Prediction of Protein-Protein Interaction Sites by Random Forest Algorithm with mRMR and IFS

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction.     

Predicting DNA-binding sites of proteins based on sequential and 3D structural information

Protein–DNA interactions play important roles in many biological processes. To understand the molecular mechanisms of protein–DNA interaction, it is necessary to identify the DNA-binding sites in DNA-binding proteins. In the last decade, computational approaches have been developed to predict protein–DNA-binding sites based solely on protein sequences. In this study, we developed a novel predictor based on support vector machine algorithm coupled with the maximum relevance minimum redundancy method followed by incremental feature selection. We incorporated not only features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure, solvent accessibility, but also five three-dimensional (3D) structural features calculated from PDB data to predict the protein–DNA interaction sites. Feature analysis showed that 3D structural features indeed contributed to the prediction of DNA-binding site and it was demonstrated that the prediction performance was better with 3D structural features than without them. It was also shown via analysis of features from each site that the features of DNA-binding site itself contribute the most to the prediction. Our prediction method may become a useful tool for identifying the DNA-binding sites and the feature analysis described in this paper may provide useful insights for in-depth investigations into the mechanisms of protein–DNA interaction.     

Prediction of tyrosine sulfation with mRMR feature selection and analysis

Protein tyrosine sulfation is a ubiquitous post-translational modification (PTM) of secreted and transmembrane proteins that pass through the Golgi apparatus. In this study, we developed a new method for protein tyrosine sulfation prediction based on a nearest neighbor algorithm with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). We incorporated features of sequence conservation, residual disorder, and amino acid factor, 229 features in total, to predict tyrosine sulfation sites. From these 229 features, 145 features were selected and deemed as the optimized features for the prediction. The prediction model achieved a prediction accuracy of 90.01% using the optimal 145-feature set. Feature analysis showed that conservation, disorder, and physicochemical/biochemical properties of amino acids all contributed to the sulfation process. Site-specific feature analysis showed that the features derived from its surrounding sites contributed profoundly to sulfation site determination in addition to features derived from the sulfation site itself. The detailed feature analysis in this paper might help understand more of the sulfation mechanism and guide the related experimental validation.     

Prediction of protein modification sites of pyrrolidone carboxylic acid using mRMR feature selection and analysis

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations.     

Prediction of protein domain with mRMR feature selection and analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28-40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.     

Prediction and analysis of protein hydroxyproline and hydroxylysine

Background

Hydroxylation is an important post-translational modification and closely related to various diseases. Besides the biotechnology experiments, in silico prediction methods are alternative ways to identify the potential hydroxylation sites.

Methodology/Principal Findings

In this study, we developed a novel sequence-based method for identifying the two main types of hydroxylation sites – hydroxyproline and hydroxylysine. First, feature selection was made on three kinds of features consisting of amino acid indices (AAindex) which includes various physicochemical properties and biochemical properties of amino acids, Position-Specific Scoring Matrices (PSSM) which represent evolution information of amino acids and structural disorder of amino acids in the sliding window with length of 13 amino acids, then the prediction model were built using incremental feature selection method. As a result, the prediction accuracies are 76.0% and 82.1%, evaluated by jackknife cross-validation on the hydroxyproline dataset and hydroxylysine dataset, respectively. Feature analysis suggested that physicochemical properties and biochemical properties and evolution information of amino acids contribute much to the identification of the protein hydroxylation sites, while structural disorder had little relation to protein hydroxylation. It was also found that the amino acid adjacent to the hydroxylation site tends to exert more influence than other sites on hydroxylation determination.

Conclusions/Significance

These findings may provide useful insights for exploiting the mechanisms of hydroxylation.     

Prediction and analysis of protein palmitoylation sites

Palmitoylation is a universal and important lipid modification, involving a series of basic cellular processes, such as membrane trafficking, protein stability and protein aggregation. With the avalanche of new protein sequences generated in the post genomic era, it is highly desirable to develop computational methods for rapidly and effectively identifying the potential palmitoylation sites of uncharacterized proteins so as to timely provide useful information for revealing the mechanism of protein palmitoylation. By using the Incremental Feature Selection approach based on amino acid factors, conservation, disorder feature, and specific features of palmitoylation site, a new predictor named IFS-Palm was developed in this regard. The overall success rate thus achieved by jackknife test on a newly constructed benchmark dataset was 90.65%. It was shown via an in-depth analysis that palmitoylation was intimately correlated with the feature of the upstream residue directly adjacent to cysteine site as well as the conservation of amino acid cysteine. Meanwhile, the protein disorder region might also play an import role in the post-translational modification. These findings may provide useful insights for revealing the mechanisms of palmitoylation.     

On-gel fluorescent visualization and the site identification of S-nitrosylated proteins

Mounting evidence indicates that S-nitrosylation of critical cysteine residues in a protein represents a common feature of protein function regulation and cell signaling. However, the progress in studying the exact role of S-nitrosylation has been hampered by a lack of rapid and accurate methods for the detection of these S-nitrosylated proteins and the exact modification sites. In this article, we report a fluorescence-based method in which the S-nitrosylated cysteines are converted into 7-amino-4-methylcoumarin-3-acetic acid (AMCA) fluorophore-labeled cysteines—termed the AMCA switch method. The labeled proteins are then analyzed by nonreducing SDS-PAGE, and the S-nitrosylated proteins can be readily detected as brilliant blue bands after the activation of ultraviolet light. Furthermore, the sites of modification can be determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS) after in-gel tryptic digestion of the fluorescent band, and the recognizable AMCA tag in the MS spectra ensures the accurate site identification of the nitrosocysteines. Therefore, our method offers some apparent advantages by direct visualization of on-gel image of S-nitrosylated proteins, shorter experiment time by skipping the anti-biotin immunoblotting step, and elimination of the potential interference of endogenous biotinylated proteins. Based on this method, we detected the S-nitrosylation and the modified site in bovine serum albumin and gankyrin after in vitro S-nitrosylation. Overall, our results indicate that the AMCA switch method is a fast and accurate method to identify the S-nitrosylated protein and the modification sites.     

Prediction of Protein Cleavage Site with Feature Selection by Random Forest

Proteinases play critical roles in both intra and extracellular processes by binding and cleaving their protein substrates. The cleavage can either be non-specific as part of degradation during protein catabolism or highly specific as part of proteolytic cascades and signal transduction events. Identification of these targets is extremely challenging. Current computational approaches for predicting cleavage sites are very limited since they mainly represent the amino acid sequences as patterns or frequency matrices. In this work, we developed a novel predictor based on Random Forest algorithm (RF) using maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The features of physicochemical/biochemical properties, sequence conservation, residual disorder, amino acid occurrence frequency, secondary structure and solvent accessibility were utilized to represent the peptides concerned. Here, we compared existing prediction tools which are available for predicting possible cleavage sites in candidate substrates with ours. It is shown that our method makes much more reliable predictions in terms of the overall prediction accuracy. In addition, this predictor allows the use of a wide range of proteinases.     

Predicting protein oxidation sites with feature selection and analysis approach

Protein oxidation is a ubiquitous post-translational modification that plays important roles in various physiological and pathological processes. Owing to the fact that protein oxidation can also take place as an experimental artifact or caused by oxygen in the air during the process of sample collection and analysis, and that it is both time-consuming and expensive to determine the protein oxidation sites purely by biochemical experiments, it would be of great benefit to develop in silico methods for rapidly and effectively identifying protein oxidation sites. In this study, we developed a computational method to address this problem. Our method was based on the nearest neighbor algorithm in which, however, the maximum relevance minimum redundancy and incremental feature selection approaches were incorporated. From the initial 735 features, 16 features were selected as the optimal feature set. Of such 16 optimized features, 10 features were associated with the position-specific scoring matrix conservation scores, three with the amino acid factors, one with the propensity of conservation of residues on protein surface, one with the side chain count of carbon atom deviation from mean, and one with the solvent accessibility. It was observed that our prediction model achieved an overall success rate of 75.82%, indicating that it is quite encouraging and promising for practical applications. Also, the 16 optimal features obtained through this study may provide useful clues and insights for in-depth understanding the action mechanism of protein oxidation.     

Predicting protein oxidation sites with feature selection and analysis approach

Protein oxidation is a ubiquitous post-translational modification that plays important roles in various physiological and pathological processes. Owing to the fact that protein oxidation can also take place as an experimental artifact or caused by oxygen in the air during the process of sample collection and analysis, and that it is both time-consuming and expensive to determine the protein oxidation sites purely by biochemical experiments, it would be of great benefit to develop in silico methods for rapidly and effectively identifying protein oxidation sites. In this study, we developed a computational method to address this problem. Our method was based on the nearest neighbor algorithm in which, however, the maximum relevance minimum redundancy and incremental feature selection approaches were incorporated. From the initial 735 features, 16 features were selected as the optimal feature set. Of such 16 optimized features, 10 features were associated with the position-specific scoring matrix conservation scores, three with the amino acid factors, one with the propensity of conservation of residues on protein surface, one with the side chain count of carbon atom deviation from mean, and one with the solvent accessibility. It was observed that our prediction model achieved an overall success rate of 75.82%, indicating that it is quite encouraging and promising for practical applications. Also, the 16 optimal features obtained through this study may provide useful clues and insights for in-depth understanding the action mechanism of protein oxidation.     

Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana

Nitric oxide (NO) is an important signaling molecule that regulates many physiological processes in plants. One of the most important regulatory mechanisms of NO is S-nitrosylation—the covalent attachment of NO to cysteine residues. Although the involvement of cysteine S-nitrosylation in the regulation of protein functions is well established, its substrate specificity remains unknown. Identification of candidates for S-nitrosylation and their target cysteine residues is fundamental for studying the molecular mechanisms and regulatory roles of S-nitrosylation in plants. Several experimental methods that are based on the biotin switch have been developed to identify target proteins for S-nitrosylation. However, these methods have their limits. Thus, computational methods are attracting considerable attention for the identification of modification sites in proteins. Using GPS-SNO version 1.0, a recently developed S-nitrosylation site-prediction program, a set of 16,610 candidate proteins for S-nitrosylation containing 31,900 S-nitrosylation sites was isolated from the entire Arabidopsis proteome using the medium threshold. In the compartments “chloroplast,” “CUL4-RING ubiquitin ligase complex,” and “membrane” more than 70% of the proteins were identified as candidates for S-nitrosylation. The high number of identified candidates in the proteome reflects the importance of redox signaling in these compartments. An analysis of the functional distribution of the predicted candidates showed that proteins involved in signaling processes exhibited the highest prediction rate. In a set of 46 proteins, where 53 putative S-nitrosylation sites were already experimentally determined, the GPS-SNO program predicted 60 S-nitrosylation sites, but only 11 overlap with the results of the experimental approach. In general, a computer-assisted method for the prediction of targets for S-nitrosylation is a very good tool; however, further development, such as including the three dimensional structure of proteins in such analyses, would improve the identification of S-nitrosylation sites.     

Prediction of lysine ubiquitination with mRMR feature selection and analysis

Ubiquitination, one of the most important post-translational modifications of proteins, occurs when ubiquitin (a small 76-amino acid protein) is attached to lysine on a target protein. It often commits the labeled protein to degradation and plays important roles in regulating many cellular processes implicated in a variety of diseases. Since ubiquitination is rapid and reversible, it is time-consuming and labor-intensive to identify ubiquitination sites using conventional experimental approaches. To efficiently discover lysine-ubiquitination sites, a sequence-based predictor of ubiquitination site was developed based on nearest neighbor algorithm. We used the maximum relevance and minimum redundancy principle to identify the key features and the incremental feature selection procedure to optimize the prediction engine. PSSM conservation scores, amino acid factors and disorder scores of the surrounding sequence formed the optimized 456 features. The Mathew’s correlation coefficient (MCC) of our ubiquitination site predictor achieved 0.142 by jackknife cross-validation test on a large benchmark dataset. In independent test, the MCC of our method was 0.139, higher than the existing ubiquitination site predictor UbiPred and UbPred. The MCCs of UbiPred and UbPred on the same test set were 0.135 and 0.117, respectively. Our analysis shows that the conservation of amino acids at and around lysine plays an important role in ubiquitination site prediction. What’s more, disorder and ubiquitination have a strong relevance. These findings might provide useful insights for studying the mechanisms of ubiquitination and modulating the ubiquitination pathway, potentially leading to potential therapeutic strategies in the future.     

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.     

Unbiased identification of cysteine S-nitrosylation sites on proteins

Covalent addition of nitric oxide (NO) to Cys-sulfur in proteins, or S-nitrosylation, plays pervasive roles in the physiological and pathophysiological modulation of mammalian protein functions. Knowledge of the specific protein Cys residues that undergo NO addition in different biological settings is fundamental to understanding NO-mediated signal transduction. Here, we describe in detail an MS-based proteomic protocol for facile, high-throughput and unbiased discovery of SNO-Cys residues in proteins from complex biological samples. The approach, termed SNOSID (SNO-Cys site identification), can be used to identify endogenous and chemically induced S-nitrosylation sites in proteins from tissues or cells. Identified SNO-Cys sites may provide insights into novel mechanisms and proteins that mediate NO bioactivities in health and disease. SNOSID builds on the biotin-switch method for covalent addition of disulfide-linked biotin at S-nitrosylation sites on proteins. Biotinylated proteins are then subjected to trypsinolysis and the resulting biotin-tagged peptides are affinity-captured on streptavidin-agarose. After selective elution with beta-mercaptoethanol, the peptides are sequenced using nanoflow liquid chromatography tandem mass spectrometry (nLC-MS/MS). Validation that identified peptide ions as originating from authentic NO-Cys-containing precursor proteins can be provided by establishing that these peptide ions are absent from control samples where S-NO bonds were subjected to prior photolysis, using a UV transilluminator. The protocol requires approximately 2 days for sample processing, including the incubation time for proteolysis. An additional 1-2 days is needed for sample analysis by nLC-MS/MS and data analysis/interpretation.     

An efficient support vector machine approach for identifying protein S-nitrosylation sites

Protein S-nitrosylation plays a key and specific role in many cellular processes. Detecting possible S-nitrosylated substrates and their corresponding exact sites is crucial for studying the mechanisms of these biological processes. Comparing with the expensive and time-consuming biochemical experiments, the computational methods are attracting considerable attention due to their convenience and fast speed. Although some computational models have been developed to predict S-nitrosylation sites, their accuracy is still low. In this work,we incorporate support vector machine to predict protein S-nitrosylation sites. After a careful evaluation of six encoding schemes, we propose a new efficient predictor, CPR-SNO, using the coupling patterns based encoding scheme. The performance of our CPR-SNO is measured with the area under the ROC curve (AUC) of 0.8289 in 10-fold cross validation experiments, which is significantly better than the existing best method GPS-SNO 1.0's 0.685 performance. In further annotating large-scale potential S-nitrosylated substrates, CPR-SNO also presents an encouraging predictive performance. These results indicate that CPR-SNO can be used as a competitive protein S-nitrosylation sites predictor to the biological community. Our CPR-SNO has been implemented as a web server and is available at http://math.cau.edu.cn/CPR -SNO/CPR-SNO.html.     

蛋白质亚硝基化检测和鉴定方法的研究进展

蛋白的亚硝基化是近期发现的一种类似于磷酸化、可逆的、不依赖于环磷酸鸟苷（cGMP）的一氧化氮修饰和调节蛋白功能的新途径。一经发现,有关亚硝基化的研究呈指数级递增。亚硝基化参与从生长发育到抗病、抗逆等多个生理和病理过程。已有大量综述对亚硝基化调控蛋白功能从而影响某一生理生化及病理过程进行了总结。但迄今为止,对检测蛋白亚硝基化的手段和鉴定亚硝基化位点的方法进行总结的文献综述仍屈指可数。据此,我们对蛋白亚硝基化检测手段的发明、改进提高、亚硝基化位点的结构特点以及亚硝基化位点预测软件的开发等进行综述,旨在为该领域内科研工作者提供方便。     

A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis

Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification accuracy of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.     

In situ detection and visualization of S-nitrosylated proteins following chemical derivatization: identification of Ran GTPase as a target for S-nitrosylation.

The formation of S-nitrosylated proteins is a nitric oxide-dependent post-translational modification important in signal transduction, yet the in situ detection of S-nitrosylated proteins remains problematic. In this study, we adapted a recently developed biotin derivatization approach to visualize S-nitrosylated proteins in intact cells. This strategy circumvents the use of antibodies directed against S-nitrosocysteine, which may have problematic specificity, due to epitope instability. Endogenous protein S-nitrosylation could be observed in intact cells and in mouse lung sections using fluorophore-conjugated streptavidin and confocal microscopy, and was enhanced by S-nitrosothiols and reduced following treatment with the nitric oxide synthase inhibitor, L-N-monomethyl arginine. Intriguingly, protein S-nitrosylation was detected mainly in the nuclear compartment of cells under baseline conditions and was enhanced when nuclear export was blocked with leptomycin B. We also determined that the small GTPase Ran, a key regulator of nucleocytoplasmic transport, is a target for S-nitrosylation. These findings demonstrate that biotin derivatization is a useful approach to detect S-nitrosylated proteins in situ in cellular compartments or tissues, and will be useful in the assessment of altered S-nitrosylation in pathological conditions.     

Identification of the cysteine nitrosylation sites in human endothelial nitric oxide synthase

S-nitrosylation, or the replacement of the hydrogen atom in the thiol group of cysteine residues by a -NO moiety, is a physiologically important posttranslational modification. In our previous work we have shown that S-nitrosylation is involved in the disruption of the endothelial nitric oxide synthase (eNOS) dimer and that this involves the disruption of the zinc (Zn) tetrathiolate cluster due to the S-nitrosylation of Cysteine 98. However, human eNOS contains 28 other cysteine residues whose potential to undergo S-nitrosylation has not been determined. Thus, the goal of this study was to identify the cysteine residues within eNOS that are susceptible to S-nitrosylation in vitro. To accomplish this, we utilized a modified biotin switch assay. Our modification included the tryptic digestion of the S-nitrosylated eNOS protein to allow the isolation of S-nitrosylated peptides for further identification by mass spectrometry. Our data indicate that multiple cysteine residues are capable of undergoing S-nitrosylation in the presence of an excess of a nitrosylating agent. All these cysteine residues identified were found to be located on the surface of the protein according to the available X-ray structure of the oxygenase domain of eNOS. Among those identified were Cys 93 and 98, the residues involved in the formation of the eNOS dimer through a Zn tetrathiolate cluster. In addition, cysteine residues within the reductase domain were identified as undergoing S-nitrosylation. We identified cysteines 660, 801, and 1113 as capable of undergoing S-nitrosylation. These cysteines are located within regions known to bind flavin mononucleotide (FMN), flavin adenine dinucleotide (FAD), and nicotinamide adenine dinucleotide (NADPH) although from our studies their functional significance is unclear. Finally we identified cysteines 852, 975/990, and 1047/1049 as being susceptible to S-nitrosylation. These cysteines are located in regions of eNOS that have not been implicated in any known biochemical functions and the significance of their S-nitrosylation is not clear from this study. Thus, our data indicate that the eNOS protein can be S-nitrosylated at multiple sites other than within the Zn tetrathiolate cluster, suggesting that S-nitrosylation may regulate eNOS function in ways other than simply by inducing dimer collapse.