From Protein Structure to Function with Bioinformatics.pdf

4 Membrane Protein Structure Prediction 101classification algorithm. BPROMPT (Taylor et al. 2003), which takes a consensusapproach, combines the outputs of five different predictors to produce an overalltopology using a Bayesian belief network, while Nilsson et al. (2002) used a simplemajority-vote approach to return the best topology from their five predictors. ThePONGO server (Amico et al. 2006) returns the results of five high scoring methodsin a graphical format for direct comparison. In most cases, but particularly proteinswhose topology is not straightforward, considering a number of predictions by differentmethods is highly advisable (Fig. 4.4).4.6.1.3 Signal Peptides and Re-Entrant HelicesOne problem faced by modern topology predictors is the discrimination betweenTM helices and other features composed largely of hydrophobic residues. Theseinclude targeting motifs such as signal peptides and signal anchors, amphipathichelices, and re-entrant helices – membrane penetrating helices that enter and exitthe membrane on the same side, common in many ion channel families (Fig. 4.5).The high similarity between such features and the hydrophobic profile of a TMhelix frequently leads to crossover between the different types of predictions.Should these elements be predicted as TM helices, the ensuing topology predictionis likely to be severely disrupted. Some prediction methods, such as SignalP(Bendtsen et al. 2004) and TargetP (Emanuelsson et al. 2007), are effective in identifyingsignal peptides, and may be used as a pre-filter prior to analysis using a TMtopology predictor. Phobius (Käll et al. 2004) uses a HMM to successfully addressthe problem of signal peptides in TM protein topology prediction, whilePolyPhobius (Käll et al. 2005) further increases accuracy by including homologyinformation. Other methods such as TOP-MOD (Viklund et al. 2006) andOCTOPUS have attempted to incorporate identification of re-entrant regions into aTM topology predictor but there is significant room for improvement. The problem,particularly regarding re-entrant helices, is the lack of reliable data with which totrain machine-learning based methods.Fig. 4.4 Using a number of methods to form a consensus topology prediction