Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

Paris, Renata De; Quevedo, Christian Vahl; Ruiz, Duncan D.; Souza, Osmar Norberto de; Barros, Rodrigo C.

doi:10.1155/2015/916240

Cited by 52 publications

(38 citation statements)

References 34 publications

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“…We used similar ideas as the approach taken in Ref. (66), focusing on the binding site’s structural fluctuations rather than the entire structure. All six clustering methods (TICA, PCA, and Gromos for backbone or C-alpha atoms and CBA) were also performed on the holo MD trajectories.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking ensemble docking methods as a scientific outreach project

Gan

Kumar²,

Chen

et al. 2020

Preprint

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The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that CatS is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

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Section: Methodsmentioning

confidence: 99%

Benchmarking ensemble docking methods as a scientific outreach project

Gan

Kumar²,

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, if a receptor conformation belongs to a cluster that interacts favorably with a specific ligand, we can assume that other conformations within the same cluster have similar structural properties in their substrate-binding cavity, and consequently, will behave similarly. Otherwise, if the interaction between the same receptor and ligand is unfavorable, we can consider that this cluster has unpromising snapshots and can be discarded to reduce the number of docking experiments on the FFR model [ 42 ]. Due to this high level of binding cavity similarity within a cluster, we used the optimal clustering solution selected by De Paris et al [ 20 ] as input to the method proposed in this study.…”

Section: Methodsmentioning

confidence: 99%

A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

et al. 2018

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BackgroundIn the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines.ResultsFirst, in order to find the most affordable cost-benefit pool of virtual machines, we evaluated the performance of the docking workflow invocations in different configurations of Azure instances. Second, we validated the gains obtained by the proposed method based on the quality of the Reduced Fully-Flexible Receptor (RFFR) models produced using AutoDock4.2. The analyses show that the proposed method reduced the model size by approximately 50% while covering at least 86% of the best docking results from the 74 ligands tested. Third, we tested our novel method using AutoDock Vina, a different docking software, and showed the positive accuracy achieved in the resulting RFFR models. Finally, our results demonstrated that the method proposed optimized ensemble docking experiments and is applicable to different docking software. In addition, it detected new binding modes, which would be unreachable if employing only the rigid structure used to generate the InhA FFR model.ConclusionsOur results showed that the selective method is a valuable strategy for optimizing ensemble docking-based experiments using different docking software. The RFFR models produced by discarding non-promising snapshots from the original model are accurately shaped for a larger number of ligands, and the elapsed time spent in the ensemble docking experiments are considerably reduced.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2222-2) contains supplementary material, which is available to authorized users.

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“…Each ligand, with its structures colored by atom type, is identified by their name and its corresponding PDB ID. The dashed circle represents the rotatable bounds selected by AutoDockTools 1.5.6 [31].…”

Section: E Statistical Analysismentioning

confidence: 99%

Clustering Molecular Dynamics trajectories with a univariate estimation of distribution algorithm

Barros

Quevedo

Paris

et al. 2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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Molecular Dynamics simulations of protein receptors are an emergent tool in rational drug discovery. Nevertheless, employing Molecular Dynamics trajectories in virtual screening of large repositories is a very costly procedure, which ultimately may become unfeasible. Data clustering have been applied in this context with the goal of reducing the overall computational cost in order to make this task feasible. In this paper, we develop a novel estimation of distribution algorithm called Clus-EDA for clustering entire trajectories using structural features from the substrate-binding cavity of the protein receptor. This novel approach is capable of reducing the original trajectory to about 4% of its original size whilst keeping all relevant information for the analysis of receptor-ligand binding. The resulting partition generated by the estimation of distribution algorithm is further validated by analyzing the interactions between 20 ligands and a Fully-Flexible Receptor model containing a 20 ns Molecular Dynamics simulation trajectory. Results show that Clus-EDA is capable of outperforming traditional clustering algorithms such as k-means and hierarchical clustering by providing the smallest variance of the free energy of binding within the conformations in each cluster.

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Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

Cited by 52 publications

References 34 publications

Benchmarking ensemble docking methods as a scientific outreach project

Benchmarking ensemble docking methods as a scientific outreach project

A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

Clustering Molecular Dynamics trajectories with a univariate estimation of distribution algorithm

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