Predicting Moonlighting Proteins from Protein Sequence

Jing Hu, Yihang Du

2023

Abstract

High-throughput proteomics projects have resulted in a rapid accumulation of protein sequences in public databases. For the majority of these proteins, limited functional information has been known so far. Moonlighting proteins (MPs) are a class of proteins which perform at least two physiologically relevant distinct biochemical or biophysical functions. These proteins play important functional roles in enzymatic catalysis process, signal transduction, cellular regulation, and biological pathways. However, it has been proven to be difficult, time-consuming, and expensive to identify MPs experimentally. Therefore, computational approaches which can predict MPs are needed. In this study, we present MPKNN, a K-nearest neighbors method which can identify MPs with high efficiency and accuracy. The method is based on the bit-score weighted Euclidean distance, which is calculated from selected features derived from protein sequence. On a benchmark dataset, our method achieved 83% overall accuracy, 0.64 MCC, 0.87 F-measure, and 0.86 AUC.

Paper Citation

in Harvard Style

Hu J. and Du Y. (2023). Predicting Moonlighting Proteins from Protein Sequence. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-631-6, SciTePress, pages 270-275. DOI: 10.5220/0011782300003414

in Bibtex Style

@conference{bioinformatics23,
author={Jing Hu and Yihang Du},
title={Predicting Moonlighting Proteins from Protein Sequence},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS},
year={2023},
pages={270-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011782300003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS
TI - Predicting Moonlighting Proteins from Protein Sequence
SN - 978-989-758-631-6
AU - Hu J.
AU - Du Y.
PY - 2023
SP - 270
EP - 275
DO - 10.5220/0011782300003414
PB - SciTePress