ISNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins.
ABSTRACT: As one of the most important and universal posttranslational modifications (PTMs) of proteins, S-nitrosylation (SNO) plays crucial roles in a variety of biological processes, including the regulation of cellular dynamics and many signaling events. Knowledge of SNO sites in proteins is very useful for drug development and basic research as well. Unfortunately, it is both time-consuming and costly to determine the SNO sites purely based on biological experiments. Facing the explosive protein sequence data generated in the post-genomic era, we are challenged to develop automated vehicles for timely and effectively determining the SNO sites for uncharacterized proteins. To address the challenge, a new predictor called iSNO-AAPair was developed by taking into account the coupling effects for all the pairs formed by the nearest residues and the pairs by the next nearest residues along protein chains. The cross-validation results on a state-of-the-art benchmark have shown that the new predictor outperformed the existing predictors. The same was true when tested by the independent proteins whose experimental SNO sites were known. A user-friendly web-server for iSNO-AAPair was established at http://app.aporc.org/iSNO-AAPair/, by which users can easily obtain their desired results without the need to follow the mathematical equations involved during its development.
Project description:Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had [Formula: see text] pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.
Project description:S-nitrosylation (SNO) is one of the most universal reversible post-translational modifications involved in many biological processes. Malfunction or dysregulation of SNO leads to a series of severe diseases, such as developmental abnormalities and various diseases. Therefore, the identification of SNO sites (SNOs) provides insights into disease progression and drug development. In this paper, a new bioinformatics tool, named PSNO, is proposed to identify SNOs from protein sequences. Firstly, we explore various promising sequence-derived discriminative features, including the evolutionary profile, the predicted secondary structure and the physicochemical properties. Secondly, rather than simply combining the features, which may bring about information redundancy and unwanted noise, we use the relative entropy selection and incremental feature selection approach to select the optimal feature subsets. Thirdly, we train our model by the technique of the k-nearest neighbor algorithm. Using both informative features and an elaborate feature selection scheme, our method, PSNO, achieves good prediction performance with a mean Mathews correlation coefficient (MCC) value of about 0.5119 on the training dataset using 10-fold cross-validation. These results indicate that PSNO can be used as a competitive predictor among the state-of-the-art SNOs prediction tools. A web-server, named PSNO, which implements the proposed method, is freely available at http://184.108.40.206:8088/PSNO/.
Project description:Lysine succinylation in protein is one type of post-translational modifications (PTMs). Succinylation is associated with some diseases and succinylated sites data just has been found in recent years in experiments. It is highly desired to develop computational methods to identify the candidate proteins and their sites. In view of this, a new predictor called iSuc-PseAAC was proposed by incorporating the peptide position-specific propensity into the general form of pseudo amino acid composition. The accuracy is 79.94%, sensitivity 51.07%, specificity 89.42% and MCC 0.431 in leave-one-out cross validation with support vector machine algorithm. It demonstrated by rigorous leave-one-out on stringent benchmark dataset that the new predictor is quite promising and may become a useful high throughput tool in this area. Meanwhile a user-friendly web-server for iSuc-PseAAC is accessible at http://app.aporc.org/iSuc-PseAAC/. Users can easily obtain their desired results without the need to understand the complicated mathematical equations presented in this paper just for its integrity.
Project description:S-nitrosylation (SNO) is a reversible protein modification that has the ability to alter the activity of target proteins. However, only a small number of SNO proteins have been found in the myocardium, and even fewer specific sites of SNO have been identified. Therefore, this study aims to characterize potential SNO sites in the myocardium. We utilized a modified version of the SNO-resin-assisted capture technique in tandem with mass spectrometry. In brief, a modified biotin switch was performed using perfused mouse heart homogenates incubated with or without the S-nitrosylating agent S-nitrosoglutathione. Our modified SNO-resin-assisted capture protocol identified 116 unique SNO-modified proteins under basal conditions, and these represent the constitutive SNO proteome. These constitutive SNO proteins are likely to be physiologically relevant targets, since nitric oxide has been shown to play an important role in the regulation of normal cardiovascular physiology. Following S-nitrosoglutathione treatment, we identified 951 unique SNO proteins, many of which contained multiple SNO sites. These proteins show the potential for SNO. This study provides novel information regarding the constitutive SNO proteome of the myocardium, as well as potential myocardial SNO sites, and yields additional information on the SNO sites for many key proteins involved in myocardial contraction, metabolism, and cellular signaling.
Project description:Previous studies have shown a role for nitric oxide and S-nitrosylation (SNO) in postconditioning (PostC), but specific SNO proteins and sites have not been identified in the myocardium after PostC. In this study, we examined SNO signaling in PostC using a Langendorff-perfused mouse heart model. After 20 min of equilibrium perfusion and 25 min of global ischemia, PostC was applied at the beginning of reperfusion with six cycles of 10 s of reperfusion and 10 s of ischemia. The total period of reperfusion was 90 min. Compared with the ischemia-reperfusion (I/R) control, PostC significantly reduced postischemic contractile dysfunction and infarct size. PostC-induced protection was blocked by treatment with N(G)-nitro-l-arginine methyl ester (l-NAME) (10 ?mol/l; a constitutive NO synthase inhibitor), but not by either ODQ (10 ?mol/l, a highly selective soluble guanylyl cyclase inhibitor) or KT5823 (1 ?mol/l, a specific protein kinase G inhibitor). Two biotin switch based methods, two dimensional CyDye-maleimide difference gel electrophoresis (2D CyDye-maleimide DIGE) and SNO-resin-assisted capture (SNO-RAC), were utilized to identify SNO-modified proteins and sites. Using 2D CyDye-maleimide DIGE analysis, PostC was found to cause a 25% or greater increase in SNO of a number of proteins, which was blocked by treatment with l-NAME in parallel with the loss of protection. Using SNO-RAC, we identified 77 unique proteins with SNO sites after PostC. These results suggest that NO-mediated SNO signaling is involved in PostC-induced cardioprotection and these data provide the first set of candidate SNO proteins in PostC hearts.
Project description:Reversible addition of NO to Cys-sulfur in proteins, a modification termed S-nitrosylation, has emerged as a ubiquitous signaling mechanism for regulating diverse cellular processes. A key first-step toward elucidating the mechanism by which S-nitrosylation modulates a protein's function is specification of the targeted Cys (SNO-Cys) residue. To date, S-nitrosylation site specification has been laboriously tackled on a protein-by-protein basis. Here we describe a high-throughput proteomic approach that enables simultaneous identification of SNO-Cys sites and their cognate proteins in complex biological mixtures. The approach, termed SNOSID (SNO Site Identification), is a modification of the biotin-swap technique [Jaffrey, S. R., Erdjument-Bromage, H., Ferris, C. D., Tempst, P. & Snyder, S. H. (2001) Nat. Cell. Biol. 3, 193-197], comprising biotinylation of protein SNO-Cys residues, trypsinolysis, affinity purification of biotinylated-peptides, and amino acid sequencing by liquid chromatography tandem MS. With this approach, 68 SNO-Cys sites were specified on 56 distinct proteins in S-nitrosoglutathione-treated (2-10 microM) rat cerebellum lysates. In addition to enumerating these S-nitrosylation sites, the method revealed endogenous SNO-Cys modification sites on cerebellum proteins, including alpha-tubulin, beta-tubulin, GAPDH, and dihydropyrimidinase-related protein-2. Whereas these endogenous SNO proteins were previously recognized, we extend prior knowledge by specifying the SNO-Cys modification sites. Considering all 68 SNO-Cys sites identified, a machine learning approach failed to reveal a linear Cys-flanking motif that predicts stable transnitrosation by S-nitrosoglutathione under test conditions, suggesting that undefined 3D structural features determine S-nitrosylation specificity. SNOSID provides the first effective tool for unbiased elucidation of the SNO proteome, identifying Cys residues that undergo reversible S-nitrosylation.
Project description:S-nitrosylation, the formation of S-nitrosothiol (SNO), is an important reversible thiol oxidation event that has been increasingly recognized for its role in cell signaling. Although many proteins susceptible to S-nitrosylation have been reported, site-specific identification of physiologically relevant SNO modifications remains an analytical challenge because of the low abundance and labile nature of this modification. Herein we present further improvement and optimization of the recently reported resin-assisted cysteinyl peptide enrichment protocol for SNO identification and its application to mouse skeletal muscle to identify specific cysteine sites sensitive to S-nitrosylation by a quantitative reactivity profiling strategy. Our results indicate that the protein- and peptide-level enrichment protocols provide comparable specificity and coverage of SNO-peptide identifications. S-nitrosylation reactivity profiling was performed by quantitatively comparing the site-specific SNO modification levels in samples treated with S-nitrosoglutathione, an NO donor, at two different concentrations (i.e., 10 and 100 ?M). The reactivity profiling experiments led to the identification of 488 SNO-modified sites from 197 proteins with specificity of ?95% at the unique peptide level, i.e., ?95% of enriched peptides contain cysteine residues as the originally SNO-modified sites. Among these sites, 281 from 145 proteins were considered more sensitive to S-nitrosylation based on the ratios of observed SNO levels between the two treatments. These SNO-sensitive sites are more likely to be physiologically relevant. Many of the SNO-sensitive proteins are localized in mitochondria, contractile fiber, and actin cytoskeleton, suggesting the susceptibility of these subcellular compartments to redox regulation. Moreover, these observed SNO-sensitive proteins are primarily involved in metabolic pathways, including the tricarboxylic acid cycle, glycolysis/gluconeogenesis, glutathione metabolism, and fatty acid metabolism, suggesting the importance of redox regulation in muscle metabolism and insulin action.
Project description:We have modified the biotin switch assay for protein S-nitrosothiols (SNOs), using resin-assisted capture (SNO-RAC). Compared with existing methodologies, SNO-RAC requires fewer steps, detects high-mass S-nitrosylated proteins more efficiently, and facilitates identification and quantification of S-nitrosylated sites by mass spectrometry. When combined with iTRAQ labeling, SNO-RAC revealed that intracellular proteins may undergo rapid denitrosylation on a global scale. This methodology is readily adapted to analyzing diverse cysteine-based protein modifications, including S-acylation.
Project description: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/.
Project description:Given the increasing number of proteins reported to be regulated by S-nitrosylation (SNO), it is considered to act, in a manner analogous to phosphorylation, as a pleiotropic regulator that elicits dual effects to regulate diverse pathophysiological processes by altering protein function, stability, and conformation change in various cancers and human disorders. Due to its importance in regulating protein functions and cell signaling, dbSNO (http://dbSNO.mbc.nctu.edu.tw) is extended as a resource for exploring structural environment of SNO substrate sites and regulatory networks of S-nitrosylated proteins. An increasing interest in the structural environment of PTM substrate sites motivated us to map all manually curated SNO peptides (4165 SNO sites within 2277 proteins) to PDB protein entries by sequence identity, which provides the information of spatial amino acid composition, solvent-accessible surface area, spatially neighboring amino acids, and side chain orientation for 298 substrate cysteine residues. Additionally, the annotations of protein molecular functions, biological processes, functional domains and human diseases are integrated to explore the functional and disease associations for S-nitrosoproteome. In this update, users are allowed to search a group of interested proteins/genes and the system reconstructs the SNO regulatory network based on the information of metabolic pathways and protein-protein interactions. Most importantly, an endogenous yet pathophysiological S-nitrosoproteomic dataset from colorectal cancer patients was adopted to demonstrate that dbSNO could discover potential SNO proteins involving in the regulation of NO signaling for cancer pathways.