MiCroKit 3.0: an integrated database of midbody, centrosome and kinetochore

During cell division/mitosis, a specific subset of proteins is spatially and temporally assembled into protein super complexes in three distinct regions, i.e. centrosome/spindle pole, kinetochore/centromere and midbody/cleavage furrow/phragmoplast/bud neck, and modulates cell division process faithfully. Although many experimental efforts have been carried out to investigate the characteristics of these proteins, no integrated database was available. Here, we present the MiCroKit database (http://microkit.biocuckoo.org) of proteins that localize in midbody, centrosome and/or kinetochore. We collected into the MiCroKit database experimentally verified microkit proteins from the scientific literature that have unambiguous supportive evidence for subcellular localization under fluorescent microscope. The current version of MiCroKit 3.0 provides detailed information for 1489 microkit proteins from seven model organisms, including Saccharomyces cerevisiae, Schizasaccharomyces pombe, Caenorhabditis elegans, Drosophila melanogaster, Xenopus laevis, Mus musculus and Homo sapiens. Moreover, the orthologous information was provided for these microkit proteins, and could be a useful resource for further experimental identification. The online service of MiCroKit database was implemented in PHP + MySQL + JavaScript, while the local packages were developed in JAVA 1.5 (J2SE 5.0)

GPS-CCD: A Novel Computational Program for the Prediction of Calpain Cleavage Sites

As one of the most essential post-translational modifications (PTMs) of proteins, proteolysis, especially calpain-mediated cleavage, plays an important role in many biological processes, including cell death/apoptosis, cytoskeletal remodeling, and the cell cycle. Experimental identification of calpain targets with bona fide cleavage sites is fundamental for dissecting the molecular mechanisms and biological roles of calpain cleavage. In contrast to time-consuming and labor-intensive experimental approaches, computational prediction of calpain cleavage sites might more cheaply and readily provide useful information for further experimental investigation. In this work, we constructed a novel software package of GPS-CCD (Calpain Cleavage Detector) for the prediction of calpain cleavage sites, with an accuracy of 89.98%, sensitivity of 60.87% and specificity of 90.07%. With this software, we annotated potential calpain cleavage sites for hundreds of calpain substrates, for which the exact cleavage sites had not been previously determined. In this regard, GPS-CCD 1.0 is considered to be a useful tool for experimentalists. The online service and local packages of GPS-CCD 1.0 were implemented in JAVA and are freely available at: http://ccd.biocuckoo.org/

Computational Analysis of Phosphoproteomics: Progresses and Perspectives

Abstract: 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

Computational Analysis of Phosphoproteomics: Progresses and Perspectives

Abstract: 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

GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.

As one of the most important and ubiquitous post-translational modifications (PTMs) of proteins, S-nitrosylation plays important roles in a variety of biological processes, including the regulation of cellular dynamics and plasticity. Identification of S-nitrosylated substrates with their exact sites is crucial for understanding the molecular mechanisms of S-nitrosylation. In contrast with labor-intensive and time-consuming experimental approaches, prediction of S-nitrosylation sites using computational methods could provide convenience and increased speed. In this work, we developed a novel software of GPS-SNO 1.0 for the prediction of S-nitrosylation sites. We greatly improved our previously developed algorithm and released the GPS 3.0 algorithm for GPS-SNO. By comparison, the prediction performance of GPS 3.0 algorithm was better than other methods, with an accuracy of 75.80%, a sensitivity of 53.57% and a specificity of 80.14%. As an application of GPS-SNO 1.0, we predicted putative S-nitrosylation sites for hundreds of potentially S-nitrosylated substrates for which the exact S-nitrosylation sites had not been experimentally determined. In this regard, GPS-SNO 1.0 should prove to be a useful tool for experimentalists. The online service and local packages of GPS-SNO were implemented in JAVA and are freely available at: http://sno.biocuckoo.org/

GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy *S⃞

Identification of protein phosphorylation sites with their cognate protein kinases (PKs) is a key step to delineate molecular dynamics and plasticity underlying a variety of cellular processes. Although nearly 10 kinase-specific prediction programs have been developed, numerous PKs have been casually classified into subgroups without a standard rule. For large scale predictions, the false positive rate has also never been addressed. In this work, we adopted a well established rule to classify PKs into a hierarchical structure with four levels, including group, family, subfamily, and single PK. In addition, we developed a simple approach to estimate the theoretically maximal false positive rates. The on-line service and local packages of the GPS (Group-based Prediction System) 2.0 were implemented in Java with the modified version of the Group-based Phosphorylation Scoring algorithm. As the first stand alone software for predicting phosphorylation, GPS 2.0 can predict kinase-specific phosphorylation sites for 408 human PKs in hierarchy. A large scale prediction of more than 13,000 mammalian phosphorylation sites by GPS 2.0 was exhibited with great performance and remarkable accuracy. Using Aurora-B as an example, we also conducted a proteome-wide search and provided systematic prediction of Aurora-B-specific substrates including protein-protein interaction information. Thus, the GPS 2.0 is a useful tool for predicting protein phosphorylation sites and their cognate kinases and is freely available on line

A summary of computational resources for protein phosphorylation. Curr. Protein Pept. Sci.,

Abstract: Protein phosphorylation is the most ubiquitous post-translational modification (PTM), and plays important roles in most of biological processes. Identification of site-specific phosphorylated substrates is fundamental for understanding the molecular mechanisms of phosphorylation. Besides experimental approaches, prediction of potential candidates with computational methods has also attracted great attention for its convenience, fast-speed and low-cost. In this review, we present a comprehensive but brief summarization of computational resources of protein phosphorylation, including phosphorylation databases, prediction of non-specific or organism-specific phosphorylation sites, prediction of kinase-specific phosphorylation sites or phospho-binding motifs, and other tools. The latest compendium of computational resources for protein phosphorylation is available at: http://gps.biocuckoo.org/links.ph