Abstract
Purpose :
Humanin (HN), the first identified mitochondrial derived peptide, contains 24 amino acids and has neuroprotective and anti-apoptotic properties. The serum HN levels decrease significantly with age and are associated with age-related diseases in rodent animal models and human clinical studies. Our preliminary study showed that the 14-amino acid long humanin-fragment (HNF14) with the sequence LLLTSEIDLPVKRR was found at higher concentrations in older-normal plasma samples compared to samples from age-related macular degeneration patients. Here, we describe the stability features of the HNF14 in different conditions using ultra-performance liquid chromatography coupled to high resolution mass spectrometry (UPLC-HRMS) and an Alfafold 2 machine learning algorithm.
Methods :
HNF14 fragments were custom synthesized in Anaspec Inc. 4 μM HNF14 solutions were prepared in HPLC grade water and phosphate buffered saline (PBS) and stored at 37°C for 28 days. 4 μM HNF14 in HPLC water and PBS were analyzed using UPLC-HRMS (Xevo® G2-XS QTof). Alfafold2 machine-learning algorithm and Google Research Colaboratory were used to predict molecular three-dimensional structure of HNF14. 3D structure of HNF14 was visualized using UCSF Chimera software.
Results :
UPLC-HRMS Analyses: Multiple charged HNF14 peptides were observed at m/z 827.5, 552.0 and 414.25, after incubation at 37 oC for 28 days in water and also PBS solutions. Oxidation of HNF14 peptides were not detected using UPLC-HRMS.
The molecular tridimensional images of HN and HNF14 were produced using the Alfafold2 and UCSF Chimera software (Figure 1). Sequences of A) HN and B) HNF14 were shown. Visualizations of surface hydrophobicity of C) HN and D) HNF14 were represented. E) HN and F) HNF14 were shown in surface model. Tridimensional structure of all atoms of G) HN and H) HNF14 fragment are represented.
Conclusions :
For the first time, stability features of the HNF14 peptides have been analyzed in detail using advanced UPLC-HRMS technologies as well as identified three-dimensional HNF14 using Alfafold2 and Google Research Colaboratory. Our results may help researchers design better in vitro and in vivo experimental parameters to further understand the critical role of HNF14 in physiological conditions and human diseases. In addition, future studies will investigate whether there are any biological functions of the HNF14.
This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.