Dataset Information

Homology modeling and epitope prediction of Der f 33.

ABSTRACT: Dermatophagoides farinae (Der f), one of the main species of house dust mites, produces more than 30 allergens. A recently identified allergen belonging to the alpha-tubulin protein family, Der f 33, has not been characterized in detail. In this study, we used bioinformatics tools to construct the secondary and tertiary structures and predict the B and T cell epitopes of Der f 33. First, protein attribution, protein patterns, and physicochemical properties were predicted. Then, a reasonable tertiary structure was constructed by homology modeling. In addition, six B cell epitopes (amino acid positions 34-45, 63-67, 103-108, 224-230, 308-316, and 365-377) and four T cell epitopes (positions 178-186, 241-249, 335-343, and 402-410) were predicted. These results established a theoretical basis for further studies and eventual epitope-based vaccine design against Der f 33.

SUBMITTER: Teng F

PROVIDER: S-EPMC5875910 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Homology modeling and epitope prediction of Der f 33.

Teng Feixiang F Sun Jinxia J Yu Lili L Li Qisong Q Cui Yubao Y

Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas 20180315 5

Dermatophagoides farinae (Der f), one of the main species of house dust mites, produces more than 30 allergens. A recently identified allergen belonging to the alpha-tubulin protein family, Der f 33, has not been characterized in detail. In this study, we used bioinformatics tools to construct the secondary and tertiary structures and predict the B and T cell epitopes of Der f 33. First, protein attribution, protein patterns, and physicochemical properties were predicted. Then, a reasonable tert ...[more]

PMID: 29561952

Similar Datasets

Project description:Rhodopsins are seven α-helical membrane proteins that are of great importance in chemistry, biology, and modern biotechnology. Any in silico study on rhodopsin properties and functioning requires a high-quality three-dimensional structure. Due to particular difficulties with obtaining membrane protein structures from the experiment, in silico prediction of the three-dimensional rhodopsin structure based only on its primary sequence is an especially important task. For the last few years, significant progress was made in the field of protein structure prediction, especially for methods based on comparative modeling. However, the majority of this progress was made for soluble proteins and further investigations are needed to achieve similar progress for membrane proteins. In this paper, we evaluate the performance of modern protein structure prediction methodologies (implemented in the Medeller, I-TASSER, and Rosetta packages) for their ability to predict rhodopsin structures. Three widely used methodologies were considered: two general methodologies that are commonly applied to soluble proteins and a methodology that uses constraints that are specific for membrane proteins. The test pool consisted of 36 target-template pairs with different sequence similarities that was constructed on the basis of 24 experimental rhodopsin structures taken from the RCSB database. As a result, we showed that all three considered methodologies allow obtaining rhodopsin structures with the quality that is close to the crystallographic one (root mean square deviation (RMSD) of the predicted structure from the corresponding X-ray structure up to 1.5 Å) if the target-template sequence identity is higher than 40%. Moreover, all considered methodologies provided structures of average quality (RMSD < 4.0 Å) if the target-template sequence identity is higher than 20%. Such structures can be subsequently used for further investigation of molecular mechanisms of protein functioning and for the development of modern protein-based biotechnologies.

Dataset Information

Homology modeling and epitope prediction of Der f 33.

Publications

Homology modeling and epitope prediction of Der f 33.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets