A model recognition approach to the prediction of all-helical membrane protein structure and topology.

This paper describes a new method for the prediction of the secondary structure and topology of integral membrane proteins based on the recognition of topological models. The method employs a set of statistical tables (log likelihoods) complied from well-characterized membrane protein data, and a novel dynamic programming algorithm to recognize membrane topology models by expectation maximization. The statistical tables show definite biases toward certain amino acid species on the inside, middle, and outside of a cellular membrane. Using a set of 83 integral membrane protein sequences taken from a variety of bacterial, plant, and animal species, and a strict jackknifing procedure, where each protein (along with any detectable homologues) is removed from the training set used to calculate the tables before prediction, the method successfully predicted 64 of the 83 topologies, and of the 37 complex multispanning topologies 34 were predicted correctly.