Clustering molecular dynamics trajectories for optimizing docking experiments
| Autor: | Renata De Paris, Rodrigo C. Barros, Osmar Norberto de Souza, Christian Vahl Quevedo, Duncan D. Ruiz |
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| Rok vydání: | 2014 |
| Předmět: |
General Computer Science
Article Subject Computer science General Mathematics Computational intelligence Cloud computing Molecular Dynamics Simulation lcsh:Computer applications to medicine. Medical informatics Machine learning computer.software_genre Ligands lcsh:RC321-571 Molecular dynamics Software Artificial Intelligence Cluster Analysis Cluster analysis lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Virtual screening business.industry Drug discovery General Neuroscience Proteins General Medicine ComputingMethodologies_PATTERNRECOGNITION lcsh:R858-859.7 Artificial intelligence Data mining business computer Algorithms Curse of dimensionality Research Article |
| Zdroj: | Computational Intelligence and Neuroscience Computational Intelligence and Neuroscience, Vol 2015 (2015) |
| ISSN: | 1687-5273 |
| Popis: | 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 thek-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. |
| Databáze: | OpenAIRE |
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