A strategic solution to optimize molecular docking simulations using Fully-Flexible Receptor models

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

doi:10.1016/j.eswa.2014.05.038

Cited by 15 publications

(11 citation statements)

References 54 publications

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“…These MD ensembles, hereby called a Fully-Flexible Receptor (FFR) model, typically hold over 10 4 MD structures. For this reason, recent studies on combining docking and MD simulations have developed novel techniques to systematically reduce the number of MD structures without losing essential information, usually employing clustering algorithms for achieving the desired reduction [5]- [7]. By clustering highly-similar MD conformations regarding their substrate-binding cavities, one can extract the most relevant information during the molecular docking experiments, reducing its overall computational cost.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Clustering algorithms used in a variety of situations, such as understanding virtual screening results [1], partitioning data sets into structurally homogeneous subsets for modeling [2, 3], and picking representative chemical structures from individual clusters [4–6]. The use of clustering algorithms to group similar conformations is the most appropriate data mining technique to distill the structural information from properties of an MD trajectory [7–10].…”

Section: Introductionmentioning

confidence: 99%

The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix

Kim¹,

Jang²,

Yadav³

et al. 2017

J Cheminform

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BackgroundThe accuracy of any 3D-QSAR, Pharmacophore and 3D-similarity based chemometric target fishing models are highly dependent on a reasonable sample of active conformations. Since a number of diverse conformational sampling algorithm exist, which exhaustively generate enough conformers, however model building methods relies on explicit number of common conformers.ResultsIn this work, we have attempted to make clustering algorithms, which could find reasonable number of representative conformer ensembles automatically with asymmetric dissimilarity matrix generated from openeye tool kit. RMSD was the important descriptor (variable) of each column of the N × N matrix considered as N variables describing the relationship (network) between the conformer (in a row) and the other N conformers. This approach used to evaluate the performance of the well-known clustering algorithms by comparison in terms of generating representative conformer ensembles and test them over different matrix transformation functions considering the stability. In the network, the representative conformer group could be resampled for four kinds of algorithms with implicit parameters. The directed dissimilarity matrix becomes the only input to the clustering algorithms.ConclusionsDunn index, Davies–Bouldin index, Eta-squared values and omega-squared values were used to evaluate the clustering algorithms with respect to the compactness and the explanatory power. The evaluation includes the reduction (abstraction) rate of the data, correlation between the sizes of the population and the samples, the computational complexity and the memory usage as well. Every algorithm could find representative conformers automatically without any user intervention, and they reduced the data to 14–19% of the original values within 1.13 s per sample at the most. The clustering methods are simple and practical as they are fast and do not ask for any explicit parameters. RCDTC presented the maximum Dunn and omega-squared values of the four algorithms in addition to consistent reduction rate between the population size and the sample size. The performance of the clustering algorithms was consistent over different transformation functions. Moreover, the clustering method can also be applied to molecular dynamics sampling simulation results.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0208-0) contains supplementary material, which is available to authorized users.

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“…In this paper, an MD ensemble is called a fully flexible receptor (FFR) model [ 5 ], which typically has over 10 4 MD structures. For this reason, recent studies on combining docking and MD simulations have created novel techniques to systematically reduce the number of MD structures without losing essential structural/dynamical information [ 6 – 8 ]. Therefore, we focus our efforts on performing cluster analysis for grouping MD conformations with high affinity in their substrate-binding cavities in order to extract the most relevant information during the molecular docking simulations, reducing its overall computational cost.…”

Section: Introductionmentioning

confidence: 99%

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

Paris

Quevedo

Ruiz

et al. 2015

Computational Intelligence and Neuroscience

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Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

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A strategic solution to optimize molecular docking simulations using Fully-Flexible Receptor models

Cited by 15 publications

References 54 publications

Clustering Molecular Dynamics trajectories with a univariate estimation of distribution algorithm

Clustering Molecular Dynamics trajectories with a univariate estimation of distribution algorithm

The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

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