ab_phd_htmtop_ismb

Refining neural network predictions for helical transmembrane proteins by dynamic programming

Rost, Burkhard; Casadio, Rita & Fariselli, Piero

In: D States et al. (eds.) "The fourth international conference on Intelligent Systems for Molecular Biology (ISMB)", St. Louis, U.S.A., Jun, 1996, Menlo Park, CA: AAAI Press, 1996, 192-200.

Abstract

For transmembrane proteins experimental determination of three-dimensional structure is problematic. However, membrane proteins have important impact for molecular biology in general, and for drug design in particular. Thus, prediction method are needed. Here we introduce a method that started from the output of a profile-based neural network system (PHDhtm; Rost, et al. 1995) . Instead of choosing the neural network output unit with maximal value as prediction, we implemented a dynamic programming-like refinement procedure that aimed at producing the best model for all transmembrane helices compatible with the neural network output. Preliminary results suggest that the refinement was clearly superior to the initial neural network system; and that, in terms of correctly predicting all transmembrane helices of a protein correctly, the method was more accurate than a previously applied empirical filter. The refined prediction was used successfully to predict transmembrane topology based on an empirical rule for the charge difference between extra- and intra-cytoplasmic regions (positive-inside rule). The resulting accuracy in predicting topology was better than 80%. Although a more thorough evaluation of the method on a larger data set will be required, the results compared favourably with alternative methods for the prediction of transmembrane helices and topology.