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.     

An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on Homo sapiens by Fusing Three Sequence Encoding Schemes with the Random Forest Classifier

Background Lysine succinylation is one of the reversible protein post-translational modifications (PTMs), which regulate the structure and function of proteins. It plays a significant role in various cellular physiologies including some diseases of human as well as many other organisms. The accurate identification of succinylation site is essential to understand the various biological functions and drug development.Methods In this study, we developed an improved method to predict lysine succinylation sites mapping on Homo sapiens by the fusion of three encoding schemes such as binary, the composition of k-spaced amino acid pairs (CKSAAP) and amino acid composition (AAC) with the random forest (RF) classifier. The prediction performance of the proposed random forest (RF) based on the fusion model in a comparison of other candidates was investigated by using 20-fold cross-validation (CV) and two independent test datasets were collected from two different sources.Results The CV results showed that the proposed predictor achieves the highest scores of sensitivity (SN) as 0.800, specificity (SP) as 0.902, accuracy (ACC) as 0.919, Mathew correlation coefficient (MCC) as 0.766 and partial AUC (pAUC) as 0.163 at a false-positive rate (FPR) = 0.10 and area under the ROC curve (AUC) as 0.958. It achieved the highest performance scores of SN as 0.811, SP as 0.902, ACC as 0.891, MCC as 0.629 and pAUC as 0.139 and AUC as 0.921 for the independent test protein set-1 and SN as 0.772, SP as 0.901, ACC as 0.836, MCC as 0.677 and pAUC as 0.141 at FPR = 0.10 and AUC as 0.923 for the independent test protein set-2. It also outperformed all the other existing prediction models.Conclusion The prediction performances as discussed in this article recommend that the proposed method might be a useful and encouraging computational resource for lysine succinylation site prediction in the case of human population.     

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.     

Role of ubiquitin in parainfluenza virus 5 particle formation

Ubiquitin is important for the budding of many retroviruses and other enveloped viruses, but the precise role of ubiquitin in virus budding remains unclear. Here, we characterized the ubiquitination of the matrix (M) protein of a paramyxovirus, parainfluenza virus 5 (PIV5). The PIV5 M protein (but not the PIV5 nucleocapsid protein) was found to be targeted for monoubiquitination in transfected mammalian cells. Major sites of ubiquitin attachment identified by mass spectrometry analysis were lysine residues at amino acid positions 79/80, 130, and 247. The cumulative mutation of lysine residues 79, 80, and 130 to arginines led to an altered pattern of M protein ubiquitination and impaired viruslike particle (VLP) production. However, the cumulative mutation of lysine residues 79, 80, 130, and 247 to arginines restored M protein ubiquitination and VLP production, suggesting that ubiquitin is attached to alternative sites on the M protein when the primary ones have been removed. Additional lysine residues were targeted for mutagenesis based on the UbiPred algorithm. An M protein with seven lysine residues changed to arginines exhibited altered ubiquitination and poor VLP production. A recombinant virus encoding an M protein with seven lysines mutated was generated, and this virus exhibited a 6-fold-reduced maximum titer, with the defect being attributed mainly to the budding of noninfectious particles. The recombinant virus was assembly deficient, as judged by the redistribution of viral M and hemagglutinin-neuraminidase proteins in infected cells. Similar assembly defects were observed for the wild-type (wt) virus after treatment with a proteasome inhibitor. Collectively, these findings suggest that the monoubiquitination of the PIV5 M protein is important for proper virus assembly and for the budding of infectious particles.     

hCKSAAP_UbSite: Improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties

As one of the most common post-translational modifications, ubiquitination regulates the quantity and function of a variety of proteins. Experimental and clinical investigations have also suggested the crucial roles of ubiquitination in several human diseases. The complicated sequence context of human ubiquitination sites revealed by proteomic studies highlights the need of developing effective computational strategies to predict human ubiquitination sites. Here we report the establishment of a novel human-specific ubiquitination site predictor through the integration of multiple complementary classifiers. Firstly, a Support Vector Machine (SVM) classier was constructed based on the composition of k-spaced amino acid pairs (CKSAAP) encoding, which has been utilized in our previous yeast ubiquitination site predictor. To further exploit the pattern and properties of the ubiquitination sites and their flanking residues, three additional SVM classifiers were constructed using the binary amino acid encoding, the AAindex physicochemical property encoding and the protein aggregation propensity encoding, respectively. Through an integration that relied on logistic regression, the resulting predictor termed hCKSAAP_UbSite achieved an area under ROC curve (AUC) of 0.770 in 5-fold cross-validation test on a class-balanced training dataset. When tested on a class-balanced independent testing dataset that contains 3419 ubiquitination sites, hCKSAAP_UbSite has also achieved a robust performance with an AUC of 0.757. Specifically, it has consistently performed better than the predictor using the CKSAAP encoding alone and two other publicly available predictors which are not human-specific. Given its promising performance in our large-scale datasets, hCKSAAP_UbSite has been made publicly available at our server (http://protein.cau.edu.cn/cksaap_ubsite/).     

Sequence and structure-based characterization of ubiquitination sites in human and yeast proteins using Chou's sample formulation

Ubiquitination is an important post-translational event responsible for half-life and turnover of proteins inside the cell. Proteins are ubiquitinated by forming an iso-peptide bond between their lysine residue and C-terminal glycine residue of ubiquitin leading to rapid degradation of proteins by 26S proteosome complex. Deregulation of ubiquitination is manifested by aberrant expression of E3-ligase activity or mutation in the surroundings of ubiquitination sites. Many new experimentally validated ubiquitinated lysines have been recently identified that motivated the study of the environments surrounding the ubiquitinated lysines. With the help of known ubiquitinated proteins, here we present a comprehensive study of sequence and spatial environment of ubiquitination sites of human and yeast proteins. To identify position-specific features, this work distinguishes the spatial environments as proximity and distal regions. Certain amino acids specific to these regions, well differentiate the ubiquitination sites from non-ubiquitination sites are revealed. Additionally, amino acid signatures that contribute for protein disordered regions and solvent accessibility of amino acids are found to be contributing factors in ubiquitination sites. These results suggest that the ubiquitination site environment of the substrate determines the recognition and unfolding of substrate to facilitate the entry into 26S proteosomal complex. We believe that these findings will help in better prediction of ubiquitination sites using the sequence and spatial information.     

Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs

As one of the most important reversible protein post-translation modifications, ubiquitination has been reported to be involved in lots of biological processes and closely implicated with various diseases. To fully decipher the molecular mechanisms of ubiquitination-related biological processes, an initial but crucial step is the recognition of ubiquitylated substrates and the corresponding ubiquitination sites. Here, a new bioinformatics tool named CKSAAP_UbSite was developed to predict ubiquitination sites from protein sequences. With the assistance of Support Vector Machine (SVM), the highlight of CKSAAP_UbSite is to employ the composition of k-spaced amino acid pairs surrounding a query site (i.e. any lysine in a query sequence) as input. When trained and tested in the dataset of yeast ubiquitination sites (Radivojac et al, Proteins, 2010, 78: 365-380), a 100-fold cross-validation on a 1∶1 ratio of positive and negative samples revealed that the accuracy and MCC of CKSAAP_UbSite reached 73.40% and 0.4694, respectively. The proposed CKSAAP_UbSite has also been intensively benchmarked to exhibit better performance than some existing predictors, suggesting that it can be served as a useful tool to the community. Currently, CKSAAP_UbSite is freely accessible at http://protein.cau.edu.cn/cksaap_ubsite/. Moreover, we also found that the sequence patterns around ubiquitination sites are not conserved across different species. To ensure a reasonable prediction performance, the application of the current CKSAAP_UbSite should be limited to the proteome of yeast.     

OH-PRED: prediction of protein hydroxylation sites by incorporating adapted normal distribution bi-profile Bayes feature extraction and physicochemical properties of amino acids

Hydroxylation of proline or lysine residues in proteins is a common post-translational modification event, and such modifications are found in many physiological and pathological processes. Nonetheless, the exact molecular mechanism of hydroxylation remains under investigation. Because experimental identification of hydroxylation is time-consuming and expensive, bioinformatics tools with high accuracy represent desirable alternatives for large-scale rapid identification of protein hydroxylation sites. In view of this, we developed a supporter vector machine-based tool, OH-PRED, for the prediction of protein hydroxylation sites using the adapted normal distribution bi-profile Bayes feature extraction in combination with the physicochemical property indexes of the amino acids. In a jackknife cross validation, OH-PRED yields an accuracy of 91.88% and a Matthew’s correlation coefficient (MCC) of 0.838 for the prediction of hydroxyproline sites, and yields an accuracy of 97.42% and a MCC of 0.949 for the prediction of hydroxylysine sites. These results demonstrate that OH-PRED increased significantly the prediction accuracy of hydroxyproline and hydroxylysine sites by 7.37 and 14.09%, respectively, when compared with the latest predictor PredHydroxy. In independent tests, OH-PRED also outperforms previously published methods.     

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.     

iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model

As one of the most important posttranslational modifications (PTMs), ubiquitination plays an important role in regulating varieties of biological processes, such as signal transduction, cell division, apoptosis, and immune response. Ubiquitination is also named “lysine ubiquitination” because it occurs when an ubiquitin is covalently attached to lysine (K) residues of targeting proteins. Given an uncharacterized protein sequence that contains many lysine residues, which one of them is the ubiquitination site, and which one is of non-ubiquitination site? With the avalanche of protein sequences generated in the postgenomic age, it is highly desired for both basic research and drug development to develop an automated method for rapidly and accurately annotating the ubiquitination sites in proteins. In view of this, a new predictor called “iUbiq-Lys” was developed based on the evolutionary information, gray system model, as well as the general form of pseudo-amino acid composition. It was demonstrated via the rigorous cross-validations that the new predictor remarkably outperformed all its counterparts. As a web-server, iUbiq-Lys is accessible to the public at http://www.jci-bioinfo.cn/iUbiq-Lys. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process.     

Prediction of protein amidation sites by feature selection and analysis

Carboxy-terminal α-amidation is a widespread post-translational modification of proteins found widely in vertebrates and invertebrates. The α-amide group is required for full biological activity, since it may render a peptide more hydrophobic and thus better be able to bind to other proteins, preventing ionization of the C-terminus. However, in particular, the C-terminal amidation is very difficult to detect because experimental methods are often labor-intensive, time-consuming and expensive. Therefore, in silico methods may complement due to their high efficiency. In this study, a computational method was developed to predict protein amidation sites, by incorporating the maximum relevance minimum redundancy method and the incremental feature selection method based on the nearest neighbor algorithm. From a total of 735 features, 41 optimal features were selected and were utilized to construct the final predictor. As a result, the predictor achieved an overall Matthews correlation coefficient of 0.8308. Feature analysis showed that PSSM conservation scores and amino acid factors played the most important roles in the α-amidation site prediction. Site-specific feature analyses showed that features derived from the amidation site itself and adjacent sites were most significant. This method presented could be used as an efficient tool to theoretically predict amidated peptides. And the selected features from our study could shed some light on the in-depth understanding of the mechanisms of the amidation modification, providing guidelines for experimental validation.     

Development of a protein–ligand-binding site prediction method based on interaction energy and sequence conservation

We present a new method for predicting protein–ligand-binding sites based on protein three-dimensional structure and amino acid conservation. This method involves calculation of the van der Waals interaction energy between a protein and many probes placed on the protein surface and subsequent clustering of the probes with low interaction energies to identify the most energetically favorable locus. In addition, it uses amino acid conservation among homologous proteins. Ligand-binding sites were predicted by combining the interaction energy and the amino acid conservation score. The performance of our prediction method was evaluated using a non-redundant dataset of 348 ligand-bound and ligand-unbound protein structure pairs, constructed by filtering entries in a ligand-binding site structure database, LigASite. Ligand-bound structure prediction (bound prediction) indicated that 74.0 % of predicted ligand-binding sites overlapped with real ligand-binding sites by over 25 % of their volume. Ligand-unbound structure prediction (unbound prediction) indicated that 73.9 % of predicted ligand-binding residues overlapped with real ligand-binding residues. The amino acid conservation score improved the average prediction accuracy by 17.0 and 17.6 points for the bound and unbound predictions, respectively. These results demonstrate the effectiveness of the combined use of the interaction energy and amino acid conservation in the ligand-binding site prediction.     

Large-scale prediction of protein ubiquitination sites using a multimodal deep architecture

Background

Ubiquitination, which is also called “lysine ubiquitination”, occurs when an ubiquitin is attached to lysine (K) residues in targeting proteins. As one of the most important post translational modifications (PTMs), it plays the significant role not only in protein degradation, but also in other cellular functions. Thus, systematic anatomy of the ubiquitination proteome is an appealing and challenging research topic. The existing methods for identifying protein ubiquitination sites can be divided into two kinds: mass spectrometry and computational methods. Mass spectrometry-based experimental methods can discover ubiquitination sites from eukaryotes, but are time-consuming and expensive. Therefore, it is priority to develop computational approaches that can effectively and accurately identify protein ubiquitination sites.

Results

The existing computational methods usually require feature engineering, which may lead to redundancy and biased representations. While deep learning is able to excavate underlying characteristics from large-scale training data via multiple-layer networks and non-linear mapping operations. In this paper, we proposed a deep architecture within multiple modalities to identify the ubiquitination sites. First, according to prior knowledge and biological knowledge, we encoded protein sequence fragments around candidate ubiquitination sites into three modalities, namely raw protein sequence fragments, physico-chemical properties and sequence profiles, and designed different deep network layers to extract the hidden representations from them. Then, the generative deep representations corresponding to three modalities were merged to build the final model. We performed our algorithm on the available largest scale protein ubiquitination sites database PLMD, and achieved 66.4% specificity, 66.7% sensitivity, 66.43% accuracy, and 0.221 MCC value. A number of comparative experiments also indicated that our multimodal deep architecture outperformed several popular protein ubiquitination site prediction tools.

Conclusion

The results of comparative experiments validated the effectiveness of our deep network and also displayed that our method outperformed several popular protein ubiquitination site prediction tools. The source codes of our proposed method are available at https://github.com/jiagenlee/deepUbiquitylation.

     

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 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.     

Site-Specific Ubiquitination Determines Lysosomal Sorting and Signal Attenuation of the Granulocyte Colony-Stimulating Factor Receptor

Ubiquitination of cytokine receptors controls intracellular receptor routing and signal duration, but the underlying molecular determinants are unclear. The suppressor of cytokine signaling protein SOCS3 drives lysosomal degradation of the granulocyte colony-stimulating factor receptor (G-CSFR), depending on SOCS3-mediated ubiquitination of a specific lysine located in a conserved juxtamembrane motif. Here, we show that, despite ubiquitination of other lysines, positioning of a lysine within the membrane-proximal region is indispensable for this process. Neither reallocation of the motif nor fusion of ubiquitin to the C-terminus of the G-CSFR could drive lysosomal routing. However, within this region, the lysine could be shifted 12 amino acids toward the C-terminus without losing its function, arguing against the existence of a linear sorting motif and demonstrating that positioning of the lysine relative to the SOCS3 docking site is flexible. G-CSFR ubiquitination peaked after endocytosis, was inhibited by methyl-β-cyclodextrin as well as hyperosmotic sucrose and severely reduced in internalization-defective G-CSFR mutants, indicating that ubiquitination mainly occurs at endosomes. Apart from elucidating structural and spatio-temporal aspects of SOCS3-mediated ubiquitination, these findings have implications for the abnormal signaling function of G-CSFR mutants found in severe congenital neutropenia, a hematopoietic disorder with a high leukemia risk.     

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.     

On the packing density of the unbound protein-protein interaction interface and its implications in dynamics

Background

In eukaryotes, ubiquitin-conjugation is an important mechanism underlying proteasome-mediated degradation of proteins, and as such, plays an essential role in the regulation of many cellular processes. In the ubiquitin-proteasome pathway, E3 ligases play important roles by recognizing a specific protein substrate and catalyzing the attachment of ubiquitin to a lysine (K) residue. As more and more experimental data on ubiquitin conjugation sites become available, it becomes possible to develop prediction models that can be scaled to big data. However, no development that focuses on the investigation of ubiquitinated substrate specificities has existed. Herein, we present an approach that exploits an iteratively statistical method to identify ubiquitin conjugation sites with substrate site specificities.

Results

In this investigation, totally 6259 experimentally validated ubiquitinated proteins were obtained from dbPTM. After having filtered out homologous fragments with 40% sequence identity, the training data set contained 2658 ubiquitination sites (positive data) and 5532 non-ubiquitinated sites (negative data). Due to the difficulty in characterizing the substrate site specificities of E3 ligases by conventional sequence logo analysis, a recursively statistical method has been applied to obtain significant conserved motifs. The profile hidden Markov model (profile HMM) was adopted to construct the predictive models learned from the identified substrate motifs. A five-fold cross validation was then used to evaluate the predictive model, achieving sensitivity, specificity, and accuracy of 73.07%, 65.46%, and 67.93%, respectively. Additionally, an independent testing set, completely blind to the training data of the predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (76.13%) and outperform other ubiquitination site prediction tool.

Conclusion

A case study demonstrated the effectiveness of the characterized substrate motifs for identifying ubiquitination sites. The proposed method presents a practical means of preliminary analysis and greatly diminishes the total number of potential targets required for further experimental confirmation. This method may help unravel their mechanisms and roles in E3 recognition and ubiquitin-mediated protein degradation.

     

Methods for quantification of in vivo changes in protein ubiquitination following proteasome and deubiquitinase inhibition

Ubiquitination plays a key role in protein degradation and signal transduction. Ubiquitin is a small protein modifier that is adducted to lysine residues by the combined function of E1, E2, and E3 enzymes and is removed by deubiquitinating enzymes. Characterization of ubiquitination sites is important for understanding the role of this modification in cellular processes and disease. However, until recently, large-scale characterization of endogenous ubiquitination sites has been hampered by the lack of efficient enrichment techniques. The introduction of antibodies that specifically recognize peptides with lysine residues that harbor a di-glycine remnant (K-ε-GG) following tryptic digestion has dramatically improved the ability to enrich and identify ubiquitination sites from cellular lysates. We used this enrichment technique to study the effects of proteasome inhibition by MG-132 and deubiquitinase inhibition by PR-619 on ubiquitination sites in human Jurkat cells by quantitative high performance mass spectrometry. Minimal fractionation of digested lysates prior to immunoaffinity enrichment increased the yield of K-ε-GG peptides three- to fourfold resulting in detection of up to ~3300 distinct K-GG peptides in SILAC triple encoded experiments starting from 5 mg of protein per label state. In total, we identify 5533 distinct K-ε-GG peptides of which 4907 were quantified in this study, demonstrating that the strategy presented is a practical approach to perturbational studies in cell systems. We found that proteasome inhibition by MG-132 and deubiquitinase inhibition by PR-619 induces significant changes to the ubiquitin landscape, but that not all ubiquitination sites regulated by MG-132 and PR-619 are likely substrates for the ubiquitin-proteasome system. Additionally, we find that the proteasome and deubiquitinase inhibitors studied induced only minor changes in protein expression levels regardless of the extent of regulation induced at the ubiquitin site level. We attribute this finding to the low stoichiometry of the majority ubiquitination sites identified in this study.