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Machine learning models for predicting configuration of modified knuckle epitope peptides of BMP-2 protein using
Ricky Anshuman Dash1, Esmaiel Jabbari1
1Biomimetic Materials and Tissue Engineering Laboratory, Chemical Engineering Department, University of South Carolina, 301 Main Street, Columbia, SC, USA, 29208. jabbari@cec.sc.edu.
Physical Chemistry Chemical Physics : PCCP
|April 4, 2025
Summary
This study developed quantitative structure-activity relationships (QSAR) using machine learning to predict structural properties of modified bone morphogenetic protein-2 knuckle epitope peptides (BMP2-KEP). This enables discovery of novel sequences for skeletal tissue engineering.
Area of Science:
- Biomaterials Science
- Computational Biology
- Protein Engineering
Background:
- High doses of bone morphogenetic proteins (BMPs) cause adverse effects in skeletal tissue regeneration.
- The BMP-2 knuckle epitope peptide (BMP2-KEP) offers an alternative for skeletal tissue engineering but has lower osteogenic activity in its free state due to a closed-arm structure.
- Developing modified BMP2-KEP sequences with open-arm structures is crucial for enhanced therapeutic potential.
Purpose of the Study:
- To develop quantitative structure-activity relationships (QSAR) for modified BMP2-KEP sequences.
- To correlate 20-mer sequences of modified BMP2-KEP with their configurational properties using machine learning (ML) models.
- To establish a predictive framework for discovering novel BMP2-KEP sequences with desired open-arm structures.
Main Methods:
- Utilized the SIMFIM mesoscale simulation model to generate structural properties (radius of gyration, end-to-end distance) for a modified BMP2-KEP sequence database.
- Employed machine learning models with amino acid descriptor (AAD) scales representing residues as input features.
- Evaluated model performance using R-squared metric and employed permutation importance and SHAP analysis to identify key residue contributions.
Main Results:
- Successfully developed and validated QSAR models capable of predicting structural properties of modified BMP2-KEP sequences.
- Identified specific residue positions and properties that significantly influence the conformational characteristics of the peptides.
- The study generated a robust database and predictive models for BMP2-KEP structural analysis.
Conclusions:
- QSAR models provide a powerful tool for understanding structure-property relationships in modified BMP2-KEP sequences.
- This approach facilitates the rational design and discovery of novel BMP2-KEP variants with optimized open-arm structures for skeletal tissue regeneration.
- The findings pave the way for engineering more effective therapeutic peptides by predicting structural configurations from sequence data.
