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Structure-based Design of Peptides with High Affinity and Specificity to HER2 Positive Tumors.

ABSTRACT: To identify peptides with high affinity and specificity against human epidermal growth factor receptor 2 (HER2), a series of peptides were designed based on the structure of HER2 and its Z(HER2:342) affibody. By using a combination protocol of molecular dynamics modeling, MM/GBSA binding free energy calculations, and binding free energy decomposition analysis, two novel peptides with 27 residues, pep27 and pep27-24M, were successfully obtained. Immunocytochemistry and flow cytometry analysis verified that both peptides can specifically bind to the extracellular domain of HER2 protein at cellular level. The Surface Plasmon Resonance imaging (SPRi) analysis showed that dissociation constants (K D) of these two peptides were around 300 nmol/L. Furthermore, fluorescence imaging of peptides against nude mice xenografted with SKBR3 cells indicated that both peptides have strong affinity and high specificity to HER2 positive tumors.

SUBMITTER: Geng L

PROVIDER: S-EPMC4533098 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Structure-based Design of Peptides with High Affinity and Specificity to HER2 Positive Tumors.

Geng Lingling L Wang Zihua Z Yang Xiaoliang X Li Dan D Lian Wenxi W Xiang Zhichu Z Wang Weizhi W Bu Xiangli X Lai Wenjia W Hu Zhiyuan Z Fang Qiaojun Q

Theranostics 20150801 10

To identify peptides with high affinity and specificity against human epidermal growth factor receptor 2 (HER2), a series of peptides were designed based on the structure of HER2 and its Z(HER2:342) affibody. By using a combination protocol of molecular dynamics modeling, MM/GBSA binding free energy calculations, and binding free energy decomposition analysis, two novel peptides with 27 residues, pep27 and pep27-24M, were successfully obtained. Immunocytochemistry and flow cytometry analysis ver ...[more]

PMID: 26284145

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Project description:BACKGROUND:Finding peptides with high binding affinity to Class I major histocompatibility complex (MHC-I) attracts intensive research, and it serves a crucial part of developing a better vaccine for precision medicine. Traditional methods cost highly for designing such peptides. The advancement of computational approaches reduces the cost of new drug discovery dramatically. Compared with flourishing computational drug discovery area, the immunology area lacks tools focused on in silico design for the peptides with high binding affinity. Attributed to the ever-expanding amount of MHC-peptides binding data, it enables the tremendous influx of deep learning techniques for modeling MHC-peptides binding. To leverage the availability of these data, it is of great significance to find MHC-peptides binding specificities. The binding motifs are one of the key components to decide the MHC-peptides combination, which generally refer to a combination of some certain amino acids at certain sites which highly contribute to the binding affinity. RESULT:In this work, we propose the Motif Activation Mapping (MAM) network for MHC-I and peptides binding to extract motifs from peptides. Then, we substitute amino acid randomly according to the motifs for generating peptides with high affinity. We demonstrated the MAM network could extract motifs which are the features of peptides of highly binding affinities, as well as generate peptides with high-affinities; that is, 0.859 for HLA-A*0201, 0.75 for HLA-A*0206, 0.92 for HLA-B*2702, 0.9 for HLA-A*6802 and 0.839 for Mamu-A1*001:01. Besides, its binding prediction result reaches the state of the art. The experiment also reveals the network is appropriate for most MHC-I with transfer learning. CONCLUSIONS:We design the MAM network to extract the motifs from MHC-peptides binding through prediction, which are proved to generate the peptides with high binding affinity successfully. The new peptides preserve the motifs but vary in sequences.

Dataset Information

Structure-based Design of Peptides with High Affinity and Specificity to HER2 Positive Tumors.

Publications

Structure-based Design of Peptides with High Affinity and Specificity to HER2 Positive Tumors.

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