{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:03:30Z","timestamp":1784750610831,"version":"3.55.0"},"reference-count":79,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T00:00:00Z","timestamp":1717632000000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Peptide- and protein-based therapeutics are becoming a promising treatment regimen for myriad diseases. Toxicity of proteins is the primary hurdle for protein-based therapies. Thus, there is an urgent need for accurate in silico methods for determining toxic proteins to filter the pool of potential candidates. At the same time, it is imperative to precisely identify non-toxic proteins to expand the possibilities for protein-based biologics. To address this challenge, we proposed an ensemble framework, called VISH-Pred, comprising models built by fine-tuning ESM2 transformer models on a large, experimentally validated, curated dataset of protein and peptide toxicities. The primary steps in the VISH-Pred framework are to efficiently estimate protein toxicities taking just the protein sequence as input, employing an under sampling technique to handle the humongous class-imbalance in the data and learning representations from fine-tuned ESM2 protein language models which are then fed to machine learning techniques such as Lightgbm and XGBoost. The VISH-Pred framework is able to correctly identify both peptides\/proteins with potential toxicity and non-toxic proteins, achieving a Matthews correlation coefficient of 0.737, 0.716 and 0.322 and F1-score of 0.759, 0.696 and 0.713 on three non-redundant blind tests, respectively, outperforming other methods by over $10\\%$ on these quality metrics. Moreover, VISH-Pred achieved the best accuracy and area under receiver operating curve scores on these independent test sets, highlighting the robustness and generalization capability of the framework. By making VISH-Pred available as an easy-to-use web server, we expect it to serve as a valuable asset for future endeavors aimed at discerning the toxicity of peptides and enabling efficient protein-based therapeutics.<\/jats:p>","DOI":"10.1093\/bib\/bbae270","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T12:40:23Z","timestamp":1717677623000},"source":"Crossref","is-referenced-by-count":27,"title":["VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1779-3150","authenticated-orcid":false,"given":"Raghvendra","family":"Mall","sequence":"first","affiliation":[{"name":"Biotechnology Research Center, Technology Innovation Institute , P.O. Box 9639, Abu Dhabi , United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankita","family":"Singh","sequence":"additional","affiliation":[{"name":"Biotechnology Research Center, Technology Innovation Institute , P.O. Box 9639, Abu Dhabi , United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0777-7720","authenticated-orcid":false,"given":"Chirag N","family":"Patel","sequence":"additional","affiliation":[{"name":"Biotechnology Research Center, Technology Innovation Institute , P.O. Box 9639, Abu Dhabi , United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7978-7771","authenticated-orcid":false,"given":"Gregory","family":"Guirimand","sequence":"additional","affiliation":[{"name":"Biotechnology Research Center, Technology Innovation Institute , P.O. Box 9639, Abu Dhabi , United Arab Emirates"},{"name":"Graduate School of Science , Technology and Innovation, , 1-1 Rokkodai-cho, Nada-ku, Kobe, 657-8501 , Japan"},{"name":"Kobe University , Technology and Innovation, , 1-1 Rokkodai-cho, Nada-ku, Kobe, 657-8501 , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filippo","family":"Castiglione","sequence":"additional","affiliation":[{"name":"Biotechnology Research Center, Technology Innovation Institute , P.O. Box 9639, Abu Dhabi , United Arab Emirates"},{"name":"Institute for Applied Computing, National Research Council of Italy , Via dei Taurini, 19, 00185, Rome , Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,6,6]]},"reference":[{"key":"2024060612324895500_ref1","article-title":"Biochemistry, proteins enzymes","volume-title":"StatPearls [Internet]","author":"Theodore Lewis and William L Stone","year":"2024"},{"key":"2024060612324895500_ref2","first-page":"118","article-title":"Protein\u2013which is best?","volume":"3","author":"Hoffman","year":"2004","journal-title":"Journal of sports science & medicine"},{"key":"2024060612324895500_ref3","doi-asserted-by":"crossref","first-page":"eabo6294","DOI":"10.1126\/sciimmunol.abo6294","article-title":"Zbp1-dependent inflammatory cell death, panoptosis, and cytokine storm disrupt ifn therapeutic efficacy during coronavirus infection","volume":"7","author":"Karki","year":"2022","journal-title":"Sci Immunol"},{"key":"2024060612324895500_ref4","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1016\/j.cell.2023.05.005","article-title":"Nlrp12-panoptosome activates panoptosis and pathology in response to heme and pamps","volume":"186","author":"Sundaram","year":"2023","journal-title":"Cell"},{"key":"2024060612324895500_ref5","doi-asserted-by":"crossref","first-page":"zcac033","DOI":"10.1093\/narcan\/zcac033","article-title":"Pancancer transcriptomic profiling identifies key panoptosis markers as therapeutic targets for oncology","volume":"4","author":"Mall","year":"2022","journal-title":"NAR cancer"},{"key":"2024060612324895500_ref6","first-page":"1","article-title":"Harnessing Qatar biobank to understand type 2 diabetes and obesity in adult qataris from the first Qatar biobank project","volume":"16","author":"Ullah","year":"2018","journal-title":"J Transl Med"},{"key":"2024060612324895500_ref7","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1038\/nature25171","article-title":"A metabolic function of fgfr3-tacc3 gene fusions in cancer","volume":"553","author":"Frattini","year":"2018","journal-title":"Nature"},{"key":"2024060612324895500_ref8","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1038\/s41398-020-00882-7","article-title":"Alzheimer\u2019s disease-related dysregulation of mrna translation causes key pathological features with ageing","volume":"10","author":"Ghosh","year":"2020","journal-title":"Transl Psychiatry"},{"key":"2024060612324895500_ref9","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.4155\/tde.13.104","article-title":"Basics and recent advances in peptide and protein drug delivery","volume":"4","author":"Bruno","year":"2013","journal-title":"Ther Deliv"},{"key":"2024060612324895500_ref10","doi-asserted-by":"crossref","first-page":"e0181748","DOI":"10.1371\/journal.pone.0181748","article-title":"Thpdb: database of fda-approved peptide and protein therapeutics","volume":"12","author":"Usmani","year":"2017","journal-title":"PloS One"},{"key":"2024060612324895500_ref11","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.tibs.2018.12.004","article-title":"Friends or foes? 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