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Most computational methods for transmembrane protein topology prediction rely on compositional bias of amino acids to locate those hydrophobic domains in transmembrane proteins. Because signal peptides also contain hydrophobic segments, these computational prediction methods often misidentify signal peptides as transmembrane proteins. Here, we present a new approach that combines the SVM-Fisher discrimination method and TMMOD - a hidden Markov model based predictor for transmembrane proteins. While TMMOD alone has already outperformed most existing methods in both identification and topology prediction, this new approach further improves the ability of TMMOD to discriminate between transmembrane proteins and signal peptide containing proteins, reducing mis-prediction of signal peptides by more than 30% in our test data.

In this work, we present a hidden Markov model for predicting the topology of transmembrane proteins. Our model differs from TMHMM (Sonnhammer et al) both in the architecture of the loop submodels on both sides of the membrane and in the model training procedure. Using maximum likelihood parameter estimation with significant regularization, the model was trained and cross-validated on two sets of sequences with known topology. On the first set of 83 sequences, the prediction accuracy of our model for membrane domain locations and topology are both 89% while TMHMM reported 83% for domain locations and 77% for topology. On the second dataset of 160 sequences, our prediction accuracies are 89% for locations and 84% for topology: both surpassing significantly those of TMHMM (83% and 77%).

Motivation: Knowledge of the transmembrane helical topology can help identify binding sites and infer functions for membrane proteins. However, because membrane proteins are hard to solubilize and purify, only a very small amount of membrane proteins have structure and topology experimentally determined. This has motivated various computational methods for predicting the topology of membrane proteins. Results: We present an improved hidden Markov model, TMMOD, for the identification and topology prediction of transmembrane proteins. Our model uses TMHMM as a prototype, but differs from TMHMM by the architecture of the submodels for loops on both sides of the membrane and also by the model training procedure. In cross-validation experiments using a set of 83 transmembrane proteins with known topology, TMMOD outperformed TMHMM and other existing methods, with an accuracy of 89% for both topology and locations. In another experiment using a separate set of 160 transmembrane proteins, TMMOD had 84% for topology and 89% for locations. When utilized for identifying transmembrane proteins from non-transmembrane proteins, particularly signal peptides, TMMOD has consistently fewer false positives than TMHMM does. Application of TMMOD to a collection of complete genomes shows that the number of predicted membrane proteins accounts for ∼20--30% of all genes in those genomes, and that the topology where both the N- and C-termini are in the cytoplasm is dominant in these organisms except for Caenorhabditis elegans. Availability: http://liao.cis.udel.edu/website/servers/TMMOD/ Contact: lliao@cis.udel.edu