Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes
Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes
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DOI：
10.1002/prot.20831
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发表时间：
2006-05-15
影响因子：
2.9
通讯作者：
Abagyan, R
中科院分区：
文献类型：
--
作者：
Bordner, AJ;Abagyan, R
由于确定所有肽-MHC 复合物的晶体结构是不可行的，因此准确预测构象是一个关键的计算问题。这些模型可用于确定结合能量、预测与 T 细胞受体的特定三元复合物的结构以及设计与这些复合物相互作用的新分子。主要困难是（1）对柔性肽的大量构象自由度进行充分采样，（2）预测结合后 MHC 界面几何形状的细微变化，以及（3）为许多结构未知的 MHC 同种异型建立模型。之前的研究通过将构象变量划分为不同的集合并分别预测来解决采样问题，而我们在内坐标中改进了偏概率蒙特卡罗对接协议，以同时优化所有肽变量的物理能量函数。我们还通过对接到 MHC 的更宽松的平滑网格表示来模仿诱导拟合，然后使用全原子 MHC 模型进行细化和重新排序。我们的方法通过比较 14 种肽交叉对接至 HLA-A*0201 和 9 种肽交叉对接至 H-2K(b) 的结果以及将肽对接至五种不同 HLA 同种异型的同源模型与一套全面的实验结构的结果进行了测试。对于与原始结合肽不同的高度灵活的十肽的交叉对接，令人惊讶地准确预测（0.75 埃主链 RMSD），以及使用同源模型对两种同种异型（平均主链 RMSD 低于 1.0 埃）进行对接预测，说明了该方法的有效性。最后，将使用预测结构计算的能量项与大数据集上的监督学习相结合，将肽分类为 HLA-A*0201 结合物或非结合物。与基于序列的预测方法相比，该模型还能够预测肽与不用于训练的不同 MHC 同种异型 (H-2K(b)) 的结合亲和力，具有相当的预测精度。
Since determining the crystallographic structure of all peptide-MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T-cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased-Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all-atom MHC model. Our method was tested by a comparison of the results of cross-docking 14 peptides into HLA-A*0201 and 9 peptides into H-2K(b) as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 angstrom backbone RMSD) for cross-docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 angstrom illustrate the method's effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA-A*0201 binders or nonbinders. In contrast with sequence-based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H-2K(b)), not used for training, with comparable prediction accuracy.