{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T09:33:40Z","timestamp":1787045620018,"version":"3.56.0"},"reference-count":79,"publisher":"American Chemical Society (ACS)","issue":"12","license":[{"start":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T00:00:00Z","timestamp":1603411200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T00:00:00Z","timestamp":1603411200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T00:00:00Z","timestamp":1603411200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100002842","name":"Chiang Mai University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002842","id-type":"DOI","asserted-by":"publisher"}]},{"name":"College of Arts, Media and Technology, Chiang Mai University"},{"DOI":"10.13039\/100012527","name":"Office of the Higher Education Commission","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100012527","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004396","name":"Thailand Research Fund","doi-asserted-by":"crossref","award":["MRG6180222"],"award-info":[{"award-number":["MRG6180222"]}],"id":[{"id":"10.13039\/501100004396","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004396","name":"Thailand Research Fund","doi-asserted-by":"crossref","award":["MRG6180226"],"award-info":[{"award-number":["MRG6180226"]}],"id":[{"id":"10.13039\/501100004396","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004396","name":"Thailand Research Fund","doi-asserted-by":"crossref","award":["RSA6280075"],"award-info":[{"award-number":["RSA6280075"]}],"id":[{"id":"10.13039\/501100004396","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Umami or the taste of monosodium glutamate represents one of the major attractive taste modalities in humans. Therefore, knowledge about biophysical and biochemical properties of the umami taste is important for both scientific research and the food industry. Experimental approaches for predicting umami peptides are labor intensive, time consuming, and expensive. To date, computational models for the prediction and analysis of umami peptides as a function of sequence information have not been developed yet. In this study, we have proposed the first sequence-based predictor named iUmami-SCM using primary sequence information for the identification and characterization of umami peptides. iUmami-SCM utilized a newly developed scoring card method (SCM) in conjunction with the propensity scores of amino acids and dipeptide. Our predictor demonstrated excellent prediction performance ability for predicting umami peptides as well as outperforming other commonly used machine learning classifiers. Particularly, iUmami-SCM afforded the highest accuracy and Matthews correlation coefficient of 0.865 and 0.679, respectively, on an independent data set. Furthermore, the analysis of SCM-derived propensity scores was performed so as to provide a more in-depth understanding and knowledge of biophysical and biochemical properties of umami intensities of peptides. To develop a convenient bioinformatics tool, the best model is deployed as a web server that is made publicly available at http:\/\/camt.pythonanywhere.com\/iUmami-SCM. The iUmami-SCM, as presented herein, serves as a powerful computational technique for large-scale umami peptide identification as well as facilitating the interpretation of umami peptides.<\/jats:p>","DOI":"10.1021\/acs.jcim.0c00707","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T07:08:40Z","timestamp":1603436920000},"page":"6666-6678","source":"Crossref","is-referenced-by-count":185,"title":["iUmami-SCM: A Novel Sequence-Based Predictor for Prediction\nand Analysis of Umami Peptides Using a Scoring Card Method with Propensity\nScores of Dipeptides"],"prefix":"10.1021","volume":"60","author":[{"given":"Phasit","family":"Charoenkwan","sequence":"first","affiliation":[{"name":"Chiang Mai University , , ,","place":["Chiang Mai, Thailand, 50200"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janchai","family":"Yana","sequence":"additional","affiliation":[{"name":"Chiang Mai Rajabhat University , , ,","place":["Chiang Mai, Thailand, 50300"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1040-663X","authenticated-orcid":true,"given":"Chanin","family":"Nantasenamat","sequence":"additional","affiliation":[{"name":"Mahidol University , , ,","place":["Bangkok, Thailand, 10700"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Mehedi","family":"Hasan","sequence":"additional","affiliation":[{"name":"Kyushu Institute of Technology , , 680-4 Kawazu , , ,","place":["Iizuka, Fukuoka, Japan, 820-8502"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3394-8709","authenticated-orcid":true,"given":"Watshara","family":"Shoombuatong","sequence":"additional","affiliation":[{"name":"Mahidol University , , ,","place":["Bangkok, Thailand, 10700"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2020,10,23]]},"reference":[{"key":"2026081804363922400_cit1","doi-asserted-by":"publisher","first-page":"2220","DOI":"10.1002\/anie.201002094","article-title":"Sweet and umami taste:\nnatural products, their chemosensory targets, and beyond","volume":"50","author":"Behrens","year":"2011","journal-title":"Angew. Chem., Int. 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Food Sci."},{"key":"2026081804363922400_cit5","volume-title":"Exploring\nQSAR: Fundamentals and\nApplications in Chemistry and Biology","author":"Hansch","year":"1995"},{"key":"2026081804363922400_cit6","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s13321-020-0408-x","article-title":"Towards reproducible computational drug discovery","volume":"12","author":"Schaduangrat","year":"2020","journal-title":"J. Cheminform."},{"key":"2026081804363922400_cit7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-319-56850-8_1","article-title":"Towards\nthe Revival of Interpretable QSAR Models","volume-title":"Advances in QSAR Modeling","author":"Shoombuatong","year":"2017"},{"key":"2026081804363922400_cit8","first-page":"688","article-title":"Towards understanding aromatase inhibitory\nactivity via QSAR modeling","volume":"17","author":"Shoombuatong","year":"2018","journal-title":"EXCLI J."},{"key":"2026081804363922400_cit9","doi-asserted-by":"publisher","first-page":"1841","DOI":"10.1007\/s12013-014-0141-z","article-title":"Interaction between\numami peptide and taste receptor T1R1\/T1R3","volume":"70","author":"Dang","year":"2014","journal-title":"Cell Biochem. 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