PREDICTING MEMBRANE-PROTEIN SECONDARY STRUCTURE - PREFERENCE FUNCTIONS METHOD FOR FINDING OPTIMAL CONFORMATIONAL PARAMETERS

An automated iterative method is developed for predicting secondary co nformation in membrane proteins. The initial set of parameters are alp ha-helix preferences and associated conformational preference function s extracted from the data set of known soluble protein structures. The secondary structure segments are assigned to each of 14 tested membra ne proteins by using the prediction method, which evaluates and compar es preference functions in the tested protein. A new set of parameters are then calculated which is based on the predicted protein structure from the previous iterative cycle. The method takes advantage of the similarities in local sequence patterns found in the tested proteins. Residues in membrane proteins are predicted with 84% accuracy and with the correlation coefficient for the alpha-helix structure equal to 0. 68, which is a considerably better performance than that of neural net work programs or Garnier-Robson's algorithm.