From Protein Structure to Function with Bioinformatics.pdf

12 Prediction of Protein Function from Theoretical Models 301procedures to determine new serine hydrolases in yeast (Baxter et al. 2004). Themain advantage of the method is that it does not rely on residue conservationacross an entire family and the key functional residues are specifically identifiedregardless of overall global sequence similarity to any other protein exhibiting thesame function. It could therefore be applicable to identification and annotation ofdifferent functional sites, including enzyme-active sites, regulatory and cofactorbindingsites.It is also worth mentioning a hybrid approach that combines protein surfaceanalysis with evolutionary methods that has been proposed by Pawlowski and coworkers(Pawlowski and Godzik 2001). By mapping different features (e.g. chargeand hydrophobicity) onto a spherical approximation of protein surface, they createdsurface maps for entire proteins. By this way, entire proteins can be compared toinfer global functional similarities, e.g. according to simple numerical measures ofmap similarity between two or more proteins. It was shown that surface map comparisonallows a better function prediction than general sequence analysis methodsand can reproduce known examples of functional variation within a divergent groupof proteins, including the detection of unexpected sets of common functional propertiesfor seemingly distant paralogs. The method, which is now available via a webserver (Sasin et al. 2007), was also shown to be robust enough to allow the use ofprotein models from comparative modelling instead of experimental structures.Other studies have addressed whether more specific function predictions can bemade as accurately for models as for experimental structures. For metal-bindingsites, the results of the MetSite method that combines sequence and structure informationwere encouraging (Sodhi et al. 2004). Although performance with modelledstructures was inferior to that with experimental structures, correct metal site predictionscould be made for around half of reliable mGenTHREADER-derivedmodels. Notably, these models are backbone-only so that performance would notbe at all affected by errors in side chain positioning. Similarly, a method for predictingDNA-binding ability using sequence information, structural asymmetry in distributionof some amino acids and dipole moments, has been benchmarked againstboth experimental structures and models (Szilagyi and Skolnick 2006). The methoduses Cα-only structures. Performance of this method vs that obtained forexperimental structures, was found to decrease only very slightly for models of upto 6 Å RMS deviation from native structure. Thus, it will be appropriate to use themethod on model structures of all kinds, including the template-free and fold recognition-derivedmodels for which lower accuracy would be expected.One of the important practical applications of protein models is for in silicoscreening against small compound databases in order to pick out likely inhibitorsfor development into drug leads (Jacobson and Sali 2004). Since the focus of thisbook is protein function, those applications are not discussed in this chapter.Nevertheless, small molecule docking, in an identical fashion as with the pharmaceuticalscenario, is now starting to be used for prediction of protein function. Inthis way, as discussed in Chapter 8, the best-fitting compounds from sets of candidatescan be hypothesised to represent true ligands (Hermann et al. 2007; Songet al. 2007). It is therefore relevant here to mention studies that benchmark the