Bioinformatic Analysis

Understanding the selectivity of neonicotinoids toward insect nAChR is essential for environment protection, human health, and insecticide resistance [7], and also a key issue for the design and structure-relationship of new derivatives. 

Researchers had proposed some residues that may contribute to the selectivity through mutation experiments [8-10], Here, through bioinformatic and statistical analysis, residue distribution differences in the ligand binding sites between arthropods and vertebrates as well as features of nicotinic leads were studied. The specihc sites that contribute to the neonicotinoids selectivity were identified and coincide well with the known experimental data. 

Subunits Residue Position Arthropods nAChRs Vertebrates nAChRs 

Selectivity of neonicotinoids. The differences between arthropods and vertebrates nAChR are clearly revealed. The pocket environments of vertebrates nAChR are 

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Once data from thousands of different sequences derived from thousands of different tissues has been compiled, bioinformatic analysis of the data allows us to identify genes that show restricted patterns of expression, assisting in both annotation of function, and association with phenotype. This analysis can be extended to look for novel markers or targets, by analyzing those novel sequences that are co-expressed with known markers or targets. This has been applied to a number of diseases already, such as prostate cancer and Parkinson s disease [120, 164]. 

In summary, it is reasonable to conclude that bioinformatic analysis of structural similarities to, or of sequence homology with, known protein allergens, and evaluation of stability with SGF, can both contribute, if interpreted cautiously, to an overall assessment of sensitizing hazard. However, these analyses, neither alone nor in combination, provide a definitive answer regarding allergenic potential, and for this reason there has been interest in exploring a more holistic approach (and more definitive assessment) that may be provided by suitable animal models. 

Lupas, a. N. and Koretke, K. K., Bioinformatic analysis of ClpS, a protein module involved in prokaryotic and eukaryotic protein degradation,... 

However, the procedure for sample comparison by bioinformatics analysis differs. The main differences are that (1) the samples from each chnical subject are treated separately during the first two phases of analysis, and (2) the method to compare samples is different ... 

Leung AK, Andersen JS, Mann M, Lamond AI. Bioinformatic analysis of the nucleolus. Biochem J 2003 376 553-569. 

He Y, Xiang Z (2010) Bioinformatics analysis of Brucella vaccines and vaccine targets using VIOLIN. Immunome Res 6(Suppl 1) S5... 

From these results, a smaller protein subset containing approximately 50-200 different, purified proteins will be generated and subjected to further qualitative and quantitative on protein biochips. Following bioinformatical analysis, protein antigens will be determined which may be associated with the respective autoimmune disease following confirmation by a second method such as western immunoblotting. 

To gain primary insights about structural features of a metabolic product encoded by a biosynthetic assembly line, bioinformatics analysis can be very helpful 8,24). The procedure can allow the prediction of putative physico-chemical properties of the putative metabolic product encoded, for example, by a cryptic biosynthetic gene cluster 138). The prediction assists in choosing adequate fermentation conditions... 

Bioinformatic analysis performed only on rodent and primate DRM datasets. 

As mentioned in Chapter 2 for the description of our Y2H library screening procedure, what we obtained after a bioinformatics analysis is the location of the interacting domain within the bait or the prey protein. In reality, we do not measure interaction between full length proteins but between domains within proteins which is a closer representation of an in vivo interaction where only some key fragments of each protein interact with one another in space. These domains were called Selected Interacting Domain or SID. It is therefore clear that bioinformatics sequence analysis that compare SIDs obtained from Y2H experiments with Interpro domains can lead to very interesting observation regarding the domain keen to interact and can help to discriminate more specifically true and false positives. 

Baggerman G, Liu F, Wets G, Schoofs L. Bioinformatic analysis of peptide precursor proteins. Ann. N.Y. Acad. Sci. 2005 1040 59-65. 

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