PREDICTION OF PROTEIN FOLDING CLASS FROM AMINO-ACID-COMPOSITION

An empirical relation between the amino acid composition and three-dim ensional folding pattern of several classes of proteins has been deter mined. Computer simulated neural networks have been used to assign pro teins to one of the following classes based on their amino acid compos ition and size: (1) 4alpha-helical bundles, (2) parallel (alpha/beta)8 barrels, (3) nucleotide binding fold, (4) immunoglobulin fold, or (5) none of these. Networks trained on the known crystal structures as we ll as sequences of closely related proteins are shown to correctly pre dict folding classes of proteins not represented in the training set w ith an average accuracy of 87%. Other folding motifs can easily be add ed to the prediction scheme once larger databases become available. An alysis of the neural network weights reveals that amino acids favoring prediction of a folding class are usually over represented in that cl ass and amino acids with unfavorable weights are underrepresented in c omposition. The neural networks utilize combinations of these multiple small variations in amino acid composition in order to make a predict ion. The favorably weighted amino acids in a given class also form the most intramolecular interactions with other residues in proteins of t hat class. A detailed examination of the contacts of these amino acids reveals some general patterns that may help stabilize each folding cl ass.