SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids
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 identifica...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Pharmaceutics |
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| Online Access: | https://www.mdpi.com/1999-4923/14/1/122 |
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| author | Phasit Charoenkwan Wararat Chiangjong Chanin Nantasenamat Mohammad Ali Moni Pietro Lio’ Balachandran Manavalan Watshara Shoombuatong |
| author_facet | Phasit Charoenkwan Wararat Chiangjong Chanin Nantasenamat Mohammad Ali Moni Pietro Lio’ Balachandran Manavalan Watshara Shoombuatong |
| author_sort | Phasit Charoenkwan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:43:08Z |
| format | Article |
| id | doaj.art-658b529cefc34c3ca01527a5bdc0dad8 |
| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-10T00:43:08Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj.art-658b529cefc34c3ca01527a5bdc0dad82023-11-23T15:04:14ZengMDPI AGPharmaceutics1999-49232022-01-0114112210.3390/pharmaceutics14010122SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino AcidsPhasit Charoenkwan0Wararat Chiangjong1Chanin Nantasenamat2Mohammad Ali Moni3Pietro Lio’4Balachandran Manavalan5Watshara Shoombuatong6Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, ThailandPediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, ThailandCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandArtificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, AustraliaDepartment of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, UKDepartment of Physiology, Ajou University School of Medicine, Suwon 16499, KoreaCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandTumor-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.https://www.mdpi.com/1999-4923/14/1/122tumor-homing peptidetherapeutic peptidescoring card methodpropensity scoremachine learningbioinformatics |
| spellingShingle | Phasit Charoenkwan Wararat Chiangjong Chanin Nantasenamat Mohammad Ali Moni Pietro Lio’ Balachandran Manavalan Watshara Shoombuatong SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids Pharmaceutics tumor-homing peptide therapeutic peptide scoring card method propensity score machine learning bioinformatics |
| title | SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids |
| title_full | SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids |
| title_fullStr | SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids |
| title_full_unstemmed | SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids |
| title_short | SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids |
| title_sort | scmthp a new approach for identifying and characterizing of tumor homing peptides using estimated propensity scores of amino acids |
| topic | tumor-homing peptide therapeutic peptide scoring card method propensity score machine learning bioinformatics |
| url | https://www.mdpi.com/1999-4923/14/1/122 |
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