Proteome analysis of human hepatocellular carcinoma tissues was conducted using two-dimensional difference gel electrophoresis coupled with mass spectrometry. Paired samples from the normal and tumor region of resected human liver were labeled with Cy3 and Cy5, respectively while the pooled standard sample was labeled with Cy2. After analysis by the DeCyder software, protein spots that exhibited at least a two-fold difference in intensity were excised for in-gel tryptic digestion and matrix-assisted laser desorption/ionization-time of flight mass spectrometry. A total of 6 and 42 proteins were successfully identified from the well- and poorly-differentiated samples, respectively. The majority of these proteins are related to detoxification/oxidative stress and metabolism. Three down-regulated metabolic enzymes, methionine adenosyltransferase, glycine N-methyltransferase, and betaine-homocysteine S-methyltransferase that are involved in the methylation cycle in the liver are of special interest. Their expression levels, especially, methionine adenosyltransferase, seemed to have a major influence on the level of S-adenosylmethionine (AdoMet), a vital intermediate metabolite required for the proper functioning of the liver. Recent work has shown that chronic deficiency in AdoMet in the liver results in spontaneous development of steatohepatitis and hepatocellular carcinoma, and hence the down-regulation of hepatic methionine adenosyltransferase in our hepatocellular carcinoma samples is in line with this observation. Moreover, when a comparison is made between the differentially expressed proteins from our human hepatocellular carcinoma samples and from the liver tissues of knockout mice deficient in methionine adenosyltransferase, there is a fairly good correlation between them.
Knowledge of protein-protein interaction is useful for elucidating protein function via the concept of 'guilt-by-association'. A statistical learning method, Support Vector Machine (SVM), has recently been explored for the prediction of protein-protein interactions using artificial shuffled sequences as hypothetical noninteracting proteins and it has shown promising results (Bock, J. R., Gough, D. A., Bioinformatics 2001, 17, 455-460). It remains unclear however, how the prediction accuracy is affected if real protein sequences are used to represent noninteracting proteins. In this work, this effect is assessed by comparison of the results derived from the use of real protein sequences with that derived from the use of shuffled sequences. The real protein sequences of hypothetical noninteracting proteins are generated from an exclusion analysis in combination with subcellular localization information of interacting proteins found in the Database of Interacting Proteins. Prediction accuracy using real protein sequences is 76.9% compared to 94.1% using artificial shuffled sequences. The discrepancy likely arises from the expected higher level of difficulty for separating two sets of real protein sequences than that for separating a set of real protein sequences from a set of artificial sequences. The use of real protein sequences for training a SVM classification system is expected to give better prediction results in practical cases. This is tested by using both SVM systems for predicting putative protein partners of a set of thioredoxin related proteins. The prediction results are consistent with observations, suggesting that real sequence is more practically useful in development of SVM classification system for facilitating protein-protein interaction prediction.
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