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Efficient and Accurate Algorithm for Cleaved Fragments Prediction (CFPA) in Protein Sequences Dataset Based on Consensus and Its Variants: A Novel Degradomics Prediction Application

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Neuroproteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1598))

Abstract

Degradomics is a novel discipline that involves determination of the proteases/substrate fragmentation profile, called the substrate degradome, and has been recently applied in different disciplines. A major application of degradomics is its utility in the field of biomarkers where the breakdown products (BDPs) of different protease have been investigated. Among the major proteases assessed, calpain and caspase proteases have been associated with the execution phases of the pro-apoptotic and pro-necrotic cell death, generating caspase/calpain-specific cleaved fragments. The distinction between calpain and caspase protein fragments has been applied to distinguish injury mechanisms. Advanced proteomics technology has been used to identify these BDPs experimentally. However, it has been a challenge to identify these BDPs with high precision and efficiency, especially if we are targeting a number of proteins at one time. In this chapter, we present a novel bioinfromatic detection method that identifies BDPs accurately and efficiently with validation against experimental data. This method aims at predicting the consensus sequence occurrences and their variants in a large set of experimentally detected protein sequences based on state-of-the-art sequence matching and alignment algorithms. After detection, the method generates all the potential cleaved fragments by a specific protease. This space and time-efficient algorithm is flexible to handle the different orientations that the consensus sequence and the protein sequence can take before cleaving. It is O(mn) in space complexity and O(Nmn) in time complexity, with N number of protein sequences, m length of the consensus sequence, and n length of each protein sequence. Ultimately, this knowledge will subsequently feed into the development of a novel tool for researchers to detect diverse types of selected BDPs as putative disease markers, contributing to the diagnosis and treatment of related disorders.

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Acknowledgment

This work was supported by a grant from the National Council for Scientific Research (CNRS) in Lebanon.

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Authors and Affiliations

  1. Faculty of Engineering and Architecture, Department of Electrical and Computer Engineering, American University of Beirut, Riad El-Solh, 11-0236, Beirut, 1107 2020, Lebanon

    Atlal El-Assaad, Zaher Dawy & Hazem Hajj

  2. Faculty of Medicine, Department of Biochemistry and Molecular Genetics, American University of Beirut, Bliss Street, DTS Bldg, Room 221, P.O. Box 110236/41, Riad El Solh, Beirut, 1107-2020, Lebanon

    Georges Nemer

  3. Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon

    Firas H. Kobeissy

  4. Department of Psychiatry, Center for Neuroproteomics and Biomarkers Research, University of Florida, Gainesville, FL, USA

    Firas H. Kobeissy

Authors
  1. Atlal El-Assaad
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  2. Zaher Dawy
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  3. Georges Nemer
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  4. Hazem Hajj
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  5. Firas H. Kobeissy
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Corresponding author

Correspondence to Firas H. Kobeissy .

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Editors and Affiliations

  1. Department of Psychiatry, University of Florida McKnight Brain Institute, Gainesville, Florida, USA

    Firas H. Kobeissy

  2. Department of Cell Biology, Microbiology, & Molecular Biology, University of South Florida, Tampa, Florida, USA

    Stanley M. Stevens, Jr.

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El-Assaad, A., Dawy, Z., Nemer, G., Hajj, H., Kobeissy, F.H. (2017). Efficient and Accurate Algorithm for Cleaved Fragments Prediction (CFPA) in Protein Sequences Dataset Based on Consensus and Its Variants: A Novel Degradomics Prediction Application. In: Kobeissy, F., Stevens, Jr., S. (eds) Neuroproteomics. Methods in Molecular Biology, vol 1598. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6952-4_17

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  • DOI: https://doi.org/10.1007/978-1-4939-6952-4_17

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6950-0

  • Online ISBN: 978-1-4939-6952-4

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