Outer membrane proteins (OMPs) are the transmembrane proteins found in the outer 
membranes of Gram-negative bacteria, mitochondria and plastids. Most prediction 
methods have focused on analogous features, such as alternating hydrophobicity 
patterns. Here, we start from the observation that almost all beta-barrel OMPs 
are related by common ancestry. We identify proteins as OMPs by detecting their 
homologous relationships to known OMPs using sequence similarity. Given an input 
sequence, HHomp builds a profile hidden Markov model (HMM) and compares it with 
an OMP database by pairwise HMM comparison, integrating OMP predictions by 
PROFtmb. A crucial ingredient is the OMP database, which contains profile HMMs 
for over 20,000 putative OMP sequences. These were collected with the 
exhaustive, transitive homology detection method HHsenser, starting from 23 
representative OMPs in the PDB database. In a benchmark on TransportDB, HHomp 
detects 63.5% of the true positives before including the first false positive. 
This is 70% more than PROFtmb, four times more than BOMP and 10 times more than 
TMB-Hunt. In Escherichia coli, HHomp identifies 57 out of 59 known OMPs and 
correctly assigns them to their functional subgroups. HHomp can be accessed at 
http://toolkit.tuebingen.mpg.de/hhomp.