From Protein Structure to Function with Bioinformatics.pdf

300 I.A. Cymerman et al.larity cases contain less information in the template about the size and physicochemicalproperties of particular residues in the target. However, not all structure-derivedproperties provide additional information with respect to the template. For SDPs thatdepend mostly on position of residues, such as exposure state, neighbourhood of buriedresidues and number of surface pockets, models do not provide added-value. It isprobably caused by the fact that buried residues are more conserved than exposedresidues, comprising protein cores that are responsible for protein integrity.For other SDPs, such as neighbourhood of exposed residues and total accessiblesurface area (ASA), models show some added-value. This is very important as residuesaccessible to the solvent are responsible for interactions with other molecules,thus determining the biological function of the protein.Finally, for properties that strongly depend on the physicochemical characteristicsof the amino acids in the sequence, such as composition of pockets andelectrostatic potential, models show large added-value. The identification ofcharged regions is of large value as they may represent binding or active sites (seeChapter 7).To summarize: generally the studies performed by Chakravarty et al. demonstratethat, with the exception of the detection of pockets, most model-derivedstructural properties exhibit some level of added-value. The more a given propertydepends on the sequence of the protein the more useful a model will be in estimatingthe value of that property. Encouragingly, depending on the feature, 25–40%sequence identity between target and template was sufficient to produce a SDPestimate of comparable accuracy to that available from an NMR structure.12.3.1 ImplementationThe knowledge about the added-value of particular structural and surface propertiesof models raises the question whether they can be also useful for the functionprediction. In 1998 Fetrow and Skolnick proposed a multi-step procedure thatenables identification of protein functional sites in low-to-moderate resolutionmodels (Fetrow and Skolnick 1998). Based on geometry, residue identity, distancesbetween alpha carbons and conformation, the active site residues become athree dimensional descriptor termed Fuzzy Functional Form (FFF). AfterwardsFFFs are used to screen the set of three dimensional models to identify withinthem those containing similar structural motifs. The usefulness of the method wasproved by the identification of the novel members of the disulphide glutaredoxin/thioredoxin protein family within in the yeast (Fetrow and Skolnick 1998) and E.coli genomes (Fetrow et al. 1998), whose function could not be identified bysequence comparison methods. The great advantage of FFF and related approachesis that the method distinguishes protein pairs with similar active sites fromproteins pairs that may have similar folds, but not necessarily similar active sites.The FFF technology was further developed to the method called active siteprofiling (Cammer et al. 2003) and was successfully combined with experimental