Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery

Stable Isotope Labeling-Based Nontargeted Strategy for Characterization of the In Vitro Metabolic Profile of a Novel Doping BPC-157 in Doping Control by UHPLC-HRMS

Traditional strategies for the metabolic profiling of doping are limited by the unpredictable metabolic pathways and the numerous proportions of background and chemical noise that lead to inadequate metabolism knowledge, thereby affecting the selection of optimal detection targets. Thus, a stable isotope labeling-based nontargeted strategy combined with ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) was first proposed for the effective and rapid metabolism analysis of small-molecule doping agents and demonstrated via its application to a novel doping BPC-157. Using 13C/15N-labeled BPC-157, a complete workflow including automatic 13C0,15N0-13C6,15N2m/z pair picking based on the characteristic behaviors of isotope pairs was constructed, and one metabolite produced by a novel metabolic pathway plus eight metabolites produced by the conventional amide-bond breaking metabolic pathway were successfully discovered from two incubation models. Furthermore, a specific method for the detection of BPC-157 and the five main metabolites in human urine was developed and validated with satisfactory detection limits (0.01~0.11 ng/mL) and excellent quantitative ability (linearity: 0.02~50 ng/mL with R2 > 0.999; relative error (RE)% < 10% and relative standard deviation (RSD)% < 5%; recovery > 90%). The novel metabolic pathway and the in vitro metabolic profile could provide new insights into the biotransformation of BPC-157 and improved targets for doping control.

PhytoSelectDBT: A database for the molecular models of anti-diabetic targets docked with bioactive peptides from selected ethno-medicinal plants

It is of interest to assess the effectiveness of bioactive peptides derived from 41 ethno-medicinal plants, classify them according to their anti-diabetic protein targets (DPP-IV, alpha-amylase, alpha-glucosidase, GRK2, GSK3B, GLP-1R, and AdipoR1), and create a web tool named PhytoSelectDBT by using the top seven peptides per target. If one of the target-based medicinal plant suggestions made by PhytoSelectDBT is unsuccessful, alternative target-based possibilities are presented by PhytoSelectDBT for treating the condition and any other related complications. The results provide a useful resource for the management of type 2 diabetes and emphasize the significance of utilising ethnomedical knowledge for the identification of potent anti-diabetic plants and their peptides. We used molecular docking to investigate interactions between anti-diabetic targets (DPP-IV, alpha-amylase, alpha-glucosidase, GRK2, GSK3B, GLP-1R, and AdipoR1) and projected bioactive peptides from 41 ethnomedicinal plants. All bioactive peptides were cross-checked against several databases to determine their allergenicity, toxicity, and cross-reactivity. The presence of B and T cell epitopes was also examined in all simulated digested bioactive peptides for reference. This data is archived at the PhytoselectDBT database.

Non-Canonical Amino Acids in Analyses of Protease Structure and Function

All known organisms encode 20 canonical amino acids by base triplets in the genetic code. The cellular translational machinery produces proteins consisting mainly of these amino acids. Several hundred natural amino acids serve important functions in metabolism, as scaffold molecules, and in signal transduction. New side chains are generated mainly by post-translational modifications, while others have altered backbones, such as the β- or γ-amino acids, or they undergo stereochemical inversion, e.g., in the case of D-amino acids. In addition, the number of non-canonical amino acids has further increased by chemical syntheses. Since many of these non-canonical amino acids confer resistance to proteolytic degradation, they are potential protease inhibitors and tools for specificity profiling studies in substrate optimization and enzyme inhibition. Other applications include in vitro and in vivo studies of enzyme kinetics, molecular interactions and bioimaging, to name a few. Amino acids with bio-orthogonal labels are particularly attractive, enabling various cross-link and click reactions for structure-functional studies. Here, we cover the latest developments in protease research with non-canonical amino acids, which opens up a great potential, e.g., for novel prodrugs activated by proteases or for other pharmaceutical compounds, some of which have already reached the clinical trial stage.

Subcutaneous Catabolism of Peptide Therapeutics: Bioanalytical Approaches and ADME Considerations

Many peptide drugs such as insulin and glucagon-like peptide (GLP-1) analogues are successfully administered subcutaneously (SC). Following SC injection, peptides may undergo catabolism in the SC compartment before entering systemic circulation, which could compromise their bioavailability and in turn affect their efficacy.This review will discuss how both technology and strategy have evolved over the past years to further elucidate peptide SC catabolism.Modern bioanalytical technologies (particularly liquid chromatography-high-resolution mass spectrometry) and bioinformatics platforms for data mining has prompted the development of in silico, in vitro and in vivo tools for characterizing peptide SC catabolism to rapidly address proteolytic liabilities and, ultimately, guide the design of peptides with improved SC bioavailability.More predictive models able to recapitulate the interplay between SC catabolism and other factors driving SC absorption are highly desirable to improve in vitro/in vivo correlations.We envision the routine incorporation of in vitro and in vivo SC catabolism studies in ADME screening funnels to develop more effective peptide drugs for SC delivery.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Doping control analysis of small peptides: A decade of progress

Small peptides are handled in the field of sports drug testing analysis as a separate group doping substances. It is a diverse group, which includes but is not limited to growth hormone releasing-factors and gonadotropin-releasing hormone analogues. Significant progress has been achieved during the past decade in the doping control analysis of these peptides. In this article, achievements in the application of liquid chromatography-mass spectrometry-based methodologies are reviewed. To meet the augmenting demands for analyzing an increasing number of samples for the presence of an increasing number of prohibited small peptides, testing methods have been drastically simplified, whilst their performance level remained constant. High-resolution mass spectrometers have been installed in routine laboratories and became the preferred detection technique. The discovery and implementation of metabolites/catabolites in testing methods led to extended detection windows of some peptides, thus, contributed to more efficient testing in the anti-doping community.