scholarly journals Haptic-Assisted Interactive Molecular Docking Incorporating Receptor Flexibility

2019 ◽  
Vol 59 (6) ◽  
pp. 2900-2912 ◽  
Author(s):  
Nick Matthews ◽  
Akio Kitao ◽  
Stephen Laycock ◽  
Steven Hayward
2016 ◽  
Author(s):  
Lucia Sessa ◽  
Luigi Di BIasi ◽  
Rosaura Parisi ◽  
Simona Concilio ◽  
Stefano Piotto

Motivation Molecular docking is an efficient method to predict the conformations adopted by the ligand within the target binding site. Usually, standard docking protocol involves only one structure to represent the receptor, overlooking the changes in the binding pocket geometry induced by ligand binding. In our previous work, we observed that different conformations of the same target show different volume and shape of the internal cavities (Sessa et al., 2016). Different ligands may stabilize different receptor conformations with different internal cavities. Consequently, the crystallographic data represent the adaptation of a protein to a particular ligand. Cross-docking is a validation procedure consisting in docking a series of ligands into different conformation of the same receptor. Since the structures of the same receptor can be rather different, the cross-docking analyses are typically very poor. In these cases the internal cavity of the buried binding pocket does not have space enough to accommodate all ligands and this can radically affect the outcome and alter the cross-docking results. The changes of the cavity volume might explain the failure of traditional docking method and support the hypothesis that a single representative structure for the receptor is not enough. Keeping target proteins flexible during the docking has a high computational cost. To overcome this limit, our docking strategy is to represent receptor flexibility through an inexpensive method that generates a series of target structures. Starting from a known target structure, we used the molecular dynamics (MD) simulations to explore the conformational changes induced by ligand binding and to collect several snapshots of receptor structures to perform the cross-docking studies. To validate the accuracy of our flexible protocol in docking, we used a set of 10 crystallographic conformations of Androgen Receptor with the same target but with a different ligand. We performed two parallel experiments of docking, one with a rigid protein target and one considering flexible receptor structures. In addition, we compared the results for both experiments in the re-docking and in the cross-docking analysis. Methods Ten receptor structures complexed with a ligand were extracted from the X-ray structures in the PDB database (Berman et al., 2000). Several conformations for each receptor were selected from the molecular dynamics simulations (MD) at regular time intervals (each 500 ps). The MD simulations were performed with the software YASARA Structure 16.2.14 (Krieger & Vriend, 2014) using AMBER14 as force field. The molecular docking simulations were performed using VINA provided in the YASARA package. "Abstract truncated at 3,000 characters - the full version is available in the pdf file"


2020 ◽  
Author(s):  
Apurba Bhattarai ◽  
Jinan Wang ◽  
Yinglong Miao

AbstractBackgroundEnsemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A1 receptor (A1AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A1AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A1AR.MethodsEnsemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A1AR.ResultsEnsemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased.ConclusionsReceptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A1AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking.General SignificanceEnsemble docking is a promising approach for drug discovery targeting flexible receptors.


2016 ◽  
Author(s):  
Lucia Sessa ◽  
Luigi Di BIasi ◽  
Rosaura Parisi ◽  
Simona Concilio ◽  
Stefano Piotto

Motivation Molecular docking is an efficient method to predict the conformations adopted by the ligand within the target binding site. Usually, standard docking protocol involves only one structure to represent the receptor, overlooking the changes in the binding pocket geometry induced by ligand binding. In our previous work, we observed that different conformations of the same target show different volume and shape of the internal cavities (Sessa et al., 2016). Different ligands may stabilize different receptor conformations with different internal cavities. Consequently, the crystallographic data represent the adaptation of a protein to a particular ligand. Cross-docking is a validation procedure consisting in docking a series of ligands into different conformation of the same receptor. Since the structures of the same receptor can be rather different, the cross-docking analyses are typically very poor. In these cases the internal cavity of the buried binding pocket does not have space enough to accommodate all ligands and this can radically affect the outcome and alter the cross-docking results. The changes of the cavity volume might explain the failure of traditional docking method and support the hypothesis that a single representative structure for the receptor is not enough. Keeping target proteins flexible during the docking has a high computational cost. To overcome this limit, our docking strategy is to represent receptor flexibility through an inexpensive method that generates a series of target structures. Starting from a known target structure, we used the molecular dynamics (MD) simulations to explore the conformational changes induced by ligand binding and to collect several snapshots of receptor structures to perform the cross-docking studies. To validate the accuracy of our flexible protocol in docking, we used a set of 10 crystallographic conformations of Androgen Receptor with the same target but with a different ligand. We performed two parallel experiments of docking, one with a rigid protein target and one considering flexible receptor structures. In addition, we compared the results for both experiments in the re-docking and in the cross-docking analysis. Methods Ten receptor structures complexed with a ligand were extracted from the X-ray structures in the PDB database (Berman et al., 2000). Several conformations for each receptor were selected from the molecular dynamics simulations (MD) at regular time intervals (each 500 ps). The MD simulations were performed with the software YASARA Structure 16.2.14 (Krieger & Vriend, 2014) using AMBER14 as force field. The molecular docking simulations were performed using VINA provided in the YASARA package. "Abstract truncated at 3,000 characters - the full version is available in the pdf file"


BMC Genomics ◽  
2011 ◽  
Vol 12 (Suppl 4) ◽  
pp. S6 ◽  
Author(s):  
Karina S Machado ◽  
Evelyn K Schroeder ◽  
Duncan D Ruiz ◽  
Elisângela ML Cohen ◽  
Osmar Norberto de Souza

2019 ◽  
Vol 18 (03) ◽  
pp. 1920001 ◽  
Author(s):  
Chung F. Wong

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking. However, it is still unclear how best to use the docking scores from multiple structures to classify compounds into actives and inactives. Previous studies have also found that the performance of classification could decrease rather than increase with the number of structures included in the ensemble. Machine learning could help to alleviate these problems.


2015 ◽  
Author(s):  
Manik Ghosh ◽  
Kamal Kant ◽  
Anoop Kumar ◽  
Padma Behera ◽  
Naresh Rangra ◽  
...  

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