Resource for benchmarking the applicability of protein structure models
Year: 2012
Type: dissertation
Abstract: The function of a protein is closely related to the structure it attains. The sequence of a\nprotein is of limited biological relevance without some knowledge of both its structure and its\nfunction; protein structures provide a wealth of information that cannot be deduced from their\nprimary sequence alone; therefore, we can get a complete understanding of protein roles by\nanalyzing them in structural terms. Structure-based methodologies are consequently regarded as\nmore robust than sequence-based ones.\nThe limiting step for these structure-based methodologies is actually having the structure of\na protein at hand. Due to the ever-increasing gap between known protein sequences and structures\nand the ever-growing number of protein structure prediction methods available, which are\nbecoming more and more accurate over time, the use of protein structure models is mandatory.\nHowever, and in spite of progress in the field of protein structure prediction, computed\nmodels often contain structural inaccuracies in both backbone and side-chain spatial coordinates;\ninstead of being discarded, these models can provide important insights into the function of the\nnative counterpart; this, in turn, demands the existence of robust methods that can effectively make\nuse of computed models in the midrange and low range of accuracy, routinely produced by\nproteome-scale protein structure modeling projects. Any structure-based algorithm that does not\nrequire high-resolution structures will prove to have a big advantage and an inestimable practical\nvalue.\nModelDB, the tool introduced here, strives to serve as a resource to test any structure-based\nmethod (such as an active site or ligand-binding site predictor) on protein structure models of\ndifferent quality. This has the final goal of benchmarking the applicability of protein structure\nmodels for a given novel algorithm. ModelDB builds sets of models of decreasing quality, which\nwe call decoys, given the sequences experimentally determined proteins. A decoy is a computergenerated protein structure that possesses some characteristics of native proteins, but is not\nbiologically real. Our system is implemented in such a way that any structure-based existing\nmethod can be tested on the real structure and on the decoy models. The next step is to\nautomatically assess at which level of quality the results of the tested method differ from those\nobtained with the native structure.\nEach decoy model is directly compared to its corresponding native structure and precise\nquality scores are computed. For a visual insight on how models of different qualities look like and\ndiffer from the native counterpart in a spatial context, they are "colored" following different colorschemes defined by the following spatial descriptors: Solvent accessibilities, secondary structures,\ncavity occurrences, average depths, protrusion indexes or burial indexes. This, in turn, allows an\neasy visualization and understanding of these parameters' variations in the protein structural\ncontext. Besides, functional annotation is provided when available, in terms of catalytic sites,\nligand-binding sites and other sites of relevance like glycosylation sites.\nThe tool is publicly available either as an on-line tool or a local application for larger\ncalculations; it makes use of other in-house tools that also exists independently on-line and for local\nuse. One of these tools, mappON, colors input structures according to diverse descriptors and\noutputs a table with the descriptors of selected residues (and those surrounding them); thus, it serves\nto analyze properties of key residues in the protein structural context and visually examine the\nresults. The other, MAP, has some features intended to deal with the common problem in\nbioinformatics of mapping sequence residues onto structures, or structure residues onto another\nstructure.\nVery few other public resources exist for readily retrieving decoy sets of protein structures,\nand we indeed have no record of any other automated pipeline for producing such decoys in an easy\nand user-friendly fashion. Our tool, apart from allowing to build new decoy sets for a given protein\na scientist is interested in, covers many more different proteins representing a bigger portion of the\nprotein structural space than any other resource. Furthermore, the on-line version has the advantage\nto let the user visually inspect and compare all the models of ranging quality for a given protein in\nthe same spatial frame. The decoy sets are conceived to test structure-based methods and define to\nwhich extent they can make use of predicted protein structure models. However, the functional\ndocumentation, the model quality estimates and the different color schemes allow many large-scale\nanalyses to be performed as well.
Author: CARBAJO PEDROSA, DANIEL
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7043242313