Proteomics algorithms

Proteomics algorithms have been developed to search databases of protein sequences for matches to short sequences determined experimentally from proteins or peptides in the laboratory,5 59 and for theoretical matches to... 

The use of proteomics exploits one of the fastest-growing analytical technologies, with scientists worldwide contributing to the protein/genome databases, and to improved algorithms to search databases and identify proteins. 

Demirev, P. A. Lin, J. S. Pineda, F. J. Fenselau, C. Bioinformatics and mass spectrometry for microorganism identification Proteome-wide post-translational modifications and database search algorithms for characterization of intact H. Pylori. Anal. Chem. 2001, 73, 4566 573. 

Methods based on liquid chromatography-mass spectrometry (LC-MS) and universally accepted search algorithms permit reliable identifications of low levels of proteins at high sensitivity [6]. Even semispecialized protein chemistry labs can readily identify proteins at the level of a few picomoles (10 pmol of a 50-kDa protein is 500 ng). Specialized groups with access to the latest advances in HPLC and mass spectrometry routinely work with subpicomolar quantities. Chemical proteomics as discussed here requires the more advanced equipment. 

MS instruments measure the mass-to-charge ratio (m/z) values of the smallest of molecules very accurately. In addition, the development of translated genomic databases and specialized software algorithms that rapidly search MS data against theoretical spectra of known or predicted proteins within databases is an important component that greatly facilitated the emergence of mass spectrometry-based proteomics as a key approach for large-scale proteomic analysis.15... 

Figure 7.5. Simulation results that elucidate how the sensitivity and the selectivity of a proteomics experiment depend on various features (a) The choice of algorithm. The probity algorithm displays better sensitivity and selectivity than an algorithm that ranks strictly based on the number of matches, (b) The search conditions. Increasing the mass window of a search 10 times when searching with data that display small mass errors yields worse sensitivity and selectivitry. (c) The quality of the data. Data with less noise yields better sensitivity and selectivity.

J. Eriksson and D. Fenyo. Probity A Protein Identification Algorithm with Accurate Assignment of the Statistical Significance of the Results./. Proteome Res., 3, no. 1 (2004) 32-36. 

F. tularensis [18] and Shigella flexneri [19]. Once immunoreactive proteins are identified, these proteins are evaluated for their potential as vaccine candidates using a multifaceted algorithm [11]. An overall strategy in proteomics-based vaccine discovery is presented in Fig. 12.2. 

Fig. 4. Application of bioinformatics tools to 2D-DIGE data analysis. Proteome data consisting of the normalized spot intensity values are exported from the image analysis software and their correlation with clinicopathological data examined. Using informatics tools including clustering algorithms and machine-learning methods, a novel cancer classification based on proteome data is established, and key proteomic features and proteins corresponding to biomarker candidates are identified.

In this study, a machine learning model system was developed to classify cell line chemosensitivity exclusively based on proteomic profiling. Using reverse-phase protein lysate microarrays, protein expression levels were measured by 52 antibodies in a panel of 60 human cancer cell (NCI-60) lines. The model system combined several well-known algorithms, including Random forests, Relief, and the nearest neighbor methods, to construct the protein expression-based chemosensitivity classifiers. 

Mian S, Ball G, Hornbuckle J, et al. A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions. Proteomics 2003 3(9) 1725-1737. 

MacCoss MJ, Wu CC, Liu H, Sadygov R, Yates JR 3rd. A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal Chem 2003 75 6912-6921. 

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