Evidence map›Paper›PMID 40703711›Full record

ArticleBiotechnologia2025

Integration of proteomics and bioinformatics in traumatic brain injury biomarker discovery.

Mohamed M Mohamed, El-Sayed A El-Absawy, Hala M Ahmed, Mohamed E Hasan

Abstract read
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Article in Biotechnologia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mohamed M MohamedDepartment of Basic Sciences, Faculty of Physical Therapy, Alryada University for Science and Technology, Egypt.
El-Sayed A El-AbsawyBioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Egypt.
Hala M AhmedDepartment of Biomedical Equipment, Faculty of Applied Health Sciences Technology, October 6 University, Egypt.
Mohamed E HasanBioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Traumatic brain injury (TBI) is a significant medical crisis with no FDA-approved therapies to improve functional outcomes. Key biomarkers, such as glial fibrillary acidic protein (GFAP), S-100 calcium-binding protein B (S-100B), and ubiquitin C-terminal hydrolase L1 (UCH-L1), are crucial for understanding TBI pathology. Materials and methods: This study integrates proteomic and bioinformatic approaches to explore established TBI biomarkers' structural and functional complexities: GFAP, S-100B, and UCH-L1. Results: Our comprehensive secondary structure and solvent accessibility assessment, conducted with PredictProtein, confirmed the predominance of alpha-helices in GFAP and S-100B, while UCH-L1 displayed a balanced mix of helices (65.00, 67.39, and 40.81%), beta strands (6.20, 0, and 17.94%), and coils (40.81, 17.94, and 41.26%). AlphaFold and I-TASSER were identified as the best servers for full-length tertiary structure prediction for the three target proteins, based on root-mean-square deviation (RMSD), TM-score, and C-score assessments. Protein motif database scans predicted four, eight, and one protein-binding motifs and two, three, and one post-translational modifications for GFAP, S-100B, and UCH-L1, respectively. Conclusions: GFAP's role in axonal transport and synaptic plasticity was emphasized through motifs such as Filament and DUF1664. S-100B's association with neuroinflammation and oxidative stress post-TBI was supported by the S-100/ICaBP-type calcium-binding domain. UCH-L1's dualistic impact on TBI was further clarified by the Peptidase_C12 motif. This approach deepens our comprehension of these biomarkers and paves the way for targeted diagnostics in TBI.

Indexed as

AlphaFoldbioinformaticsGFAPproteomicsS-100Bstructural biomarkerstraumatic brain injury (TBI)UCH-L1

Identifiers

PMID40703711
PMCPMC12281496

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.