Wenzhen Li, Jinyi Wei, Qianhu Jiang, Yuwei Zhou, Xingru Yan, Changcheng Xiang* and Jian Huang* Pages 1 - 11 ( 11 )
The application of therapeutic peptides in clinical practice has significantly progressed in the past decades. However, immunogenicity remains an inevitable and crucial issue in the development of therapeutic peptides. The prediction of antigenic peptides presented by MHC class II is a critical approach to evaluating the immunogenicity of therapeutic peptides. With the continuous upgrade of algorithms and databases in recent years, the prediction accuracy has been significantly improved. This has made in silico evaluation an important component of immunogenicity assessment in therapeutic peptide development. In this review, we summarize the development of peptide-MHC-II binding prediction methods for antigenic peptides presented by MHC class II molecules and provide a systematic explanation of the most advanced ones, aiming to deepen our understanding of this field that requires particular attention.
Therapeutic peptides, immunogenicity, anti-drug antibodies, major histocompatibility complex, machine learning, deep learning, human leukocyte antigen.