From Protein Structure to Function with Bioinformatics.pdf

102 T. Nugent and D.T. JonesFig. 4.5 Potassium channel subunit from Streptomyces lividans showing a short re-entrant helix(in the centre at the top). PDB code 1r3j4.6.2 Beta-Barrel ProteinsThe relative abundance of alpha-helical TM proteins in both complete proteomes and3D databases, when compared to beta-barrel TM proteins has resulted in the latterclass being somewhat overshadowed in terms of efforts to predict structure and topology.Perhaps another reason is the relative ease with which alpha-helical TM helicescan be predicted due to their enrichment of hydrophobic residues. The anti-parallelbeta-strands of beta-barrel TM proteins contain alternating polar and hydrophobicamino acids, allowing the hydrophobic residues to orientate towards the membranewhile the polar residues are oriented toward the solvent-exposed surface. Early methodsused to predict such beta-strands relied on sliding window-based hydrophobicityanalyses in order to capture the alternating patterns (Schirmer and Cowan 1993), whileother approaches included the construction of special empirical rules using amino acidpropensities and prior knowledge of the structural nature of the proteins (Gromiha andPonnuswamy 1993). As the number of structures of beta-barrel proteins known atatomic resolution increased, machine learning based methods began to emerge trainedon these larger datasets. These include NNs (Jacoboni et al. 2001; Gromiha et al.2004), HMMs (Martelli et al. 2002; Liu et al. 2003; Bagos et al. 2004) and SVM predictors(Park et al. 2005), using single sequences and multiple sequence alignments. Aselection of machine learning-based beta-barrel predictors can be found in Table 4.4.4.6.3 Whole Genome AnalysisLarge-scale genomics and proteomics projects are frequently identifying novelproteins, many of which are of unknown localisation and function. While some ofthe methods outlined above can accurately predict TM topology, fewer are suitable