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An Integrated Approach for Microprotein Identification and Sequence Analysis
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Augmented sequence features and subcellular localization for functional characterization of unknown protein

Saurabh Agrawal1, Dilip Singh Sisodia2, Naresh Kumar Nagwani2

  • 1Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India. sagrawal.phd2018.cs@nitrr.ac.in.

Medical & Biological Engineering & Computing
|September 21, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces novel feature augmentation techniques to improve the prediction of protein subcellular localization for both known and unknown protein sequences (UPS). Enhanced feature sets significantly boost prediction accuracy across various machine learning models.

Keywords:
Augmented sequence featuresEvolutionary informationFunctional characterizationSequence featuresSubcellular localization

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput techniques generate numerous unknown protein sequences (UPS) requiring functional characterization.
  • Protein subcellular localization is crucial for understanding protein function, disease mechanisms, and drug repositioning.
  • Existing feature extraction methods often yield suboptimal prediction performance for protein sequences.

Purpose of the Study:

  • To develop and evaluate two novel feature augmentation strategies for protein sequences.
  • To enhance the prediction accuracy of protein subcellular localization, particularly for unknown protein sequences.
  • To compare the performance of individual and ensemble machine learning models using augmented features.

Main Methods:

  • Two feature augmentation techniques were developed, integrating sequence-induced, physicochemical, and evolutionary information.
  • Augmented features were designed to retain sequence-order information and protein-residue properties.
  • Bacterial Gram-Positive (G+) and Gram-Negative (G-) datasets were preprocessed, and feature vectors were generated for model training and evaluation.

Main Results:

  • The Decision Tree (C4.5) algorithm achieved the highest accuracy (99.57% on G+ and 97.47% on G-) for known protein sequences.
  • For unknown protein sequences (UPS), Support Vector Machine (SVM) yielded 85.17% accuracy on G+ datasets, and Multi-Layer Perceptron (MLP) achieved 82.45% on G- datasets.
  • Ensemble methods and individual classifiers demonstrated improved performance with the augmented feature sets.

Conclusions:

  • The proposed feature augmentation methods effectively improve protein subcellular localization prediction accuracy.
  • The integrated features provide a robust approach for characterizing both known and unknown protein sequences.
  • This work offers valuable tools for advancing functional genomics and drug discovery through improved protein analysis.