Dataset Information

Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning.

ABSTRACT: The placing of novel or new-in-the-context proteins on the market, appearing in genetically modified foods, certain bio-pharmaceuticals and some household products leads to human exposure to proteins that may elicit allergic responses. Accurate methods to detect allergens are therefore necessary to ensure consumer/patient safety. We demonstrate that it is possible to reach a new level of accuracy in computational detection of allergenic proteins by presenting a novel detector, Detection based on Filtered Length-adjusted Allergen Peptides (DFLAP). The DFLAP algorithm extracts variable length allergen sequence fragments and employs modern machine learning techniques in the form of a support vector machine. In particular, this new detector shows hitherto unmatched specificity when challenged to the Swiss-Prot repository without appreciable loss of sensitivity. DFLAP is also the first reported detector that successfully discriminates between allergens and non-allergens occurring in protein families known to hold both categories. Allergenicity assessment for specific protein sequences of interest using DFLAP is possible via ulfh@slv.se.

SUBMITTER: Soeria-Atmadja D

PROVIDER: S-EPMC1540723 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning.

Soeria-Atmadja D D Lundell T T Gustafsson M G MG Hammerling U U

Nucleic acids research 20060823 13

The placing of novel or new-in-the-context proteins on the market, appearing in genetically modified foods, certain bio-pharmaceuticals and some household products leads to human exposure to proteins that may elicit allergic responses. Accurate methods to detect allergens are therefore necessary to ensure consumer/patient safety. We demonstrate that it is possible to reach a new level of accuracy in computational detection of allergenic proteins by presenting a novel detector, Detection based on ...[more]

PMID: 16977698

Similar Datasets

Project description:KIBRA-initially identified as a neuronal associated protein is now shown to be functionally associated with other tissue types as well. KIBRA interacts with dyenin light chain 1 and this interaction is essential for oestrogen receptor transactivation in breast cancer cells. KIBRA as a substrate of Cdk1, Aurora kinase and ERK plays an important role in regulating cell cycle, cell proliferation and migration. Despite these evidences, the exact role of KIBRA in cancer progression is not known. We studied the expression of KIBRA in breast tissues and breast cancer cell lines by western blotting, immunohistochemisry (IHC) and RT-PCR. Stable over expression and knockdown clones were generated to study the transforming properties of KIBRA by conventional assays. Xenograft studies were performed in nude mice to study the in vivo tumourigenic efficacy of KIBRA. qPCR array was performed to understand the molecular mechanism behind oncogenic activity of KIBRA. Our results showed that KIBRA is upregulated in breast cancer cells and in malignant human breast tumours by both western blotting and IHC. Interestingly, we found that KIBRA expression level goes up with increase in breast cancer progression in well-established MCF10A model system. Further, results from stable overexpression clones of KIBRA in fibroblasts (Rat-1) and epithelial breast cancer cells (ZR75) and lentiviral short hairpin RNA-mediated knockdown (KD) clones of KIBRA in ZR75 showed increase in transforming properties with KIBRA overexpression and vice-versa. Results also showed that fibroblasts stably overexpressing KIBRA showed increased tumourigenic potential in nude mice. By adopting a quantitative PCR array-based approach, we identified RASSF1A, a tumour suppressor, as a transcriptional target of KIBRA. This is the first study to demonstrate the in vivo tumourigenic property of KIBRA in a nude mouse model and also unravel the underlying molecular mechanism of KIBRA-mediated transformation via repression of RASSF1A.British Journal of Cancer advance online publication, 29 June 2017; doi:10.1038/bjc.2017.192 www.bjcancer.com.

Dataset Information

Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning.

Publications

Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets