Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO
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Date
2024-12-01
Authors
Khan, Rayyan
Pokorná, Petra
Štourač, Jan
Borko, Simeon
Dobiáš, Adam
Planas-Iglesias, Joan
Mazurenko, Stanislav
Arefiev, Ihor
Pinto, Gaspar P.
Szotkowská, Veronika
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Altmetrics
Abstract
Next-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/.
Description
Citation
Computational and Structural Biotechnology Journal. 2024, vol. 24, issue 1, p. 734-738.
https://www.sciencedirect.com/science/article/pii/S2001037024003982
https://www.sciencedirect.com/science/article/pii/S2001037024003982
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en