SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.

Authors
Charoenkwan, Phasit 
Chiangjong, Wararat  ORCID logo  https://orcid.org/0000-0002-2984-3330
Nantasenamat, Chanin  ORCID logo  https://orcid.org/0000-0003-1040-663X
Lio', Pietro 

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Article
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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.

Publication Date
2022-01-04
Online Publication Date
2022-01-04
Acceptance Date
2021-12-28
Keywords
bioinformatics, machine learning, propensity score, scoring card method, therapeutic peptide, tumor-homing peptide
Journal Title
Pharmaceutics
Journal ISSN
1999-4923
1999-4923
Volume Title
14
Publisher
MDPI AG
Sponsorship
National Research Foundation of Korea (NRF) (2021R1A2C1014338)