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Published on: December 6, 2017
Machine learning-guided discovery and design of non-hemolytic peptides
Fabien Plisson1, Obed Ramírez-Sánchez2, Cristina Martínez-Hernández2
1CONACYT, Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación Y de Estudios Avanzados del IPN, 36824, Irapuato, Guanajuato, Mexico. fabien.plisson@cinvestav.mx.
Machine learning models predict antimicrobial peptide (AMP) hemolytic activity, identifying non-hemolytic candidates for drug design. This approach reduces toxicity concerns for novel peptide therapeutics against resistant infections.
Area of Science:
- Biochemistry
- Computational Biology
- Drug Discovery
Background:
- Peptide-based drug design faces challenges in clinical trial progression, particularly concerning therapeutic efficacy and safety.
- Antimicrobial peptides (AMPs) show promise against antibiotic-resistant infections but often exhibit hemolytic toxicity, limiting their clinical use.
- Machine learning (ML) offers efficient prediction of biological activities from peptide sequences but requires robust methods for reliable predictions and defining applicability domains.
Purpose of the Study:
- To develop and validate machine learning models combined with outlier detection for predicting hemolytic activity in antimicrobial peptides (AMPs).
- To facilitate the discovery of novel, non-hemolytic AMPs and guide the design of safer peptide therapeutics.
- To establish guidelines for designing peptides with reduced hemolytic activity.
Main Methods:
- Development of gradient boosting classifiers for predicting peptide hemolytic activity from primary sequences.
- Implementation of multivariate outlier detection methods to identify peptides outside the reliable prediction domain.
- Application of the combined approach to a library of AMPs for identifying non-hemolytic candidates.
Main Results:
- Gradient boosting models achieved 95-97% accuracy in predicting the hemolytic nature of peptide sequences.
- Approximately 70% of known AMPs were predicted as hemolytic.
- Multivariate outlier detection identified 273 AMPs (approximately 9%) with unreliable predictions.
- The integrated approach identified 34 high-confidence non-hemolytic natural AMPs and enabled the de novo design of 507 non-hemolytic peptides.
Conclusions:
- Machine learning models, enhanced by outlier detection, provide a robust framework for predicting and minimizing hemolytic activity in AMPs.
- This strategy accelerates the discovery of safer peptide therapeutics and offers valuable guidelines for future peptide design.
- The findings support the development of novel peptide-based treatments for antibiotic-resistant infections with improved safety profiles.
