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A Protocol for Computer-Based Protein Structure and Function Prediction
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Prediction of protein structural class based on symmetrical recurrence quantification analysis.

Ines Abdennaji1, Mourad Zaied1, Jean-Marc Girault1

  • 1Research Team in Intelligent Machines, National School of Engineers of Gabes, B.P. W, 6072 Gabes, Tunisia; GSII ESEO - LAUM UMR CNRS 6613, 49000 Angers, France.

Computational Biology and Chemistry
|February 25, 2021
PubMed
Summary

Predicting protein structural class from low similarity sequences is crucial for drug design. A novel method using sequence transformation and symmetrical recurrence quantification analysis achieved 100% accuracy with Random Forest, outperforming existing approaches.

Keywords:
LDAMachine learningProtein structural classesRandom ForestRecurrence plotSVMSymmetrical recurrence quantification analysisSymmetry

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein structural class prediction is vital for understanding protein function and facilitating drug design.
  • Predicting structural class for sequences with low similarity presents a significant computational challenge.
  • Existing methods often struggle with accuracy and generalization for diverse protein families.

Purpose of the Study:

  • To develop and evaluate a novel computational method for predicting protein structural class, particularly for low similarity sequences.
  • To assess the efficacy of symmetrical recurrence quantification analysis as a feature extraction technique.
  • To compare the performance of different machine learning classifiers for this prediction task.

Main Methods:

  • Protein sequences were transformed into DNA and subsequently into binary sequences.
  • Symmetrical recurrence quantification analysis was applied to extract 8 features per symmetry plot.
  • Machine learning algorithms including Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) were employed and compared.

Main Results:

  • The proposed method, combining sequence transformation and symmetrical recurrence quantification analysis, demonstrated high predictive performance.
  • The Random Forest classifier, when used with the extracted features, achieved an outstanding overall accuracy of 100%.
  • The approach proved effective in avoiding overfitting, indicating robust generalization capabilities.

Conclusions:

  • Symmetrical recurrence quantification analysis is a powerful feature extraction method for protein sequence data.
  • The Random Forest classifier, coupled with this feature extraction technique, offers a superior solution for protein structural class prediction.
  • This method holds significant promise for advancing applications in drug design and protein functional analysis.