PCPMer is a software tool that can automatically detect motifs in a protein family and subsequently identifies related members in protein sequence database. The program calculates conservation of PCP descriptors, quantitative descriptors for amino acids based on multi-dimensional scaling of 237 physical-chemical properties,  to define a motif. Each motif is described as profile that consists of average vector magnitude, standard deviation and relative entropy. Applications of PCPMer includes:

1. Identification of conserved residues, motifs that can be used for experimental mutational design to analyze function of a protein family.

2. To identify structural and functionally realted sequences that share similar motifs.

3. For large-scale datamining and annotation of translated genomic sequences

Distribution of the eigenvalues of the components, computed from 237 normalized properties (black bars). Indices for the components are sorted according to decreasing order of the eigenvalues. As a control, eigenvalues of a matrix derived from 237 random uniformly distributed vectors are shown in white bars.Comparison of distances between amino acids in the original property space and component subspaces was performed. We calculated a linear correlation coefficient between distances of all amino acid pairs in the original 237-dimensional property space and in subspaces of n components with n varying from 2 to 20. Correlation plot between distances of amino acid pairs in the five-dimensional space formed by the first five components (D5) and distances in the original property space (D237) showed that a original distribution can be reproduced (r= 0.992). J Mol Modeling (2001) 7:445-453 .

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