Xinjiao Gao

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

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.

CSS-Palm 2.0: an updated software for palmitoylation sites prediction

Protein palmitoylation is an essential post-translational lipid modification of proteins, and reversibly orchestrates a variety of cellular processes. Identification of palmitoylated proteins with their sites is the foundation for understanding molecular mechanisms and regulatory roles of palmitoylation. Contrasting to the labor-intensive and time-consuming experimental approaches, in silico prediction of palmitoylation sites has attracted much attention as a popular strategy. In this work, we updated our previous CSS-Palm into version 2.0. An updated clustering and scoring strategy (CSS) algorithm was employed with great improvement. The leave-one-out validation and 4-, 6-, 8- and 10-fold cross-validations were adopted to evaluate the prediction performance of CSS-Palm 2.0. Also, an additional new data set not included in training was used to test the robustness of CSS-Palm 2.0. By comparison, the performance of CSS-Palm was much better than previous tools. As an application, we performed a small-scale annotation of palmitoylated proteins in budding yeast. The online service and local packages of CSS-Palm 2.0 were freely available at: http://bioinformatics.lcd-ustc.org/css_palm.

Systematic study of protein sumoylation: Development of a site‐specific predictor of SUMOsp 2.0

Protein sumoylation is an important reversible post‐translational modification on proteins, and orchestrates a variety of cellular processes. Recently, computational prediction of sumoylation sites has attracted much attention for its cost‐efficiency and power in genomic data mining. In this work, we developed SUMOsp 2.0, an accurate computing program with an improved group‐based phosphorylation scoring algorithm. Our analysis demonstrated that SUMOsp 2.0 has greater prediction accuracy than SUMOsp 1.0 and other existing tools, with a sensitivity of 88.17% and a specificity of 92.69% under the medium threshold. Previously, several large‐scale experiments have identified a list of potential sumoylated substrates in Saccharomyces cerevisiae and Homo sapiens; however, the exact sumoylation sites in most of these proteins remain elusive. We have predicted potential sumoylation sites in these proteins using SUMOsp 2.0, which provides a great resource for researchers and an outline for further mechanistic studies of sumoylation in cellular plasticity and dynamics. The online service and local packages of SUMOsp 2.0 are freely available at: http://sumosp.biocuckoo.org/.

GPS 2.1: enhanced prediction of kinase-specific phosphorylation sites with an algorithm of motif length selection

As the most important post-translational modification of proteins, phosphorylation plays essential roles in all aspects of biological processes. Besides experimental approaches, computational prediction of phosphorylated proteins with their kinase-specific phosphorylation sites has also emerged as a popular strategy, for its low-cost, fast-speed and convenience. In this work, we developed a kinase-specific phosphorylation sites predictor of GPS 2.1 (Group-based Prediction System), with a novel but simple approach of motif length selection (MLS). By this approach, the robustness of the prediction system was greatly improved. All algorithms in GPS old versions were also reserved and integrated in GPS 2.1. The online service and local packages of GPS 2.1 were implemented in JAVA 1.5 (J2SE 5.0) and freely available for academic researches at: http://gps.biocuckoo.org.

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/.

Septin 7 Interacts with Centromere-associated Protein E and Is Required for Its Kinetochore Localization

Chromosome segregation in mitosis is orchestrated by dynamic interaction between spindle microtubules and the kinetochore. Septin (SEPT) belongs to a conserved family of polymerizing GTPases localized to the metaphase spindle during mitosis. Previous study showed that SEPT2 depletion results in chromosome mis-segregation correlated with a loss of centromere-associated protein E (CENP-E) from the kinetochores of congressing chromosomes (1). However, it has remained elusive as to whether CENP-E physically interacts with SEPT and how this interaction orchestrates chromosome segregation in mitosis. Here we show that SEPT7 is required for a stable kinetochore localization of CENP-E in HeLa and MDCK cells. SEPT7 stabilizes the kinetochore association of CENP-E by directly interacting with its C-terminal domain. The region of SEPT7 binding to CENP-E was mapped to its C-terminal domain by glutathione S-transferase pull-down and yeast two-hybrid assays. Immunofluorescence study shows that SEPT7 filaments distribute along the mitotic spindle and terminate at the kinetochore marked by CENP-E. Remarkably, suppression of synthesis of SEPT7 by small interfering RNA abrogated the localization of CENP-E to the kinetochore and caused aberrant chromosome segregation. These mitotic defects and kinetochore localization of CENP-E can be successfully rescued by introducing exogenous GFP-SEPT7 into the SEPT7-depleted cells. These SEPT7-suppressed cells display reduced tension at kinetochores of bi-orientated chromosomes and activated mitotic spindle checkpoint marked by Mad2 and BubR1 labelings on these misaligned chromosomes. These findings reveal a key role for the SEPT7-CENP-E interaction in the distribution of CENP-E to the kinetochore and achieving chromosome alignment. We propose that SEPT7 forms a link between kinetochore distribution of CENP-E and the mitotic spindle checkpoint.

PhosSNP for Systematic Analysis of Genetic Polymorphisms That Influence Protein Phosphorylation

We are entering the era of personalized genomics as breakthroughs in sequencing technology have made it possible to sequence or genotype an individual person in an efficient and accurate manner. Preliminary results from HapMap and other similar projects have revealed the existence of tremendous genetic variations among world populations and among individuals. It is important to delineate the functional implication of such variations, i.e. whether they affect the stability and biochemical properties of proteins. It is also generally believed that the genetic variation is the main cause for different susceptibility to certain diseases or different response to therapeutic treatments. Understanding genetic variation in the context of human diseases thus holds the promise for “personalized medicine.” In this work, we carried out a genome-wide analysis of single nucleotide polymorphisms (SNPs) that could potentially influence protein phosphorylation characteristics in human. Here, we defined a phosphorylation-related SNP (phosSNP) as a non-synonymous SNP (nsSNP) that affects the protein phosphorylation status. Using an in-house developed kinase-specific phosphorylation site predictor (GPS 2.0), we computationally detected that ∼70% of the reported nsSNPs are potential phosSNPs. More interestingly, ∼74.6% of these potential phosSNPs might also induce changes in protein kinase types in adjacent phosphorylation sites rather than creating or removing phosphorylation sites directly. Taken together, we proposed that a large proportion of the nsSNPs might affect protein phosphorylation characteristics and play important roles in rewiring biological pathways. Finally, all phosSNPs were integrated into the PhosSNP 1.0 database, which was implemented in JAVA 1.5 (J2SE 5.0). The PhosSNP 1.0 database is freely available for academic researchers.

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/.

GPS-YNO2: computational prediction of tyrosine nitration sites in proteins

The last decade has witnessed rapid progress in the identification of protein tyrosine nitration (PTN), which is an essential and ubiquitous post-translational modification (PTM) that plays a variety of important roles in both physiological and pathological processes, such as the immune response, cell death, aging and neurodegeneration. Identification of site-specific nitrated substrates is fundamental for understanding the molecular mechanisms and biological functions of PTN. In contrast with labor-intensive and time-consuming experimental approaches, here we report the development of the novel software package GPS-YNO2 to predict PTN sites. The software demonstrated a promising accuracy of 76.51%, a sensitivity of 50.09% and a specificity of 80.18% from the leave-one-out validation. As an example application, we predicted potential PTN sites for hundreds of nitrated substrates which had been experimentally detected in small-scale or large-scale studies, even though the actual nitration sites had still not been determined. Through a statistical functional comparison with the nitric oxide (NO) dependent reversible modification of S-nitrosylation, we observed that PTN prefers to attack certain fundamental biological processes and functions. These prediction and analysis results might be helpful for further experimental investigation. Finally, the online service and local packages of GPS-YNO2 1.0 were implemented in JAVA and freely available at: .

A Summary of Computational Resources for Protein Phosphorylation

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.php.