An improved hidden Markov model for transmembrane topology prediction

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 sub models 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%).(1)