Prediction of solvent accessibility of amino acid residues: critical aspects

Abstract.

For predicting solvent accessibility from the sequence of amino acids in proteins, we use a logistic function trained on a non-redundant protein database. Using a principal component analysis, we find that the prediction can be considered, in a good approximation, as a monofactorial problem: a crossed effect of the burial propensity of amino acids and of their locations at positions flanking the amino acid of interest. Complementary effects depend on the presence of certain amino acids (mostly P, G and C) at given positions. We have refined the predictive model (1) by adding supplementary input data, (2) by using a strategy of prediction correction and (3) by adapting the decision rules according to the amino acid type. We obtain a best score of 77.6% correct prediction for a relative accessibility of 9%. However, compared to trivial strategy only based upon the frequencies of buried or exposed residues, the gain is less than 4%.