Dataset Information

A new approach to assess and predict the functional roles of proteins across all known structures.

ABSTRACT: The three dimensional atomic structures of proteins provide information regarding their function; and codified relationships between structure and function enable the assessment of function from structure. In the current study, a new data mining tool was implemented that checks current gene ontology (GO) annotations and predicts new ones across all the protein structures available in the Protein Data Bank (PDB). The tool overcomes some of the challenges of utilizing large amounts of protein annotation and measurement information to form correspondences between protein structure and function. Protein attributes were extracted from the Structural Biology Knowledgebase and open source biological databases. Based on the presence or absence of a given set of attributes, a given protein's functional annotations were inferred. The results show that attributes derived from the three dimensional structures of proteins enhanced predictions over that using attributes only derived from primary amino acid sequence. Some predictions reflected known but not completely documented GO annotations. For example, predictions for the GO term for copper ion binding reflected used information a copper ion was known to interact with the protein based on information in a ligand interaction database. Other predictions were novel and require further experimental validation. These include predictions for proteins labeled as unknown function in the PDB. Two examples are a role in the regulation of transcription for the protein AF1396 from Archaeoglobus fulgidus and a role in RNA metabolism for the protein psuG from Thermotoga maritima.

SUBMITTER: Julfayev ES

PROVIDER: S-EPMC3089730 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The IgE response has been associated with both allergic reactions and immunity to metazoan parasites. Recently, we hypothesized that all environmental allergens bear structural homology to IgE-binding antigens from metazoan parasites and that this homology defines the relatively small number of protein families containing allergenic targets. In this study, known allergen structures (Pfam domains) from major environmental allergen families were used to predict allergen-like (SmProfilin, SmVAL-6, SmLipocalin, SmHSP20, Sm triosephosphate isomerase, SmThioredoxin, Sm superoxide dismutase, SmCyclophilin, and Sm phosphoglycerate kinase) and non-allergen-like [Sm dynein light chain (SmDLC), SmAldolase SmAK, SmUbiquitin, and Sm14-3-3] proteins in Schistosoma mansoni. Recombinant antigens were produced in Escherichia coli and IgG1, IgG4, and IgE responses against them measured in a cohort of people (n = 222) infected with S. mansoni. All allergen-like antigens were targeted by IgE responses in infected subjects, whilst IgE responses to the non-allergen-like antigens, SmAK, SmUbiquitin, and Sm14-3-3 were essentially absent being of both low prevalence and magnitude. Two new IgE-binding Pfam domain families, not previously described in allergen family databases, were also found, with prevalent IgE responses against SmDLC (PF01221) and SmAldolase (PF00274). Finally, it was demonstrated that immunoregulatory serological processes typically associated with allergens also occurred in responses to allergen-like proteins in S. mansoni infections, including the production of IgG4 in people responding with IgE and the down-regulation of IgE in response to increased antigen exposure from S. mansoni eggs. This study establishes that structures of known allergens can be used to predict IgE responses against homologous parasite allergen-like molecules (parallergens) and that serological responses with IgE/IgG4 to parallergens mirror those seen against allergens, supporting our hypothesis that allergenicity is rooted in expression of certain protein domain families in metazoan parasites.

Project description:BACKGROUND:Most methods for predicting functional sites in protein 3D structures, rely on information on related proteins and cannot be applied to proteins with no known relatives. Another limitation of these methods is the lack of a well annotated set of functional sites to use as benchmark for validating their predictions. Experimental findings and theoretical considerations suggest that residues involved in function often contribute unfavorably to the native state stability. We examine the possibility of systematically exploiting this intrinsic property to identify functional sites using an original procedure that detects destabilizing regions in protein structures. In addition, to relate destabilizing regions to known functional sites, a novel benchmark consisting of a diverse set of hand-curated protein functional sites is derived. RESULTS:A procedure for detecting clusters of destabilizing residues in protein structures is presented. Individual residue contributions to protein stability are evaluated using detailed atomic models and a force-field successfully applied in computational protein design. The most destabilizing residues, and some of their closest neighbours, are clustered into destabilizing regions following a rigorous protocol. Our procedure is applied to high quality apo-structures of 63 unrelated proteins. The biologically relevant binding sites of these proteins were annotated using all available information, including structural data and literature curation, resulting in the largest hand-curated dataset of binding sites in proteins available to date. Comparing the destabilizing regions with the annotated binding sites in these proteins, we find that the overlap is on average limited, but significantly better than random. Results depend on the type of bound ligand. Significant overlap is obtained for most polysaccharide- and small ligand-binding sites, whereas no overlap is observed for most nucleic acid binding sites. These differences are rationalised in terms of the geometry and energetics of the binding site. CONCLUSION:We find that although destabilizing regions as detected here can in general not be used to predict binding sites in protein structures, they can provide useful information, particularly on the location of functional sites that bind polysaccharides and small ligands. This information can be exploited in methods for predicting function in protein structures with no known relatives. Our publicly available benchmark of hand-curated functional sites in proteins should help other workers derive and validate new prediction methods.

Dataset Information

A new approach to assess and predict the functional roles of proteins across all known structures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets