Case study: using sequence homology to identify putative phosphorylation sites in an evolutionarily distant species (honeybee)

New challenges in knowledge extraction include interpreting and classifying data sets while simultaneously considering related information to confirm results or identify false positives. We discuss a data fusion algorithmic framework targeted at this problem. It includes separate base classifiers for each data type and a fusion method for combining the individual classifiers. The fusion method is an extension of current ensemble classification techniques and has the advantage of allowing data to remain in heterogeneous databases. In this paper, we focus on the applicability of this framework to the protein phosphorylation prediction problem. Key words: ensemble classification, phosphorylation, base classifier 1

Supplementary Information for:

Src activation by b-adrenoreceptors is a key switch

Analysis of the phosphoproteome by MS has become a key technology for the characterization of dynamic regulatory processes in the cell, since kinase and phosphatase action underlie many major biological functions. However, the addition of a phosphate group to a suitable side chain often confounds informatic analysis by generating product ion spectra that are more difficult to interpret (and consequently identify) relative to unmodified peptides. Collectively, these challenges have motivated bioinformaticians to create novel software tools and pipelines to assist in the identification of phosphopeptides in proteomic mixtures, and help pinpoint or “localize ” the most likely site of modification in cases where there is ambiguity. Here we review the challenges to be met and the informatics solutions available to address them for phosphoproteomic analysis, as well as highlighting the difficulties associated with using them and the implications for data standards. Keywords: Bioinformatics / Data processing and analysis / Phosphoproteomics / Technology

Copyright © 2014 Zainab Jahangir et al.This is an open access article distributed under theCreative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Impaired insulin signaling has been thought of as important step in both Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM). Posttranslational modifications (PTMs) regulate functions and interaction of insulin with insulin receptors substrates (IRSs) and activate insulin signaling downstream pathways via autophosphorylation on several tyrosine (TYR) residues on IRSs. Two important insulin receptor substrates 1 and 2 are widely expressed in human, and alternative phosphorylation on their serine (Ser) and threonine (Thr) residues has been known to block the Tyr phosphorylation of IRSs, thus inhibiting insulin signaling and promoting insulin resistance. Like phosphorylation, O-glycosylation modification is important PTM and inhibits phosphorylation on same or neighboring Ser/Thr residues, often called Yin Yang sites. Both IRS-1 and IRS-2 have been shown to be O-glycosylated; however exact sites are not determined yet. In this study, by using neuronal network based prediction methods, we found more than 50 Ser/Thr residues that have potential to be O-glycosylated and may act as possible sites as well. Moreover, alternative