ConPred_elite: a highly reliable approach to transmembrane topology prediction

Authors:
Jun-Xiong Xia;Masami Ikeda;Toshio Shimizu
Affiliations:
Department of Electronic Information System Engineering, Faculty of Science and Technology, Hirosaki University, 3 Bunkyo-cho, Hirosaki 036-8561, Japan;Department of Electronic Information System Engineering, Faculty of Science and Technology, Hirosaki University, 3 Bunkyo-cho, Hirosaki 036-8561, Japan and Science of Bioresources Program, The Uni ...;Department of Electronic Information System Engineering, Faculty of Science and Technology, Hirosaki University, 3 Bunkyo-cho, Hirosaki 036-8561, Japan
Venue:
Computational Biology and Chemistry
Year:
2004

Citing 1
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ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology

A hidden Markov model with molecular mechanics energy-scoring function for transmembrane helix prediction

Computational Biology and Chemistry

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Abstract

The function of transmembrane (TM) proteins is closely correlated to their TM topology; large quantities of highly reliable TM topology data are becoming increasingly required. We present a new consensus approach for TM topology prediction (ConPred_elite) that can predict the whole topology with accuracies of 0.98 for prokaryotic and 0.95 for eukaryotic proteins on a dataset of experimentally-characterized TM topologies. The predicted yield on the dataset is 30.4% for prokaryotic and 21.5% for eukaryotic proteins. Applying ConPred_elite to predicted TM proteins extracted from 29 prokaryotic and 10 eukaryotic proteomes, we obtained 3871 and 7271 highly reliable TM topologies (yields, 19.8 and 13.3%), respectively. The predicted TM topology data may contribute to further research into a comprehensive functional classification and identification of TM proteins based on information of the topology.