select ad.sno,ad.journal,ad.title,ad.author_names,ad.abstract,ad.abstractlink,j.j_name,vi.* from articles_data ad left join journals j on j.journal=ad.journal left join vol_issues vi on vi.issue_id_en=ad.issue_id where ad.sno_en='7920' and ad.lang_id='9' and j.lang_id='9' and vi.lang_id='9' Amino acid biophysical properties in the statistical predict | 7920
రోగనిరోధక పరిశోధన

రోగనిరోధక పరిశోధన
అందరికి ప్రవేశం

ISSN: 1745-7580

నైరూప్య

Amino acid biophysical properties in the statistical prediction of peptide-MHC class I binding

Surajit Ray and Thomas B Kepler

Background: A key step in the development of an adaptive immune response to pathogens or vaccines is the binding of short peptides to molecules of the Major Histocompatibility Complex (MHC) for presentation to T lymphocytes, which are thereby activated and differentiate into effector and memory cells. The rational design of vaccines consists in part in the identification of appropriate peptides to effect this process. There are several algorithms currently in use for making such predictions, but these are limited to a small number of MHC molecules and have good but imperfect prediction power. Results: We have undertaken an exploration of the power gained by taking advantage of a natural representation of the amino acids in terms of their biophysical properties. We used several wellknown statistical classifiers using either a naive encoding of amino acids by name or an encoding by biophysical properties. In all cases, the encoding by biophysical properties leads to substantially lower misclassification error. Conclusion: Representation of amino acids using a few important bio-physio-chemical property provide a natural basis for representing peptides and greatly improves peptide-MHC class I binding prediction.

నిరాకరణ: ఈ సారాంశం కృత్రిమ మేధస్సు సాధనాలను ఉపయోగించి అనువదించబడింది మరియు ఇంకా సమీక్షించబడలేదు లేదా ధృవీకరించబడలేదు.
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