Reiterated runs of standard docking protocols usually provide a collection of possible binding modes rather than pinpoint a single solution. Usually, this ensemble is then ranked by means of an energy-based scoring function. However, since many degrees of approximation have to be introduced in the computation of the binding free energy, scoring functions cannot always rank the experimental pose among the top scorers. Cluster analysis might help to overcome this limit, provided that data clusterability has been earlier assessed. In this paper, first, we present a modified version of a test earlier developed by Hopkins to assess whether or not docking outputs show the natural tendency to be grouped in clusters. Then, we report the results of a comparative study on the application of different hierarchical-agglomerative cluster rules to partition docking outputs. The rule that was able to best manage the observed data was finally applied to the whole ensemble of poses collected from several docking tools. The combination of the average linkage rule with the cutting function developed by Sutcliffe and co-workers turned out to be an approach that meets all of the criteria required for a robust clustering protocol. Furthermore, a consensus clustering allowed us to identify the pose closest to the experimental one within a statistically significant cluster, whose number was always of few units.

A comparative study on the application of hierarchical-agglomerative clustering approaches to organize outputs of reiterated docking runs.

BOTTEGONI, GIOVANNI;CAVALLI, ANDREA;RECANATINI, MAURIZIO
2006

Abstract

Reiterated runs of standard docking protocols usually provide a collection of possible binding modes rather than pinpoint a single solution. Usually, this ensemble is then ranked by means of an energy-based scoring function. However, since many degrees of approximation have to be introduced in the computation of the binding free energy, scoring functions cannot always rank the experimental pose among the top scorers. Cluster analysis might help to overcome this limit, provided that data clusterability has been earlier assessed. In this paper, first, we present a modified version of a test earlier developed by Hopkins to assess whether or not docking outputs show the natural tendency to be grouped in clusters. Then, we report the results of a comparative study on the application of different hierarchical-agglomerative cluster rules to partition docking outputs. The rule that was able to best manage the observed data was finally applied to the whole ensemble of poses collected from several docking tools. The combination of the average linkage rule with the cutting function developed by Sutcliffe and co-workers turned out to be an approach that meets all of the criteria required for a robust clustering protocol. Furthermore, a consensus clustering allowed us to identify the pose closest to the experimental one within a statistically significant cluster, whose number was always of few units.
2006
Bottegoni G.; Cavalli A.; Recanatini M.
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/27292
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? 9
  • Scopus 54
  • ???jsp.display-item.citation.isi??? 52
social impact