Current Proteomics - Volume 10, Issue 1, 2013

Membrane protein, which is closely related to cellular biological functions, is a vital component of cell membranes. Predicting membrane protein and its types is a challenging task that offers promising results. This paper proposes a novel membrane detection method that is more efficient and accurate than traditional technologies. Two methods were used to extract features from protein sequences. The 20-D feature was extracted from the position-specific scoring matrix of proteins, and the 188-D feature was extracted based on protein composition and physical-chemical properties. These features, together with a novel ensemble voting strategy that was derived from the theorem of minimal false classified samples set, were employed to improve classification performance. The proposed method offers efficient memory usage and accurate predictions. By using the jackknife test on the 20-D feature, the proposed method obtained 91.2% accuracy in distinguishing membrane proteins and 86.1% accuracy in predicting membrane protein types. Two interesting discoveries are presented: 1) approximately 12% of total enzymes are membrane proteins, and 2) membrane proteins occupy a higher proportion in alternative splicing peptides than normal proteins. A new membrane protein dataset which contains 7388 membrane protein sequences is built by using the latest Swiss-Prot database. Furthermore, a Web server and software called BinMemPredict is developed, which is freely accessible to the public at http://datamining.xmu.- edu.cn/software/bmp.

Animals produce a large variety of antimicrobial peptides that play an important role in natural innate immunity and controlling microbial infections. Snakes are classified into the phylum Chordata and class Reptilia, within the animal kingdom. Snake venoms are extensive mixtures that contain a large number of biologically active proteins/ peptides that represent a promising source of potential therapeutics for both humans and animals. These components are extensively studied for a wide range of pharmacological properties; however, it is quite exceptional that very little is relatively known about antimicrobial activity associated with venoms to date. In this review, we emphasize the available literature linked to antimicrobial activity of venom proteins such as L-amino acid oxidase (LAAO), phospholipase A2 (PLA2), peptides and snake cathelicidin. We propose a model for antimicrobial action in comparison with existing mechanisms. Structure and function of snake venom proteins/peptides in relation to antimicrobial activity and its involvement in molecular pharmacology thoroughly discussed. Nevertheless, snake venom enzymes and various classes of peptides have unique pharmacological properties, enhanced properties of antimicrobial effects against various bacterial infections, as well as varying levels of toxicity on eukaryotic and prokaryotic cells. In conclusions, these peptide-based molecules may ultimately be used as alternative drugs to replace chemical antibiotics that increasingly become less useful due to highly-evolved resistance mechanisms employed by various microbial pathogens.

Hepatocellular carcinoma (HCC) with more than 700,000 deaths every year is the most prevalent type of liver cancer and a global concern. It is the fifth most common cancer worldwide and has a poor general prognosis. Chronic hepatitis B virus (HBV) infection is a major cause of HCC. The HBV-infected individual has 100 times higher risk of developing HCC. The x protein of HBV (HBx) has been shown to involve in the development of HCC. In this study, the association between HBV replication and the host cell metabolism is investigated. HepG2 cells are transfected with different genotypes of HBx and total proteins are extracted and analyzed using LC-MS/MS. Our proteomics results indicates that a number of glycolytic enzymes including glyceraldehyde-3-phosphate dehydrogenase (GAPDH), pyruvate kinase (PK), Phosphoglyceratekinase (PGK) and Lactate dehydrogenase (LDH) are significantly up-regulated in HepG2 cells transfected by HBx comparing with control group. These findings suggest that HBV replication could alter host cell metabolism by increasing the rate of glycolysis to provide important metabolic requirements for nucleotides, amino acids and lipids synthesis. Hence, our proteomics approach may provide candidate biomarkers to improve the diagnosis of HBVrelated HCC patients.

The failure of proteins to fold correctly result in amyloidosis. Therefore, amyloid plaque prediction has become significant to narrow down the exploration of anti- amyloidosis and related drugs. In this research article, we propose a unique hybrid approach to computationally predict the formation of amyloid plaques by exploiting diversity in the feature vector extracted from protein sequences and structures. The diversity in the sequence of feature space is exploited using structure dependent features besides the physico-chemical information from amino acid chemistry and frequency spectrum based parameters. We explored the prediction capability with independent and integrated feature vectors by an ensemble machine learning classifier, Random Forests. Computational analysis evidence that the assimilation of diverse feature set outperform individual feature array with a balanced prediction accuracy of 0.830 and Receiver Characteristic Curve area of 0.918 on stratified10-fold cross-validation test.

Hyperhomocysteinemia (HHcy): A condition that epidemiological studies have shown to be associated with increased risk of vascular disease and type 2 diabetes (T2D), arises from disrupted homocysteine (Hcy) and glucose metabolism. Our previous studies evaluating the regulation of Hcy metabolism from the molecular, cell, animal and population levels have demonstrated that utilization of Hcy by the trans-sulfuration and remethylation pathways is regulated by omega-3 polyunsaturated fatty acids (PUFA). This interaction was then identified as having protective effects on the cardiovascular system. Furthermore, observational studies we conducted found potential beneficial effects of omega-3 PUFA on insulin sensitivity and T2D. To date, no literature reports have discussed the mechanism behind metabolism of omega-3 PUFA, Hcy and glucose from a nutriproteomic and nutrigenomic point of view. This review comprehensively summarizes the metabolism of omega-3 PUFA, Hcy and glucose; and their regulation by omega-3 PUFA on critical gene expression, a key enzyme activity involved in the Hcy metabolic and insulin signaling pathways. In summary, high dietary omega-3 PUFA decreases blood Hcy and glucose concentrations. Omega-3 PUFA decreases the concentration of Hcy despite increasing MAT activity and up-regulating MAT mRNA expression through compensatory cystathionine-g-lyase mRNA expression, both of which are involved in Hcy metabolism. Omega-3 PUFA had a beneficial effect on insulin sensitivity and directly targeted insulin signaling pathway via increasing insulin receptor number, tyrosine phosphrylation of IRS-1, serine phosphorylation of Akt, gene expression of IRS-1, IRS-2 and GLUT-4 in different tissues. More research is warranted to explore the precise mechanism.

The central nervous system (CNS) encompasses complex groups of cells that coexist to form the functional units of brain mainly comprising three cell types: neurons, oligodendrocytes and astrocytes. The network of these cells through interaction between each other leads to formation of complex organizations such as nodes of ranvier, paranodes, juxtaparanodes etc. Therefore, regions of molecular complexity in brain have different protein/lipid combinatorial complexities. Isolation and enrichment of these domains/regions are crucial for the maximum representation of the protein complement,which otherwise goes undetected due to its miniscule amounts in the whole brain homogenate. Thus preparation of enriched fractions representing the proteins of these complexities is important for detection of molecules of pathogenic and diagnostic significance in diseases. Apart from enrichment, the proteins from the enriched fractions have to be solubilized into an appropriate sample format suitable for resolving into its individual protein components. This holds the key to identification of novel molecules important in neurodegenerative diseases. Thus, sample processing for understanding the organizational and functional proteomics becomes an extremely important step for fruitful results to avoid neuroproteomics biases. This review focuses on the functional units of brain, significance of certain molecules in these organizations and how current techniques are meaningfully employed towards neuroproteomics. In addition, we review how custom techniques of sample preparation for protein analysis are being tailored to address issues for a better analysis of protein complement from specialized regions of the functional units of brain in discovering biomarkers pertaining to neurodegenerative diseases.