HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials
HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials
复制标题
HLaffy：通过学习位置特异性配对电位来估计 1 类 HLA 分子的肽亲和力
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
N. Chandra
中科院分区：
文献类型：
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作者：
S. Mukherjee;C. Bhattacharyya;N. Chandra
动机
T细胞表位是启动适应性免疫应答的分子关键。T细胞表位的鉴定也是合理疫苗设计的关键步骤。大多数可用的方法是由信息学驱动的，并且严重依赖于实验获得的训练数据。对来自免疫表位数据库（IEDB）的几个等位基因的训练集的分析表明，肽空间的采样非常稀疏，覆盖了可能的九聚体空间的一小部分，并且也严重偏斜，从而限制了表位预测的范围。
结果
我们提出了一种新的表位预测方法，有四个不同的计算模块：（i）结构建模，估计统计对电位和约束推导，（ii）隐式建模和相互作用分析，（iii）特征表示和结合亲和力预测和（iv）使用图形模型提取肽序列签名预测HLA I类等位基因的表位。
结论
HLaffy是一种新的有效的表位预测方法，其通过估计肽-HLA复合物的结合强度来预测任何1类HLA等位基因的表位，所述肽-HLA复合物的结合强度是通过学习对肽结合重要的成对电位来实现的。它依赖于肽-HLA识别的机制理解的强度，并提供每个等位基因的总配体空间的估计。HLaffy的性能被认为是优于目前可用的方法上级。
可用性和执行
该方法可通过网络服务器http://proline.biochem.iisc.ernet.in/HLaffy访问
接触
：nchandra@biochem.iisc.ernet.in网站
补充资料
补充数据可在Bioinformatics在线获得。
MOTIVATION
T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction.
RESULTS
We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles.
CONCLUSIONS
HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods.
AVAILABILITY AND IMPLEMENTATION
The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy
CONTACT
: nchandra@biochem.iisc.ernet.in
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.