Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences

Angiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inhibiting angiogenesis, are promising candidates in cancer treatment. We propose AAPL, a sequence-based predictor to identify AAPs with machine learning...

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Bibliographic Details
Published in:Scientific reports 2024-06, Vol.14 (1), p.14387-10, Article 14387
Main Authors: Lee, Yun-Chen, Yu, Jen-Chieh, Ni, Kuan, Lin, Yu-Chuan, Chen, Ching-Tai
Format: Article
Language:English
Subjects:
631/114/1305
631/114/2397
Algorithms
Amino Acid Sequence
Amino acids
Angiogenesis
Angiogenesis Inhibitors - chemistry
Angiogenesis Inhibitors - pharmacology
Cancer therapies
Cell proliferation
Correlation coefficient
Humanities and Social Sciences
Humans
Hydrophobicity
Learning algorithms
Machine Learning
Metastases
multidisciplinary
Peptides
Peptides - chemistry
Peptides - pharmacology
Predictions
Science
Science (multidisciplinary)
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Summary:Angiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inhibiting angiogenesis, are promising candidates in cancer treatment. We propose AAPL, a sequence-based predictor to identify AAPs with machine learning models of improved prediction accuracy. Each peptide sequence was transformed to a vector of 4335 numeric values according to 58 different feature types, followed by a heuristic algorithm for feature selection. Next, the hyperparameters of six machine learning models were optimized with respect to the feature subset. We considered two datasets, one with entire peptide sequences and the other with 15 amino acids from peptide N-termini. AAPL achieved Matthew’s correlation coefficients of 0.671 and 0.756 for independent tests based on the two datasets, respectively, outperforming existing predictors by a range of 5.3% to 24.6%. Further analyses show that AAPL yields higher prediction accuracy for peptides with more hydrophobic residues, and fewer hydrophilic and charged residues. The source code of AAPL is available at https://github.com/yunzheng2002/Anti-angiogenic .
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-65062-9