Drug repurposing to new medical uses and chiral switches are elements of secondary pharmaceuticals. This article focuses on drug repurposing/chiral switches of the diastereomeric quasi-enantiomeric antimalarial quinine and antiarrhythmic quinidine, based on the histories of these drugs (1638–2022), applying a widened scope. Quinine, an essential medicine, changed the world. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs outside the scope of the original medical indications. Potential drugs are not included in the definition of drug repurposing. Drug repurposing may be within or outside the therapeutic group, e.g., quinidine to quinine repurposing, from treatment of arrhythmia or severe malaria to uncomplicated malaria. The scope of chiral switches included racemate to single enantiomer and other switches of the status of chirality, e.g., racemate and quasi-racemate to scalemic mixtures. There are 16 quinine/quinidine stereoisomers. Given the multiple pharmacological activities of Cinchona alkaloid stereoisomers, this article calls for subjecting them to comprehensive drug repurposing/chiral switch searches for new medical uses.
The role of peptides is nowadays relevant in fields such as drug discovery and biotechnology. Computational analyses are required to study their properties and gain insights into rational design strategies. Both natural and modified peptides containing non-natural amino acids require customized tools to run sequence and structure-based studies. PepFun 2.0 is a new version of the python package for the study of natural and modified peptides using a set of modules to analyze the sequence and structure of the molecules. PepFun 2.0 comprises five main modules for different tasks such as sequence alignments, prediction of properties, generation of conformers, modification of structures, detection of protein-peptide interactions, and extra functions to include peptides containing non-natural amino acids. The code and tutorial are available at: https://github.com/rochoa85/PepFun2
Metabolic reprogramming is a major hallmark of malignant transformation in cancer, and part of the so-called Warburg effect, in which the upregulation of glutamine catabolism plays a major role. The glutaminase enzymes convert glutamine to glutamate, which initiates this pathway. Inhibition of different forms of glutaminase (KGA, GAC, or LGA) demonstrated potential as an emerging anti-cancer therapeutic strategy. The regulation of these enzymes, and the molecular basis for their inhibition, have been the focus of much recent research. This review will explore the recent progress in understanding the molecular basis for activation and inhibition of different forms of glutaminase, as well as the recent focus on combination therapies of glutaminase inhibitors with other anti-cancer drugs.
Aim: This study aimed to develop a chemoinformatic tool for extracting natural product information from academic literature. Materials & methods: Machine learning graph embeddings were used to extract knowledge from a knowledge graph, connecting properties, molecular data and BERTopic topics. Results: Metapath2Vec performed best in extracting compound names and showed improvement over evaluation stages. Embedding Propagation on Heterogeneous Networks achieved the best performance in extracting bioactivity information. Metapath2Vec excelled in extracting species information, while DeepWalk and Node2Vec performed well in one stage for species location extraction. Embedding Propagation on Heterogeneous Networks consistently improved performance and achieved the best overall scores. Unsupervised embeddings effectively extracted knowledge, with different methods excelling in different scenarios. Conclusion: This research establishes a foundation for frameworks in knowledge extraction, benefiting sustainable resource use.
Aim: This work aims to contribute toward development of preventive measures for the control of monkeypox (mpox) virus disease through computational design of a multiepitope vaccine. Methods: To accomplish this, we employed a robust immunoinformatics approach to design a putative chimeric vaccine candidate from 18 viral transmembrane proteins. Results: The resulting chimeric vaccine candidate is a 76.4 kDa protein containing 687 amino acids with an estimated isoelectric point of 9.39. In addition, it was predicted to adopt a stable 3D conformation that harbors discontinuous B-cell epitopes and strongly interacts with key immune receptors. Conclusion: The designed hypothetical antigen is a valuable addition to the collection of prospective vaccine candidates for future development and trials against the re-emerging mpox disease.
