Idiopathic pulmonary fibrosis is an incurable disease of unknown etiology. Acute exacerbation of idiopathic pulmonary fibrosis is associated with high mortality. Excessive apoptosis of lung epithelial cells occurs in pulmonary fibrosis acute exacerbation. We recently identified corisin, a proapoptotic peptide that triggers acute exacerbation of pulmonary fibrosis. Here, we provide insights into the mechanism underlying the processing and release of corisin. Furthermore, we demonstrate that an anticorisin monoclonal antibody ameliorates lung fibrosis by significantly inhibiting acute exacerbation in the human transforming growth factorβ1 model and acute lung injury in the bleomycin model. By investigating the impact of the anticorisin monoclonal antibody in a general model of acute lung injury, we further unravel the potential of corisin to impact such diseases. These results underscore the role of corisin in the pathogenesis of acute exacerbation of pulmonary fibrosis and acute lung injury and provide a novel approach to treating this incurable disease.

Excipients are major components of drugs and are used to improve drug attributes such as stability and appearance. Excipients approved by the U.S. Food and Drug Administration (FDA) are regarded as safe for humans in allowed concentrations, but their potential interactions with drug targets have not been investigated systematically, which might influence a drug’s efficacy. Deep learning models have been used for the identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand–protein interaction. One-shot learning techniques provide a potential approach to address this low data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models to data sets that include ligands binding to G-protein-coupled receptors (GPCRs) and kinases. The predicted results suggest that one-shot learning could be used for predicting ligand–protein interactions, and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning can be used to make accurate predictions for excipient–protein interactions, and these methods could be used for selecting excipients with limited drug–protein interactions.

Lanthipeptides are ribosomally synthesized and post-translationally modified peptides that are generated from precursor peptides through a dehydration and cyclization process. ProcM, a class II lanthipeptide synthetase, demonstrates high substrate tolerance....

Lanthipeptides are ribosomally synthesized and post-translationally modified pep- tides that are generated from precursor peptides through a dehydration and cycliza- tion process in the biosynthetic pathways. In contrast to most other lanthipeptide synthetases, ProcM, a class II lanthipeptide synthetase, demonstrates high substrate tolerance. It is enigmatic that a single enzyme can catalyze the cyclization process of a diverse range of substrates with high fidelity. Previous studies suggested that the site- selectivity of lanthionine formation is determined by substrate sequence rather than by the enzyme. However, exactly how substrate sequence contributes to site-selective lanthipeptide biosynthesis is not clear. In this study, we performed molecular dynamic simulations for ProcA3.3 core peptide variants to explore how the predicted solution structure of the substrate without enzyme correlates to final product formation. Our simulation results support a model in which the secondary structure of the core peptide controls the ring pattern of the final product. We also demonstrate that the dehydra- tion step in the biosynthesis pathway does not influence the site-selectivity of ring formation. In addition, we performed simulation for the core peptides of ProcA1.1 and 2.8, which are well-suited candidates to investigate the connection between order of ring formation and solution structure. Simulation results indicate that in both cases, C-terminal ring formation is more likely which was supported by experimental results. Our findings indicate that the substrate sequence and its solution structure can be used to predict the site-selectivity and order of ring formation, and that secondary structure is a crucial factor influencing the site-selectivity. Taken together, these findings will fa- cilitate our understanding of the lanthipeptide biosynthetic mechanism and accelerate bioengineering efforts for lanthipeptide-derived products.

Excipients are a major component of drugs and are used to improve drugs attributes such as stability and appearance. Excipients approved by Food and Drug Administration (FDA) are regarded as safe for human in allowed concentration, but their potential interaction with drug targets have not been investigated systematically, which might influence drugs efficacy. Deep learning models have been used for identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand-protein interaction. One-shot learning techniques provide a potential approach to address this low-data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models on datasets that include ligands binding to G-Protein Coupled Receptors (GPCRs) and Kinases. The predicted results suggest that one-shot learning models could be used for predicting ligand-protein interaction and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning models can be used to make accurate prediction for excipient-protein interactions and these methods could be used for selecting excipients with limited drug-protein interactions.