Protein network analysis

Background Untreatable metastasis, rather than the primary tumor, is the cause of mortality in breast cancer. Myeloid-derived suppressor cells (MDSCs) are hema-topoetic cells that home specifically to the tumors and have a major role in tumor invasion and metastasis and the development of resistance to chemotherapy. MDSCs proliferate in response to tumors and accumulate in the spleen, from which they can be isolated using their Grl and CDllb surface markers. The objective was to use label-free mass spectrometry and shotgun proteomics to characterize MDSCs that associate with two mouse cell lines derived from the same tumor, one from the primary tumor (67NR) and the other from cells that have already metastasized to various organs (4T1). Spectral counting, for quantification, and protein network analysis were used to search for MDSC biomarkers characteristic to metastasis. 

Sheehan KM, Calvert VS, Kay EW et al (2005) Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma. Mol Cell Proteomics 4 346-355... 

Proteomics includes a variety of technologies that include differential protein display on gels, protein chips, quantitation of protein amoimts, analysis of post-translational modifications, characterization of protein complexes and networks and bioinformatics. All this information in combination with genome and phenotype studies will ultimately yield a comprehensive picture of a cellular or tissue proteome (Wasinger and Corthals 2002). 

Fardet, A., Baldwin, P.M., Bertrand, D., Bouchet, B., Gallant, D.J., and Barry, J.L. 1998. Textural image analysis of pasta protein networks to determine influence of technological processes. Cereal Chem. 75(5), 699-704. 

Figure 12.6. Secondary structure prediction of duck lysozyme at NPS . The predicted secondary structures of duck lysozyme at Network Protein Sequence Analysis (NPS ) with GOR IV method are depicted in different representations.

The method of Rost and Sander (Rost and Sander, 1993), which combines neural networks with multiple sequence alignments known as PHD, is available from the PredictProtein (Rost, 1996) server of Columbia University (http //cubic.bioc. columbia.edu/predictprotein/). This Web site offers the comprehensive protein sequence analysis and structure prediction (Figure 12.8). For the secondary structure prediction, choose Submit a protein sequence for prediction to open the submission form. Enter e-mail address, paste the sequence, choose options, and then click the... 

Figure 12,7. Secondary structure consensus prediction at NPS . Network Protein Sequence Analysis (NPS ) offers numerous methods for secondary structure prediction of proteins. The secondary structure consensus prediction (Sec.Cons.) of duck lysozyme is derived from simultaneous execution of predictions with more than one methods.

Mawuenyega KG, Forst CV, Dobos KM et al (2005) Mycobacterium tuberculosis functional network analysis by global subcellular protein profiling. Mol Biol Cell 16 396-404... 

Wilcox, G. L Poliac, M. O. Sc Liebman, M. N. (1991). Neural network analysis of protein tertiary structure. Tetrahedron Comp Meth 3,191-211. 

Table 11.1 Neural network applications for protein sequence analysis.

Neural network applications for protein sequence analysis are summarized in Table 11.1. Like the DNA coding region recognition problem, signal peptide prediction (11.2) involves both search for content and search for signal tasks. An effective means for protein sequence analysis is reverse database searching to detect functional motifs or sites (11.3) and identify protein families (11.4). Most of the functional motifs are also... 

How have neural networks been used in genome informatics applications In Part II, we have summarized them based on the types of applications for DNA sequence analysis, protein structure prediction and protein sequence analysis. Indeed, the development of neural network applications over the years has resulted in many successful and widely used systems. Current state-of-the-art systems include those for gene recognition, secondary structure prediction, protein classification, signal peptide recognition, and peptide design, to name just a few. 

Soreghan BA, Lu BW, Thomas SN, Duff K, Rakhmatulin EA, Nikolskaya T, Chen T, Yang AJ (2005) Using proteomics and network analysis to elucidate die consequences of synapdc protein oxidadon in a PSl + AbetaPP mouse model of Alzheimer s disease. J Alzheimers Dis 8 227—241. 

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