Open AccessA dissimilarity matrix between protein atom classes basedon Gaussian mixtures
Author(s) -
VilleVeikko Rantanen,
Mats Gyllenberg,
Timo Koski,
Mark S. Johnson
Publication year - 2002
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/18.9.1257
Subject(s) - gaussian , atom (system on chip) , cluster analysis , protein data bank , multidimensional scaling , mixture model , hierarchical clustering , ligand (biochemistry) , matrix (chemical analysis) , protein data bank (rcsb pdb) , basis (linear algebra) , protein ligand , protein structure , computer science , algorithm , chemistry , artificial intelligence , mathematics , computational chemistry , machine learning , stereochemistry , geometry , embedded system , receptor , organic chemistry , chromatography , biochemistry
Previously, Rantanen et al. (2001; J. Mol. Biol., 313, 197-214) constructed a protein atom-ligand fragment interaction library embodying experimentally solved, high-resolution three-dimensional (3D) structural data from the Protein Data Bank (PDB). The spatial locations of protein atoms that surround ligand fragments were modeled with Gaussian mixture models, the parameters of which were estimated with the expectation-maximization (EM) algorithm. In the validation analysis of this library, there was strong indication that the protein atom classification, 24 classes, was too large and that a reduction in the classes would lead to improved predictions.
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