MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.

Prevention and Treatment of Respiratory Distress Syndrome in Preterm Neonates

Respiratory distress syndrome (RDS) impacts a high proportion of preterm neonates, resulting in significant morbidity and mortality. Advances in pharmacotherapy, specifically antenatal corticosteroids and postnatal surfactant therapy, have significantly reduced the incidence and impact of neonatal RDS. Antenatal corticosteroids accelerate fetal lung maturation by increasing the activity of enzymes responsible for surfactant biosynthesis, resulting in improved lung compliance. Maternal antenatal corticosteroid treatment has improved survival of preterm neonates and lowered the incidence of brain injury. After birth, exogenous surfactant administration improves lung compliance and oxygenation, resulting in reductions in the incidence of pneumothorax and of death. Future research will identify the optimal surfactant product, timing of the initial dose, and mode of delivery.