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

## TL;DR

This paper explores the structural and functional properties of key TBI biomarkers using proteomics and bioinformatics to better understand their roles in traumatic brain injury.

## Contribution

The study provides new insights into the structural and functional complexities of GFAP, S-100B, and UCH-L1 using advanced bioinformatics tools.

## Key findings

- Alpha-helices dominate the secondary structure of GFAP and S-100B, while UCH-L1 has a balanced mix of helices, beta strands, and coils.
- AlphaFold and I-TASSER were identified as the best tools for predicting the tertiary structures of the three proteins.
- Protein motifs and post-translational modifications were predicted, offering insights into the functional roles of GFAP, S-100B, and UCH-L1 in TBI.

## Abstract

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.

This study integrates proteomic and bioinformatic approaches to explore established TBI biomarkers’ structural and functional complexities: GFAP, S-100B, and UCH-L1.

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.

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.