MONET: A Database for Prediction of Neoantigens Derived From Microsatellite Loci

Authors

Nan Deng
Krishna M Sinha
Eduardo Vilar

Publication Date

1-1-2024

Journal

Frontiers in Immunology

DOI

10.3389/fimmu.2024.1394593

PMID

3883577

PMCID

PMC11148240

PubMedCentral® Posted Date

5-21-2024

PubMedCentral® Full Text Version

Post-print

Abstract

BACKGROUND: Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET).

METHOD: MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions.

RESULTS: MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. < 25%) using a set of experimentally validated neoAgs.

CONCLUSION: MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.

Keywords

Humans, Microsatellite Repeats, Databases, Genetic, Antigens, Neoplasm, Microsatellite Instability, Frameshift Mutation, Software, Computational Biology, Neoplasms

Published Open-Access

yes

Recommended Citation

Nan Deng, Krishna M Sinha, and Eduardo Vilar, "MONET: A Database for Prediction of Neoantigens Derived From Microsatellite Loci" (2024). Faculty, Staff and Student Publications. 5081.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5081