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

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cam.issuedOnline2022-01-04
dc.contributor.authorCharoenkwan, Phasit
dc.contributor.authorChiangjong, Wararat
dc.contributor.authorNantasenamat, Chanin
dc.contributor.authorMoni, Mohammad Ali
dc.contributor.authorLio', Pietro
dc.contributor.authorManavalan, Balachandran
dc.contributor.authorShoombuatong, Watshara
dc.contributor.orcidChiangjong, Wararat [0000-0002-2984-3330]
dc.contributor.orcidNantasenamat, Chanin [0000-0003-1040-663X]
dc.contributor.orcidMoni, Mohammad Ali [0000-0003-0756-1006]
dc.contributor.orcidManavalan, Balachandran [0000-0003-0697-9419]
dc.contributor.orcidShoombuatong, Watshara [0000-0002-3394-8709]
dc.date.accessioned2022-02-22T02:03:26Z
dc.date.available2022-02-22T02:03:26Z
dc.date.issued2022-01-04
dc.date.updated2022-02-22T02:03:24Z
dc.description.abstractTumor-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.
dc.identifier.doi10.17863/CAM.81714
dc.identifier.eissn1999-4923
dc.identifier.issn1999-4923
dc.identifier.otherPMC8779003
dc.identifier.other35057016
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334301
dc.languageeng
dc.language.isoeng
dc.publisherMDPI AG
dc.publisher.urlhttp://dx.doi.org/10.3390/pharmaceutics14010122
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 101534003
dc.sourceessn: 1999-4923
dc.subjectbioinformatics
dc.subjectmachine learning
dc.subjectpropensity score
dc.subjectscoring card method
dc.subjecttherapeutic peptide
dc.subjecttumor-homing peptide
dc.titleSCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.
dc.typeArticle
dcterms.dateAccepted2021-12-28
prism.issueIdentifier1
prism.publicationNamePharmaceutics
prism.volume14
pubs.funder-project-idNational Research Foundation of Korea (NRF) (2021R1A2C1014338)
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
rioxxterms.versionVoR
rioxxterms.versionofrecord10.3390/pharmaceutics14010122

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