Predicting Mhc Class II Restricted T-Cell Epitopes

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Project title

Code/計畫編號

NSC100-2311-B019-001

Translated Name/計畫中文名

第二類型主要組織相容性複合物T細胞抗原決定位的預測

Project Coordinator/計畫主持人

Kuan Y. Chang

Funding Organization/主管機關

National Science and Technology Council

Department/Unit

Department of Computer Science and Engineering

Website

https://www.grb.gov.tw/search/planDetail?id=2359754

Year

2011

Start date/計畫起

01-08-2011

Expected Completion/計畫迄

31-07-2012

Bugetid/研究經費

700千元

ResearchField/研究領域

生物科學
資訊科學--軟體

"第二類型主要組織相容性複合物T細胞抗原決定位 (MHC class II restricted T-cell epitopes) 的預測主要是用電腦幫助辨別新的、未知的T細胞抗原決定位。已經有不少預測方法應用在此問題，但這些方法並無提供令人滿意的實驗結果。問題出在這些計算方法並非針對免疫問題設計、無完整地運用序列與結構的資訊和忽略生物醫學科學家真正的需要。本計劃的目標就是對T細胞抗原決定位做詳細的檢查，進而建立一個強而有力的預測系統。三大研究方向，分別是T細胞抗原決定位的序列對齊方法、以原子為主的黏合潛能和預測的準確度 (precision)。據我們所知，還未有人對這些重要的研究方向，在此問題上做詳盡探討。三個具體的研究目標如下: (1) 設計一套多用途以化學結構特徵為底的多序列對齊方式，來協助對齊多變的T細胞抗原決定位序列。 (2) 檢驗以統計方式、原子為主的黏合潛能，來辨別能與第二類型主要組織相容性複合物作用的肽。(3) 延續我們之前的研究，評估以不同的方式，來提升T細胞抗原決定位預測的準確度。最後，為驗證此電腦模型，將以實驗的方式檢驗被預測、新的T細胞抗原決定位。""Predicting MHC class II restricted T-cell epitopes aims to identify novel epitopes in vivo. Many computational approaches have been applied to this problem, but none provides a satisfying experimental result. The complexity of this problem are hardly captured by the computational methods because these methods were not originally designed for immunological purposes, do not fully utilize the sequence and structure information, and pay little attention to what biomedical researchers want. The purpose of this proposal is to establish a powerful and robust prediction system by dissecting the nature of T-cell epitopes. Three important aspects of this problem, to our knowledge none has been examined thoroughly, will be studied: epitope sequence alignments, atom-based binding potentials, and prediction precision. The three specific aims are as follows: (1) Develop a novel chemical-structure-based multiple sequence alignment of peptides by a Gibbs sampling algorithm. (2) Investigate statistical atom-based potentials in distinguishing peptides bound to MHC class II molecules. (3) Evaluate methodology in enhancing prediction precision for identifying MHC class II restricted T-cell epitopes. Besides, in order to validate the computational model, novel T-cell epitopes predicted by this model will be examined experimentally"

Keyword(s)

生物資訊
計算生物學
T細胞抗原決定位的預測
第二類型主要組織相容性複合物
Bioinformatics
Computational Biology
T-cell Epitope prediction
MHC class II molecules

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Predicting Mhc Class II Restricted T-Cell Epitopes

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