Abstract
Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95–97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.
Highlights
Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design
The predictive power of machine learning models depends on several factors; the input datasets, the quality of independent variables/features and the type of algorithms
In order to compare our models with the performances of online services H emoPred55, HemoPI19 and HLPpred-Fuse[21] to predict the hemolytic nature and activity of peptides, we used the 3 publicly available HemoPI datasets
Summary
Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. We developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Machine learning-guided methods are a cost-effective and less time-consuming strategy than acquiring data through in vitro and in vivo experiments This strategy limits the number of candidates from large peptide libraries by predicting and ranking their biological activities from sequences, so-called Quantitative Structure–Activity/Property Relationship (QSA/PR) studies. Most antimicrobial peptides (AMPs) present in preclinical/clinical applications are applied topically, in part due to their hemolytic activity. None has defined the domains of applicability of their models, which could extrapolate its predictive power after submitting novel sequences on its online platform
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