Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

Bartuzi, Damian; Kaczor, Agnieszka A.; Targowska-Duda, Katarzyna M.; Matosiuk, Dariusz

doi:10.3390/molecules22020340

Cited by 57 publications

(35 citation statements)

References 166 publications

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“…The type of molecule to be evaluated and its physico-chemical characteristics also offer different challenges for virtual screening, in terms of docking and its ability to discriminate between actives and decoys. For each specific target, the decoys included in the DUD-E were generated by having similar 1-D physico-chemical properties to the actives from which they originated, to remove bias [32]. Hence, to analyze how the different substrate properties affected the discriminating ability of each target, the physical properties of all actives identified in the ligands ranked as the top 1% were evaluated and compared with the other actives that were ranked the worst.…”

Section: Substratesmentioning

confidence: 99%

“…In this study, two of the most commonly used docking tools-AutoDock (version 4.2.6) and Vina (AutoDock Vina)-were evaluated for different types of targets and ligands, using an unbiased reference validation set-Directory of Useful Decoys-Enhanced (DUD-E) [31]. Both docking programs are widely used to this day, for a large diversity of targets and problems [32][33][34][35][36][37][38][39].AutoDock 4 is a well-known docking program developed by Morris and co-workers [40][41][42][43] at the Scripps Research Institute. Its free availability to academic users, together with the good accuracy and high versatility shown, had made it a very popular first choice for new users and have contributed to a widespread use of AutoDock, well portrayed in its impressively high number of citations.…”

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Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Vieira

Sousa

2019

Applied Sciences

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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.of the poses previously created, discriminating between the best and not so good alternatives [1,5]. This estimate, which in some cases is a prediction of the free energy of binding, must be able to discriminate between molecules that bind to the target and those that do not [23]. When looking at the two enantiomers, for example, it is still not possible to identify the most active form with most of the scoring functions used by the most common docking software [24].Even with all the significant improvements in computational power and docking software, considering all interactions that happen when a ligand binds to its target is an extremely challenging task. In order to be rigorous, the scoring functions would have to be much more complex, involve quantum calculations and, thus, these assays would turn out to be considerably expensive and time-consuming. When applying a virtual screening protocol, one wishes to screen very large databases of compounds in a relatively small period of time and, therefore, scoring functions are often simplified to improve the speed and cost of the computational screenings [6]. These simplifications come with a cost in accuracy, which might not be problematic for one ligand-target situation but takes a much more challenging scope when talking about virtual screening of thousands or millions of compounds [5].The goal of virtual screening (VS) is to guide the selection of molecules for experimental testing. In these assays, millions of compounds are docked into one specific target and only a selection of the top scores proceeds for experimental testing. If a scoring functions fails to identify a potential strong binder, then, it remains hidden among those million compounds, despite their pharmacological potential. In fact, that is one of the main problems in VS, the false negatives, or molecules that the docki...

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Section: Substratesmentioning

confidence: 99%

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confidence: 99%

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Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Vieira

Sousa

2019

Applied Sciences

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“…There are few available methods that enable docking small molecules to GPCRs [7][8][9][10]. These may also be applied to short peptides, up to 5 residues [11].…”

Section: Introductionmentioning

confidence: 99%

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

Badaczewska-Dawid

Kmiecik

Koliński

2020

Preprint

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The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein-peptide complex structures and model scoring. We evaluated the proposed protocol on the set of 7 different GPCR-peptide complexes (including one containing a cyclic peptide) for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface RMSD measure.

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“…In this setting, structure-based computational methods may be used to accelerate the discovery of high affinity ligands. [2][3][4][5][6][7][8][9][10][11][12][13] The computational challenge of structure-based ligand design may be considered to comprise two main components. The first is prediction of the bound conformation, or pose, of a candidate ligand, typically by fast, ligand-protein docking algorithms.…”

Section: Introductionmentioning

confidence: 99%

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

Wagner

Churas

Liu

et al. 2018

Preprint

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SUMMARYDocking calculations can be used to accelerate drug discovery by providing predictions of the poses of candidate ligands bound to a targeted protein. However, studies in the literature use varied docking methods, and it is not clear which work best, either in general or for specific protein targets. In addition, a complete docking calculation requires components beyond the docking algorithm itself, such as preparation of the protein and ligand for calculations, and it is difficult to isolate which aspects of a method are most in need of improvement. To address such issues, we have developed the Continuous Evaluation of Ligand Protein Predictions (CELPP), a weekly blinded challenge for automated docking workflows. Participants in CELPP create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new (never before released) protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. CELPP is a new cyberinfrastructure resource to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

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Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

Cited by 57 publications

References 166 publications

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

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