SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.
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Authors
Charoenkwan, Phasit
Publication Date
2022-01-04Journal Title
Pharmaceutics
ISSN
1999-4923
Publisher
MDPI AG
Volume
14
Issue
1
Language
eng
Type
Article
This Version
VoR
Metadata
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Charoenkwan, P., Chiangjong, W., Nantasenamat, C., Moni, M. A., Lio, P., Manavalan, B., & Shoombuatong, W. (2022). SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.. Pharmaceutics, 14 (1) https://doi.org/10.3390/pharmaceutics14010122
Abstract
Tumor-homing peptides (THPs) are small peptides that can recognize and bind cancer cells specifically. To gain a better understanding of THPs' functional mechanisms, the accurate identification and characterization of THPs is required. Although some computational methods for in silico THP identification have been proposed, a major drawback is their lack of model interpretability. In this study, we propose a new, simple and easily interpretable computational approach (called SCMTHP) for identifying and analyzing tumor-homing activities of peptides via the use of a scoring card method (SCM). To improve the predictability and interpretability of our predictor, we generated propensity scores of 20 amino acids as THPs. Finally, informative physicochemical properties were used for providing insights on characteristics giving rise to the bioactivity of THPs via the use of SCMTHP-derived propensity scores. Benchmarking experiments from independent test indicated that SCMTHP could achieve comparable performance to state-of-the-art method with accuracies of 0.827 and 0.798, respectively, when evaluated on two benchmark datasets consisting of Main and Small datasets. Furthermore, SCMTHP was found to outperform several well-known machine learning-based classifiers (e.g., decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes and partial least squares regression) as indicated by both 10-fold cross-validation and independent tests. Finally, the SCMTHP web server was established and made freely available online. SCMTHP is expected to be a useful tool for rapid and accurate identification of THPs and for providing better understanding on THP biophysical and biochemical properties.
Keywords
Bioinformatics, Therapeutic peptide, Machine Learning, Propensity Score, Tumor-homing Peptide, Scoring Card Method
Sponsorship
National Research Foundation of Korea (NRF) (2021R1A2C1014338)
Identifiers
PMC8779003, 35057016
External DOI: https://doi.org/10.3390/pharmaceutics14010122
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334301
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