Luo Heng, Ye Hao, Ng Hui Wen, Shi Leming, Tong Weida, Mendrick Donna L, Hong Huixiao
National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA. ; University of Arkansas at Little Rock/University of Arkansas for Medical Sciences Bioinformatics Graduate Program, Little Rock, AR, USA.
National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.
Bioinform Biol Insights. 2015 Oct 11;9(Suppl 3):21-9. doi: 10.4137/BBI.S29466. eCollection 2015.
As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA-peptide binding prediction. We also summarize the descriptors based on which the HLA-peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA-peptide binding prediction method based on network analysis.
作为人类主要组织相容性复合体，人类白细胞抗原（HLA）具有将抗原肽呈递给T细胞受体以进行免疫识别和反应的重要功能。解读和预测HLA-肽结合对于研究T细胞表位、免疫反应以及药物不良反应机制至关重要。我们综述了用于HLA-肽结合预测的不同类型机器学习方法和工具。我们还总结了构建HLA-肽结合预测模型所基于的描述符，并讨论了当前方法的局限性和挑战。最后，我们对基于网络分析的HLA-肽结合预测方法给出未来展望。
