The use of functional domains to improve transmembrane protein topology prediction.
Transmembrane proteins affect vital cellular functions and pathogenesis, and are a focus of drug design. It is difficult to obtain diffraction quality crystals to study transmembrane protein structure. Computational tools for transmembrane protein topology prediction fill in the gap between the abundance of transmembrane proteins and the scarcity of known membrane protein structures. Their prediction accuracy is still inadequate: TMHMM, the current state-of-the-art method, has less than 52% accuracy in topology prediction on one set of transmembrane proteins of known topology. Based on the observation that there are functional domains that occur preferentially internal or external to the membrane, we have extended the model of TMHMM to incorporate functional domains, using a probabilistic approach originally developed for computational gene finding. Our extension is better than TMHMM in predicting the topology of transmembrane proteins. As prediction of functional domain improves, our system's prediction accuracy will likely improve as well.
Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Calgary, HS-1150, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada. ewxu@ucalgary.ca
Emily W
EW
Xu
Paul
P
Kearney
Daniel G
DG
Brown
D019295:Computational Biology; D003198:Computer Simulation; D008390:Markov Chains; D008565:Membrane Proteins / Q000737:chemistry* / Q000235:genetics; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D016415:Sequence Alignment / Q000706:statistics & numerical data
D008565:Membrane Proteins