Xuebiao Yao

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/.

PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory

BackgroundAs a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated.ResultsIn this work, we present a novel, versatile and comprehensive program, PPSP (Prediction of PK-specific Phosphorylation site), deployed with approach of Bayesian decision theory (BDT). PPSP could predict the potential phosphorylation sites accurately for ~70 PK (Protein Kinase) groups. Compared with four existing tools Scansite, NetPhosK, KinasePhos and GPS, PPSP is more accurate and powerful than these tools. Moreover, PPSP also provides the prediction for many novel PKs, say, TRK, mTOR, SyK and MET/RON, etc. The accuracy of these novel PKs are also satisfying.ConclusionTaken together, we propose that PPSP could be a potentially powerful tool for the experimentalists who are focusing on phosphorylation substrates with their PK-specific sites identification. Moreover, the BDT strategy could also be a ubiquitous approach for PTMs, such as sumoylation and ubiquitination, etc.

A Novel Set of DNA Methylation Markers in Urine Sediments for Sensitive/Specific Detection of Bladder Cancer

Purpose: This study aims to provide a better set of DNA methylation markers in urine sediments for sensitive and specific detection of bladder cancer. Experimental Design: Fifty-nine tumor-associated genes were profiled in three bladder cancer cell lines, a small cohort of cancer biopsies and urine sediments by methylation-specific PCR. Twenty-one candidate genes were then profiled in urine sediments from 132 bladder cancer patients (8 cases for stage 0a; 68 cases for stage I; 50 cases for stage II; 4 cases for stages III; and 2 cases for stage IV), 23 age-matched patients with noncancerous urinary lesions, 6 neurologic diseases, and 7 healthy volunteers. Results: Despite six incidences of four genes reported in 3 of 23 noncancerous urinary lesion patients analyzed, cancer-specific hypermethylation in urine sediments were reported for 15 genes (P < 0.05). Methylation assessment of an 11-gene set (SALL3, CFTR, ABCC6, HPR1, RASSF1A, MT1A, RUNX3, ITGA4, BCL2, ALX4, MYOD1, DRM, CDH13, BMP3B, CCNA1, RPRM, MINT1, and BRCA1) confirmed the existing diagnosis of 121 among 132 bladder cancer cases (sensitivity, 91.7%) with 87% accuracy. Significantly, more than 75% of stage 0a and 88% of stage I disease were detected, indicating its value in the early diagnosis of bladder cancer. Interestingly, the cluster of reported methylation markers used in the U.S. bladder cancers is distinctly different from that identified in this study, suggesting a possible epigenetic disparity between the American and Chinese cases. Conclusions: Methylation profiling of an 11-gene set in urine sediments provides a sensitive and specific detection of bladder cancer.

SUMOsp: a web server for sumoylation site prediction

Systematic dissection of the sumoylation proteome is emerging as an appealing but challenging research topic because of the significant roles sumoylation plays in cellular dynamics and plasticity. Although several proteome-scale analyzes have been performed to delineate potential sumoylatable proteins, the bona fide sumoylation sites still remain to be identified. Previously, we carried out a genome-wide analysis of the SUMO substrates in human nucleus using the putative motif ψ-K-X-E and evolutionary conservation. However, a highly specific predictor for in silico prediction of sumoylation sites in any individual organism is still urgently needed to guide experimental design. In this work, we present a computational system SUMOsp—SUMOylation Sites Prediction, based on a manually curated dataset, integrating the results of two methods, GPS and MotifX, which were originally designed for phosphorylation site prediction. SUMOsp offers at least as good prediction performance as the only available method, SUMOplot, on a very large test set. We expect that the prediction results of SUMOsp combined with experimental verifications will propel our understanding of sumoylation mechanisms to a new level. SUMOsp has been implemented on a freely accessible web server at: .

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/.

Structure of a CENP-A–histone H4 heterodimer in complex with chaperone HJURP

In higher eukaryotes, the centromere is epigenetically specified by the histone H3 variant Centromere Protein-A (CENP-A). Deposition of CENP-A to the centromere requires histone chaperone HJURP (Holliday junction recognition protein). The crystal structure of an HJURP-CENP-A-histone H4 complex shows that HJURP binds a CENP-A-H4 heterodimer. The C-terminal β-sheet domain of HJURP caps the DNA-binding region of the histone heterodimer, preventing it from spontaneous association with DNA. Our analysis also revealed a novel site in CENP-A that distinguishes it from histone H3 in its ability to bind HJURP. These findings provide key information for specific recognition of CENP-A and mechanistic insights into the process of centromeric chromatin assembly.

The membrane-recruitment-and-recycling hypothesis of gastric HCl secretion

In the unstimulated oxyntic (or parietal) cell, the primary pump for gastric HCl secretion, the H+/K+-ATPase, is retained within the cytoplasm in a membranous compartment of tubulovesicles. Neural or hormonal stimulation of acid secretion induces extensive membrane transformations consistent with a fusion and recruitment of tubulovesicles to the apical plasma membrane. The consequent placement of H+/K+-ATPase in parallel with K(+) and Cl(-) channels provides the necessary ionic flow and ATP-driven exchange for net HCl secretion. Current evidence is consistent with a recruitment and recycling of membrane transporters, such as H+/K+-ATPase, through docking/fusion machinery analogous to that in many other systems.

Stabilization of PML nuclear localization by conjugation and oligomerization of SUMO-3

The PML gene of acute promyelocytic leukemia (APL) encodes a cell-growth and tumor suppressor. PML localizes to discrete nuclear bodies (NBs) that are disrupted in APL cells, resulting from a reciprocal chromosome translocation t (15;17). Here we show that the nuclear localization of PML is also regulated by SUMO-3, one of the three recently identified SUMO isoforms in human cells. SUMO-3 bears similar subcellular distribution to those of SUMO-1 and -2 in the interphase nuclear body, which is colocalized with PML protein. However, both SUMO-2 and -3 are also localized to nucleoli, a region lacking SUMO-1. Immunoprecipitated PML protein bears SUMO-3 moiety in a covalently modified form, supporting the notion that PML is conjugated by SUMO-3. To determine the functional relevance of SUMO-3 conjugation on PML molecular dynamics, we suppressed SUMO-3 protein expression using a siRNA-mediated approach. Depletion of SUMO-3 markedly reduced the number of PML-containing NBa and their integrity, which is rescued by exogenous expression of SUMO-3 but not SUMO-1 or SUMO-2. The specific requirement of SUMO-3 for PML nuclear localization is validated by expression of SUMO-3 conjugation defective mutant. Moreover, we demonstrate that oligomerization of SUMO-3 is required for PML retention in the nucleus. Taken together, our studies provide first line of evidence showing that SUMO-3 is essential for PML localization and offer novel insight into the pathobiochemistry of APL.