algorithms of the best ligand pose search, global and local optimizations, ligand and target protein flexibility, and the simplifications made for the docking accelerating. Apart from other recent reviews focused mainly on the performance of different docking programs, in this work, an attempt is made to extract the most important functional characteristics defining the docking accuracy. Also a roadmap for increasing the docking accuracy is proposed. This is based on the new generation of docking programs which have been realized recently. These programs and respective new global optimization algorithms are described shortly. Results: Several popular conventional docking programs are considered. Their search of the best ligand pose is based explicitly or implicitly on the global optimization problem. Several algorithms are used to solve this problem, and among them, the heuristic genetic algorithm is distinguished by its popularity and an elaborate design. All conventional docking programs for their acceleration use the preliminary calculated grids of protein-ligand interaction potentials or preferable points of protein and ligand conjugation. These approaches and commonly used fitting parameters restrict strongly the docking accuracy. Solvent is considered in exceedingly simplified approaches in the course of the global optimization and the search for the best ligand poses. More accurate approaches on the base of implicit solvent models are used frequently for more careful binding energy calculations after docking. The new generation of docking programs are developed recently. They find the spectrum of low energy minima of a protein-ligand complex including the global minimum. These programs should be more accurate because they do not use a preliminary calculated grid of protein-ligand interaction potentials and other simplifications, the energy of any conformation of the molecular system is calculated in the frame of a given force field and there are no fitting parameters. A new docking algorithm is developed and fulfilled specially for the new docking programs. This algorithm allows docking a flexible ligand into a flexible protein with several dozen mobile atoms on the base of the global energy minimum search. Such docking results in improving the accuracy of ligand positioning in the docking process. The adequate choice of the method of molecular energy calculations also results in the better docking positioning accuracy. An advancement in the application of quantum chemistry methods to docking and scoring is revealed. Conclusion: The findings of this review confirm the great demand in docking programs for discovery of new medicine substances with the help of molecular modeling. New trends in docking programs design are revealed. These trends are focused on the increase of the docking accuracy at the expense of more accurate molecular energy calculations without any fitting parameters, including quantum-chemical methods and implicit solvent models, and by using new global optimization algorithms which make it possible to treat flexibility of ligands and mobility of protein atoms simultaneously. Finally, it is shown that all the necessary prerequisites for increasing the docking accuracy can be accomplished in practice.

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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 (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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Huang, Sheng-You, and Xiaoqin Zou. "Advances and Challenges in Protein-Ligand Docking." International Journal of Molecular Sciences 11, no. 8 (2010): 3016–34. http://dx.doi.org/10.3390/ijms11083016.

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Thilagavathi, Ramasamy, and Ricardo L. Mancera. "Ligand−Protein Cross-Docking with Water Molecules." Journal of Chemical Information and Modeling 50, no. 3 (2010): 415–21. http://dx.doi.org/10.1021/ci900345h.

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Taufer, M., R. Armen, Jianhan Chen, P. Teller, and C. Brooks. "Computational multiscale modeling in protein--ligand docking." IEEE Engineering in Medicine and Biology Magazine 28, no. 2 (2009): 58–69. http://dx.doi.org/10.1109/memb.2009.931789.

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Cole, Jason C., Christopher W. Murray, J. Willem M. Nissink, Richard D. Taylor, and Robin Taylor. "Comparing protein-ligand docking programs is difficult." Proteins: Structure, Function, and Bioinformatics 60, no. 3 (2005): 325–32. http://dx.doi.org/10.1002/prot.20497.

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Hoffmann, Daniel, Bernd Kramer, Takumi Washio, Torsten Steinmetzer, Matthias Rarey, and Thomas Lengauer. "Two-Stage Method for Protein−Ligand Docking." Journal of Medicinal Chemistry 42, no. 21 (1999): 4422–33. http://dx.doi.org/10.1021/jm991090p.

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Lybrand, Terry P. "Ligand—protein docking and rational drug design." Current Opinion in Structural Biology 5, no. 2 (1995): 224–28. http://dx.doi.org/10.1016/0959-440x(95)80080-8.

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Oferkin, I. V., A. V. Sulimov, E. V. Katkova, et al. "Supercomputer investigation of the protein-ligand system low-energy minima." Biomeditsinskaya Khimiya 61, no. 6 (2015): 712–16. http://dx.doi.org/10.18097/pbmc20156106712.

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The accuracy ofthe protein-ligand binding energy calculations andligand positioning isstrongly influenced by the choice of the docking target function. This work demonstrates the evaluation of the five different target functions used in docking: functions based on MMFF94 force field and functions based on PM7 quantum-chemical method accounting orwithout accounting the implicit solvent model (PCM, COSMO or SGB). For these purposes the ligand positions corresponding to the minima of the target function and the experimentally known ligand positions in the protein active site (crystal ligand positions) were compared. Each function was examined on the same test-set of 16 protein-ligand complexes. The new parallelized docking program FLM based on Monte Carlo search algorithm was developed to perform the comprehensive low-energy minima search and to calculate the protein-ligand binding energy. This study demonstrates that the docking target function based on the MMFF94 force field can be used to detect the crystal or near crystal positions of the ligand by the finding the low-energy local minima spectrum of the target function. The importance of solvent accounting in the docking process for the accurate ligand positioning is also shown. The accuracy of the ligand positioning as well as the correlation between the calculated and experimentally determined protein-ligand binding energies are improved when the MMFF94 force field is substituted by the new PM7 method with implicit solvent accounting.

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Wong, Chung F. "Flexible ligand–flexible protein docking in protein kinase systems." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1784, no. 1 (2008): 244–51. http://dx.doi.org/10.1016/j.bbapap.2007.10.005.

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Shin, Woong-Hee, and Chaok Seok. "GalaxyDock: Protein–Ligand Docking with Flexible Protein Side-chains." Journal of Chemical Information and Modeling 52, no. 12 (2012): 3225–32. http://dx.doi.org/10.1021/ci300342z.

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Oferkin, Igor V., Ekaterina V. Katkova, Alexey V. Sulimov, et al. "Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima." Advances in Bioinformatics 2015 (November 26, 2015): 1–12. http://dx.doi.org/10.1155/2015/126858.

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The adequate choice of the docking target function impacts the accuracy of the ligand positioning as well as the accuracy of the protein-ligand binding energy calculation. To evaluate a docking target function we compared positions of its minima with the experimentally known pose of the ligand in the protein active site. We evaluated five docking target functions based on either the MMFF94 force field or the PM7 quantum-chemical method with or without implicit solvent models: PCM, COSMO, and SGB. Each function was tested on the same set of 16 protein-ligand complexes. For exhaustive low-energy minima search the novel MPI parallelized docking program FLM and large supercomputer resources were used. Protein-ligand binding energies calculated using low-energy minima were compared with experimental values. It was demonstrated that the docking target function on the base of the MMFF94 force field in vacuo can be used for discovery of native or near native ligand positions by finding the low-energy local minima spectrum of the target function. The importance of solute-solvent interaction for the correct ligand positioning is demonstrated. It is shown that docking accuracy can be improved by replacement of the MMFF94 force field by the new semiempirical quantum-chemical PM7 method.

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Fu, Yi, Ji Zhao, and Zhiguo Chen. "Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein." Computational and Mathematical Methods in Medicine 2018 (December 4, 2018): 1–12. http://dx.doi.org/10.1155/2018/3502514.

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Protein-ligand interactions are a necessary prerequisite for signal transduction, immunoreaction, and gene regulation. Protein-ligand interaction studies are important for understanding the mechanisms of biological regulation, and they provide a theoretical basis for the design and discovery of new drug targets. In this study, we analyzed the molecular interactions of protein-ligand which was docked by AutoDock 4.2 software. In AutoDock 4.2 software, we used a new search algorithm, hybrid algorithm of random drift particle swarm optimization and local search (LRDPSO), and the classical Lamarckian genetic algorithm (LGA) as energy optimization algorithms. The best conformations of each docking algorithm were subjected to molecular dynamic (MD) simulations to further analyze the molecular mechanisms of protein-ligand interactions. Here, we analyze the binding energy between protein receptors and ligands, the interactions of salt bridges and hydrogen bonds in the docking region, and the structural changes during complex unfolding. Our comparison of these complexes highlights differences in the protein-ligand interactions between the two docking methods. It also shows that salt bridge and hydrogen bond interactions play a crucial role in protein-ligand stability. The present work focuses on extracting the deterministic characteristics of docking interactions from their dynamic properties, which is important for understanding biological functions and determining which amino acid residues are crucial to docking interactions.

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Huang, Sheng-You, Min Li, Jianxin Wang, and Yi Pan. "HybridDock: A Hybrid Protein–Ligand Docking Protocol Integrating Protein- and Ligand-Based Approaches." Journal of Chemical Information and Modeling 56, no. 6 (2015): 1078–87. http://dx.doi.org/10.1021/acs.jcim.5b00275.

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Kannadasan, R., M. S. Saleembasha, and I. Arnold Emerson. "A Frame Work for Learning Drug Designing through Molecular Modelling Software Techniques and Biological Databases for Protein-Ligand Interactions." International Journal of Engineering Research in Africa 27 (December 2016): 111–18. http://dx.doi.org/10.4028/www.scientific.net/jera.27.111.

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Applications of computer and information technology are indispensable in various fields especially in the field of biology. The use of computer aided tools plays a key role in solving biological problems. The spontaneous process of molecular docking is important for finding potentially strong candidate of drug for various viruses. The binding of protein receptors with ligand molecules is essential in drug discovery process. The aim of molecular docking tools is to predict the interaction between protein and ligand. This review outlines the major tools for protein - ligand docking which in turn emphasize the importance of molecular docking in modern drug discovery process.

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May, Andreas, and Martin Zacharias. "Accounting for global protein deformability during protein–protein and protein–ligand docking." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1754, no. 1-2 (2005): 225–31. http://dx.doi.org/10.1016/j.bbapap.2005.07.045.

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Chen, Jui-Le, Chun-Wei Tsai, Ming-Chao Chiang, and Chu-Sing Yang. "A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/909717.

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The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algorithm works by leveraging two high-performance operators: (1) thenovelmigration (information exchange) operator is designed specially for cloud-based environments to reduce the computation time; (2) theefficientoperator is aimed at filtering out the worst search directions. Our simulation results illustrate that the proposed method outperforms the other docking algorithms compared in this paper in terms of both the computation time and the quality of the end result.

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Lexa, Katrina W., and Heather A. Carlson. "Protein flexibility in docking and surface mapping." Quarterly Reviews of Biophysics 45, no. 3 (2012): 301–43. http://dx.doi.org/10.1017/s0033583512000066.

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AbstractStructure-based drug design has become an essential tool for rapid lead discovery and optimization. As available structural information has increased, researchers have become increasingly aware of the importance of protein flexibility for accurate description of the native state. Typical protein–ligand docking efforts still rely on a single rigid receptor, which is an incomplete representation of potential binding conformations of the protein. These rigid docking efforts typically show the best performance rates between 50 and 75%, while fully flexible docking methods can enhance pose prediction up to 80–95%. This review examines the current toolbox for flexible protein–ligand docking and receptor surface mapping. Present limitations and possibilities for future development are discussed.

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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 (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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Seifert, Markus H. J. "ProPose: Steered Virtual Screening by Simultaneous Protein−Ligand Docking and Ligand−Ligand Alignment." Journal of Chemical Information and Modeling 45, no. 2 (2005): 449–60. http://dx.doi.org/10.1021/ci0496393.

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Yang, Jinsol, Minkyung Baek, and Chaok Seok. "GalaxyDock3: Protein–ligand docking that considers the full ligand conformational flexibility." Journal of Computational Chemistry 40, no. 31 (2019): 2739–48. http://dx.doi.org/10.1002/jcc.26050.

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Fukunishi, Yoshifumi, Yumiko Mizukoshi, Koh Takeuchi, Ichio Shimada, Hideo Takahashi, and Haruki Nakamura. "Protein–ligand docking guided by ligand pharmacophore-mapping experiment by NMR." Journal of Molecular Graphics and Modelling 31 (November 2011): 20–27. http://dx.doi.org/10.1016/j.jmgm.2011.08.002.

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Ng, Marcus C. K., Simon Fong, and Shirley W. I. Siu. "PSOVina: The hybrid particle swarm optimization algorithm for protein–ligand docking." Journal of Bioinformatics and Computational Biology 13, no. 03 (2015): 1541007. http://dx.doi.org/10.1142/s0219720015410073.

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Protein–ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden–Fletcher–Goldfarb–Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein–ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51–60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein–ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

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UEHARA, Shota, Kazuhiro FUJIMOTO, and Shigenori TANAKA. "Protein-Ligand Docking Using Artificial Bee Colony Algorithm." Journal of Computer Chemistry, Japan 13, no. 3 (2014): 163–64. http://dx.doi.org/10.2477/jccj.2014-0020.

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Cabrera, Álvaro Cortés, Javier Klett, Helena G. Dos Santos, et al. "CRDOCK: An Ultrafast Multipurpose Protein–Ligand Docking Tool." Journal of Chemical Information and Modeling 52, no. 8 (2012): 2300–2309. http://dx.doi.org/10.1021/ci300194a.

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Purisima, Enrico O., and Hervé Hogues. "Protein–Ligand Binding Free Energies from Exhaustive Docking." Journal of Physical Chemistry B 116, no. 23 (2012): 6872–79. http://dx.doi.org/10.1021/jp212646s.

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Stahl, Martin, and Hans-Joachim Böhm. "Development of filter functions for protein–ligand docking." Journal of Molecular Graphics and Modelling 16, no. 3 (1998): 121–32. http://dx.doi.org/10.1016/s1093-3263(98)00018-7.

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Sousa, Sérgio Filipe, Pedro Alexandrino Fernandes, and Maria João Ramos. "Protein-ligand docking: Current status and future challenges." Proteins: Structure, Function, and Bioinformatics 65, no. 1 (2006): 15–26. http://dx.doi.org/10.1002/prot.21082.

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Zhao, Yong, and Michel F. Sanner. "Protein–ligand docking with multiple flexible side chains." Journal of Computer-Aided Molecular Design 22, no. 9 (2007): 673–79. http://dx.doi.org/10.1007/s10822-007-9148-5.

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Shin, Woong-Hee, Lim Heo, Juyong Lee, Junsu Ko, Chaok Seok, and Jooyoung Lee. "LigDockCSA: Protein-ligand docking using conformational space annealing." Journal of Computational Chemistry 32, no. 15 (2011): 3226–32. http://dx.doi.org/10.1002/jcc.21905.

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Gautam, Priyanka. "Preliminary Study on the Effect of Quinolone against Rec A Protein for the Possible Role in the Treatment of Tuberculosis through Molecular Docking." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 227–32. http://dx.doi.org/10.22214/ijraset.2021.36198.

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Tuberculosis is a type of ancient, chronic disease which affects humans and caused by Mycobacterium tuberculosis. They affect the lungs and other organs. The treatment is curable but in some cases it is fatal if not treated properly. The molecular docking method was used to see the interaction of the protein with the ligand. Thus, molecular docking was used to analyse the Rec A (PDB ID 1U94) target protein with their known type of ligand by using molecular docking tools. The Rec A (PDB ID 1U94) structure of protein was downloaded through online database. The best ligand after molecular docking was Quinolone, which may act as a drug after in vitro and in vivo studies.

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Lin, Hang, and Shirley Siu. "A Hybrid Cuckoo Search and Differential Evolution Approach to Protein–Ligand Docking." International Journal of Molecular Sciences 19, no. 10 (2018): 3181. http://dx.doi.org/10.3390/ijms19103181.

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Protein–ligand docking is a molecular modeling technique that is used to predict the conformation of a small molecular ligand at the binding pocket of a protein receptor. There are many protein–ligand docking tools, among which AutoDock Vina is the most popular open-source docking software. In recent years, there have been numerous attempts to optimize the search process in AutoDock Vina by means of heuristic optimization methods, such as genetic and particle swarm optimization algorithms. This study, for the first time, explores the use of cuckoo search (CS) to solve the protein–ligand docking problem. The result of this study is CuckooVina, an enhanced conformational search algorithm that hybridizes cuckoo search with differential evolution (DE). Extensive tests using two benchmark datasets, PDBbind 2012 and Astex Diverse set, show that CuckooVina improves the docking performances in terms of RMSD, binding affinity, and success rate compared to Vina though it requires about 9–15% more time to complete a run than Vina. CuckooVina predicts more accurate docking poses with higher binding affinities than PSOVina with similar success rates. CuckooVina’s slower convergence but higher accuracy suggest that it is better able to escape from local energy minima and improves the problem of premature convergence. As a summary, our results assure that the hybrid CS–DE process to continuously generate diverse solutions is a good strategy to maintain the proper balance between global and local exploitation required for the ligand conformational search.

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Naqvi, Ahmad Abu Turab, Taj Mohammad, Gulam Mustafa Hasan, and Md Imtaiyaz Hassan. "Advancements in Docking and Molecular Dynamics Simulations Towards Ligand-receptor Interactions and Structure-function Relationships." Current Topics in Medicinal Chemistry 18, no. 20 (2018): 1755–68. http://dx.doi.org/10.2174/1568026618666181025114157.

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Protein-ligand interaction is an imperative subject in structure-based drug design and protein function prediction process. Molecular docking is a computational method which predicts the binding of a ligand molecule to the particular receptor. It predicts the binding pose, strength and binding affinity of the molecules using various scoring functions. Molecular docking and molecular dynamics simulations are widely used in combination to predict the binding modes, binding affinities and stability of different protein-ligand systems. With advancements in algorithms and computational power, molecular dynamics simulation is now a fundamental tool to investigative bio-molecular assemblies at atomic level. These methods in association with experimental support have been of great value in modern drug discovery and development. Nowadays, it has become an increasingly significant method in drug discovery process. In this review, we focus on protein-ligand interactions using molecular docking, virtual screening and molecular dynamics simulations. Here, we cover an overview of the available methods for molecular docking and molecular dynamics simulations, and their advancement and applications in the area of modern drug discovery. The available docking software and their advancement including application examples of different approaches for drug discovery are also discussed. We have also introduced the physicochemical foundations of molecular docking and simulations, mainly from the perception of bio-molecular interactions.

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Xiao, Wei, Disha Wang, Zihao Shen, Shiliang Li, and Honglin Li. "Multi-Body Interactions in Molecular Docking Program Devised with Key Water Molecules in Protein Binding Sites." Molecules 23, no. 9 (2018): 2321. http://dx.doi.org/10.3390/molecules23092321.

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Water molecules play an important role in modeling protein-ligand interactions. However, traditional molecular docking methods often ignore the impact of the water molecules by removing them without any analysis or keeping them as a static part of the proteins or the ligands. Hence, the accuracy of the docking simulations will inevitably be damaged. Here, we introduce a multi-body docking program which incorporates the fixed or the variable number of the key water molecules in protein-ligand docking simulations. The program employed NSGA II, a multi-objective optimization algorithm, to identify the binding poses of the ligand and the key water molecules for a protein. To this end, a force-field-based hydration-specific scoring function was designed to favor estimate the binding affinity considering the key water molecules. The program was evaluated in aspects of the docking accuracy, cross-docking accuracy, and screening efficiency. When the numbers of the key water molecules were treated as fixed-length optimization variables, the docking accuracy of the multi-body docking program achieved a success rate of 80.58% for the best RMSD values for the recruit of the ligands smaller than 2.0 Å. The cross-docking accuracy was investigated on the presence and absence of the key water molecules by four protein targets. The screening efficiency was assessed against those protein targets. Results indicated that the proposed multi-body docking program was with good performance compared with the other programs. On the other side, when the numbers of the key water molecules were treated as variable-length optimization variables, the program obtained comparative performance under the same three evaluation criterions. These results indicated that the multi-body docking with the variable numbers of the water molecules was also efficient. Above all, the multi-body docking program developed in this study was capable of dealing with the problem of the water molecules that explicitly participating in protein-ligand binding.

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Merugu, Ramchander, Uttam Kumar Neerudu, Karunakar Dasa, and Kalpana V. Singh. "Molecular docking studies of deacetylbisacodyl with intestinal sucrase-maltase enzyme." International Journal of Advances in Scientific Research 2, no. 12 (2017): 191. http://dx.doi.org/10.7439/ijasr.v2i12.3821.

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Molecular docking of sucrase-isomaltase with ligand deacetylbisacodyl when subjected to docking analysis using docking server, predicted in-silico result with a free energy of -3.36 Kcal/mol which was agreed well with physiological range for protein-ligand interaction, making bisacodyl probable potent anti-isomaltase molecule. According to docking server Inhibition constant is 5.98Mm. which predicts that the ligand is going to inhibits enzyme and result in a clinically relevant drug interaction with a substrate for the enzyme. Hydrogen bond with bond length 3.45is formed between Pro 64 (A) of target and of ligand, which is again indicative of the docking between target and ligand. Excellent electrostatic interactions of polar, hydrophobic, pi-pi and Van der walls are observed. The proteinligand interaction study showed 6 amino acid residues interaction with the ligand.

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Ulzurrun, Eugenia, Yorley Duarte, Esteban Perez-Wohlfeil, Fernando Gonzalez-Nilo, and Oswaldo Trelles. "PLIDflow: an open-source workflow for the online analysis of protein–ligand docking using galaxy." Bioinformatics 36, no. 14 (2020): 4203–5. http://dx.doi.org/10.1093/bioinformatics/btaa481.

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Abstract Motivation Molecular docking is aimed at predicting the conformation of small-molecule (ligands) within an identified binding site (BS) in a target protein (receptor). Protein–ligand docking plays an important role in modern drug discovery and biochemistry for protein engineering. However, efficient docking analysis of proteins requires prior knowledge of the BS, which is not always known. The process which covers BS identification and protein–ligand docking usually requires the combination of different programs, which require several input parameters. This is furtherly aggravated when factoring in computational demands, such as CPU-time. Therefore, these types of simulation experiments can become a complex process for researchers without a background in computer sciences. Results To overcome these problems, we have designed an automatic computational workflow (WF) to process protein–ligand complexes, which runs from the identification of the possible BSs positions to the prediction of the experimental binding modes and affinities of the ligand. This open-access WF runs under the Galaxy platform that integrates public domain software. The results of the proposed method are in close agreement with state-of-the-art docking software. Availability and implementation Software is available at: https://pistacho.ac.uma.es/galaxy-bitlab. Contact euv@uma.es Supplementary information Supplementary data are available at Bioinformatics online.

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Jakhar, Ritu, Mehak Dangi, Alka Khichi, and Anil Kumar Chhillar. "Relevance of Molecular Docking Studies in Drug Designing." Current Bioinformatics 15, no. 4 (2020): 270–78. http://dx.doi.org/10.2174/1574893615666191219094216.

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Molecular Docking is used to positioning the computer-generated 3D structure of small ligands into a receptor structure in a variety of orientations, conformations and positions. This method is useful in drug discovery and medicinal chemistry providing insights into molecular recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery (CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment of targets and ligand. Over the last decade, advances in the field of computational, proteomics and genomics have also led to the development of different docking methods which incorporate protein-ligand flexibility and their different binding conformations. Receptor flexibility accounts for more accurate binding pose predictions and a more rational depiction of protein binding interactions with the ligand. Protein flexibility has been included by generating protein ensembles or by dynamic docking methods. Dynamic docking considers solvation, entropic effects and also fully explores the drug-receptor binding and recognition from both energetic and mechanistic point of view. Though in the fast-paced drug discovery program, dynamic docking is computationally expensive but is being progressively used for screening of large compound libraries to identify the potential drugs. In this review, a quick introduction is presented to the available docking methods and their application and limitations in drug discovery.

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Cavasotto, Claudio N., and Ruben A. Abagyan. "Protein Flexibility in Ligand Docking and Virtual Screening to Protein Kinases." Journal of Molecular Biology 337, no. 1 (2004): 209–25. http://dx.doi.org/10.1016/j.jmb.2004.01.003.

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Jiménez-Luna, José, Alberto Cuzzolin, Giovanni Bolcato, Mattia Sturlese, and Stefano Moro. "A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection." Molecules 25, no. 11 (2020): 2487. http://dx.doi.org/10.3390/molecules25112487.

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While a plethora of different protein–ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein–ligand pair. In this study, we developed a machine-learning model that uses a combination of convolutional and fully connected neural networks for the task of predicting the performance of several popular docking protocols given a protein structure and a small compound. We also rigorously evaluated the performance of our model using a widely available database of protein–ligand complexes and different types of data splits. We further open-source all code related to this study so that potential users can make informed selections on which protocol is best suited for their particular protein–ligand pair.

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Journal articles on the topic 'Protein-ligand docking'

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