Development of a PNGase Rc Column for Online Deglycosylation of Complex Glycoproteins during HDX-MS.

Protein glycosylation is one of the most common PTMs and many cell surface receptors, extracellular proteins, and biopharmaceuticals are glycosylated. However, HDX-MS analysis of such important glycoproteins has so far been limited by difficulties in determining the HDX of the protein segments that contain glycans. We have developed a column containing immobilized PNGase Rc (from Rudaea cellulosilytica) that can readily be implemented into a conventional HDX-MS setup to allow improved analysis of glycoproteins. We show that HDX-MS with the PNGase Rc column enables efficient online removal of N-linked glycans and the determination of the HDX of glycosylated regions in several complex glycoproteins. Additionally, we use the PNGase Rc column to perform a comprehensive HDX-MS mapping of the binding epitope of a mAb to c-Met, a complex glycoprotein drug target. Importantly, the column retains high activity in the presence of common quench-buffer additives like TCEP and urea and performed consistent across 114 days of extensive use. Overall, our work shows that HDX-MS with the integrated PNGase Rc column can enable fast and efficient online deglycosylation at harsh quench conditions to provide comprehensive analysis of complex glycoproteins.

Machine learning approaches for diagnosing depression using EEG: A review.

Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.