Bioinformatics Approach to Understanding Interacting Pathways in Neuropsychiatric Disorders

Alawieh, Ali; Sabra, Zahraa; Nokkari, Amaly; El-Assaad, Atlal; Mondello, Stefania; Zaraket, Fadi A.; Fadlallah, Bilal H.; Kobeissy, Firas

doi:10.1007/978-1-4939-0847-9_9

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…However, caspase-3 differs from calpain-2 in the requirement for Ca2. Furthermore, what distinguishes caspase-3 from other proteases is that it has a crucial role in apoptosis in many different cell types27.…”

Section: Resultsmentioning

confidence: 99%

“…to identify different proteases and their subsequent proteolytic substrates/degradome in a defined pathophysiological condition1. Recently, the use of bioinformatics tools as means for data mining has spanned different fields in cancer, neuroscience and biochemistry research23. Degradomics as a discipline has benefitted from data mining strategies as tools to predict degradome specific substrates in silico 4567.…”

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confidence: 99%

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Novel Bioinformatics–Based Approach for Proteomic Biomarkers Prediction of Calpain-2 & Caspase-3 Protease Fragmentation: Application to βII-Spectrin Protein

El-Assaad

Dawy

Nemer

et al. 2017

Sci Rep

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The crucial biological role of proteases has been visible with the development of degradomics discipline involved in the determination of the proteases/substrates resulting in breakdown-products (BDPs) that can be utilized as putative biomarkers associated with different biological-clinical significance. In the field of cancer biology, matrix metalloproteinases (MMPs) have shown to result in MMPsgenerated protein BDPs that are indicative of malignant growth in cancer, while in the field of neural injury, calpain-2 and caspase-3 proteases generate BDPs fragments that are indicative of different neural cell death mechanisms in different injury scenarios. Advanced proteomic techniques have shown a remarkable progress in identifying these BDPs experimentally. In this work, we present a bioinformatics-based prediction method that identifies protease-associated BDPs with high precision and efficiency. The method utilizes state-of-the-art sequence matching and alignment algorithms. It starts by locating consensus sequence occurrences and their variants in any set of protein substrates, generating all fragments resulting from cleavage. The complexity exists in space O(mn) as well as in O(Nmn) time, where N, m, and n are the number of protein sequences, length of the consensus sequence, and length per protein sequence, respectively. Finally, the proposed methodology is validated against βII-spectrin protein, a brain injury validated biomarker.Degradomics discipline has been recently introduced to depict the application of an omics approach (genomics and proteomics etc.) to identify different proteases and their subsequent proteolytic substrates/degradome in a defined pathophysiological condition 1 . Recently, the use of bioinformatics tools as means for data mining has spanned different fields in cancer, neuroscience and biochemistry research 2,3 . Degradomics as a discipline has benefitted from data mining strategies as tools to predict degradome specific substrates in silico [4][5][6][7] . However, the application of bioinformatic tools on degradomics analysis requires different types of sequencing matching algorithms making it one of the challenging fields despite its potential beneficial outcomes mainly in clinical and diagnostic research. Knuth et al. developed an algorithm that only finds exact matches of a subsequence of size m in a sequence of size n in O(m + n) 8 . It is worth to know that other algorithms have identified sequence variants with comparable complexity, but not with the same fidelity. Lipman et al. devised a heuristic algorithm called FAST Protein (FASTP) 9 ; it is based on alignment approach and is both rapid and sensitive in finding similarities between any amino acid subsequence and matching sequences in a database. Yet, it does not cover all regions, as it starts with an anchoring scheme that identifies identical regions using a replaceability matrix 9 . Similarly, Altschul et al. developed another heuristic algorithm BLAST, along with its variations; this algorithm supposedly supersedes FASTP ...

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

Novel Bioinformatics–Based Approach for Proteomic Biomarkers Prediction of Calpain-2 & Caspase-3 Protease Fragmentation: Application to βII-Spectrin Protein

El-Assaad

Dawy

Nemer

et al. 2017

Sci Rep

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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

El-Assaad

Dawy

Nemer

et al. 2017

Methods in Molecular Biology

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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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Genome-wide association study for the level of prolificacy in Cameroon’s native goat

Kouam Simo,

Meutchieye,

Wouobeng

et al. 2024

Journal of Applied Animal Research

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Bioinformatics Approach to Understanding Interacting Pathways in Neuropsychiatric Disorders

Cited by 4 publications

References 48 publications

Novel Bioinformatics–Based Approach for Proteomic Biomarkers Prediction of Calpain-2 & Caspase-3 Protease Fragmentation: Application to βII-Spectrin Protein

Novel Bioinformatics–Based Approach for Proteomic Biomarkers Prediction of Calpain-2 & Caspase-3 Protease Fragmentation: Application to βII-Spectrin Protein

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

Genome-wide association study for the level of prolificacy in Cameroon’s native goat

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