NBA-Palm: prediction of palmitoylation site implemented in Naïve Bayes algorithm

BACKGROUND: Protein palmitoylation, an essential and reversible post-translational modification (PTM), has been implicated in cellular dynamics and plasticity. Although numerous experimental studies have been performed to explore the molecular mechanisms underlying palmitoylation processes, the intrinsic feature of substrate specificity has remained elusive. Thus, computational approaches for palmitoylation prediction are much desirable for further experimental design. RESULTS: In this work, we present NBA-Palm, a novel computational method based on Naïve Bayes algorithm for prediction of palmitoylation site. The training data is curated from scientific literature (PubMed) and includes 245 palmitoylated sites from 105 distinct proteins after redundancy elimination. The proper window length for a potential palmitoylated peptide is optimized as six. To evaluate the prediction performance of NBA-Palm, 3-fold cross-validation, 8-fold cross-validation and Jack-Knife validation have been carried out. Prediction accuracies reach 85.79% for 3-fold cross-validation, 86.72% for 8-fold cross-validation and 86.74% for Jack-Knife validation. Two more algorithms, RBF network and support vector machine (SVM), also have been employed and compared with NBA-Palm. CONCLUSION: Taken together, our analyses demonstrate that NBA-Palm is a useful computational program that provides insights for further experimentation. The accuracy of NBA-Palm is comparable with our previously described tool CSS-Palm. The NBA-Palm is freely accessible from:

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:

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

BACKGROUND: As 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. RESULTS: In 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. CONCLUSION: Taken 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

MeMo: a web tool for prediction of protein methylation modifications

Protein methylation is an important and reversible post-translational modification of proteins (PTMs), which governs cellular dynamics and plasticity. Experimental identification of the methylation site is labor-intensive and often limited by the availability of reagents, such as methyl-specific antibodies and optimization of enzymatic reaction. Computational analysis may facilitate the identification of potential methylation sites with ease and provide insight for further experimentation. Here we present a novel protein methylation prediction web server named MeMo, protein methylation modification prediction, implemented in Support Vector Machines (SVMs). Our present analysis is primarily focused on methylation on lysine and arginine, two major protein methylation sites. However, our computational platform can be easily extended into the analyses of other amino acids. The accuracies for prediction of protein methylation on lysine and arginine have reached 67.1 and 86.7%, respectively. Thus, the MeMo system is a novel tool for predicting protein methylation and may prove useful in the study of protein methylation function and dynamics. The MeMo web server is available at:

GPS: a comprehensive www server for phosphorylation sites prediction

Protein phosphorylation plays a fundamental role in most of the cellular regulatory pathways. Experimental identification of protein kinases' (PKs) substrates with their phosphorylation sites is labor-intensive and often limited by the availability and optimization of enzymatic reactions. Recently, large-scale analysis of the phosphoproteome by the mass spectrometry (MS) has become a popular approach. But experimentally, it is still difficult to distinguish the kinase-specific sites on the substrates. In this regard, the in silico prediction of phosphorylation sites with their specific kinases using protein's primary sequences may provide guidelines for further experimental consideration and interpretation of MS phosphoproteomic data. A variety of such tools exists over the Internet and provides the predictions for at most 30 PK subfamilies. We downloaded the verified phosphorylation sites from the public databases and curated the literature extensively for recently found phosphorylation sites. With the hypothesis that PKs in the same subfamily share similar consensus sequences/motifs/functional patterns on substrates, we clustered the 216 unique PKs in 71 PK groups, according to the BLAST results and protein annotations. Then, we applied the group-based phosphorylation scoring (GPS) method on the data set; here, we present a comprehensive PK-specific prediction server GPS, which could predict kinase-specific phosphorylation sites from protein primary sequences for 71 different PK groups. GPS has been implemented in PHP and is available on a www server at

TTK kinase is essential for the centrosomal localization of TACC2

AbstractChromosome segregation in mitosis is orchestrated by dynamic interaction between spindle microtubule and the kinetochore. Our recent ultrastructural studies demonstrated a dynamic distribution of TTK, from the kinetochore to the centrosome, as cell enters into anaphase. Here, we show that a centrosomal protein TACC2 is phosphorylated in mitosis by TTK signaling pathway. TACC2 was pulled down by wild type TTK but not kinase death mutant, suggesting the potential phosphorylation-mediated interaction between these two proteins. Our immunofluorescence studies revealed that both TTK and TACC2 are located to the centrosome. Interestingly, expression of kinase death mutant of TTK eliminated the centrosomal localization of TACC2 but not other centrosomal proteins such as γ-tubulin and NuMA, a phenotype seen in TTK-depleted cells. In these centrosomal TACC2-liberated cells, chromosomes were lagging and mis-aligned. In addition, the distance between two centrosomes was markedly reduced, suggesting that centrosomal TACC2 is required for mitotic spindle maintenance. The inter-relationship between TTK and TACC2 established here provides new avenue to study centrosome and spindle dynamics underlying cell divisional control

CENP-E forms a link between attachment of spindle microtubules to kinetochores and the mitotic checkpoint. Nat Cell Biol

Here we show that suppression of synthesis of the microtubule motor CENP-E (centromere-associated protein E), a component of the kinetochore corona fibres of mammalian centromeres, yields chromosomes that are chronically mono-orientated, with spindles that are flattened along the plane of the substrate. Despite apparently normal microtubule numbers and the continued presence at kinetochores of other microtubule motors, spindle poles fragment in the absence of CENP-E, which implicates this protein in delivery of components from kinetochores to poles. CENP-E represents a link between attachment of spindle microtubules and the mitotic checkpoint signalling cascade, as depletion of this motor leads to profound checkpoint activation, whereas immunoprecipitation reveals a nearly stoichiometric association of CENP-E with the checkpoint kinase BubR1 during mitosis. hromosome movements during mitosis are orchestrated primarily by the interaction of spindle microtubules with the kinetochore 1 , the site for attachment of spindle microtubules to the centromere. In addition to providing a physical link between chromosomes and spindle microtubules, the kinetochore has an active function in chromosomal segregation through microtubule motors located at or near i

SENP1 regulates IFN-γ−STAT1 signaling through STAT3−SOCS3 negative feedback loop

Interferon-γ (IFN-γ) triggers macrophage for inflammation response by activating the intracellular JAK−STAT1 signaling. Suppressor of cytokine signaling 1 (SOCS1) and protein tyrosine phosphatases can negatively modulate IFN-γ signaling. Here, we identify a novel negative feedback loop mediated by STAT3−SOCS3, which is tightly controlled by SENP1 via de-SUMOylation of protein tyrosine phosphatase 1B (PTP1B), in IFN-γ signaling. SENP1-deficient macrophages show defects in IFN-γ signaling and M1 macrophage activation. PTP1B in SENP1-deficient macrophages is highly SUMOylated, which reduces PTP1B-induced de-phosphorylation of STAT3. Activated STAT3 then suppresses STAT1 activation via SOCS3 induction in SENP1-deficient macrophages. Accordingly, SENP1-deficient macrophages show reduced ability to resist Listeria monocytogenes infection. These results reveal a crucial role of SENP1-controlled STAT1 and STAT3 balance in macrophage polarization