DATABASE ENRICHMENTS OF MAO-B THROUGH ENSEMBLE DOCKING

Objective: The recent growth of highly resoluted crystallographic structures, together with the continuous improvements of the computing power, has established molecular docking as a leading drug design technique. However, the problems concerning the receptor flexibility and the lowered ability of docking software to correctly score the occurred interactions in some receptors are still relevant. Methods: Recently, several research groups have reported an enhancement in enrichment values when ensemble docking has been applied. Therefore, we utilized the latest technique for a dataset of Monoamine Oxidase–B (MAO-B) inhibitors. The docking program GOLD 5.3 was used in our study. Several docking parameters (grid space, scoring functions and ligand flexibility) were altered in order to achieve the optimal docking protocol. Results: The results of 200 000+docking simulations are represented in a modest table. The ensembled simulations demonstrated low ability of the docking software to correctly score the actives seeded in the dataset. However, the superimposed complex-1S3B-1OJA-1OJC, achieved a moderate enrichment value equaled to 9. No significant improvements were noted when five complexed receptors were employed. Conclusion: As a conclusion, it should be noted that in some cases the ensemble docking enhanced the database enrichments, however overall the value is not suitable for future virtual screening. Further investigations in that area should be considered.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

MotivationRecent efforts to computationally identify inhibitors for SARS-CoV-2 proteins have largely ignored the issue of receptor flexibility. We have implemented a computational tool for ensemble docking with the SARS-CoV-2 proteins, including the main protease (Mpro), papain-like protease (PLpro) and RNA-dependent RNA polymerase (RdRp).ResultsEnsembles of other SARS-CoV-2 proteins are being prepared and made available through a user-friendly docking interface. Plausible binding modes between conformations of a selected ensemble and an uploaded ligand are generated by DINC, our parallelized meta-docking tool. Binding modes are scored with three scoring functions, and account for the flexibility of both the ligand and receptor. Additional details on our methods are provided in the supplementary material.Availabilitydinc-covid.kavrakilab.orgSupplementary informationDetails on methods for ensemble generation and docking are provided as supplementary data [email protected], [email protected]

Heteroditopic receptor flexibility – an important design principle for effective ion pair extractants based on carboxylates studies

Despite the continuous development of heteroditopic molecular receptors with the ability to transfer salts from the aqueous to the organic phase as symport, the factors contributing to the effectiveness of...

Receptor dynamics in molecular recognition by Cryo-EM and molecular simulation

: The appropriate selection of initial receptor structure has been the "cornerstone" or foundation of successful structure-based virtual screening (SBVS), and plagued the structure-based design with a significantly practical problem to determine the major physiological states or important transition states of receptors (e.g. proteins with multiple low-energy conformations and ligand-dependent conformational dynamics). It is well known that current SBVS methods lack capacity to capture and characterize the intrinsic receptor flexibility with ideal cost-effectiveness. In recent years, cryoelectron microscopy (cryo-EM) has been routinely applied in the determination of biomolecular assemblies within physiological state. In this work, we review the roles of cryo-EM and ensemble docking methods to present the intrinsically dynamic behavior of biomacromolecules, as well as the ever-improving estimation of ligand binding affinities and receptor-ligand thermodynamics. Finally, we also provide an attitude for the further researches on the modeling receptor dynamics.

Retrospective Ensemble Docking of Allosteric Modulators in an Adenosine G-Protein-Coupled Receptor

BackgroundEnsemble 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.

Improving ensemble docking for drug discovery by machine learning

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.