alpha-helical transmembrane (TM) proteins play important and diverse functional 
roles in cells. The ability to predict the topology of these proteins is 
important for identifying functional sites and inferring function of membrane 
proteins. This paper presents a Hidden Markov Model (referred to as HMM_RA) that 
can predict the topology of alpha-helical transmembrane proteins with improved 
performance. HMM_RA adopts the same structure as the HMMTOP method, which has 
five modules: inside loop, inside helix tail, membrane helix, outside helix tail 
and outside loop. Each module consists of one or multiple states. HMM_RA allows 
using reduced alphabets to encode protein sequences. Thus, each state of HMM_RA 
is associated with n emission probabilities, where n is the size of the reduced 
alphabet set. Direct comparisons using two standard data sets show that HMM_RA 
consistently outperforms HMMTOP and TMHMM in topology prediction. Specifically, 
on a high-quality data set of 83 proteins, HMM_RA outperforms HMMTOP by up to 
7.6% in topology accuracy and 6.4% in alpha-helices location accuracy. On the 
same data set, HMM_RA outperforms TMHMM by up to 6.4% in topology accuracy and 
2.9% in location accuracy. Comparison also shows that HMM_RA achieves comparable 
performance as Phobius, a recently published method.