Topology Prediction of α-Helical Transmembrane Proteins

Membrane proteins fulfil a number of tasks in cells, including signalling, cell-cell interaction, and the transportation of molecules. The prominence of these tasks makes membrane proteins an important target for clinical drugs. Because of the decreasing price of sequencing, the number of sequences known is increasing at such a rate that manual annotations cannot compete. Here, topology prediction is a way to provide additional information. It predicts the location and number of transmembrane helices in the protein and the orientation inside the membrane. An important factor to detect transmembrane helices is their hydrophobicity, which can be calculated using dedicated scales. In the first paper, we studied the difference between several hydrophobicity scales and evaluated their performance. We showed that while they appear to be similar, their performance for topology prediction differs significantly. The better performing scales appear to measure the probability of amino acids to be within a transmembrane helix, instead of just being located in a hydrophobic environment. Around 20% of the transmembrane helices are too hydrophilic to explain their insertion with hydrophobicity alone. These are referred to as marginally hydrophobic helices. In the second paper, we studied three of these helices experimentally and performed an analysis on membrane proteins. The experiments show that for all three helices positive charges on the N-terminal side of the subsequent helix are important to insert, but only two need the subsequent helix. Additionally, the analysis shows that not only the N-terminal helices are more hydrophobic, but also the C-terminal transmembrane helices. In Paper III, the finding from the second paper was used to improve the topology prediction. By extending our hidden Markov model with N- and C-terminal helix states, we were able to set stricter cut-offs. This improved the general topology prediction and in particular miss-prediction in large N- and C-terminal domains, as well the separation between transmembrane and non-transmembrane proteins. Lastly, we contribute several new features to our consensus topology predictor, TOPCONS. We added states for the detection of signal peptides to its hidden Markov model and thus reduce the over-prediction of transmembrane helices. With a new method for the generation of profile files, it is possible to increase the size of the database used to find homologous proteins and decrease the running time by 75%