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Published on: January 7, 2019
Mechanism-Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy
Wen Zhang1, Shengxin Lu1, Guangyong Zheng1
1State Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine, Shanghai Frontiers Science Center of Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research and Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Abstract:
Oncolytic peptides (OPs) represent a promising class of cancer therapeutics capable of rapidly lysing tumor cells and activating antitumor immunity. However, accurate in silico identification of potent OPs remains challenging due to limited datasets and high false-positive rates. Here, we present MISPOP (Mechanism-Informed Screening Pipeline for Oncolytic Peptides), an integrated machine learning framework that combines eXtreme Gradient Boosting, deep neural networks, and transfer learning into a high-confidence ensemble model augmented with physicochemical priors. Applied to a natural peptide library of 1033 sequences, MISPOP prioritized 16 candidates, among which five were synthesized and evaluated across three tumor cell lines. Dermaseptin-S9 exhibited the most favorable therapeutic index. Molecular dynamics simulations revealed its deep insertion into lipid bilayers and stable peptide-membrane interactions, while in vitro assays confirmed pronounced membrane disruption and induction of immunogenic cell death. In a B16F10 melanoma model, Dermaseptin-S9 achieved over 92% tumor growth inhibition without evident systemic toxicity. Collectively, these findings demonstrate that embedding biochemical priors into ensemble learning can markedly improve predictive accuracy and enable the discovery of potent OPs, offering a generalizable paradigm for accelerating peptide-based oncotherapy development.
Insights
MISPOP, a machine learning tool, identifies potent oncolytic peptides (OPs) for cancer therapy. It successfully discovered Dermaseptin-S9, which effectively inhibited tumor growth with minimal toxicity.
Area of Science:
- Biochemistry
- Computational Biology
- Oncology
Background:
- Oncolytic peptides (OPs) show promise in cancer treatment by destroying tumor cells and stimulating anti-cancer immune responses.
- Identifying effective OPs computationally is difficult due to small datasets and many false positives.
Purpose of the Study:
- To develop an advanced machine learning framework, MISPOP, for accurate in silico identification of potent oncolytic peptides.
- To validate the efficacy of computationally identified peptides in preclinical cancer models.
Main Methods:
- Developed MISPOP, an ensemble machine learning model integrating eXtreme Gradient Boosting, deep neural networks, and transfer learning.
- Augmented the model with physicochemical properties and biochemical priors for enhanced accuracy.
- Screened a library of 1033 natural peptides, synthesized top candidates, and performed in vitro and in vivo evaluations.
Main Results:
- MISPOP prioritized 16 peptide candidates, with five synthesized and tested.
- Dermaseptin-S9 demonstrated a high therapeutic index, effectively disrupting tumor cell membranes and inducing immunogenic cell death.
- In a melanoma model, Dermaseptin-S9 achieved over 92% tumor growth inhibition with no significant systemic toxicity.
Conclusions:
- Integrating biochemical knowledge into ensemble machine learning significantly improves the prediction of potent oncolytic peptides.
- MISPOP offers a generalizable approach to accelerate the discovery and development of peptide-based cancer therapies.
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