Protein Fold Prediction In the Context of Fine-Grained Classifications

摘要

Predicting a protein fold and implied function from the amino acid sequence is a problem of great interest. We have developed a neural networks (NN) based expert system which, given a classification of protein folds, can assign a protein to a folding class using primary sequence data. It addresses the inverse protein folding problem fro ma taxonometric rathre than threadign perspective. Recent classifications suggest the existence of approx 80-350 differnet folds. The occurrence of several representatives for each fold allows extraction of the common features of its members. Our method (i) provides a global description of a protein sequence in terms of the biochemical and structural properties of the constituent amino acids, (ii) combines the descriptors usign Nns allowing discrimination of members of a given folding class from members of all other folding classes and (iii) uses a voting procedure among predictions based on differnet descriptors to decide on the final assignment.