MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs

Background

Carbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson’s disease, and Alzheimer’s disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures.

Results

By manually curating experimental data from research articles, we obtained 332, 144, 135, and 140 verified substrate sites for K (lysine), R (arginine), T (threonine), and P (proline) residues, respectively, from 241 carbonylated proteins. In order to examine the informative attributes for classifying between carbonylated and non-carbonylated sites, multifarious features including composition of twenty amino acids (AAC), composition of amino acid pairs (AAPC), position-specific scoring matrix (PSSM), and positional weighted matrix (PWM) were investigated in this study. Additionally, in an attempt to explore the motif signatures of carbonylation sites, an iterative statistical method was adopted to detect statistically significant dependencies of amino acid compositions between specific positions around substrate sites. Profile hidden Markov model (HMM) was then utilized to train a predictive model from each motif signature. Moreover, based on the method of support vector machine (SVM), we adopted it to construct an integrative model by combining the values of bit scores obtained from profile HMMs. The combinatorial model could provide an enhanced performance with evenly predictive sensitivity and specificity in the evaluation of cross-validation and independent testing.

Conclusion

This study provides a new scheme for exploring potential motif signatures at substrate sites of protein carbonylation. The usefulness of the revealed motifs in the identification of carbonylated sites is demonstrated by their effective performance in cross-validation and independent testing. Finally, these substrate motifs were adopted to build an available online resource (MDD-Carb, http://csb.cse.yzu.edu.tw/MDDCarb/) and are also anticipated to facilitate the study of large-scale carbonylated proteomes.

     

GSHSite: Exploiting an Iteratively Statistical Method to Identify S-Glutathionylation Sites with Substrate Specificity

S-glutathionylation, the covalent attachment of a glutathione (GSH) to the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-glutathionylation remains unknown. Based on a total of 1783 experimentally identified S-glutathionylation sites from mouse macrophages, this work presents an informatics investigation on S-glutathionylation sites including structural factors such as the flanking amino acids composition and the accessible surface area (ASA). TwoSampleLogo presents that positively charged amino acids flanking the S-glutathionylated cysteine may influence the formation of S-glutathionylation in closed three-dimensional environment. A statistical method is further applied to iteratively detect the conserved substrate motifs with statistical significance. Support vector machine (SVM) is then applied to generate predictive model considering the substrate motifs. According to five-fold cross-validation, the SVMs trained with substrate motifs could achieve an enhanced sensitivity, specificity, and accuracy, and provides a promising performance in an independent test set. The effectiveness of the proposed method is demonstrated by the correct identification of previously reported S-glutathionylation sites of mouse thioredoxin (TXN) and human protein tyrosine phosphatase 1b (PTP1B). Finally, the constructed models are adopted to implement an effective web-based tool, named GSHSite (http://csb.cse.yzu.edu.tw/GSHSite/), for identifying uncharacterized GSH substrate sites on the protein sequences.     

Investigation and identification of functional post-translational modification sites associated with drug binding and protein-protein interactions

Background

Protein post-translational modification (PTM) plays an essential role in various cellular processes that modulates the physical and chemical properties, folding, conformation, stability and activity of proteins, thereby modifying the functions of proteins. The improved throughput of mass spectrometry (MS) or MS/MS technology has not only brought about a surge in proteome-scale studies, but also contributed to a fruitful list of identified PTMs. However, with the increase in the number of identified PTMs, perhaps the more crucial question is what kind of biological mechanisms these PTMs are involved in. This is particularly important in light of the fact that most protein-based pharmaceuticals deliver their therapeutic effects through some form of PTM. Yet, our understanding is still limited with respect to the local effects and frequency of PTM sites near pharmaceutical binding sites and the interfaces of protein-protein interaction (PPI). Understanding PTM’s function is critical to our ability to manipulate the biological mechanisms of protein.

Results

In this study, to understand the regulation of protein functions by PTMs, we mapped 25,835 PTM sites to proteins with available three-dimensional (3D) structural information in the Protein Data Bank (PDB), including 1785 modified PTM sites on the 3D structure. Based on the acquired structural PTM sites, we proposed to use five properties for the structural characterization of PTM substrate sites: the spatial composition of amino acids, residues and side-chain orientations surrounding the PTM substrate sites, as well as the secondary structure, division of acidity and alkaline residues, and solvent-accessible surface area. We further mapped the structural PTM sites to the structures of drug binding and PPI sites, identifying a total of 1917 PTM sites that may affect PPI and 3951 PTM sites associated with drug-target binding. An integrated analytical platform (CruxPTM), with a variety of methods and online molecular docking tools for exploring the structural characteristics of PTMs, is presented. In addition, all tertiary structures of PTM sites on proteins can be visualized using the JSmol program.

Conclusion

Resolving the function of PTM sites is important for understanding the role that proteins play in biological mechanisms. Our work attempted to delineate the structural correlation between PTM sites and PPI or drug-target binding. CurxPTM could help scientists narrow the scope of their PTM research and enhance the efficiency of PTM identification in the face of big proteome data. CruxPTM is now available at http://csb.cse.yzu.edu.tw/CruxPTM/.

     

Mutually dependent degradation of Ama1p and Cdc20p terminates APC/C ubiquitin ligase activity at the completion of meiotic development in yeast

Background

The execution of meiotic nuclear divisions in S. cerevisiae is regulated by protein degradation mediated by the anaphase promoting complex/cyclosome (APC/C) ubiquitin ligase. The correct timing of APC/C activity is essential for normal chromosome segregation. During meiosis, the APC/C is activated by the association of either Cdc20p or the meiosis-specific factor Ama1p. Both Ama1p and Cdc20p are targeted for degradation as cells exit meiosis II with Cdc20p being destroyed by APC/C^Ama1. In this study we investigated how Ama1p is down regulated at the completion of meiosis.

Findings

Here we show that Ama1p is a substrate of APC/C^Cdc20 but not APC/C^Cdh1 in meiotic cells. Cdc20p binds Ama1p in vivo and APC/C^Cdc20 ubiquitylates Ama1p in vitro. Ama1p ubiquitylation requires one of two degradation motifs, a D-box and a “KEN-box” like motif called GxEN. Finally, Ama1p degradation does not require its association with the APC/C via its conserved APC/C binding motifs (C-box and IR) and occurs simultaneously with APC/C^Ama1-mediated Cdc20p degradation.

Conclusions

Unlike the cyclical nature of mitotic cell division, meiosis is a linear pathway leading to the production of quiescent spores. This raises the question of how the APC/C is reset prior to spore germination. This and a previous study revealed that Cdc20p and Ama1p direct each others degradation via APC/C-dependent degradation. These findings suggest a model that the APC/C is inactivated by mutual degradation of the activators. In addition, these results support a model in which Ama1p and Cdc20p relocate to the substrate address within the APC/C cavity prior to degradation.

     

Proteomic Analyses Reveal Divergent Ubiquitylation Site Patterns in Murine Tissues

Posttranslational modifications of proteins increase the complexity of the cellular proteome and enable rapid regulation of protein functions in response to environmental changes. Protein ubiquitylation is a central regulatory posttranslational modification that controls numerous biological processes including proteasomal degradation of proteins, DNA damage repair and innate immune responses. Here we combine high-resolution mass spectrometry with single-step immunoenrichment of di-glycine modified peptides for mapping of endogenous putative ubiquitylation sites in murine tissues. We identify more than 20,000 unique ubiquitylation sites on proteins involved in diverse biological processes. Our data reveals that ubiquitylation regulates core signaling pathways common for each of the studied tissues. In addition, we discover that ubiquitylation regulates tissue-specific signaling networks. Many tissue-specific ubiquitylation sites were obtained from brain highlighting the complexity and unique physiology of this organ. We further demonstrate that different di-glycine-lysine-specific monoclonal antibodies exhibit sequence preferences, and that their complementary use increases the depth of ubiquitylation site analysis, thereby providing a more unbiased view of protein ubiquitylation.Ubiquitin is a small 76-amino-acid protein that is conjugated to the ε-amino group of lysines in a highly orchestrated enzymatic cascade involving ubiquitin activating (E1), ubiquitin conjugating (E2), and ubiquitin ligase (E3) enzymes (1). Ubiquitylation is involved in the regulation of diverse cellular processes including protein degradation (2, 3, 4), DNA damage repair (5, 6), DNA replication (7), cell surface receptor endocytosis, and innate immune signaling (8, 9). Deregulation of protein ubiquitylation is implicated in the development of cancer and neurodegenerative diseases (10, 11). Inhibitors targeting the ubiquitin proteasome system are used in the treatment of hematologic malignancies such as multiple myeloma (12, 13).Recent developments in the mass spectrometry (MS)-based proteomics have greatly expedited proteome-wide analysis of posttranslational modifications (PTMs) (14–17). Large-scale mapping of ubiquitylation sites by mass spectrometry is based on the identification of the di-glycine remnant that results from trypsin digestion of ubiquitylated proteins and remains attached to ubiquitylated lysines (18). Recently, two monoclonal antibodies were developed that specifically recognize di-glycine remnant modified peptides enabling their efficient enrichment from complex peptide mixtures (19, 20). These antibodies have been used to identify thousands of endogenous ubiquitylation sites in human cells, and to quantify site-specific changes in ubiquitylation in response to different cellular perturbations (20–22). It should be noted that the di-glycine remnant is not specific for proteins modified by ubiquitin but also proteins modified by NEDD8 or ISG15 generate an identical di-glycine remnant on modified lysines making it impossible to distinguish between these modifications by mass spectrometry. However, expression of NEDD8 in mouse tissues was shown to be developmentally down-regulated (23), and ISG15 expression in bovine tissues is low in the absence of interferon stimulation (24). In cell culture experiments it was shown that a great majority of sites identified using di-glycine-lysine-specific antibodies stems from ubiquitylated peptides (20).The rates of cell proliferation and protein turnover in mammals vary dramatically between different tissues. Immortalized cell lines, often derived from cancer, are selected for high proliferation rates and fail to represent the complex conditions in tissues. Tissue proteomics can help to gain a more comprehensive understanding of physiological processes in multicellular organisms. Analysis of tissue proteome and PTMs can provide important insights into tissue-specific processes and signaling networks that regulate these processes (25–32). In addition, development of mass spectrometric methods for analysis of PTMs in diseased tissues might lead to the identification of biomarkers.In this study, we combined high-resolution mass spectrometry with immunoenrichment of di-glycine modified peptides to investigate endogenous ubiquitylation sites in murine tissues. We identified more than 20,000 ubiquitylation sites from five different murine tissues and report the largest ubiquitylation dataset obtained from mammalian tissues to date. Furthermore, we compared the performance of the two monoclonal di-glycine-lysine-specific antibodies available for enrichment of ubiquitylated peptides, and reveal their relative preferences for different amino acids flanking ubiquitylation sites.     

SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity

S-nitrosylation, the covalent attachment of a nitric oxide to (NO) the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-nitrosylation remains unknown. Based on a total of 586 experimentally identified S-nitrosylation sites from SNAP/L-cysteine-stimulated mouse endothelial cells, this work presents an informatics investigation on S-nitrosylation sites including structural factors such as the flanking amino acids composition, the accessible surface area (ASA) and physicochemical properties, i.e. positive charge and side chain interaction parameter. Due to the difficulty to obtain the conserved motifs by conventional motif analysis, maximal dependence decomposition (MDD) has been applied to obtain statistically significant conserved motifs. Support vector machine (SVM) is applied to generate predictive model for each MDD-clustered motif. According to five-fold cross-validation, the MDD-clustered SVMs could achieve an accuracy of 0.902, and provides a promising performance in an independent test set. The effectiveness of the model was demonstrated on the correct identification of previously reported S-nitrosylation sites of Bos taurus dimethylarginine dimethylaminohydrolase 1 (DDAH1) and human hemoglobin subunit beta (HBB). Finally, the MDD-clustered model was adopted to construct an effective web-based tool, named SNOSite (http://csb.cse.yzu.edu.tw/SNOSite/), for identifying S-nitrosylation sites on the uncharacterized protein sequences.     

Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments

Background

Motif analysis methods have long been central for studying biological function of nucleotide sequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked by an experimentally acquired functional property such as gene expression or protein binding affinity. Current motif discovery tools suffer from limitations in searching large motif spaces, and thus more complex motifs may not be included. There is thus a need for motif analysis methods that are tailored for analyzing specific complex motifs motivated by biological questions and hypotheses rather than acting as a screen based motif finding tool.

Methods

We present Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented motifs in ranked lists of sequences. Regmex uses regular expressions to define motifs or families of motifs and embedded Markov models to calculate exact p-values for motif observations in sequences. Biases in motif distributions across ranked sequence lists are evaluated using random walks, Brownian bridges, or modified rank based statistics. A modular setup and fast analytic p value evaluations make Regmex applicable to diverse and potentially large-scale motif analysis problems.

Results

We demonstrate use cases of combined motifs on simulated data and on expression data from micro RNA transfection experiments. We confirm previously obtained results and demonstrate the usability of Regmex to test a specific hypothesis about the relative location of microRNA seed sites and U-rich motifs. We further compare the tool with an existing motif discovery tool and show increased sensitivity.

Conclusions

Regmex is a useful and flexible tool to analyze motif hypotheses that relates to large data sets in functional genomics. The method is available as an R package (https://github.com/muhligs/regmex).

     

Usa1 Protein Facilitates Substrate Ubiquitylation through Two Separate Domains

Background

Defects in protein folding are recognized as the root of many neurodegenerative disorders. In the endoplasmic reticulum (ER), secretory proteins are subjected to a stringent quality control process to eliminate misfolded proteins by the ER-associated degradation (ERAD) pathway. A novel ERAD component Usa1 was recently identified. However, the specific role of Usa1 in ERAD remains obscure.

Methodology/Principal Findings

Here, we demonstrate that Usa1 is important for substrate ubiquitylation. Furthermore, we defined key cis-elements of Usa1 essential for its degradation function. Interestingly, a putative proteasome-binding motif is dispensable for the functioning of Usa1 in ERAD. We identify two separate cytosolic domains critical for Usa1 activity in ERAD, one of which is involved in binding to the Ub-protein ligase Hrd1/Hrd3. Usa1 may have another novel role in substrate ubiquitylation that is separate from the Hrd1 association.

Conclusions/Significance

We conclude that Usa1 has two important roles in ERAD substrate ubiquitylation.     

The predictive performance of short-linear motif features in the prediction of calmodulin-binding proteins

Background

The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins.

Results

We propose a new method for the prediction of CaM-binding proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding proteins and 193 mitochondrial proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF).

Conclusions

Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding proteins.

     

Mitofusin 1 is degraded at G<Subscript>2</Subscript>/M phase through ubiquitylation by MARCH5

Background

Mitochondria exhibit a dynamic morphology in cells and their biogenesis and function are integrated with the nuclear cell cycle. In mitotic cells, the filamentous network structure of mitochondria takes on a fragmented form. To date, however, whether mitochondrial fusion activity is regulated in mitosis has yet to be elucidated.

Findings

Here, we report that mitochondria were found to be fragmented in G₂ phase prior to mitotic entry. Mitofusin 1 (Mfn1), a mitochondrial fusion protein, interacted with cyclin B1, and their interactions became stronger in G₂/M phase. In addition, MARCH5, a mitochondrial E3 ubiquitin ligase, reduced Mfn1 levels and the MARCH5-mediated Mfn1 ubiquitylation were enhanced in G₂/M phase.

Conclusions

Mfn1 is degraded through the MARCH5-mediated ubiquitylation in G₂/M phase and the cell cycle-dependent degradation of Mfn1 could be facilitated by interaction with cyclin B1/Cdk1 complexes.

     

Fast subcellular localization by cascaded fusion of signal-based and homology-based methods

Background

The functions of proteins are closely related to their subcellular locations. In the post-genomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means.

Results

This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by a cascaded fusion of cleavage site prediction and profile alignment. Specifically, the informative segments of protein sequences are identified by a cleavage site predictor using the information in their N-terminal shorting signals. Then, the sequences are truncated at the cleavage site positions, and the shortened sequences are passed to PSI-BLAST for computing their profiles. Subcellular localization are subsequently predicted by a profile-to-profile alignment support-vector-machine (SVM) classifier. To further reduce the training and recognition time of the classifier, the SVM classifier is replaced by a new kernel method based on the perturbational discriminant analysis (PDA).

Conclusions

Experimental results on a new dataset based on Swiss-Prot Release 57.5 show that the method can make use of the best property of signal- and homology-based approaches and can attain an accuracy comparable to that achieved by using full-length sequences. Analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without significant reduction in subcellular localization accuracy. It was found that PDA enjoys a short training time as compared to the conventional SVM. We advocate that the method will be important for biologists to conduct large-scale protein annotation or for bioinformaticians to perform preliminary investigations on new algorithms that involve pairwise alignments.