Computational proteomics

Maggio ET, Ramnarayan K. Recent developments in computational proteomics. Trends Biotechnol 2001 19 266-272. 

Computation proteome annotation is the process of proteome database mining, which includes structure/fold prediction and functionality assignment. Methodologies of secondary structure prediction and problems of protein folding are discussed. Approaches to identify functional sites are presented. Protein structure databases are surveyed. Secondary structure predictions and pattern/fold recognition of proteins using the Internet resources are described. 

Pfeifer, N. et al. Statistical learning of peptide retention behavior in chromatographic separations a new kernel-based approach for computational proteomics. BMC Bioinformatics 2007, 8, 468 http //www.biomedcentral.coni/1471-2105/8/468. 

Computational proteomics refers to the large-scale generation and analysis of 3D protein structural information. Accurate prediction of protein contact maps is the beginning and essential step for computational proteomics. The major resource for computational proteomics is the currently available information on protein and nucleic acid structures. The 3D-GENOM1CS (www.sbg.bio.ic.au.uk/3dgenomics/) and PDB (http // www.rcsb.org/pdb/), and other databases provide a broad range of structural and functional annotations for proteins from sequenced genomes and protein 3D structures, which make a solid foundation for computational proteomics. 

Ramos, M.J. (ed.) (2008) Computational Proteomics, Transworld Research Network. 

It is interesting to note that the foremost challenges for the detailed modeling of the intact organism (computing time, complexity of interactions, model selection) are very similar to those entailed by the analysis of proteomic or genomic data. In the clinical case, complexity shifts from the richness of the data set to the model formulation, whereas in the proteomic-genomic case the main source of difficulties is the sheer size of the data set however, at least at present, interpretative tools are rather uncomplicated. 

The statistical degree of overlapping (SDO) and 2D autocovariance function (ACVF) methods have been applied to 2D-PAGE maps (Marchetti et al., 2004 Pietrogrande et al., 2002, 2003, 2005, 2006a Campostrini et al., 2005) the means for extracting information from the experimental data and their relevance to proteomics are discussed in the following. The procedures were validated on computer-simulated maps. Their applicability to real samples was tested on reference maps obtained from literature sources. Application to experimental maps is also discussed. 

Although, MediChem is a biosecurity products manufacturer, its biotechnology-based R D capabilities are worth mentioning here. The attended markets include Medical, Laboratory, Veterinary, and Environmental sectors. Medicinal chemistry services and drug discovery form the basis of the company, though their capabilities might be applied in a broader range of sectors. These capabilities comprise the areas of Proteomics, Combinatorial and Computational Chemistry, Medicinal Chemistry, Enzymes, Process Development, Analytical and Separations Chemistry, Chemical Synthesis and Scale Up. 

Computational methods have been applied to determine the connections in systems that are not well-defined by canonical pathways. This is either done by semi-automated and/or curated literature causal modeling [1] or by statistical methods based on large-scale data from expression or proteomic studies (a mostly theoretical approach is given by reference [2] and a more applied approach is in reference [3]). Many methods, including clustering, Bayesian analysis and principal component analysis have been used to find relationships and "fingerprints" in gene expression data [4]. 

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