Current Protein and Peptide Science - Volume 12, Issue 7, 2011

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Phosphorylation is one of the most essential post-translational modifications (PTMs) of proteins, regulates a variety of cellular signaling pathways, and at least partially determines the biological diversity. Recent progresses in phosphoproteomics have identified more than 100,000 phosphorylation sites, while this number will easily exceed one million in the next decade. In this regard, how to extract useful information from flood of phosphoproteomics data has emerged as a great challenge. In this review, we summarized the leading edges on computational analysis of phosphoproteomics, including discovery of phosphorylation motifs from phosphoproteomics data, systematic modeling of phosphorylation network, analysis of genetic variation that influences phosphorylation, and phosphorylation evolution. Based on existed knowledge, we also raised several perspectives for further studies. We believe that integration of experimental and computational analyses will propel the phosphoproteomics research into a new phase.

As social network analysis is gaining popularity in modeling real world problems, the task of applying the social network model concepts and notions to biological data is still one of the most attractive research problems to be addressed. According, our work described in this paper focuses on a particular set of genes that reside on the community boundaries in gene co-expression networks. Stemmed from community mining problem in social networks, peripheries of communities (i.e., boundaries) can be used to aid certain biological analysis. The proposed method consists of three parts: 1) Finding communities of gene co-expression networks through clustering. 2) Analyzing stability of community structures by Monte Carlo method. 3) Designing of dynamic adoption of boundaries using geometric convexity. We validated our findings using breast cancer gene expression data from various studies. Our approach contributes to the new branch of applying social network mechanisms in biological data analysis, leading to new data mining strategies implied by witnessing social behaviors in gene expression analysis.

Protein-Protein Interaction (PPI) prediction is a well known problem in Bioinformatics, for which a large number of techniques have been proposed in the past. However, prediction results have not been sufficiently satisfactory for guiding biologists in web-lab experiments. One reason is that not all useful information, such as pairwise protein interaction information based on sequence alignment, has been integrated together in PPI prediction. Alignment is a basic concept to measure sequence similarity in Proteomics that has been used in a number of applications ranging from protein recognition to protein subcellular localization. In this article, we propose a novel integrated approach to predicting PPI based on sequence alignment by jointly using a k-Nearest Neighbor classifier (SA-kNN) and a Support Vector Machine (SVM). SVM is a machine learning technique used in a wide range of Bioinformatics applications, thanks to the ability to alleviate the overfitting problems. We demonstrate that in our approach the two methods, SA-kNN and SVM, are complementary, which are combined in an ensemble to overcome their respective limitations. While the SVM is trained on Amino Acid (AA) compositions and protein signatures mined from literature, the SA-kNN makes use of the similarity of two protein pairs through alignment. Experimentally, our technique leads to a significant gain in accuracy, precision and sensitivity measures at ∼5%, 16% and 10% respectively.

Much has been now recognized on the rice (Oryza sativa L.) proteomics by using the powerful experimental tool two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). 2D-PAGE can be utilized for monitoring global changes of quantitative protein expression in specific tissues under various conditions. However, systematic annotations of the protein spots generated by 2D-PAGE are still limited for rice. In this study, a new approach for Oryza sativa proteome annotation based on the 2D-gel maps was developed. Based on the publicly available 2D-PAGE data of rice, 11,201 gel spots were annotated accounting for 87.2% of the total spots on the gel maps. Gel spot alignments were performed for the annotated gel maps belonging to 23 rice tissues or organelles. In summary, 253 alignments between 23 tissues or organelles were performed, and 26,207 co-expressed proteins were identified using our analytical strategy. Largescale bi-cluster analysis of 23 tissues/organelles proteomes of rice was carried out to detect novel functional proteins. Function and pathway analysis identified a number of common gene products with great potential in regulating specific physiological and biochemical events within various rice tissues/organelles. It also suggested that the tissue- or organellespecific proteins might be responsible for the functional divergence of these tissues or organelles. Taken together, this study provides us new strategies and informative resources for rice proteome research based on 2D-PAGE data.

Inferring transcriptional regulatory networks from high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed TReNGO (Transcriptional Regulatory Networks reconstruction based on Global Optimization), a global and threshold-free algorithm with simulated annealing for inferring regulatory networks by the integration of ChIP-chip and expression data. Superior to existing methods, TReNGO was expected to find the optimal structure of transcriptional regulatory networks without any arbitrary thresholds or predetermined number of transcriptional modules (TMs). TReNGO was applied to both synthetic data and real yeast data in the rapamycin response. In these applications, we demonstrated an improved functional coherence of TMs and TF (transcription factor)- target predictions by TReNGO when compared to GRAM, COGRIM or to analyzing ChIP-chip data alone. We also demonstrated the ability of TReNGO to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.

Plants are sessile organisms subjected to many environmental adversities. For their survival they must sense and respond to biotic and abiotic stresses efficiently. During this process, protein kinases are essential in the perception of environmental stimuli, triggering signaling cascades. Kinases are among the largest and most important gene families for biotechnological purposes, bringing many challenges to the bioinformaticians due to the combination of conserved domains besides diversified regions. Cowpea [Vigna unguiculata (L.) Walp.] is an important legume that is adapted to different agroclimatic conditions, including drought, humidity and a range of temperatures. For this crop, the association of the SuperSAGE method with high-throughput sequencing technology would generate reliable transcriptome profiles with millions of tags counted and statistically analyzed. An approach evaluating biotic and abiotic stresses was carried out generating over 13 million cowpea SuperSAGE tags available from leaves/roots of plants under abiotic (mechanical injury and salinity) or biotic (CABMV, Cowpea aphid born mosaic virus) stresses. The annotation and identification of tags linked by BlastN to previously well described ESTs, allowed the posterior identification of kinases. The annotation efficiency depended on the database used, with the KEGG figuring as a good source for annotated ESTs especially when complemented by an independent Gene Ontology categorization, as well as the Gene Index using selected species. The use of different approaches allowed the identification of 1,350 kinase candidates considering biotic libraries and 2,268 regarding abiotic libraries, based on a combination of both, adequate descriptions and GO terms. Additional searches in kinase specific databases allowed the identification of a relatively low number of additional kinases, uncovering the lack of kinase databases for non-model organisms, especially plants. Concerning the kinase families, a total of 713 potential kinases were classified into 13 families of the CMGC and STE groups. Concerning the differentially expressed kinases, 169 of the 713 potential kinases were identified (p < 0.05), 100 up- and 69 down-regulated when comparing distinct libraries, allowing the generation of a comprehensive panel of the differentially expressed kinases under biotic and abiotic stresses in a non-model plant as cowpea.

The identification of molecular mechanisms underlying enzyme cold adaptation is a hot-topic both for fundamental research and industrial applications. In the present contribution, we review the last decades of structural computational investigations on cold-adapted enzymes in comparison to their warm-adapted counterparts. Comparative sequence and structural studies allow the definition of a multitude of adaptation strategies. Different enzymes carried out diverse mechanisms to adapt to low temperatures, so that a general theory for enzyme cold adaptation cannot be formulated. However, some common features can be traced in dynamic and flexibility properties of these enzymes, as well as in their intra- and inter-molecular interaction networks. Interestingly, the current data suggest that a family-centered point of view is necessary in the comparative analyses of cold- and warm-adapted enzymes. In fact, enzymes belonging to the same family or superfamily, thus sharing at least the three-dimensional fold and common features of the functional sites, have evolved similar structural and dynamic patterns to overcome the detrimental effects of low temperatures.