Quantitative characterization of protein tertiary motifs.

A quantitative feature-vector representation/model of tertiary structural motifs of proteins is presented. Multiclass logistic regression and a probabilistic neural network were employed to apply this representation to large data sets in order to classify them into major families of distinct motif types (including those of functional importance) with high statistical confidence. Scatter plots of random samples of these motifs were obtained through two-dimensional transformation of the feature vector by metric MDS (multidimensional scaling). The plots showed distinct clusters and shapes for different families and demonstrated the relevance and importance of the proposed quantitative feature-vector representation for characterizing protein tertiary structural motifs. The relative importance of the features was analyzed. The scope of the present work to investigate Nature's prioritization and optimization of functional motif structures is highlighted.