From Protein Structure to Function with Bioinformatics.pdf

4 Membrane Protein Structure Prediction 99Fig. 4.3 A Kyte-Doolittle hydropathy plot. The protein sequence is scanned with a sliding windowof size 19–21 residues. At each position, the mean hydrophobic index of the amino acidswithin the window is calculated and that value plotted as the midpoint of the window. This plotrepresents a TM protein with 4 TM heliceshave since been replaced by machine learning approaches which prevail overhydrophobicity methods due to their probabilistic formulation. A selection ofmachine learning-based predictors can be found in Table 4.3.Hidden Markov models (HMMs) were first applied to TM topology prediction inTMHMM (Krogh et al. 2001) and HMMTOP (Tusnády and Simon 1998), and haveproved highly successful. TMHMM implements a cyclic model with seven states fora TM helix, while HMMTOP uses HMMs to distinguish between five structuralstates [helix core, inside loop, outside loop, helix caps (C and N) and globulardomains]. These states are connected by transition probabilities before dynamicprogramming is used to match a sequence against a model with the most probabletopology. HMMTOP also allows constrained predictions to be made, where specificresidues can be fixed to a topological location based on experimental data.Neural networks (NNs) are employed by methods including PHDhtm (Rostet al. 1996) and MEMSAT3 (Jones 2007). PHDhtm uses multiple sequence alignmentsto perform a consensus prediction of TM helices by combining two NNs.The first creates a ‘sequence-to-structure’ network which represents the structuralpropensity of the central residue in a window. A ‘structure-to-structure’ networkthen smoothes these propensities to predict TM helices, before the positive-insiderule is applied to produce an overall topology. MEMSAT3 uses a neural networkand dynamic programming in order to predict not only TM helices, but also to scorethe topology and to identify possible signal peptides. Additional evolutionary informationprovided by multiple sequence alignments led to prediction accuraciesincreasing to as much as 80% using one dataset (Jones 2007) .