Sumoylation Sites In Proteins Research Articles

SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.

SUMOylation is an essential post-translational modification system with the ability to regulate nearly all aspects of cellular physiology. Three major paralogues SUMO1, SUMO2and SUMO3 form a covalent bond between the small ubiquitin-like modifier with lysine residues at consensus sites in protein substrates. Biochemical studies continue to identify unique biological functions for protein targets conjugated to SUMO1 versus the highly homologous SUMO2 and SUMO3 paralogues. Yet, the field has failed to harness contemporary AI approaches including pre-trained protein language models to fully expand and/or recognize the SUMOylated proteome. Herein, we present a novel, deep learning-based approach called SumoPred-PLM for human SUMOylation prediction with sensitivity, specificity, Matthew's correlation coefficient, and accuracy of 74.64%, 73.36%, 0.48% and 74.00%, respectively, on the CPLM 4.0 independent test dataset. In addition, this novel platform uses contextualized embeddings obtained from a pre-trained protein language model, ProtT5-XL-UniRef50 to identify SUMO2/3-specific conjugation sites. The results demonstrate that SumoPred-PLM is a powerful and unique computational tool to predict SUMOylation sites in proteins and accelerate discovery.

ISUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC.

Sumoylation is the post-translational modification that is involved in the adaption of the cells and the functional properties of a large number of proteins. Sumoylation has key importance in subcellular concentration, transcriptional synchronization, chromatin remodeling, response to stress, and regulation of mitosis. Sumoylation is associated with developmental defects in many human diseases such as cancer, Huntington’s, Alzheimer’s, Parkinson’s, Spin cerebellar ataxia 1, and amyotrophic lateral sclerosis. The covalent bonding of Sumoylation is essential to inheriting part of the operative characteristics of some other proteins. For that reason, the prediction of the Sumoylation site has significance in the scientific community. A novel and efficient technique is proposed to predict the Sumoylation sites in proteins by incorporating Chou’s Pseudo Amino Acid Composition (PseAAC) with statistical moments-based features. The outcomes from the proposed system using 10 fold cross-validation testing are 94.51%, 94.24%, 94.79% and 0.8903% accuracy, sensitivity, specificity and MCC, respectively. The performance of the proposed system is so far the best in comparison to the other state-of-the-art methods. The codes for the current study are available on the GitHub repository using the link: https://github.com/csbioinfopk/iSumoK-PseAAC.

How the Artificial Intelligence Tool pSumo-CD is Working for Predicting Sumoylation Sites in Proteins

How the Artificial Intelligence Tool pSumo-CD is Working for Predicting Sumoylation Sites in Proteins

SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure.

Post Translational Modification (PTM) is defined as the modification of amino acids along the protein sequences after the translation process. These modifications significantly impact on the functioning of proteins. Therefore, having a comprehensive understanding of the underlying mechanism of PTMs turns out to be critical in studying the biological roles of proteins. Among a wide range of PTMs, sumoylation is one of the most important modifications due to its known cellular functions which include transcriptional regulation, protein stability, and protein subcellular localization. Despite its importance, determining sumoylation sites via experimental methods is time-consuming and costly. This has led to a great demand for the development of fast computational methods able to accurately determine sumoylation sites in proteins. In this study, we present a new machine learning-based method for predicting sumoylation sites called SumSec. To do this, we employed the predicted secondary structure of amino acids to extract two types of structural features from neighboring amino acids along the protein sequence which has never been used for this task. As a result, our proposed method is able to enhance the sumoylation site prediction task, outperforming previously proposed methods in the literature. SumSec demonstrated high sensitivity (0.91), accuracy (0.94) and MCC (0.88). The prediction accuracy achieved in this study is 21% better than those reported in previous studies. The script and extracted features are publicly available at: https://github.com/YosvanyLopez/SumSec.

IAcet-Sumo: Identification of lysine acetylation and sumoylation sites in proteins by multi-class transformation methods

MotivationPosttranslational modification (PTM) is a biological mechanism involved in the enzymatic modification of proteins after translation by ribosomes. Two or more modifications occurring at one residue can be transformed into a multi-label system. Two or more simultaneous modifications on a residue is more common than single PTMs. Lysine residues in proteins can be subjected to a variety of PTMs, such as ubiquitination, acetylation, sumoylation, methylation, and succinylation. Identification of uncharacterized sequences in proteins is a highly significant and state-of-the-art issue. Notably, in order to provide a method of processing multi-label sequences of lysine residues, it is highly desirable to develop computational methods to predict lysine acetylation and sumoylation modifications. ResultsIn this paper, we first launched an integrated approach, known as the five-step prediction method (FSPM), to solve the problem effectively by (1) using one-sided selection (OSS) to deal with imbalanced data, (2) extracting binary features from protein sequences, (3) incorporating binary relevance, classifier chains and multi-class transformation methods to simplify multi-label problems, (4) constructing different classifiers, and (5) implementing cross-validation and evaluating these classifiers. In 10-fold cross-validation, FSPM achieved an accuracy of 61.49% and an absolute-true rate of 60.17%. The results showed that FSPM is accurate and could be used as a powerful engine in multi-label systems. We also conducted a variety of statistical analyses of the predicted results to discuss the biological functions of lysine acetylation and sumoylation.

PSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.

Sumoylation is a post-translational modification (PTM) process, in which small ubiquitin-related modifier (SUMO) is attaching by covalent bonds to substrate protein. It is critical to many different biological processes such as replicating genome, expressing gene, localizing and stabilizing proteins; unfortunately, it is also involved with many major disorders including Alzheimer's and Parkinson's diseases. Therefore, for both basic research and drug development, it is important to identify the sumoylation sites in proteins. To address such a problem, we developed a predictor called pSumo-CD by incorporating the sequence-coupled information into the general pseudo-amino acid composition (PseAAC) and introducing the covariance discriminant (CD) algorithm, in which a bias-adjustment term, which has the function to automatically adjust the errors caused by the bias due to the imbalance of training data, had been incorporated. Rigorous cross-validations indicated that the new predictor remarkably outperformed the existing state-of-the-art prediction method for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for pSumo-CD has been established at http://www.jci-bioinfo.cn/pSumo-CD, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. jjia@gordonlifescience.org, xxiao@gordonlifescience.org or kcchou@gordonlifescience.orgSupplementary information: Supplementary data are available at Bioinformatics online.

A Chemical and Enzymatic Approach to Study Site-Specific Sumoylation.

A variety of cellular pathways are regulated by protein modifications with ubiquitin-family proteins. SUMO, the Small Ubiquitin-like MOdifier, is covalently attached to lysine on target proteins via a cascade reaction catalyzed by E1, E2, and E3 enzymes. A major barrier to understanding the diverse regulatory roles of SUMO has been a lack of suitable methods to identify protein sumoylation sites. Here we developed a mass-spectrometry (MS) based approach combining chemical and enzymatic modifications to identify sumoylation sites. We applied this method to analyze the auto-sumoylation of the E1 enzyme in vitro and compared it to the GG-remnant method using Smt3-I96R as a substrate. We further examined the effect of smt3-I96R mutation in vivo and performed a proteome-wide analysis of protein sumoylation sites in Saccharomyces cerevisiae. To validate these findings, we confirmed several sumoylation sites of Aos1 and Uba2 in vivo. Together, these results demonstrate that our chemical and enzymatic method for identifying protein sumoylation sites provides a useful tool and that a combination of methods allows a detailed analysis of protein sumoylation sites.

Prediction of sumoylation sites in proteins using linear discriminant analysis

Sumoylation is a multifunctional post-translation modification (PTM) in proteins by the small ubiquitin-related modifiers (SUMOs), which have relations to ubiquitin in molecular structure. Sumoylation has been found to be involved in some cellular processes. It is very significant to identify the exact sumoylation sites in proteins for not only basic researches but also drug developments. Comparing with time exhausting experiment methods, it is highly desired to develop computational methods for prediction of sumoylation sites as a complement to experiment in the post-genomic age. In this work, three feature constructions (AAIndex, position-specific amino acid propensity and modification of composition of k-space amino acid pairs) and five different combinations of them were used to construct features. At last, 178 features were selected as the optimal features according to the Mathew's correlation coefficient values in 10-fold cross validation based on linear discriminant analysis. In 10-fold cross-validation on the benchmark dataset, the accuracy and Mathew's correlation coefficient were 86.92% and 0.6845. Comparing with those existing predictors, SUMO_LDA showed its better performance.

In vivo localization and identification of SUMOylated proteins in the brain of His 6 -HA-SUMO1 knock-in mice

SUMOylation, an essential posttranslational protein modification, is involved in many eukaryotic cellular signaling pathways. The identification of SUMOylated proteins is difficult, because SUMOylation sites in proteins are hard to predict, SUMOylated protein states are transient in vivo and labile in vitro, only a small substrate fraction is SUMOylated in vivo, and identification tools for natively SUMOylated proteins are rare. To solve these problems, we generated knock-in mice expressing His(6)-HA-SUMO1. By anti-HA immunostaining, we show that SUMO1 conjugates in neurons are only detectable in nuclei and annulate lamellae. By anti-HA affinity purification, we identified several hundred candidate SUMO1 substrates, of which we validated Smchd1, Ctip2, TIF1γ, and Zbtb20 as novel substrates. The knock-in mouse represents an excellent mammalian model for studies on SUMO1 localization and screens for SUMO1 conjugates in vivo.

Identification of protein SUMOylation sites by mass spectrometry using combined microwave‐assisted aspartic acid cleavage and tryptic digestion

Small-ubiquitin-like modifier (SUMO) is a posttranslational modifier of protein substrates at lysine residues that conjugates to proteins in response to various changes in the cell. As a result of SUMO modification, marked changes in transcription regulation, DNA repair, subcellular localization and mitosis, among other cellular processes, are known to occur. However, although the identification of ubiquitylation sites by mass spectrometry is aided in part by the presence of a small di-amino acid GlyGly "tag" that remains on lysine residues following tryptic digestion, SUMOylation poses a particular challenge as the absence of a basic residue near to the SUMO C-terminus results in a significant 27 or 32-amino-acid sequence branch conjugated to the substrate peptide. MS/MS analyses of these branch peptides generally reveal abundant fragment ions resulting from cleavage of the SUMO tail, but which obscure those needed for characterizing the target peptide sequence. Other approaches for identifying SUMO substrates exist and include overexpression of the SUMO isoforms using an N-terminal histidine tag, as well as site-directed mutagenesis of the C-terminal end of the SUMO sequence. Here, we employ combined enzymatic/chemical approaches, which serve to shorten the SUMO tag and thus help to simplify SUMO spectra, making interpretation of mass spectra and location of the SUMOylation site easier. As described in this report, we demonstrate a method for identifying SUMOylation sites using three commercially available SUMO- modified isoforms and by employing acid-only and acid/trypsin cleavage strategies. These approaches were carried out using MALDI-time-of-flight (TOF) and LC/MS instrumentation, along with collision induced dissociation (CID) and electron transfer dissociation (ETD).

Predicting protein sumoylation sites from sequence features

Protein sumoylation is a post-translational modification that plays an important role in a wide range of cellular processes. Small ubiquitin-related modifier (SUMO) can be covalently and reversibly conjugated to the sumoylation sites of target proteins, many of which are implicated in various human genetic disorders. The accurate prediction of protein sumoylation sites may help biomedical researchers to design their experiments and understand the molecular mechanism of protein sumoylation. In this study, a new machine learning approach has been developed for predicting sumoylation sites from protein sequence information. Random forests (RFs) and support vector machines (SVMs) were trained with the data collected from the literature. Domain-specific knowledge in terms of relevant biological features was used for input vector encoding. It was shown that RF classifier performance was affected by the sequence context of sumoylation sites, and 20 residues with the core motif ΨKXE in the middle appeared to provide enough context information for sumoylation site prediction. The RF classifiers were also found to outperform SVM models for predicting protein sumoylation sites from sequence features. The results suggest that the machine learning approach gives rise to more accurate prediction of protein sumoylation sites than the other existing methods. The accurate classifiers have been used to develop a new web server, called seeSUMO (http://bioinfo.ggc.org/seesumo/), for sequence-based prediction of protein sumoylation sites.

Protein sumoylation sites prediction based on two-stage feature selection

Protein sumoylation is one of the most important post-translational modifications. Accurate prediction of sumoylation sites is very useful for the analysis of proteome. Though the putative motif Psi K XE can be used, optimization of prediction models still remains a challenge. In this study, we developed a prediction system based on feature selection strategy. A total of 1,272 peptides with 14 residues from SUMOsp (Xue et al. [8] Nucleic Acids Res 34:W254-W257, 2006) were investigated in this study, including 212 substrates and 1,060 non-substrates. Among the substrates, only 162 substrates comply to the motif Psi K XE. First, 1,272 substrates were divided into training set and test set. All the substrates were encoded into feature vectors by hundreds of amino acid properties collected by Amino Acid Index Database (AAIndex, http://www.genome.jp/aaindex ). Then, mRMR (minimum redundancy-maximum relevance) method was applied to extract the most informative features. Finally, Nearest Neighbor Algorithm (NNA) was used to produce the prediction models. Tested by Leave-one-out (LOO) cross-validation, the optimal prediction model reaches the accuracy of 84.4% for the training set and 76.4% for the test set. Especially, 180 substrates were correctly predicted, which was 18 more than using the motif Psi K XE. The final selected features indicate that amino acid residues with two-residue downstream and one-residue upstream of the sumoylation sites play the most important role in determining the occurrence of sumoylation. Based on the feature selection strategy, our prediction system can not only be used for high throughput prediction of sumoylation sites but also as a tool to investigate the mechanism of sumoylation.

Predicting the protein SUMO modification sites based on Properties Sequential Forward Selection (PSFS)

Protein SUMO modification is an important post-translational modification and the optimization of prediction methods remains a challenge. Here, by using Support Vector Machines algorithm (SVM), a novel computational method was developed for SUMO modification site prediction based on Sequential Forward Selection (SFS) of hundreds of amino acid properties, which are collected by Amino Acid Index database ( http://www.genome.jp/aaindex). Our method also compares with the 0/1 system, in which the 20 amino acids are represented by 20-dimensional vectors ( A = 00000000000000000001, C = 00000000000000000010 and so on). The overall accuracy of leave-one-out cross-validation for our method reaches 89.18%, which is higher than 0/1 system. It indicated that the SUMO modification prediction process is highly related to the amino acid property and this approach here provide a helpful tool for further investigation of the SUMO modification and identification of sumoylation sites in proteins. The software is available at http://www.biosino.org/sumo.

A Method of Mapping Protein Sumoylation Sites by Mass Spectrometry Using a Modified Small Ubiquitin-like Modifier 1 (SUMO-1) and a Computational Program

Post-translational modification by small ubiquitin-like modifier 1 (SUMO-1) is a highly conserved process from yeast to humans and plays important regulatory roles in many cellular processes. Sumoylation occurs at certain internal lysine residues of target proteins via an isopeptide bond linkage. Unlike ubiquitin whose carboxyl-terminal sequence is RGG, the tripeptide at the carboxyl terminus of SUMO is TGG. The presence of the arginine residue at the carboxyl terminus of ubiquitin allows tryptic digestion of ubiquitin conjugates to yield a signature peptide containing a diglycine remnant attached to the target lysine residue and rapid identification of the ubiquitination site by mass spectrometry. The absence of lysine or arginine residues in the carboxyl terminus of mammalian SUMO makes it difficult to apply this approach to mapping sumoylation sites. We performed Arg scanning mutagenesis by systematically substituting amino acid residues surrounding the diglycine motif and found that a SUMO variant terminated with RGG can be conjugated efficiently to its target protein under normal sumoylation conditions. We developed a Programmed Data Acquisition (PDA) mass spectrometric approach to map target sumoylation sites using this SUMO variant. A web-based computational program designed for efficient identification of the modified peptides is described.

Defining the SUMO-modified Proteome by Multiple Approaches in Saccharomyces cerevisiae*

SUMO, or Smt3 in Saccharomyces cerevisiae, is a ubiquitin-like protein that is post-translationally attached to multiple proteins in vivo. Many of these substrate modifications are cell cycle-regulated, and SUMO conjugation is essential for viability in most eukaryotes. However, only a limited number of SUMO-modified proteins have been definitively identified to date, and this has hampered study of the mechanisms by which SUMO ligation regulates specific cellular pathways. Here we use a combination of yeast two-hybrid screening, a high copy suppressor selection with a SUMO isopeptidase mutant, and tandem mass spectrometry to define a large set of proteins (>150) that can be modified by SUMO in budding yeast. These three approaches yielded overlapping sets of proteins with the most extensive set by far being those identified by mass spectrometry. The two-hybrid data also yielded a potential SUMO-binding motif. Functional categories of SUMO-modified proteins include SUMO conjugation system enzymes, chromatin- and gene silencing-related factors, DNA repair and genome stability proteins, stress-related proteins, transcription factors, proteins involved in translation and RNA metabolism, and a variety of metabolic enzymes. The results point to a surprisingly broad array of cellular processes regulated by SUMO conjugation and provide a starting point for detailed studies of how SUMO ligation contributes to these different regulatory mechanisms.