1. PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.
- Author
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Zhai S, Tan Y, Zhu C, Zhang C, Gao Y, Mao Q, Zhang Y, Duan H, and Yin Y
- Subjects
- Peptides, Cyclic chemistry, Peptides, Cyclic pharmacology, Peptides, Cyclic chemical synthesis, Macrocyclic Compounds chemistry, Macrocyclic Compounds pharmacology, Macrocyclic Compounds chemical synthesis, Molecular Structure, Humans, Peptides chemistry, Peptides pharmacology, Structure-Activity Relationship, Dose-Response Relationship, Drug, Deep Learning
- Abstract
Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer's effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC
50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer's skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)- Published
- 2024
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