DOCKING STUDIES OF CURCUMIN AS A POTENTIAL LEAD COMPOUND TO DEVELOP NOVEL DIPEPTYDYL PEPTIDASE-4 INHIBITORS
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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)Keywords:
AutoDock
Docking (animal)
Lead compound
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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DOCK
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On the quest of novel therapeutics, molecular docking methods have proven to be valuable tools for screening large libraries of compounds determining the interactions of potential drugs with the target proteins. A widely used docking approach is the simulation of the docking process guided by a binding energy function. On the basis of the molecular docking program autodock , we present pso @ autodock as a tool for fast flexible molecular docking. Our novel Particle Swarm Optimization (PSO) algorithms var CPSO and var CPSO‐ls are suited for rapid docking of highly flexible ligands. Thus, a ligand with 23 rotatable bonds was successfully docked within as few as 100 000 computing steps (rmsd = 0.87 Å), which corresponds to only 10% of the computing time demanded by autodock . In comparison to other docking techniques as gold 3.0, dock 6.0, flexx 2.2.0, autodock 3.05, and sodock , pso @ autodock provides the smallest rmsd values for 12 in 37 protein–ligand complexes. The average rmsd value of 1.4 Å is significantly lower then those obtained with the other docking programs, which are all above 2.0 Å. Thus, pso @ autodock is suggested as a highly efficient docking program in terms of speed and quality for flexible peptide–protein docking and virtual screening studies.
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Protein–ligand docking
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ABSTRACT In the past, many benchmarking studies have been performed on protein-protein and protein-ligand docking however there is no study on peptide-ligand docking. In this study, we evaluated the performance of seven widely used docking methods (AutoDock, AutoDock Vina, DOCK 6, PLANTS, rDock, GEMDOCK and GOLD) on a dataset of 57 peptide-ligand complexes. Though these methods have been developed for docking ligands to proteins but we evaluate their ability to dock ligands to peptides. First, we compared TOP docking pose of these methods with original complex and achieved average RMSD from 4.74Å for AutoDock to 12.63Å for GEMDOCK. Next we evaluated BEST docking pose of these methods and achieved average RMSD from 3.82Å for AutoDock to 10.83Å for rDock. It has been observed that ranking of docking poses by these methods is not suitable for peptide-ligand docking as performance of their TOP pose is much inferior to their BEST pose. AutoDock clearly shows better performance compared to the other six docking methods based on their TOP docking poses. On the other hand, difference in performance of different docking methods (AutoDock, AutoDock Vina, PLANTS and DOCK 6) was marginal when evaluation was based on their BEST docking pose. Similar trend has been observed when performance is measured in terms of success rate at different cut-off values. In order to facilitate scientific community a web server PLDbench has been developed ( http://webs.iiitd.edu.in/raghava/pldbench/ ).
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A new application of DrugScore is reported in which the knowledge-based pair potentials serve as objective function in docking optimizations. The Lamarckian genetic algorithm of AutoDock is used to search for favorable ligand binding modes guided by DrugScore grids as representations of the protein binding site. The approach is found to be successful in many cases where DrugScore-based re-ranking of already docked ligand conformations does not yield satisfactory results. Compared to the AutoDock scoring function, DrugScore yields slightly superior results in flexible docking.
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Protein–ligand docking
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INTRODUCTION Computer prediction of the interaction between enzymes and small molecules has now advanced to the point that it allows accurate prediction of bound conformations and binding constants. For instance, the program AutoDock allows consistent computational docking of flexible ligands with about a dozen torsional degrees of freedom, and the empirical free-energy force field provides predicted energies that are accurate to within ~2 kcal/mol, or an ~30-fold difference in binding constants. Thus, these methods can easily separate compounds with micromolar and nanomolar binding constants from those with millimolar binding constants, and can often rank molecules with finer differences in affinity. Computational docking methods can be used to screen a variety of possible compounds, searching for new compounds with specific binding properties or testing a range of modifications of an existing compound. The approach has been successful in numerous cases, most notably, the discovery of human immunodeficiency virus (HIV) protease inhibitors. This protocol presents a detailed outline and advice for use of AutoDock and its graphical interface, AutoDock Tools, to analyze biomolecular complexes using computational docking. The first step is to prepare the coordinate files for the docking molecule and the target molecule. The second step is the calculation of the affinity grid for the target molecule. In the third step, the docking molecule is docked with the affinity grid, and, finally, the results are analyzed.
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Using the popular program AutoDock, computer-aided docking of small ligands with 6 or fewer rotatable bonds, is reasonably fast and accurate. However, docking large ligands using AutoDock's recommended standard docking protocol is less accurate and computationally slow. In our earlier work, we presented a novel AutoDock-based incremental protocol (DINC) that addresses the limitations of AutoDock's standard protocol by enabling improved docking of large ligands. Instead of docking a large ligand to a target protein in one single step as done in the standard protocol, our protocol docks the large ligand in increments. In this paper, we present three detailed examples of docking using DINC and compare the docking results with those obtained using AutoDock's standard protocol. We summarize the docking results from an extended docking study that was done on 73 protein-ligand complexes comprised of large ligands. We demonstrate not only that DINC is up to 2 orders of magnitude faster than AutoDock's standard protocol, but that it also achieves the speed-up without sacrificing docking accuracy. We also show that positional restraints can be applied to the large ligand using DINC: this is useful when computing a docked conformation of the ligand. Finally, we introduce a webserver for docking large ligands using DINC. Docking large ligands using DINC is significantly faster than AutoDock's standard protocol without any loss of accuracy. Therefore, DINC could be used as an alternative protocol for docking large ligands. DINC has been implemented as a webserver and is available at http://dinc.kavrakilab.org . Applications such as therapeutic drug design, rational vaccine design, and others involving large ligands could benefit from DINC and its webserver implementation.
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Protein–ligand docking
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AutoDock
Docking (animal)
Protein–ligand docking
Drug Design
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It is well known that computer-aided docking of large ligands, with many rotatable bonds, is extremely difficult. AutoDock is a widely used docking program that can dock small ligands, with upto 5 or 6 rotatable bonds, accurately and quickly. Docking of larger ligands, however, is not very accurate and is computationally expensive. In this paper we present an AutoDock-based incremental docking protocol which docks a large ligand to its target protein in increments. A fragment of the large ligand is first chosen and then docked. Best docked conformations are incrementally grown and docked again, and this process is repeated until all the atoms of the ligand are docked. Each docking operation is performed using AutoDock. However, in each docking operation only a small number of rotatable bonds are allowed to rotate. We did a systematic docking study on a dataset of 73 protein-ligand complexes derived from the core set of PDBbind database. The number of rotatable bonds in the ligands vary from 7 to 30. Docking experiments were done to evaluate the docking performance of the incremental protocol in comparison to AutoDock's standard protocol. Results from the study show that, on average over the dataset, docking of large ligands using our incremental protocol is 23-fold computationally faster than docking using AutoDock's standard protocol and also has comparable or better accuracy. We propose that, for docking large ligands, our incremental protocol can be used as an alternative to AutoDock's standard protocol.
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Protein–ligand docking
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Reliability in docking of ligand molecules to proteins or other targets is an important challenge for molecular modeling. Applications of the docking technique include not only prediction of the binding mode of novel drugs, but also other problems like the study of protein-protein interactions. Here we present a study on the reliability of the results obtained with the popular AutoDock program. We have performed systematical studies to test the ability of AutoDock to reproduce eight different protein/ligand complexes for which the structure was known, without prior knowledge of the binding site. More specifically, we look at factors influencing the accuracy of the final structure, such as the number of torsional degrees of freedom in the ligand. We conclude that the Autodock program package is able to select the correct complexes based on the energy without prior knowledge of the binding site. We named this application blind docking, as the docking algorithm is not able to "see" the binding site but can still find it. The success of blind docking represents an important finding in the era of structural genomics.
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Protein–ligand docking
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Molecular docking is a widely used method to predict the binding modes of small-molecule ligands to the target binding site. However, it remains a challenge to identify the correct binding conformation and the corresponding binding affinity for a series of structurally similar ligands, especially those with weak binding. An understanding of the various relative attributes of popular docking programs is required to ensure a successful docking outcome. In this study, we systematically compared the performance of three popular docking programs, Autodock, Autodock Vina, and Surflex-Dock for a series of structurally similar weekly binding flavonoids (22) binding to the estrogen receptor alpha (ERα). For these flavonoids-ERα interactions, Surflex-Dock showed higher accuracy than Autodock and Autodock Vina. The hydrogen bond overweighting by Autodock and Autodock Vina led to incorrect binding results, while Surflex-Dock effectively balanced both hydrogen bond and hydrophobic interactions. Moreover, the selection of initial receptor structure is critical as it influences the docking conformations of flavonoids-ERα complexes. The flexible docking method failed to further improve the docking accuracy of the semi-flexible docking method for such chemicals. In addition, binding interaction analysis revealed that 8 residues, including Ala350, Glu353, Leu387, Arg394, Phe404, Gly521, His524, and Leu525, are the key residues in ERα-flavonoids complexes. This work provides reference for assessing molecular interactions between ERα and flavonoid-like chemicals and provides instructive information for other environmental chemicals.
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