Transmembrane beta-barrel (TMB) proteins are embedded in the outer membrane of 
Gram-negative bacteria, mitochondria, and chloroplasts. The cellular location 
and functional diversity of beta-barrel outer membrane proteins (omps) makes 
them an important protein class. At the present time, very few nonhomologous TMB 
structures have been determined by X-ray diffraction because of the experimental 
difficulty encountered in crystallizing transmembrane proteins. A novel method 
using pairwise interstrand residue statistical potentials derived from globular 
(nonouter membrane) proteins is introduced to predict the supersecondary 
structure of transmembrane beta-barrel proteins. The algorithm transFold employs 
a generalized hidden Markov model (i.e., multitape S-attribute grammar) to 
describe potential beta-barrel supersecondary structures and then computes by 
dynamic programming the minimum free energy beta-barrel structure. Hence, the 
approach can be viewed as a "wrapping" component that may capture folding 
processes with an initiation stage followed by progressive interaction of the 
sequence with the already-formed motifs. This approach differs significantly 
from others, which use traditional machine learning to solve this problem, 
because it does not require a training phase on known TMB structures and is the 
first to explicitly capture and predict long-range interactions. TransFold 
outperforms previous programs for predicting TMBs on smaller (<or=200 residues) 
proteins and matches their performance for straightforward recognition of longer 
proteins. An exception is for multimeric porins where the algorithm does perform 
well when an important functional motif in loops is initially identified. We 
verify our simulations of the folding process by comparing them with 
experimental data on the functional folding of TMBs. A Web server running 
transFold is available and outputs contact predictions and locations for 
sequences predicted to form TMBs.