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Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs

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Computational Methods for Predicting Post-Translational Modification Sites

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2499))

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Abstract

Peroxiredoxins (Prxs) are a protein superfamily, present in all organisms, that play a critical role in protecting cellular macromolecules from oxidative damage but also regulate intracellular and intercellular signaling processes involving redox-regulated proteins and pathways. Bioinformatic approaches using computational tools that focus on active site-proximal sequence fragments (known as active site signatures) and iterative clustering and searching methods (referred to as TuLIP and MISST) have recently enabled the recognition of over 38,000 peroxiredoxins, as well as their classification into six functionally relevant groups. With these data providing so many examples of Prxs in each class, machine learning approaches offer an opportunity to extract additional information about features characteristic of these protein groups.

In this study, we developed a novel computational method named “RF-Prx” based on a random forest (RF) approach integrated with K-space amino acid pairs (KSAAP) to identify peroxiredoxins and classify them into one of six subgroups. Our process performed in a superior manner compared to other machine learning classifiers. Thus the RF approach integrated with K-space amino acid pairs enabled the detection of class-specific conserved sequences outside the known functional centers and with potential importance. For example, drugs designed to target Prx proteins would likely suffer from cross-reactivity among distinct Prxs if targeted to conserved active sites, but this may be avoidable if remote, class-specific regions could be targeted instead.

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Authors and Affiliations

  1. Department of Computer Science, Jamoum University College, Umm Al-Qura University, Jamoum, Saudi Arabia

    Hussam AL-Barakati

  2. Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC, USA

    Robert H. Newman

  3. Computer Science Department, College of Computing, Michigan Technological University, Houghton, MI, USA

    Dukka B. KC

  4. Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Leslie B. Poole

Authors
  1. Hussam AL-Barakati
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  2. Robert H. Newman
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  3. Dukka B. KC
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  4. Leslie B. Poole
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Corresponding authors

Correspondence to Dukka B. KC or Leslie B. Poole .

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Editors and Affiliations

  1. Michigan Technological University, Houghton, MI, USA

    Dukka B. KC

1 Electronic Supplementary Material

Table S1

Results of tenfold cross-validation using individual and cumulative features (PDF 425 kb)

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AL-Barakati, H., Newman, R.H., KC, D.B., Poole, L.B. (2022). Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs. In: KC, D.B. (eds) Computational Methods for Predicting Post-Translational Modification Sites. Methods in Molecular Biology, vol 2499. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2317-6_8

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  • DOI: https://doi.org/10.1007/978-1-0716-2317-6_8

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