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IdentPMP: identification of moonlighting proteins in plants using sequence-based learning models.

Xinyi Liu1, Yueyue Shen1, Youhua Zhang1

  • 1School of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui, China.

Peerj
|August 26, 2021
PubMed
Summary

This study introduces IdentPMP, a new tool for identifying plant moonlighting proteins. It improves prediction accuracy for plant proteins, which have unique cellular and protein characteristics compared to animals and microorganisms.

Keywords:
eXtreme gradient boostingBenchmark data setPlant moonlighting proteinPrediction tool

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Area of Science:

  • Plant biology
  • Bioinformatics
  • Computational biology

Background:

  • Moonlighting proteins perform multiple functions, but existing prediction tools are inaccurate for plants.
  • Plant-specific tools are needed due to differences in plant cells and proteins.

Purpose of the Study:

  • Develop a specialized prediction tool for plant moonlighting proteins.
  • Create a benchmark dataset for plant moonlighting protein research.

Main Methods:

  • Constructed a plant protein dataset and reduced redundancy.
  • Applied feature selection, normalization, and dimensionality reduction.
  • Compared machine learning models, optimizing parameters to select the best algorithm (XGBoost).

Main Results:

  • Developed IdentPMP using XGBoost, achieving higher AUPRC (0.43) and AUC (0.68) than non-plant specific methods.
  • Demonstrated the necessity of a plant-specific prediction tool and benchmark dataset.
  • Launched a free web-based version of IdentPMP.

Conclusions:

  • IdentPMP offers improved accuracy for identifying plant moonlighting proteins.
  • The study highlights the importance of plant-specific bioinformatics tools.
  • IdentPMP is accessible online for researchers.