From Protein Structure to Function with Bioinformatics.pdf

2 Fold Recognition 43query sequence or template sequence, they are used for both and compared to oneanother (Fig. 2.4e). Each position in a sequence can be considered as a vector ofprobabilities. In the case of simple profiles, one has a 20 dimensional probabilityvector (1 dimension per amino acid type). A position in the query sequence is similarto a position in a template structure if they are under similar evolutionary pressures,which would be reflected in them having similar probability vectors. Manydifferent techniques have recently been devised to compare such vectors (the simplestbeing a dot product), almost all of which surpass the simpler sequence-profilescoring approaches (e.g. Rychlewski et al. 2000; Ohlsen et al. 2004; Soeding 2005;Bennett-Lovsey et al. 2008).In the light of the success of profile-profile methods, many groups generalisedthe process to include secondary structure profiles, where instead of a simple3-state prediction of alpha helix, beta strand or coil, a probability was calculatedfor each state and treated as a vector with some evidence of improved performance(Tang et al. 2003; Bennett-Lovsey et al. 2008). This is shown schematicallyin Fig. 2.4f.However, as the power of profiles grew due to improved sequence databases,more careful profile construction and more intelligent profile-profile matchingalgorithms, the value of the additional predicted structural information seemedto diminish relative to its initially critical role in the early techniques of Bowieet al. (1991). The most successful techniques for predicting secondary structuretend to rely on a machine learning algorithm such as artificial neural networksor support vector machines, trained on windows of sequence profiles that havebeen generated by PSI-BLAST. The reason for only marginal gains from usingthis information probably stems from a lack of novel or ‘orthogonal’ data. Thesource data used to make the secondary structure prediction is often the sameprofile used in the sequence matching. Thus it may be speculated that much ofthe information in the secondary structure prediction is probably alreadyencoded in the profile from which it was derived.2.3.3 Fold Classification and Support Vector MachinesFold recognition is a classification problem. It can be cast as a series of questionsregarding whether a given sequence adopts one or other of a variety of folds. Assuch, it is a problem amenable to the techniques developed in the machine learningfield. Given known features of the query sequence, s, such as its amino acid composition,its sequence relatives, secondary structure prediction and so on, we wishto determine the most likely fold that s belongs to out of some set of folds F. Suchclassifiers can be broadly classified as generative or discriminative. A typicalgenerative classifier is a Naïve Bayes classifier. The idea here is to determine therelative importance of each feature (the parameters of the model) in predicting thefold by examining the frequencies with which each feature is associated with eachclass in some training set.