Dataset Information

Identification of four novel types of in vitro protein modifications.

ABSTRACT: In vitro chemical modifications in proteins, introduced during sample preparation, can complicate mass spectra and increase the potential for false-positive identifications. While several in vitro protein modifications have been described previously, additional types of such modifications may exist. Here, we report discovery of four types of in vitro protein modifications, identified by HPLC/MS/MS analysis and nonrestrictive protein sequence alignment by PTMap, an algorithm recently developed in our laboratory. These novel in vitro modifications included ethylation of aspartate and glutamate (+28 Da), esterification of aspartate and glutamate by glycerol (+74 Da), loss of 19 Da from lysine, and addition of 108 Da to cysteine. We confirmed that these modifications occurred in vitro and not in vivo in control experiments designed to avoid conditions likely to induce the modifications. We propose a plausible molecular mechanism for the -19 Da modification of lysine. Our study therefore conclusively identifies several novel in vitro protein modifications, suggests ways to avoid these modifications, and highlights the possibility of misidentification of peptides because of in vitro modifications.

SUBMITTER: Xing G

PROVIDER: S-EPMC2911956 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of four novel types of in vitro protein modifications.

Xing Gang G Zhang Junmei J Chen Yue Y Zhao Yingming Y

Journal of proteome research 20080904 10

In vitro chemical modifications in proteins, introduced during sample preparation, can complicate mass spectra and increase the potential for false-positive identifications. While several in vitro protein modifications have been described previously, additional types of such modifications may exist. Here, we report discovery of four types of in vitro protein modifications, identified by HPLC/MS/MS analysis and nonrestrictive protein sequence alignment by PTMap, an algorithm recently developed in ...[more]

PMID: 18767873

Similar Datasets

Project description:IntroductionCancer consistently remains one of the top causes of death in the United States every year, with many cancer deaths preventable if detected early. Circulating serum miRNAs are a promising, minimally invasive supplement or even an alternative to many current screening procedures. Many studies have shown that different serum miRNAs can discriminate healthy individuals from those with certain types of cancer. Although many of those miRNAs are often reported to be significant in one cancer type, they are also altered in other cancer types. Currently, very few studies have investigated serum miRNA biomarkers for multiple cancer types for general cancer screening purposes.MethodTo identify serum miRNAs that would be useful in screening multiple types of cancers, microarray cancer datasets were curated, yielding 13 different types of cancer with a total of 3352 cancer samples and 2809 non-cancer samples. The samples were divided into training and validation sets. One hundred random forest models were built using the training set to select candidate miRNAs. The selected miRNAs were then used in the validation set to see how well they differentiate cancer from normal samples in an independent dataset. Furthermore, the interactions between these miRNAs and their target mRNAs were investigated.ResultThe random forest models achieved an average of 97% accuracy in the training set with 95% bootstrap confidence interval of 0.9544 to 0.9778. The selected miRNAs were hsa-miR-663a, hsa-miR-6802-5p, hsa-miR-6784-5p, hsa-miR-3184-5p, and hsa-miR-8073. Each miRNA exhibited high area under the curve (AUC) value using receiver operating characteristic analysis. Moreover, the combination of four out of five miRNAs achieved the highest AUC value of 0.9815 with high sensitivity of 0.9773, indicating that these miRNAs have a high potential for cancer screening. miRNA-mRNA and protein-protein interaction analysis provided insights into how these miRNAs play a role in cancer.

Dataset Information

Identification of four novel types of in vitro protein modifications.

Publications

Identification of four novel types of in vitro protein modifications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets