A general clustering approach with application to the Miyazawa-Jernigan potentials for amino acids.
In this article, we address the problem of classification of amino acids. Starting from the Miyazawa-Jernigan matrix obtained from the relative positions of amino acids in the crystal structure of globular proteins, we develop a fully unsupervised method of classification for the amino acids. The method is based in the subdominant ultrametric associated to the distance induced by the Miyazawa-Jernigan matrix and the maximum likelihood principle to determine the cluster structure. We obtain a classification consistent with the five groups used in the literature, although with some peculiarities. We also show the stability of our results against changes of the method used to classify the amino acids. Proteins 2004.
Esteve
J G
JG
Departamento de Física Teórica, Facultad de Ciencias, Universidad de Zaragoza, Zaragoza, Spain. esteve@unizar.es
Falceto
F
F
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry / Q000145:classification*; D016000:Cluster Analysis; D008958:Models, Molecular; D015233:Models, Statistical; D015394:Molecular Structure; D025341:Principal Component Analysis; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry
D000596:Amino Acids; D011506:Proteins