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Alexander A Kantardjiev - One of the best experts on this subject based on the ideXlab platform.
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Quantum.Ligand.Dock: Protein-Ligand Docking with quantum entanglement refinement on a GPU system.
Nucleic acids research, 2012Co-Authors: Alexander A KantardjievAbstract:Quantum.Ligand.Dock (Protein-Ligand Docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of Protein-Ligand interactions via high-performance Docking code. The main flavour of our approach is a combination of fast search with a special account for overlooked physical interactions. On the one hand, we take care of self-consistency and proton equilibria mutual effects of Docking partners. On the other hand, Quantum.Ligand.Dock is the the only Docking server offering such a subtle supplement to protein Docking algorithms as quantum entanglement contributions. The motivation for development and proposition of the method to the community hinges upon two arguments-the fundamental importance of quantum entanglement contribution in molecular interaction and the realistic possibility to implement it by the availability of supercomputing power. The implementation of sophisticated quantum methods is made possible by parallelization at several bottlenecks on a GPU supercomputer. The high-performance implementation will be of use for large-scale virtual screening projects, structural bioinformatics, systems biology and fundamental research in understanding Protein-Ligand recognition. The design of the interface is focused on feasibility and ease of use. Protein and ligand molecule structures are supposed to be submitted as atomic coordinate files in PDB format. A customization section is offered for addition of user-specified charges, extra ionogenic groups with intrinsic pK(a) values or fixed ions. Final predicted complexes are ranked according to obtained scores and provided in PDB format as well as interactive visualization in a molecular viewer. Quantum.Ligand.Dock server can be accessed at http://87.116.85.141/LigandDock.html.
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Quantum.Ligand.Dock: protein–ligand Docking with quantum entanglement refinement on a GPU system
Nucleic Acids Research, 2012Co-Authors: Alexander A KantardjievAbstract:Quantum.Ligand.Dock (protein–ligand Docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of protein–ligand interactions via high-performance Docking code. The main flavour of our approach is a combination of fast search with a special account for overlooked physical interactions. On the one hand, we take care of self-consistency and proton equilibria mutual effects of Docking partners. On the other hand, Quantum.Ligand.Dock is the the only Docking server offering such a subtle supplement to protein Docking algorithms as quantum entanglement contributions. The motivation for development and proposition of the method to the community hinges upon two arguments—the fundamental importance of quantum entanglement contribution in molecular interaction and the realistic possibility to implement it by the availability of supercomputing power. The implementation of sophisticated quantum methods is made possible by parallelization at several bottlenecks on a GPU supercomputer. The high-performance implementation will be of use for large-scale virtual screening projects, structural bioinformatics, systems biology and fundamental research in understanding protein–ligand recognition. The design of the interface is focused on feasibility and ease of use. Protein and ligand molecule structures are supposed to be submitted as atomic coordinate files in PDB format. A customization section is offered for addition of user-specified charges, extra ionogenic groups with intrinsic pKa values or fixed ions. Final predicted complexes are ranked according to obtained scores and provided in PDB format as well as interactive visualization in a molecular viewer. Quantum.Ligand.Dock server can be accessed at http://87.116.85.141/LigandDock.html.
Xiaoqin Zou - One of the best experts on this subject based on the ideXlab platform.
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Challenges, Applications, and Recent Advances of Protein-Ligand Docking in Structure-Based Drug Design
Molecules (Basel Switzerland), 2014Co-Authors: Sam Z. Grinter, Xiaoqin ZouAbstract:The Docking methods used in structure-based virtual database screening offer the ability to quickly and cheaply estimate the affinity and binding mode of a ligand for the protein receptor of interest, such as a drug target. These methods can be used to enrich a database of compounds, so that more compounds that are subsequently experimentally tested are found to be pharmaceutically interesting. In addition, like all virtual screening methods used for drug design, structure-based virtual screening can focus on curated libraries of synthesizable compounds, helping to reduce the expense of subsequent experimental verification. In this review, we introduce the Protein-Ligand Docking methods used for structure-based drug design and other biological applications. We discuss the fundamental challenges facing these methods and some of the current methodological topics of interest. We also discuss the main approaches for applying Protein-Ligand Docking methods. We end with a discussion of the challenging aspects of evaluating or benchmarking the accuracy of Docking methods for their improvement, and discuss future directions.
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Advances and Challenges in Protein-Ligand Docking
International journal of molecular sciences, 2010Co-Authors: Sheng-you Huang, Xiaoqin ZouAbstract:Molecular Docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular Docking is Protein-Ligand Docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in Protein-Ligand Docking. Challenges and possible future directions are discussed in the Conclusion.
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Scoring functions and their evaluation methods for Protein-Ligand Docking: Recent advances and future directions
Physical Chemistry Chemical Physics, 2010Co-Authors: Sheng-you Huang, Sam Z. Grinter, Xiaoqin ZouAbstract:The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of Protein-Ligand interactions is still a challenge in molecular Docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for Protein-Ligand Docking. The commonly-used assessment criteria and publicly available Protein-Ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.
Chaok Seok - One of the best experts on this subject based on the ideXlab platform.
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GalaxyDock3: Protein–ligand Docking that considers the full ligand conformational flexibility
Journal of computational chemistry, 2019Co-Authors: Jinsol Yang, Minkyung Baek, Chaok SeokAbstract:Predicting conformational changes of both the protein and the ligand is a major challenge when a Protein-Ligand complex structure is predicted from the unbound protein and ligand structures. Herein, we introduce a new Protein-Ligand Docking program called GalaxyDock3 that considers the full ligand conformational flexibility by explicitly sampling the ligand ring conformation and allowing the relaxation of the full ligand degrees of freedom, including bond angles and lengths. This method is based on the previous version (GalaxyDock2) which performs the global optimization of a designed score function. Ligand ring conformation is sampled from a ring conformation library constructed from structure databases. The GalaxyDock3 score function was trained with an additional bonded energy term for the ligand on a large set of complex structures. The performance of GalaxyDock3 was improved compared to GalaxyDock2 when predicted ligand conformation was used as the input for Docking, especially when the input ligand conformation differs significantly from the crystal conformation. GalaxyDock3 also compared favorably with other available Docking programs on two benchmark tests that contained diverse ligand rings. The program is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html. © 2019 Wiley Periodicals, Inc.
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GalaxyDock BP2 score: a hybrid scoring function for accurate Protein-Ligand Docking.
Journal of computer-aided molecular design, 2017Co-Authors: Minkyung Baek, Woong-hee Shin, Hwan Won Chung, Chaok SeokAbstract:Protein–ligand Docking is a useful tool for providing atomic-level understanding of protein functions in nature and design principles for artificial ligands or proteins with desired properties. The ability to identify the true binding pose of a ligand to a target protein among numerous possible candidate poses is an essential requirement for successful protein–ligand Docking. Many previously developed Docking scoring functions were trained to reproduce experimental binding affinities and were also used for scoring binding poses. However, in this study, we developed a new Docking scoring function, called GalaxyDock BP2 Score, by directly training the scoring power of binding poses. This function is a hybrid of physics-based, empirical, and knowledge-based score terms that are balanced to strengthen the advantages of each component. The performance of the new scoring function exhibits significant improvement over existing scoring functions in decoy pose discrimination tests. In addition, when the score is used with the GalaxyDock2 protein–ligand Docking program, it outperformed other state-of-the-art Docking programs in Docking tests on the Astex diverse set, the Cross2009 benchmark set, and the Astex non-native set. GalaxyDock BP2 Score and GalaxyDock2 with this score are freely available at http://galaxy.seoklab.org/softwares/galaxydock.html.
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GalaxyDock2: Protein–ligand Docking using beta‐complex and global optimization
Journal of computational chemistry, 2013Co-Authors: Woong-hee Shin, Jae-kwan Kim, Deok-soo Kim, Chaok SeokAbstract:In this article, an enhanced version of GalaxyDock protein–ligand Docking program is introduced. GalaxyDock performs conformational space annealing (CSA) global optimization to find the optimal binding pose of a ligand both in the rigid-receptor mode and the flexible-receptor mode. Binding pose prediction has been improved compared to the earlier version by the efficient generation of high-quality initial conformations for CSA using a preDocking method based on a beta-complex derived from the Voronoi diagram of receptor atoms. Binding affinity prediction has also been enhanced by using the optimal combination of energy components, while taking into consideration the energy of the unbound ligand state. The new version has been tested in terms of binding mode prediction, binding affinity prediction, and virtual screening on several benchmark sets, showing improved performance over the previous version and AutoDock, on which the GalaxyDock energy function is based. GalaxyDock2 also performs better than or comparable to other state-of-the-art Docking programs. GalaxyDock2 is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html. © 2013 Wiley Periodicals, Inc.
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GalaxyDock: Protein–Ligand Docking with Flexible Protein Side-chains
Journal of chemical information and modeling, 2012Co-Authors: Woong-hee Shin, Chaok SeokAbstract:An important issue in developing protein–ligand Docking methods is how to incorporate receptor flexibility. Consideration of receptor flexibility using an ensemble of precompiled receptor conformations or by employing an effectively enlarged binding pocket has been reported to be useful. However, direct consideration of receptor flexibility during energy optimization of the docked conformation has been less popular because of the large increase in computational complexity. In this paper, we present a new Docking program called GalaxyDock that accounts for the flexibility of preselected receptor side-chains by global optimization of an AutoDock-based energy function trained for flexible side-chain Docking. This method was tested on 3 sets of protein–ligand complexes (HIV-PR, LXRβ, cAPK) and a diverse set of 16 proteins that involve side-chain conformational changes upon ligand binding. The cross-Docking tests show that the performance of GalaxyDock is higher or comparable to previous flexible Docking metho...
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LigDockCSA: Protein-Ligand Docking using conformational space annealing.
Journal of computational chemistry, 2011Co-Authors: Woong-hee Shin, Chaok Seok, Lim Heo, Juyong Lee, Jooyoung LeeAbstract:Protein–ligand Docking techniques are one of the essential tools for structure-based drug design. Two major components of a successful Docking program are an efficient search method and an accurate scoring function. In this work, a new Docking method called LigDockCSA is developed by using a powerful global optimization technique, conformational space annealing (CSA), and a scoring function that combines the AutoDock energy and the piecewise linear potential (PLP) torsion energy. It is shown that the CSA search method can find lower energy binding poses than the Lamarckian genetic algorithm of AutoDock. However, lower-energy solutions CSA produced with the AutoDock energy were often less native-like. The loophole in the AutoDock energy was fixed by adding a torsional energy term, and the CSA search on the refined energy function is shown to improve the Docking performance. The performance of LigDockCSA was tested on the Astex diverse set which consists of 85 protein–ligand complexes. LigDockCSA finds the best scoring poses within 2 A root-mean-square deviation (RMSD) from the native structures for 84.7% of the test cases, compared to 81.7% for AutoDock and 80.5% for GOLD. The results improve further to 89.4% by incorporating the conformational entropy. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011
Shirley W. I. Siu - One of the best experts on this subject based on the ideXlab platform.
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Chaos-embedded particle swarm optimization approach for Protein-Ligand Docking and virtual screening
Journal of cheminformatics, 2018Co-Authors: Hio Kuan Tai, Siti Azma Jusoh, Shirley W. I. SiuAbstract:Protein-Ligand Docking programs are routinely used in structure-based drug design to find the optimal binding pose of a ligand in the protein’s active site. These programs are also used to identify potential drug candidates by ranking large sets of compounds. As more accurate and efficient Docking programs are always desirable, constant efforts focus on developing better Docking algorithms or improving the scoring function. Recently, chaotic maps have emerged as a promising approach to improve the search behavior of optimization algorithms in terms of search diversity and convergence speed. However, their effectiveness on Docking applications has not been explored. Herein, we integrated five popular chaotic maps—logistic, Singer, sinusoidal, tent, and Zaslavskii maps—into PSOVina $$^{{\mathrm{2LS}}}$$ 2 LS , a recent variant of the popular AutoDock Vina program with enhanced global and local search capabilities, and evaluated their performances in ligand pose prediction and virtual screening using four Docking benchmark datasets and two virtual screening datasets. Pose prediction experiments indicate that chaos-embedded algorithms outperform AutoDock Vina and PSOVina in ligand pose RMSD, success rate, and run time. In virtual screening experiments, Singer map-embedded PSOVina $$^{{\mathrm{2LS}}}$$ 2 LS achieved a very significant five- to sixfold speedup with comparable screening performances to AutoDock Vina in terms of area under the receiver operating characteristic curve and enrichment factor. Therefore, our results suggest that chaos-embedded PSOVina methods might be a better option than AutoDock Vina for Docking and virtual screening tasks. The success of chaotic maps in Protein-Ligand Docking reveals their potential for improving optimization algorithms in other search problems, such as protein structure prediction and folding. The Singer map-embedded PSOVina $$^{{\mathrm{2LS}}}$$ 2 LS which is named PSOVina-2.0 and all testing datasets are publicly available on https://cbbio.cis.umac.mo/software/psovina .
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A Hybrid Cuckoo Search and Differential Evolution Approach to Protein⁻Ligand Docking.
International journal of molecular sciences, 2018Co-Authors: Hang Lin, Shirley W. I. SiuAbstract: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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CEC - Improving the efficiency of PSOVina for Protein-Ligand Docking by two-stage local search
2016 IEEE Congress on Evolutionary Computation (CEC), 2016Co-Authors: Hio Kuan Tai, Hang Lin, Shirley W. I. SiuAbstract:Protein-Ligand Docking programs are valuable tools in the modern drug discovery process for predicting the complex structure of a small molecule ligand and the target protein. Often, the configurational search algorithm in the Docking tool consists of global search and local search. The former is to explore widely for promising regions in the search space and the latter is to optimize a candidate solution to a local optimum. However, accurate local search methods such as gradient-based Newton methods are very costly. In this investigation, we present a new approach to enhance the time efficiency of a Docking program by introducing a two-stage local search method. Given a candidate solution, a rough local search is performed in the first stage to determine the potentiality of the solution. Only if the solution is promising, the second stage with a full local search will be performed. Our method has been realized in the PSOVina Docking program and tested on two data sets. The experimental results show that two-stage local search achieves almost 2x speedup to conventional one-stage method, it also enhances the prediction performance of the Docking method in terms of increased success rate and RMSD.
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PSOVina: The hybrid particle swarm optimization algorithm for Protein-Ligand Docking.
Journal of bioinformatics and computational biology, 2015Co-Authors: Simon Fong, Shirley W. I. SiuAbstract: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 .
Sheng-you Huang - One of the best experts on this subject based on the ideXlab platform.
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Advances and Challenges in Protein-Ligand Docking
International journal of molecular sciences, 2010Co-Authors: Sheng-you Huang, Xiaoqin ZouAbstract:Molecular Docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular Docking is Protein-Ligand Docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in Protein-Ligand Docking. Challenges and possible future directions are discussed in the Conclusion.
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Scoring functions and their evaluation methods for Protein-Ligand Docking: Recent advances and future directions
Physical Chemistry Chemical Physics, 2010Co-Authors: Sheng-you Huang, Sam Z. Grinter, Xiaoqin ZouAbstract:The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of Protein-Ligand interactions is still a challenge in molecular Docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for Protein-Ligand Docking. The commonly-used assessment criteria and publicly available Protein-Ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.
