Computer-Assisted Protein Domain Boundary Prediction Using the Dom-Pred Server
Kevin Bryson, Domenico Cozzetto and David T. Jones
Affiliation: Department of Computer Science, University College London, Gower Street, London WC1E 6BT,United Kingdom.
Domain prediction from sequence is a particularly challenging task, and currently, a large variety of different methodologies are employed to tackle the task. Here we try to classify these diverse approaches into a number of broad categories. Completely automatic domain prediction from sequence alone is currently fraught with problems, but this should not be so surprising since human experts currently have significant disagreement on domain assignment even when given the structures. It can be argued that we should only test the domain prediction methods on benchmark data that human experts agree upon and this is the approach we take in this paper. Even for the data sets on which human experts agree, automatic structure-based domain assignment still cannot always agree, and so again it is still unlikely that domain prediction methods will reliably obtain correct results completely automatically. We make the argument that computerassisted domain prediction is a more achievable goal. With this aim in mind, we present the DomPred server. This server provides the user with the results from two completely different categories of method (DPS and DomSSEA). In this paper, each method is individually benchmarked against one of the latest domain prediction benchmarks to provide information about their respective reliabilities. A variety of different benchmark scores are employed since the accuracy of a domain prediction method depends critically on what types of results one wishes to obtain (single/multi-domain classification, domain number, residue linker positions, etc.). Also both of these methods, implemented within the DomPred server, can suggest alternative domain predictions, allowing the user to make the final decision based on these results and applying their own background knowledge to the problem. The DomPred server is available from the URL: http://bioinf.cs.ucl.ac.uk/software.html.
Keywords: domain homology prediction methods, domain recognition methods, PSI-BLAST, DomSSEA Method, benchmarking, Normalized Domain Overlap (NDO)
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