Protein Domain Boundary Predictions: A Structural Biology Perspective

Kirillova, Svetlana; Kumar, Suresh; Carugo, Oliviero

Data were obtained from the CASP7 web page (http://predictioncenter.gc.ucdavis.edu/casp7/). Table 1 shows the bioinformatics tools that are freely available and that were used in CASP7. Protein domain prediction methods can be classified into three main categories [42] : i) homology prediction; ii) domain recognition; iii) new domain prediction methods. The 14 prediction methods regarded in present study include all types of approaches. The homology prediction is presented by the chop [48, 49] methods that assign the query sequence to known PDB chains. Dsp [42] uses in addition more general properties of sequence conservation throughout the protein and it can be considered as lying between domain homology and new domain predictions. Domssea [42] belongs to the domain recognition approaches. It is based on the assumption that secondary structure is a more conserved feature of proteins with similar folds than sequence. Domssea aligns the secondary structure predicted for a query protein against a database of 3D domain structures and derives the domain boundaries from the known domain with the most similar secondary structure. Robetta [50] applies BLAST/PSI-BLAST for domain homology prediction and it uses FFAS03 and 3D-Jury to find remote homologues of known domain structure. Hhpred [51] is a server for remote homology detection and for structure prediction using pairwise comparison of profile hidden Markov models (HMMs). In the foldpro [52] method the structural relevance of the query-template pairs is extracted from global profile-profile alignments in combination with predicted secondary structure, relative solvent accessibility, contact map and beta-strand pairing using support vector machines. Distill [53] provides prediction of Contact Density defined as the Principal Eigenvector (PE) of a residue contact map. This information is an important intermediate step towards ab initio prediction of protein structure and is used to identify domains. Baker generates 3D protein models using the de novo prediction algorithm Rosetta and then assigns domain boundaries using Taylor's structure-based do- maopus http://sigler.bioch.bcm.tmc.edu/CASP7-DOM/ * metadp http://meta-dp.cse.buffalo.edu [54] main identification technique. Maopus performs a template screening with PSI-BLAST and FFAS03. The SKELEFOLD approach implemented in Maopus is a de novo folding algorithm that uses vector representations of secondary structural elements; domain boundaries are defined with three sequence-based filters. In the domfold method, the output from DomSSEA, DISOPRED and HHsearch is parsed to form a consensus. Metadp [54] and NNput are meta servers that comprise a number of domain prediction methods.