基于知识的MHC I类结合肽结构预测:23个配合物的研究

Ora Schueler-Furman , Ron Elber , Hanah Margalit
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引用次数: 51

摘要

背景:t细胞抗原肽与MHC分子的结合是其免疫原性的先决条件。基于蛋白质序列识别结合肽的能力对肽疫苗的合理设计具有重要意义。由于肽序列本身不能完全解释肽结合的要求,因此应考虑结构方面的考虑,并有望改进预测算法。这种算法的第一步需要对mhc结合槽中的肽结构进行准确和快速的建模。结果:我们在建模算法的开发中使用了23个已解决的肽- mhc I类复合物作为结构信息的来源。以多肽骨架和MHC结构作为预测模板。侧链构象是基于旋转体库构建的,使用“死角消除”方法。一个简单的能量函数选择一个给定序列的转子的有利组合。它进一步从有限的库中选择正确的主干结构。评估了不同参数对预测质量的影响。该算法利用包含已解复合物中肽侧链信息的特定旋转体库,正确识别85%(92%)的侧链并选择正确的主干链。在交叉验证下,70%(78%)的残基(埋藏的残基)和大部分主干被正确预测。肽侧链之间的相互作用对预测质量的影响可以忽略不计。结论:多肽侧链的结构取决于与MHC和多肽主链的相互作用,预测结果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出正确的主干。交叉验证下的性能损害表明,目前,特定的转子库不具有令人满意的代表性。随着数据的增加,预测可能会有所改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge-based structure prediction of MHC class I bound peptides: a study of 23 complexes

Background: 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.

Results: 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.

Conclusions: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.

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