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.