Software-aided detection and structural characterization of cyclic peptide metabolites in biological matrix by high-resolution mass spectrometry

• Main Authors: Ming Yao (Author), Tingting Cai (Author), Eva Duchoslav (Author), Li Ma (Author), Xu Guo (Author), Mingshe Zhu (Author)
• Format: Book
• Published: Elsevier, 2020-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2095-1779; 10.1016/j.jpha.2020.05.012
• Keywords: Atrial natriuretic peptide; Metabolism of cyclic peptide; High resolution mass spectrometry; Insulin; Software-aided data processing; Therapeutics. Pharmacology; RM1-950
• Source: Journal of Pharmaceutical Analysis, Vol 10, Iss 3, Pp 240-246 (2020)

Compared to their linear counterparts, cyclic peptides show better biological activities, such as antibacterial, immunosuppressive, and anti-tumor activities, and pharmaceutical properties due to their conformational rigidity. However, cyclic peptides could form numerous putative metabolites from potential hydrolytic cleavages and their fragments are very difficult to interpret. These characteristics pose a great challenge when analyzing metabolites of cyclic peptides by mass spectrometry. This study was to assess and apply a software-aided analytical workflow for the detection and structural characterization of cyclic peptide metabolites. Insulin and atrial natriuretic peptide (ANP) as model cyclic peptides were incubated with trypsin/chymotrypsin and/or rat liver S9, followed by data acquisition using TripleTOF® 5600. Resultant full-scan MS and MS/MS datasets were automatically processed through a combination of targeted and untargeted peak finding strategies. MS/MS spectra of predicted metabolites were interrogated against putative metabolite sequences, in light of a, b, y and internal fragment series. The resulting fragment assignments led to the confirmation and ranking of the metabolite sequences and identification of metabolic modification. As a result, 29 metabolites with linear or cyclic structures were detected in the insulin incubation with the hydrolytic enzymes. Sequences of twenty insulin metabolites were further determined, which were consistent with the hydrolytic sites of these enzymes. In the same manner, multiple metabolites of insulin and ANP formed in rat liver S9 incubation were detected and structurally characterized, some of which have not been previously reported. The results demonstrated the utility of software-aided data processing tool in detection and identification of cyclic peptide metabolites.