N-Linked Glycosylation Prevents Deamidation of Glycopeptide and Glycoprotein.

Deamidation has been recognized as a common spontaneous pathway of protein degradation and a prevalent concern in the pharmaceutical industry; deamidation caused the reduction of protein/peptide drug efficacy and shelf life in several cases. More importantly, deamidation of physiological proteins is related to several human diseases and considered a "timer" for the diseases. N-linked glycosylation has a variety of significant biological functions, and it interestingly occurs right on the deamidation site-asparagine. It has been perceived that N-glycosylation could prevent deamidation, but experimental support is still lacking for clearly understanding the role of N-glycosylation on deamidation. Our results presented that deamidation is prevented by naturally occurring N-linked glycosylation. Glycopeptides and corresponding nonglycosylated peptides were used to compare their deamidation rates. All the nonglycosylated peptides have different half-lives ranging from one to 20 days, for the corresponding glycosylated peptides; all the results showed that the deamidation reaction was significantly reduced by the introduction of N-linked glycosylation. A glycoprotein, RNase B, also showed a significantly elongated deamidation half-life compared to nonglycosylated protein RNase A. At last, N-linked glycosylation on INGAP-P, a therapeutic peptide, increased the deamidation half-life of INGAP-P as well as its therapeutic potency.

Author Correction: Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations.

Cancer is a comparatively well-studied disease, yet despite decades of intense focus, we demonstrate here using data from The Cancer Genome Atlas that a substantial number of genes implicated in cancer are relatively poorly studied. Those genes will likely be missed by any data analysis pipeline, such as enrichment analysis, that depends exclusively on annotations for understanding biological function. There is no indication that the amount of research - indicated by number of publications - is correlated with any objective metric of gene significance. Moreover, these genes are not missing at random but reflect that our information about genes is gathered in a biased manner: poorly studied genes are more likely to be primate-specific and less likely to have a Mendelian inheritance pattern, and they tend to cluster in some biological processes and not others. While this likely reflects both technological limitations as well as the fact that well-known genes tend to gather more interest from the research community, in the absence of a concerted effort to study genes in an unbiased way, many genes (and biological processes) will remain opaque.