CarbonylDB: a curated data-resource of protein carbonylation sites

Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites

N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future.

CURTAIN-A unique web-based tool for exploration and sharing of MS-based proteomics data

To facilitate analysis and sharing of mass spectrometry (MS)-based proteomics data, we created online tools called CURTAIN (https://curtain.proteo.info) and CURTAIN-PTM (https://curtainptm.proteo.info) with an accompanying series of video tutorials (https://www.youtube.com/@CURTAIN-me6hl). These are designed to enable non-MS experts to interactively peruse volcano plots and deconvolute primary experimental data so that replicates can be visualized in bar charts or violin plots and exported in publication-ready format. They also allow assessment of overall experimental quality by correlation matrix and profile plot analysis. After making a selection of protein "hits", the user can analyze known domain structure, AlphaFold predicted structure, reported interactors, relative expression as well as disease links. CURTAIN-PTM permits analysis of all identified PTM sites on protein(s) of interest with selected databases. CURTAIN-PTM also links with the Kinase Library to predict upstream kinases that may phosphorylate sites of interest. We provide examples of the utility of CURTAIN and CURTAIN-PTM in analyzing how targeted degradation of the PPM1H Rab phosphatase that counteracts the Parkinson's LRRK2 kinase impacts cellular protein levels and phosphorylation sites. We also reanalyzed a ubiquitylation dataset, characterizing the PINK1-Parkin pathway activation in primary neurons, revealing data of interest not highlighted previously. CURTAIN and CURTAIN-PTM are free to use and open source, enabling researchers to share and maximize the impact of their proteomics data. We advocate that MS data published in volcano plot format be reported containing a shareable CURTAIN weblink, thereby allowing readers to better analyze and exploit the data.

CarSitePred: an integrated algorithm for identifying carbonylated sites based on KNDUA-LNDOT resampling technique

Carbonylated sites are the determining factors for functional changes or deletions in carbonylated proteins, so identifying carbonylated sites is essential for understanding the process of protein carbonylated and exploring the pathogenesis of related diseases. The current wet experimental methods for predicting carbonylated modification sites ae not only expensive and time-consuming, but also have limited protein processing capabilities and cannot meet the needs of researchers. The identification of carbonylated sites using computational methods not only improves the functional characterization of proteins, but also provides researchers with free tools for predicting carbonylated sites. Therefore, it is essential to establish a model using computational methods that can accurately predict protein carbonylated sites. In this study, a prediction model, CarSitePred, is proposed to identify carbonylation sites. In CarSitePred, specific location amino acid hydrophobic hydrophilic, one-to-one numerical conversion of amino acids, and AlexNet convolutional neural networks convert preprocessed carbonylated sequences into valid numerical features. The K-means Normal Distribution-based Undersampling Algorithm (KNDUA) and Localized Normal Distribution Oversampling Technology (LNDOT) were firstly proposed and employed to balance the K, P, R and T carbonylation training dataset. And for the first time, carbonylation modification sites were transformed into the form of images and directly inputted into AlexNet convolutional neural network to extract features for fitting SVM classifiers. The 10-fold cross-validation and independent testing results show that CarSitePred achieves better prediction performance than the best currently available prediction models. Availability: https://github.com/zuoyun123/CarSitePred.Communicated by Ramaswamy H. Sarma.

O-GlcNAcPRED-DL: Prediction of Protein O-GlcNAcylation Sites Based on an Ensemble Model of Deep Learning

O-linked β-N-acetylglucosamine (O-GlcNAc) is a post-translational modification (i.e., O-GlcNAcylation) on serine/threonine residues of proteins, regulating a plethora of physiological and pathological events. As a dynamic process, O-GlcNAc functions in a site-specific manner. However, the experimental identification of the O-GlcNAc sites remains challenging in many scenarios. Herein, by leveraging the recent progress in cataloguing experimentally identified O-GlcNAc sites and advanced deep learning approaches, we establish an ensemble model, O-GlcNAcPRED-DL, a deep learning-based tool, for the prediction of O-GlcNAc sites. In brief, to make a benchmark O-GlcNAc data set, we extracted the information on O-GlcNAc from the recently constructed database O-GlcNAcAtlas, which contains thousands of experimentally identified and curated O-GlcNAc sites on proteins from multiple species. To overcome the imbalance between positive and negative data sets, we selected five groups of negative data sets in humans and mice to construct an ensemble predictor based on connection of a convolutional neural network and bidirectional long short-term memory. By taking into account three types of sequence information, we constructed four network frameworks, with the systematically optimized parameters used for the models. The thorough comparison analysis on two independent data sets of humans and mice and six independent data sets from other species demonstrated remarkably increased sensitivity and accuracy of the O-GlcNAcPRED-DL models, outperforming other existing tools. Moreover, a user-friendly Web server for O-GlcNAcPRED-DL has been constructed, which is freely available at http://oglcnac.org/pred_dl.

Specific detection of protein carbonylation sites by 473 nm photodissociation mass spectrometry

The study of protein oxidation remains a challenge despite the biomedical interest in reliable biomarkers of oxidative stress. This is particularly true for carbonylations although, recently, liquid chromatography-mass spectrometry techniques (LC-MS) have been proposed to detect this non-enzymatic and poorly distributed oxidative modification of proteins using untargeted or carbonyl-reactive probe methods. These methods proved to be feasible but could not preserve the dynamic range of the protein sample, making it impossible to quantify oxidatively modified proteoforms compared with native proteoforms. Here, we propose an innovative method based on the implementation of a reactive carbonyl probe conjugated with a laser-sensitive chromophore, dabcyl-aminooxy, which confers optical specificity to the LC-MS approach. In addition, our protein carbonyl detection method allows us to localize individual carbonylation sites by observing fragments of derivatized oxidized peptides. Two model proteins, alpha-synuclein and beta-lactoglobulin, were oxidized and carbonylation sites were detected, resulting in the identification of respectively 34 and 77 different carbonylated amino acids. Thus, we demonstrated the application of a direct and sensitive method for studying protein carbonylation sites in complex protein extracts.

Empirical comparison and analysis of machine learning-based approaches for druggable protein identification

Efficiently and precisely identifying drug targets is crucial for developing and discovering potential medications. While conventional experimental approaches can accurately pinpoint these targets, they suffer from time constraints and are not easily adaptable to high-throughput processes. On the other hand, computational approaches, particularly those utilizing machine learning (ML), offer an efficient means to accelerate the prediction of druggable proteins based solely on their primary sequences. Recently, several state-of-the-art computational methods have been developed for predicting and analyzing druggable proteins. These computational methods showed high diversity in terms of benchmark datasets, feature extraction schemes, ML algorithms, evaluation strategies and webserver/software usability. Thus, our objective is to reexamine these computational approaches and conduct a comprehensive assessment of their strengths and weaknesses across multiple aspects. In this study, we deliver the first comprehensive survey regarding the state-of-the-art computational approaches for in silico prediction of druggable proteins. First, we provided information regarding the existing benchmark datasets and the types of ML methods employed. Second, we investigated the effectiveness of these computational methods in druggable protein identification for each benchmark dataset. Third, we summarized the important features used in this field and the existing webserver/software. Finally, we addressed the present constraints of the existing methods and offer valuable guidance to the scientific community in designing and developing novel prediction models. We anticipate that this comprehensive review will provide crucial information for the development of more accurate and efficient druggable protein predictors.

DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet

As a key issue in orchestrating various biological processes and functions, protein post-translational modification (PTM) occurs widely in the mechanism of protein's function of animals and plants. Glutarylation is a type of protein-translational modification that occurs at active ε-amino groups of specific lysine residues in proteins, which is associated with various human diseases, including diabetes, cancer, and glutaric aciduria type I. Therefore, the issue of prediction for glutarylation sites is particularly important. This study developed a brand-new deep learning-based prediction model for glutarylation sites named DeepDN_iGlu via adopting attention residual learning method and DenseNet. The focal loss function is utilized in this study in place of the traditional cross-entropy loss function to address the issue of a substantial imbalance in the number of positive and negative samples. It can be noted that DeepDN_iGlu based on the deep learning model offers a greater potential for the glutarylation site prediction after employing the straightforward one hot encoding method, with Sensitivity (Sn), Specificity (Sp), Accuracy (ACC), Mathews Correlation Coefficient (MCC), and Area Under Curve (AUC) of 89.29%, 61.97%, 65.15%, 0.33 and 0.80 accordingly on the independent test set. To the best of the authors' knowledge, this is the first time that DenseNet has been used for the prediction of glutarylation sites. DeepDN_iGlu has been deployed as a web server (https://bioinfo.wugenqiang.top/~smw/DeepDN_iGlu/) that is available to make glutarylation site prediction data more accessible.

qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast

Post-translational modifications (PTMs) are critical molecular mechanisms that regulate protein functions temporally and spatially in various organisms. Since most PTMs are dynamically regulated, quantifying PTM events under different states is crucial for understanding biological processes and diseases. With the rapid development of high-throughput proteomics technologies, massive quantitative PTM proteome datasets have been generated. Thus, a comprehensive one-stop data resource for surfing big data will benefit the community. Here, we updated our previous phosphorylation dynamics database qPhos to the qPTM (http://qptm.omicsbio.info). In qPTM, 11 482 553 quantification events among six types of PTMs, including phosphorylation, acetylation, glycosylation, methylation, SUMOylation and ubiquitylation in four different organisms were collected and integrated, and the matched proteome datasets were included if available. The raw mass spectrometry based false discovery rate control and the recurrences of identifications among datasets were integrated into a scoring system to assess the reliability of the PTM sites. Browse and search functions were improved to facilitate users in swiftly and accurately acquiring specific information. The results page was revised with more abundant annotations, and time-course dynamics data were visualized in trend lines. We expected the qPTM database to be a much more powerful and comprehensive data repository for the PTM research community.

Analysis of Brain Protein Stability Changes in Mouse Models of Normal Aging and α-Synucleinopathy Reveals Age- and Disease-Related Differences

Here, we utilize the stability of proteins from rates of oxidation (SPROX) technique, to profile the thermodynamic stabilities of proteins in brain tissue cell lysates from Huα-Syn(A53T) transgenic mice at three time points including at 1 month (n = 9), at 6 months (n = 7), and at the time (between 9 and 16 months) a mouse became symptomatic (n = 8). The thermodynamic stability profiles generated here on 332 proteins were compared to thermodynamic stability profiles generated on the same proteins from similarly aged wild-type mice using a two-way unbalanced analysis of variance (ANOVA) analysis. This analysis identified a group of 22 proteins with age-related protein stability changes and a group of 11 proteins that were differentially stabilized in the Huα-Syn(A53T) transgenic mouse model. A total of 9 of the 11 proteins identified here with disease-related stability changes have been previously detected in human cerebral spinal fluid and thus have potential utility as biomarkers of Parkinson's disease (PD). The differential stability observed for one protein, glutamate decarboxylase 2 (Gad2), with an age-related change in stability, was consistent with the differential presence of a known, age-related truncation product of this protein, which is shown here to have a higher folding stability than full-length Gad2. Mass spectrometry data were deposited at ProteomeXchange (PXD016985).

Protein Carbonylation: Emerging Roles in Plant Redox Biology and Future Prospects

Plants are sessile in nature and they perceive and react to environmental stresses such as abiotic and biotic factors. These induce a change in the cellular homeostasis of reactive oxygen species (ROS). ROS are known to react with cellular components, including DNA, lipids, and proteins, and to interfere with hormone signaling via several post-translational modifications (PTMs). Protein carbonylation (PC) is a non-enzymatic and irreversible PTM induced by ROS. The non-enzymatic feature of the carbonylation reaction has slowed the efforts to identify functions regulated by PC in plants. Yet, in prokaryotic and animal cells, studies have shown the relevance of protein carbonylation as a signal transduction mechanism in physiological processes including hydrogen peroxide sensing, cell proliferation and survival, ferroptosis, and antioxidant response. In this review, we provide a detailed update on the most recent findings pertaining to the role of PC and its implications in various physiological processes in plants. By leveraging the progress made in bacteria and animals, we highlight the main challenges in studying the impacts of carbonylation on protein functions in vivo and the knowledge gap in plants. Inspired by the success stories in animal sciences, we then suggest a few approaches that could be undertaken to overcome these challenges in plant research. Overall, this review describes the state of protein carbonylation research in plants and proposes new research avenues on the link between protein carbonylation and plant redox biology.

CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques

BACKGROUND

Carbonylation is a non-enzymatic irreversible protein post-translational modification, and refers to the side chain of amino acid residues being attacked by reactive oxygen species and finally converted into carbonyl products. Studies have shown that protein carbonylation caused by reactive oxygen species is involved in the etiology and pathophysiological processes of aging, neurodegenerative diseases, inflammation, diabetes, amyotrophic lateral sclerosis, Huntington's disease, and tumor. Current experimental approaches used to predict carbonylation sites are expensive, time-consuming, and limited in protein processing abilities. Computational prediction of the carbonylation residue location in protein post-translational modifications enhances the functional characterization of proteins.

RESULTS

In this study, an integrated classifier algorithm, CarSite-II, was developed to identify K, P, R, and T carbonylated sites. The resampling method K-means similarity-based undersampling and the synthetic minority oversampling technique (SMOTE-KSU) were incorporated to balance the proportions of K, P, R, and T carbonylated training samples. Next, the integrated classifier system Rotation Forest uses "support vector machine" subclassifications to divide three types of feature spaces into several subsets. CarSite-II gained Matthew's correlation coefficient (MCC) values of 0.2287/0.3125/0.2787/0.2814, False Positive rate values of 0.2628/0.1084/0.1383/0.1313, False Negative rate values of 0.2252/0.0205/0.0976/0.0608 for K/P/R/T carbonylation sites by tenfold cross-validation, respectively. On our independent test dataset, CarSite-II yield MCC values of 0.6358/0.2910/0.4629/0.3685, False Positive rate values of 0.0165/0.0203/0.0188/0.0094, False Negative rate values of 0.1026/0.1875/0.2037/0.3333 for K/P/R/T carbonylation sites. The results show that CarSite-II achieves remarkably better performance than all currently available prediction tools.

CONCLUSION

The related results revealed that CarSite-II achieved better performance than the currently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http://47.100.136.41:8081/.

Human carbonic anhydrases and post-translational modifications: a hidden world possibly affecting protein properties and functions

Human carbonic anhydrases (CAs) have become a well-recognized target for the design of inhibitors and activators with biomedical applications. Accordingly, an enormous amount of literature is available on their biochemical, functional and structural aspects. Nevertheless post-translational modifications (PTMs) occurring on these enzymes and their functional implications have been poorly investigated so far. To fill this gap, in this review we have analysed all PTMs occurring on human CAs, as deriving from the search in dedicated databases, showing a widespread occurrence of modification events in this enzyme family. By combining these data with sequence alignments, inspection of 3 D structures and available literature, we have summarised the possible functional implications of these PTMs. Although in some cases a clear correlation between a specific PTM and the CA function has been highlighted, many modification events still deserve further dedicated studies.

Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method

As a newly discovered protein posttranslational modification, histone lysine crotonylation (Kcr) involved in cellular regulation and human diseases. Various proteomics technologies have been developed to detect Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and labor-intensive, which is difficult to widely popularize in large-scale species. Computational approaches are cost-effective and can be used in a high-throughput manner to generate relatively precise identification. In this study, we develop a deep learning-based method termed as Deep-Kcr for Kcr sites prediction by combining sequence-based features, physicochemical property-based features and numerical space-derived information with information gain feature selection. We investigate the performances of convolutional neural network (CNN) and five commonly used classifiers (long short-term memory network, random forest, LogitBoost, naive Bayes and logistic regression) using 10-fold cross-validation and independent set test. Results show that CNN could always display the best performance with high computational efficiency on large dataset. We also compare the Deep-Kcr with other existing tools to demonstrate the excellent predictive power and robustness of our method. Based on the proposed model, a webserver called Deep-Kcr was established and is freely accessible at http://lin-group.cn/server/Deep-Kcr.

Carbonylation of proteins-an element of plant ageing

Carbonylation-ROS-dependent posttranslational modification of proteins-may be regarded as one of the important events in the process of ageing or senescence in plants. Ageing is the progressive process starting from seed development (plants) and birth (animals). The life-span of living organisms depends on many factors and stresses, which influence reactive oxygen species (ROS) level. The imbalance of their production and scavenging causes pathophysiological conditions that accelerate ageing. ROS modify nucleic acids, lipids, sugars and proteins. The level of carbonylated proteins can serve as an indicator of an oxidative cellular status. Several pathways of protein carbonylation, e.g. the conjugation with reactive carbonyl species, and/or a direct metal-catalysed oxidative attack on amino acids residues are known. Dysfunctional carbonylated proteins are more prone to degradation or form aggregates when the proteolytic machinery is inhibited, as observed in ageing. Protein carbonylation may contribute to formation of organelle-specific signal and to the control of protein quality. Carbonylated proteins are formed during the whole plant life; nevertheless, accelerated ageing stimulates the accumulation of carbonyl derivatives. In the medicine-related literature, concerned ageing and ROS-mediated protein modifications, this topic is extensively analysed, in comparison to the plant science. In plant science, ageing and senescence are considered to describe slightly different processes (physiological events). However, senescence (Latin: senēscere) means "to grow old". This review describes the correlation of protein carbonylation level to ageing or/and senescence in plants. Comparing data from the area of plant and animal research, it is assumed that some basic mechanism of time-dependent alterations in the cellular biochemical processes are common and the protein carbonylation is one of the important causes of ageing.

Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes

BACKGROUND

The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work.

RESULTS

Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings.

CONCLUSION

We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone.

Protein Carbonylation and Glycation in Legume Nodules

Nitrogen fixation is an agronomically and environmentally important process catalyzed by bacterial nitrogenase within legume root nodules. These unique symbiotic organs have high metabolic rates and produce large amounts of reactive oxygen species that may modify proteins irreversibly. Here, we examined two types of oxidative posttranslational modifications of nodule proteins: carbonylation, which occurs by direct oxidation of certain amino acids or by interaction with reactive aldehydes arising from cell membrane lipid peroxides; and glycation, which results from the reaction of lysine and arginine residues with reducing sugars or their autooxidation products. We used a strategy based on the enrichment of carbonylated peptides by affinity chromatography followed by liquid chromatography-tandem mass spectrometry to identify 369 oxidized proteins in bean (Phaseolus vulgaris) nodules. Of these, 238 corresponded to plant proteins and 131 to bacterial proteins. Lipid peroxidation products induced most carbonylation sites. This study also revealed that carbonylation has major effects on two key nodule proteins. Metal-catalyzed oxidation caused the inactivation of malate dehydrogenase and the aggregation of leghemoglobin. In addition, numerous glycated proteins were identified in vivo, including three key nodule proteins: sucrose synthase, glutamine synthetase, and glutamate synthase. Label-free quantification identified 10 plant proteins and 18 bacterial proteins as age-specifically glycated. Overall, our results suggest that the selective carbonylation or glycation of crucial proteins involved in nitrogen metabolism, transcriptional regulation, and signaling may constitute a mechanism to control cell metabolism and nodule senescence.

Protein Carbonylation and Glycation in Legume Nodules

Nitrogen fixation is an agronomically and environmentally important process catalyzed by bacterial nitrogenase within legume root nodules. These unique symbiotic organs have high metabolic rates and produce large amounts of reactive oxygen species that may modify proteins irreversibly. Here, we examined two types of oxidative posttranslational modifications of nodule proteins: carbonylation, which occurs by direct oxidation of certain amino acids or by interaction with reactive aldehydes arising from cell membrane lipid peroxides; and glycation, which results from the reaction of lysine and arginine residues with reducing sugars or their autooxidation products. We used a strategy based on the enrichment of carbonylated peptides by affinity chromatography followed by liquid chromatography-tandem mass spectrometry to identify 369 oxidized proteins in bean (Phaseolus vulgaris) nodules. Of these, 238 corresponded to plant proteins and 131 to bacterial proteins. Lipid peroxidation products induced most carbonylation sites. This study also revealed that carbonylation has major effects on two key nodule proteins. Metal-catalyzed oxidation caused the inactivation of malate dehydrogenase and the aggregation of leghemoglobin. In addition, numerous glycated proteins were identified in vivo, including three key nodule proteins: sucrose synthase, glutamine synthetase, and glutamate synthase. Label-free quantification identified 10 plant proteins and 18 bacterial proteins as age-specifically glycated. Overall, our results suggest that the selective carbonylation or glycation of crucial proteins involved in nitrogen metabolism, transcriptional regulation, and signaling may constitute a mechanism to control cell metabolism and nodule senescence.