{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T05:53:09Z","timestamp":1791438789440,"version":"4.3.3"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biotech &amp;amp; Bioengineering"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Komagataella phaffii<\/jats:italic>\n                    is a commercially valuable yeast widely used in biotechnology due to its Crabtree\u2010negative metabolism, which enables it to sustain respiratory growth and efficiently utilize carbon sources even in the presence of excess glucose. To better understand and predict its metabolic behavior, genome\u2010scale metabolic models are commonly employed. Within these models, Gene\u2013Protein\u2013Reaction (GPR) associations play a crucial role in the predictive performance of models. The previously developed\n                    <jats:italic>i<\/jats:italic>\n                    Pichia genome\u2010scale metabolic model for\n                    <jats:italic>K. phaffii<\/jats:italic>\n                    was further refined in this study through extensive manual curation of GPR associations, resulting in an updated model termed\n                    <jats:italic>i<\/jats:italic>\n                    Pichia2. GPR associations were manually curated for 862 reactions based on the enzymatic functions and subcellular localizations of the associated proteins, using information obtained from the KEGG, NCBI, and UniProtKB databases. Gene essentiality analyses were subsequently performed using\n                    <jats:italic>i<\/jats:italic>\n                    Pichia2, and the predicted results were compared with experimental data reported in the literature. This refinement of GPR associations improved the overall predictive performance of the model, with prediction accuracy increasing from 0.71 in\n                    <jats:italic>i<\/jats:italic>\n                    Pichia to 0.727 in\n                    <jats:italic>i<\/jats:italic>\n                    Pichia2. Similarly, the Matthews correlation coefficient (MCC) increased substantially from 0.1075 to 0.24, indicating improved agreement between predicted and experimentally observed gene essentiality phenotypes. Compared with\n                    <jats:italic>i<\/jats:italic>\n                    Pichia,\n                    <jats:italic>i<\/jats:italic>\n                    Pichia2 predicted fewer true positives (313 vs. 391), false positives (69 vs. 108), and false negatives (67 vs. 71), while the number of true negatives increased from 36 to 49. The higher accuracy and MCC obtained with\n                    <jats:italic>i<\/jats:italic>\n                    Pichia2, despite its lower overall MEMOTE score (%34) compared with the original\n                    <jats:italic>i<\/jats:italic>\n                    Pichia model (%52), highlights that model quality and predictive performance represent distinct aspects of genome\u2010scale metabolic model evaluation. Overall, these results demonstrate that manual refinement of GPR associations can improve the predictive performance of GEMs without modifying their underlying reaction network. The improved GPR associations in\n                    <jats:italic>i<\/jats:italic>\n                    Pichia2 are therefore expected to support more reliable identification of gene knockout targets and facilitate metabolic engineering and rational strain design in\n                    <jats:italic>K. phaffii<\/jats:italic>\n                    .\n                  <\/jats:p>","DOI":"10.1002\/bit.70402","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T05:13:02Z","timestamp":1791436382000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Curation of Gene\u2013Protein\u2013Reaction Associations Enhances the\n                    <i>i<\/i>\n                    Pichia Genome\u2010Scale Metabolic Model of\n                    <i>Komagataella phaffii<\/i>"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9788-2019","authenticated-orcid":false,"given":"P\u0131nar","family":"Kocaba\u015f","sequence":"first","affiliation":[{"name":"Department of Bioengineering Ege University \u0130zmir T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"issue":"3","key":"e_1_2_10_2_1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1002980","article-title":"The RAVEN Toolbox and Its Use for Generating a Genome\u2010Scale Metabolic Model for Penicillium chrysogenum","volume":"9","author":"Agren R.","year":"2013","journal-title":"PLoS Computational Biology"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1002\/bit.26363","article-title":"Transcriptional Engineering of the Glyceraldehyde\u20103\u2010phosphate Dehydrogenase Promoter for Improved Heterologous Protein Production in Pichia pastoris","volume":"114","author":"Ata \u00d6.","year":"2017","journal-title":"Biotechnology and Bioengineering"},{"issue":"1","key":"e_1_2_10_4_1","doi-asserted-by":"crossref","DOI":"10.1186\/1752-0509-2-14","article-title":"Three Factors Underlying Incorrect In Silico Predictions of Essential Metabolic Genes","volume":"2","author":"Becker S. 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