Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO

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dc.contributor.authorKhan, Rayyancs
dc.contributor.authorPokorná, Petracs
dc.contributor.authorŠtourač, Jancs
dc.contributor.authorBorko, Simeoncs
dc.contributor.authorDobiáš, Adamcs
dc.contributor.authorPlanas-Iglesias, Joancs
dc.contributor.authorMazurenko, Stanislavcs
dc.contributor.authorArefiev, Ihorcs
dc.contributor.authorPinto, Gaspar P.cs
dc.contributor.authorSzotkowská, Veronikacs
dc.contributor.authorŠtěrba, Jaroslavcs
dc.contributor.authorDamborský, Jiřícs
dc.contributor.authorSlabý, Ondřejcs
dc.contributor.authorBednář, Davidcs
dc.coverage.issue1cs
dc.coverage.volume24cs
dc.date.accessioned2025-06-11T05:56:33Z
dc.date.available2025-06-11T05:56:33Z
dc.date.issued2024-12-01cs
dc.description.abstractNext-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. However, computational pipelines that handle biological data must integrate multiple software tools, which can add complexity and hinder nonspecialist users from accessing the pipeline. Here, we have developed an online user-friendly web server tool PredictONCO that is fully automated and has a low barrier to access. The tool models the structure of the mutant protein in the first step. Next, it calculates the protein stability change, pocket level information, evolutionary conservation, and changes in ionisation of catalytic amino acid residues, and uses them as the features in the machine-learning predictor. The XGBoost-based predictor was validated on an independent subset of held-out data, demonstrating areas under the receiver operating characteristic curve (ROC) of 0.97 and 0.94, and the average precision from the precision-recall curve of 0.99 and 0.94 for structure-based and sequence-based predictions, respectively. Finally, PredictONCO calculates the docking results of small molecules approved by regulatory authorities. We demonstrate the applicability of the tool by presenting its usage for variants in two cancer-associated proteins, cellular tumour antigen p53 and fibroblast growth factor receptor FGFR1. Our free web tool will assist with the interpretation of data from next-generation sequencing and navigate treatment strategies in clinical oncology: https://loschmidt.chemi.muni.cz/predictonco/.en
dc.formattextcs
dc.format.extent734-738cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationComputational and Structural Biotechnology Journal. 2024, vol. 24, issue 1, p. 734-738.en
dc.identifier.doi10.1016/j.csbj.2024.11.026cs
dc.identifier.issn2001-0370cs
dc.identifier.other197612cs
dc.identifier.urihttps://hdl.handle.net/11012/251909
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofComputational and Structural Biotechnology Journalcs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2001037024003982cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2001-0370/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectPrecision oncologyen
dc.subjectWebserveren
dc.subjectMutationen
dc.subjectPredictionen
dc.subjectTreatmenten
dc.subjectNext-generation sequencingen
dc.subjectVirtual screeningen
dc.subjectOncogenicityen
dc.subjectAutomationen
dc.subjectMachine learningen
dc.titleAnalysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCOen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-197612en
sync.item.dbtypeVAVen
sync.item.insts2025.06.11 07:56:33en
sync.item.modts2025.06.11 07:33:36en
thesis.grantorVysoké učení technické v Brně. Fakulta informačních technologií. Ústav informačních systémůcs
thesis.grantorVysoké učení technické v Brně. . Loschmidtovy laboratořecs
thesis.grantorVysoké učení technické v Brně. . Fakultní nemocnice u sv. Anny v Brněcs
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