Visual clustering approach for docking results from vina and autodock

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AudienciaPúblico en generales_ES
CoberturaMéxico[ 143]es_ES
Fecha de ingreso2026-10-05T16:35:05Z
Fecha de publicación2017-01-01
ResumenAutoDock Tools allows the analysis of docking files and is used to represent clustering conformations, yet it analyses only one docking file at a time and the method applied to represent the clustering complicates the visualization of clustering conformations. The creation of a plugin called PyDRA for the molecular visualizer PyMOL resolves that problem and allows to simultaneously process more than one docking file for the two types of file format from AutoDock 4.2 and Vina 1.1 (dlg and pdbqt). Moreover, this plugin facilitates the visualization of conformations through two clustering methods. The first method is a K-RMSD algorithm, which is based on the clustering through RMSD and enables the interactive visualization groups through a treemap. And the other one is based on a hierarchical clustering algorithm, using an algorithm of average distances which generates a dendrogram that offers the possibility to explore sequentially the groups that illustrate best the docking. The results obtained with the visualization methods implemented showed that the treemap, due to the implemented colour bar, facilitates to identify the clusters that have a greater affinity to the protein at a glance, and to determine which of the clusters hold a greater number of elements, on the other hand, the dendrogram shows a detailed analyses of the hierarchical clustering, which also enables the user to distinguish the clustering regardless the size of the window, as well as to differentiate each cluster and conformation in order to gain insight of docking results of Autdock and Vina. The fact that both visualizations are connected to PyMOL increases its ability of discernment.es_ES
Doihttps://doi.org/10.1007/978-3-319-59650-1_29es_ES
ISBN978-331959649-5es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/6524
IdiomaInglés[20]es_ES
EditorialSpringer Verlages_ES
RelaciónLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)es_ES
URL relacionadohttps://doi.org/10.1007/978-3-319-59650-1_29es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Palabra claveClusteringes_ES
Palabra claveHierarchicales_ES
Palabra claveK-RMSDes_ES
Palabra claveMolecular dockinges_ES
Palabra clavePyMOLes_ES
Palabra clavePythones_ES
TítuloVisual clustering approach for docking results from vina and autodockes_ES
TipoPonenciaes_ES
ArbitradoHa sido Arbitradoes_ES
AutorVarela-Salinas, Génesis
AutorGarcía-Pérez, Carlos Armando
AutorPeláez, Rafael
AutorRodríguez, Adolfo J.
AutorVarela-Salinas, Génesises_ES
AutorGarcía-Pérez, Carlos Armandoes_ES
AutorPeláez, Rafaeles_ES
AutorRodríguez, Adolfo J.es_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Rango de páginas342-353es_ES
URL relacionadahttps://doi.org/10.1007/978-3-319-59650-1_29
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen10334 LNCSes_ES

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