Protein informatics

Kinoshita K, Nakamura H. Protein informatics towards fimction identification. Curr Opin Struct Biol 2003 13 396 00. 

Chemistry of Amino Acids Computational Chemistry in Biology Energetics of Protein Folding Protein Informatics Physical Chemistry in Biology... 

A regularly formed crystal of reasonable size (typically >500 pm in each dimension) is required for X-ray diffraction. Samples of pure protein are screened against a matrix of buffers, additives, or precipitants for conditions under which they form crystals. This can require many thousands of trials and has benefited from increased automation over the past five years. Most large crystallographic laboratories now have robotics systems, and the most sophisticated also automate the visualization of the crystallization experiments, to monitor the appearance of crystalline material. Such developments [e.g., Ref. 1] are adding computer visualization and pattern recognition to the informatics requirements. 

For many proteins, it is possible to generate structures of protein-ligand complexes quite rapidly. It is therefore not uncommon for many hundreds of structures to be determined in support of a drug discovery and optimization project. The major challenge for this level of throughput is informatics support. It is also this type of crystallography that is most in need of semiautomated procedures for structure solution and model building (see Section 12.6). 

An J, Totrov M, Abagyan R. Comprehensive identification of druggable protein ligand binding sides. Genome Informatics 2004 15 31-41. 

The remainder of this chapter will focus on some of the applications of structural informatics and the TIP database. We will use the Key Applications column in Fig. 2 as an outline, describing the applications enabled by understanding 1) protein structural relationships 2) binding site relationships and 3) ligand binding mode relationships. 

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.

The other approach is based largely on informatics. In such an approach, tumors would be profiled in contrast to normal tissue. Tumor-specific markers would be identified via microarrays, as has been done in many publications already. From here, the list of tumor-specific markers would be analyzed to determine if any of these markers represented proteins which were likely to be secreted out of the cell and which may be detected in the peripheral blood stream. Preferably multiple markers would be identified that could be tested using multiplex ELISA assays (antibody arrays). Such work will take time, however, because once the potential markers are identified, antibodies must be generated, validated, and tested for effectiveness as an early diagnostic tool. Such work is being done, but little has been published so far. 

IP3—inositol 1,4,5-triphosphate ISP—ice structuring protein IT—informatic technology... 

Aptamers placed on a matrix bind specific proteins from dilute solution in the context of many other proteins [22]. Thus, an array of different aptamers on a chip could be used to capture different proteins (from blood, for example) at specific locations (in X, Y space) for subsequent (protein) quantitative measurements and informatics (Figure 20.5) ... 

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