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='10' and j.lang_id='10' and vi.lang_id='10' Amino acid biophysical properties in the statistical predict | 7920
நோயெதிர்ப்பு ஆராய்ச்சி

நோயெதிர்ப்பு ஆராய்ச்சி
திறந்த அணுகல்

ஐ.எஸ்.எஸ்.என்: 1745-7580

சுருக்கம்

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.

மறுப்பு: இந்த சுருக்கமானது செயற்கை நுண்ணறிவு கருவிகளைப் பயன்படுத்தி மொழிபெயர்க்கப்பட்டது மற்றும் இன்னும் மதிப்பாய்வு செய்யப்படவில்லை அல்லது சரிபார்க்கப்படவில்லை.
Top