Schueler-Furman O, Elber R, Margalit H
Department of Molecular Genetics and Biotechnology, The Hebrew University, Hadassah Medical School, Jerusalem, Israel.
Fold Des. 1998;3(6):549-64. doi: 10.1016/S1359-0278(98)00070-4.
The binding of T-cell antigenic peptides to MHC molecules is a prerequisite for their immunogenicity. The ability to identify binding peptides based on the protein sequence is of great importance to the rational design of peptide vaccines. As the requirements for peptide binding cannot be fully explained by the peptide sequence per se, structural considerations should be taken into account and are expected to improve predictive algorithms. The first step in such an algorithm requires accurate and fast modeling of the peptide structure in the MHC-binding groove.
We have used 23 solved peptide-MHC class I complexes as a source of structural information in the development of a modeling algorithm. The peptide backbones and MHC structures were used as the templates for prediction. Sidechain conformations were built based on a rotamer library, using the 'dead end elimination' approach. A simple energy function selects the favorable combination of rotamers for a given sequence. It further selects the correct backbone structure from a limited library. The influence of different parameters on the prediction quality was assessed. With a specific rotamer library that incorporates information from the peptide sidechains in the solved complexes, the algorithm correctly identifies 85% (92%) of all (buried) sidechains and selects the correct backbones. Under cross-validation, 70% (78%) of all (buried) residues are correctly predicted and most of all backbones. The interaction between peptide sidechains has a negligible effect on the prediction quality.
The structure of the peptide sidechains follows from the interactions with the MHC and the peptide backbone, as the prediction is hardly influenced by sidechain interactions. The proposed methodology was able to select the correct backbone from a limited set. The impairment in performance under cross-validation suggests that, currently, the specific rotamer library is not satisfactorily representative. The predictions might improve with an increase in the data.
T细胞抗原肽与MHC分子的结合是其免疫原性的前提条件。基于蛋白质序列识别结合肽的能力对于肽疫苗的合理设计至关重要。由于肽结合的要求不能仅由肽序列本身完全解释，因此应考虑结构因素，并有望改进预测算法。这种算法的第一步需要在MHC结合槽中对肽结构进行准确快速的建模。
我们使用23个已解析的肽-MHC I类复合物作为结构信息源来开发建模算法。肽主链和MHC结构用作预测模板。基于旋转异构体库，采用“死端消除”方法构建侧链构象。一个简单的能量函数为给定序列选择旋转异构体的有利组合。它进一步从有限的库中选择正确的主链结构。评估了不同参数对预测质量的影响。使用一个包含已解析复合物中肽侧链信息的特定旋转异构体库，该算法能正确识别所有（埋藏）侧链的85%（92%）并选择正确的主链。在交叉验证下，所有（埋藏）残基的70%（78%）被正确预测，并且大多数主链也被正确预测。肽侧链之间的相互作用对预测质量的影响可忽略不计。
肽侧链的结构源于与MHC和肽主链的相互作用，因为预测几乎不受侧链相互作用的影响。所提出的方法能够从有限的集合中选择正确的主链。交叉验证下性能的下降表明，目前特定的旋转异构体库代表性不够令人满意。随着数据的增加，预测可能会有所改善。
