Large-scale purification of a deprotected macrocyclic peptide by supercritical fluid chromatography (SFC) integrated with liquid chromatography in discovery chemistry.

A macrocyclic peptide A was successfully purified in large quantities (∼30 g) in >95 % purity by an integrated two-step orthogonal purification process combining supercritical fluid chromatography (SFC) with medium-pressure reverse-phase liquid chromatography (MP-RPLC). MP-RPLC was used to fractionate the crude peptide A, remove unwanted trifluoroacetic acid (TFA) originating from the peptide A cleavage off the resin, and convert the peptide A into ammonium acetate salt form, prior to the final purification by SFC. A co-solvent of methanol/acetonitrile containing ammonium acetate and water in CO2 was developed on a Waters BEH 2-Ethylpyridine column. The developed SFC method was readily scaled up onto a 5 cm diameter column to process multi-gram quantities of the MP-RPLC fraction to reach > 95 % purity with a throughput/productivity of 0.96 g/h. The incorporation of SFC with MP-RPLC has been demonstrated to have a broader application in other large-scale polypeptide purifications.

Disulfide bridge-targeted metabolome mining unravels an antiparkinsonian peptide.

Peptides are a particular molecule class with inherent attributes of some small-molecule drugs and macromolecular biologics, thereby inspiring continuous searches for peptides with therapeutic and/or agrochemical potentials. However, the success rate is decreasing, presumably because many interesting but less-abundant peptides are so scarce or labile that they are likely 'overlooked' during the characterization effort. Here, we present the biochemical characterization and druggability improvement of an unprecedented minor fungal RiPP (ribosomally synthesized and post-translationally modified peptide), named acalitide, by taking the relevant advantages of metabolomics approach and disulfide-bridged substructure which is more frequently imprinted in the marketed peptide drug molecules. Acalitide is biosynthetically unique in the macrotricyclization via two disulfide bridges and a protease (AcaB)-catalyzed lactamization of AcaA, an unprecedented precursor peptide. Such a biosynthetic logic was successfully re-edited for its sample supply renewal to facilitate the identification of the in vitro and in vivo antiparkinsonian efficacy of acalitide which was further confirmed safe and rendered brain-targetable by the liposome encapsulation strategy. Taken together, the work updates the mining strategy and biosynthetic complexity of RiPPs to unravel an antiparkinsonian drug candidate valuable for combating Parkinson's disease that is globally prevailing in an alarming manner.

Cyclic ß2,3-amino acids improve the serum stability of macrocyclic peptide inhibitors targeting the SARS-CoV-2 main protease.

Due to their constrained conformations, cyclic ß2,3-amino acids (cßAA) are key building blocks that can fold peptides into compact and rigid structures, improving peptidase resistance and binding affinity to target proteins, due to their constrained conformations. Although the translation efficiency of cßAAs is generally low, our engineered tRNA, referred to as tRNAPro1E2, enabled efficient incorporation of cßAAs into peptide libraries using the flexible in vitro translation (FIT) system. Here we report on the design and application of a macrocyclic peptide library incorporating 3 kinds of cßAAs: (1R,2S)-2-aminocyclopentane carboxylic acid (ß1), (1S,2S)-2-aminocyclohexane carboxylic acid (ß2), and (1R,2R)-2-aminocyclopentane carboxylic acid. This library was applied to an in vitro selection against the SARS-CoV-2 main protease (Mpro). The resultant peptides, BM3 and BM7, bearing one ß2 and two ß1, exhibited potent inhibitory activities with IC50 values of 40 and 20 nM, respectively. BM3 and BM7 also showed remarkable serum stability with half-lives of 48 and >168 h, respectively. Notably, BM3A and BM7A, wherein the cßAAs were substituted with alanine, lost their inhibitory activities against Mpro and displayed substantially shorter serum half-lives. This observation underscores the significant contribution of cßAA to the activity and stability of peptides. Overall, our results highlight the potential of cßAA in generating potent and highly stable macrocyclic peptides with drug-like properties.

Development, Characterization, and Radiation Dosimetry Studies of 18F-BMS-986229, a 18F-Labeled PD-L1 Macrocyclic Peptide PET Tracer.

PURPOSE: In cancer immunotherapy, the blockade of the interaction between programmed death-1 and its ligand (PD-1:PD-L1) has proven to be one of the most promising strategies. However, as mechanisms of resistance to PD-1/PD-L1 inhibition include variability in tumor cell PD-L1 expression in addition to standard tumor biopsy PD-L1 immunohistochemistry (IHC), a comprehensive and quantitative approach for measuring PD-L1 expression is required. Herein, we report the development and characterization of an 18F-PD-L1-binding macrocyclic peptide as a PET tracer for the comprehensive evaluation of tumor PD-L1 expression in cancer patients. PROCEDURES: 18F-BMS-986229 was characterized for PD-L1 expression assessment by autoradiography or PET imaging. 18F-BMS-986229 was utilized to evaluate tumor PD-L1 target engagement in competition with a macrocyclic peptide inhibitor of PD-L1 (BMS-986189) over a range of doses using PET imaging. A whole-body radiation dosimetry study of 18F-BMS-986229 in healthy non-human primates (NHPs) was performed. RESULTS: In vitro autoradiography showed an 8:1 binding ratio in L2987(PD-L1 +) vs. HT-29 (PD-L1-) tumors, more than 90% of which could be blocked with 1 nM of BMS-986189. Ex vivo autoradiography showed that 18F-BMS-986229 detection was penetrant over a series of sections spanning the entire L2987 tumor. In vivo PET imaging in mice demonstrated a 5:1 tracer uptake ratio (at 90-100 min after tracer administration) in L2987 vs. HT-29 tumors and demonstrated 83%-93% specific binding of BMS-986189 within those dose ranges. In a healthy NHP dosimetry study, the resultant whole-body effective dose was 0.025 mSv/MBq. CONCLUSION: 18F-BMS-986229 has been preclinically characterized and exhibits high target specificity, low background uptake, and a short blood half-life supportive of same day imaging in the clinic. As the PET tracer, 18F-BMS-986229 shows promise in the quantification of PD-L1 expression, and its use in monitoring longitudinal changes in patients may provide insights into PD-1:PD-L1 immuno-therapy treatment outcomes.

PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer's effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer's skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides.