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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F24%3A00080739" target="_blank" >RIV/00159816:_____/24:00080739 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/24:00138217 RIV/00216305:26230/24:PU156207 RIV/65269705:_____/24:00080739

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2001037024003982?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2001037024003982?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csbj.2024.11.026" target="_blank" >10.1016/j.csbj.2024.11.026</a>

Result language

angličtina

Original language name

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

Original language description

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/.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10608 - Biochemistry and molecular biology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

—

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computational and Structural Biotechnology Journal

ISSN

2001-0370

e-ISSN

2001-0370

Volume of the periodical

24

Issue of the periodical within the volume

DEC 2024

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

5

Pages from-to

734-738

UT code for WoS article

001372518900001

EID of the result in the Scopus database

2-s2.0-85210774075