Glycan composition of serum alpha-fetoprotein in patients with hepatocellular carcinoma and non-seminomatous germ cell tumour

Although estimation of serum alpha-fetoprotein (AFP) is widely used in the diagnosis of hepatocellular carcinoma (HCC) and non-seminomatous germ cell tumours (NSGCT), the clinical usefulness of this test is limited by a low specificity. However, there exist glycoforms of AFP which may be more specific for particular tumours. Previously, detailed analysis has been prevented by the low levels of AFP in human serum. We report here the application of fluorescence labelling, sequential exoglycosidase digestion, high-performance liquid chromatography and matrix-assisted laser desorption ionization in time-of-flight mass spectrometry, to determine the glycan structures of purified serum AFP from patients with HCC and NSGCT. Eleven major glycans were found, of which seven were N-linked, and four were O-linked, to the protein backbone. The structure of the N-linked glycans (all of bi-antennary complex-type with varying degrees of sialylation, fucosylation and galactosylation) were consistent with those previously reported. The O-linked glycans (three mucin O-GalNAc type glycans with variable degrees of sialylation, one O-HexNAc monosaccharide glycan) have not previously been reported. The finding of mucin O-GalNAc type glycans was supported by the prediction of potential O-GalNAc glycosylation sites on the protein backbone by analysis of the AFP structure by molecular modelling. With knowledge of these structures it may be possible to develop more specific assays for the detection of HCC and NSGCT. © 1999 Cancer Research Campaign © 1999 Cancer Research Campaig

Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Article discussing the performance of different computational methods for prediction of glycosylation sites from amino acid sequences

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine