ClusterWaters: Cluster Conserved Waters

Description Usage Arguments Details Value Author(s) References


Cluster the conserved waters.


ClusterWaters(data, cutoff.cluster, cluster.method = "complete")



The water oxygens' X, Y, and Z coordinates, B-values, and occupancy values.


Numerical value provided by the user for the distance between water oxygen atoms to form a cluster; default: 2.4 Angstroms.


Method of clustering the waters; default is "complete". Any other method accepted by the hclust function is appropriate. The original method used by Sanschagrin and Kuhn is the complete linkage clustering method and is the default. Other options include "ward.D" (equivilant to the only Ward option in R versions 3.0.3 and earlier), "ward.D2" (implements Ward's 1963 criteria; see Murtagh and Legendre 2014), "single" (related to the minimal spanning tree method and adopts a "friend of friends" clustering method), along with "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC). Due to size limitations with stats::hclust() – specifically the "size cannot be NA nor exceed 65536" – fastcluster::hclust() is being used because it is a complete replacement of stats::hclust(), is fast (compared to stats::hclust()), and is able to accommodate dissimilarity matrices with more than 2^16 (65,536) observations.


Calculate the conserved waters using a collection of crystallographic protein structures.


This function returns:


Emilio Xavier Esposito


Paul C Sanschagrin and Leslie A Kuhn. Cluster analysis of consensus water sites in thrombin and trypsin shows conservation between serine proteases and contributions to ligand specificity. Protein Science, 1998, 7 (10), pp 2054-2064.
DOI: 10.1002/pro.5560071002
PMID: 9792092
WatCH webpage

Fionn Murtagh and Pierre Legendre. Ward's Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward's Criterion? Journal of Classification, 2014, 31, (3), pp 274-295.
DOI: 10.1007/s00357-014-9161-z

Daniel Müllner. fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python. Journal of Statistical Software, 2013, 53 (9)
DOI: 10.18637/jss.v053.i09 fastcluster webpage

vanddraabe documentation built on June 8, 2019, 1:03 a.m.