Systematic analysis of human lysine acetylation proteins and accurate prediction of human lysine acetylation through bi-relative adapted binomial score Bayes feature representation

Current computational tools for protein lysine acylation site prediction

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

MSTL-Kace: Prediction of Prokaryotic Lysine Acetylation Sites Based on Multistage Transfer Learning Strategy

As one of the most important post-translational modifications (PTM), lysine acetylation (Kace) plays an important role in various biological activities. Traditional experimental methods for identifying Kace sites are inefficient and expensive. Instead, several machine learning methods have been developed for Kace site prediction, and hand-crafted features have been used to encode the protein sequences. However, there are still two challenges: the complex biological information may be under-represented by these manmade features and the small sample issue of some species needs to be addressed. We propose a novel model, MSTL-Kace, which was developed based on transfer learning strategy with pretrained bidirectional encoder representations from transformers (BERT) model. In this model, the high-level embeddings were extracted from species-specific BERT models, and a two-stage fine-tuning strategy was used to deal with small sample issue. Specifically, a domain-specific BERT model was pretrained using all of the sequences in our data sets, which was then fine-tuned, or two-stage fine-tuned based on the training data set of each species to obtain the species-specific BERT models. Afterward, the embeddings of residues were extracted from the fine-tuned model and fed to the different downstream learning algorithms. After comparison, the best model for the six prokaryotic species was built by using a random forest. The results for the independent test sets show that our model outperforms the state-of-the-art methods on all six species. The source codes and data for MSTL-Kace are available at https://github.com/leo97king/MSTL-Kace.

Adenosine triphosphate can act as a determinant of lysine acetylation of non-native and native substrates

The protein lysine acetylation includes acetyl-CoA (AcCoA) or acetyl phosphate (AcP)-mediated nonenzymatic acetylation, and enzymatic acetylation. It is widespread in the proteomes but the acetylation levels of most sites are very low. A thorough understanding of the determinants of low acetylation levels is highly important for elucidating the physiological relevance of lysine acetylation. In this study, we constructed a non-native substrate library containing 24 synthesized polypeptides, and we showed that ATP could inhibit the AcCoA-mediated nonenzymatic acetylation of these polypeptides through LC-MS/MS analysis. The acetyltransferase PatZ could acetylated these non-native substrates, and the PatZ-catalyzed acetylation of the polypeptides was also inhibited by ATP. Furthermore, the Western blot showed that ATP also inhibited the nonenzymatic (AcCoA or AcP-mediated) and enzymatic (PatZ-catalyzed) acetylation of acetyl-CoA synthetase Acs, which is a native substrate for acetylation. ATP can also inhibit the autoacetylation of acetyltransferase PatZ. Besides, both ADP and AMP could enhance the AcP-mediated acetylation of Acs, but ADP slightly inhibited the AcCoA-mediated acetylation of Acs. However, both ADP and AMP had no evident inhibition on the PatZ-catalyzed acetylation of Acs. Based on these results, we proposed that ATP can act as an inhibitor of acetylation, and it may regulate the function of PatZ by inhibiting its autoacetylation and compensate for the function of deacetylase CobB.

Systematic Analysis of Lysine Acetylation Reveals Diverse Functions in Azorhizobium caulinodans Strain ORS571

Protein acetylation can quickly modify the physiology of bacteria to respond to changes in environmental or nutritional conditions, but little information on these modifications is available in rhizobia. In this study, we report the lysine acetylome of Azorhizobium caulinodans strain ORS571, a model rhizobium isolated from stem nodules of the tropical legume Sesbania rostrata that is capable of fixing nitrogen in the free-living state and during symbiosis. Antibody enrichment and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis were used to characterize the acetylome. There are 2,302 acetylation sites from 982 proteins, accounting for 20.8% of the total proteins. Analysis of the acetylated motifs showed the preferences for the amino acid residues around acetylated lysines. The response regulator CheY1, previously characterized to be involved in chemotaxis in strain ORS571, was identified as an acetylated protein, and a mutation of the acetylated site of CheY1 significantly impaired the strain's motility. In addition, a Zn+-dependent deacetylase (AZC_0414) was characterized, and the construction of a deletion mutant strain showed that it played a role in chemotaxis. Our study provides the first global analysis of lysine acetylation in ORS571, suggesting that acetylation plays a role in various physiological processes. In addition, we demonstrate its involvement in the chemotaxis process. The acetylome of ORS571 provides insights to investigate the regulation mechanism of rhizobial physiology. IMPORTANCE Acetylation is an important modification that regulates protein function and has been found to regulate physiological processes in various bacteria. The physiology of rhizobium A. caulinodans ORS571 is regulated by multiple mechanisms both when free living and in symbiosis with the host; however, the regulatory role of acetylation is not yet known. Here, we took an acetylome-wide approach to identify acetylated proteins in A. caulinodans ORS571 and performed clustering analyses. Acetylation of chemotaxis proteins was preliminarily investigated, and the upstream acetylation-regulating enzyme involved in chemotaxis was characterized. These findings provide new insights to explore the physiological mechanisms of rhizobia.

Cross-Talk of Protein Expression and Lysine Acetylation in Response to TMV Infection in Nicotiana benthamiana

Lysine acetylation (K^ac), a reversible PTM, plays an essential role in various biological processes, including those involving metabolic pathways, pathogen resistance, and transcription, in both prokaryotes and eukaryotes. TMV, the major factor that causes the poor quality of Solanaceae crops worldwide, directly alters many metabolic processes in tobacco. However, the extent and function of K^ac during TMV infection have not been determined. The validation test to detect K^ac level and viral expression after TMV infection and Nicotinamide (NAM) treatment clarified that acetylation was involved in TMV infection. Furthermore, we comprehensively analyzed the changes in the proteome and acetylome of TMV-infected tobacco (Nicotiana benthamiana) seedlings via LC-MS/MS in conjunction with highly sensitive immune-affinity purification. In total, 2082 lysine-acetylated sites on 1319 proteins differentially expressed in response to TMV infection were identified. Extensive bioinformatic studies disclosed changes in acetylation of proteins engaged in cellular metabolism and biological processes. The vital influence of K^ac in fatty acid degradation and alpha-linolenic acid metabolism was also revealed in TMV-infected seedlings. This study first revealed K^ac information in N. benthamiana under TMV infection and expanded upon the existing landscape of acetylation in pathogen infection.

Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation

Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein-protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.

STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction

Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.

Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

BACKGROUND

Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins.

METHOD

We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories.

RESULTS

In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN .

CONCLUSIONS

The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.

Comprehensive profiling of protein lysine acetylation and its overlap with lysine succinylation in the Porphyromonas gingivalis fimbriated strain ATCC 33277

Porphyromonas gingivalis is a pathogen closely associated with periodontal and systemic infections. Recently, lysine acetylation (Kac) and lysine succinylation (Ksuc) have been identified in bacterial proteins with diverse biological and pathological functions. The Ksuc of P. gingivalis ATCC 33277 has been characterized in our previous work, and here, we report the systematic analysis of Kac and its crosstalk with Ksuc in this bacterium. A combination of the affinity enrichment by the acetyl-lysine antibody with highly sensitive LC-MS/MS was used to identify the lysine-acetylated proteins and sites in P. gingivalis ATCC 33277. A total of 1,112 lysine-acetylated sites matching 438 proteins were identified. These proteins involved in several cellular processes, especially those proteins related to protein biosynthesis and central metabolism had a high tendency to be lysine acetylated. Moreover, lysine sites flanked by tyrosine, phenylalanine, and histidine in the +1 position, as well as residue lysine in position +4 to +5, were the targets of Kac. Additionally, proteins involved in adhesins, gingipains, black pigmentation, and oxidative stress resistance were identified as substrates of Kac. Collectively, these results suggest Kac may play a critical role in the regulation of physiology and virulence of P. gingivalis. Furthermore, we discovered that, Ksuc and Kac were extensively overlapped in P. gingivalis ATCC 33277, especially in proteins related to ribosomes and metabolism. This study provides a significant beginning for further investigating the role of Kac and Ksuc in the pathogenicity of P. gingivalis.

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

Acetylome analysis of lysine acetylation in the plant pathogenic bacterium Brenneria nigrifluens

Protein lysine acetylation, a dynamic and reversible posttranslational modification, plays a crucial role in several cellular processes, including cell cycle regulation, metabolism, enzymatic activities, and protein interactions. Brenneria nigrifluens is a pathogen of walnut trees with shallow bark canker and can cause serious disease in walnut trees. Until now, a little has been known about the roles of lysine acetylation in plant pathogenic bacteria. In the present study, the lysine acetylome of B. nigrifluens was determined by high‐resolution LC‐MS/MS analysis. In total, we identified 1,866 lysine acetylation sites distributed in 737 acetylated proteins. Bioinformatics results indicated that acetylated proteins participate in many different biological functions in B. nigrifluens. Four conserved motifs, namely, LK^ac, K^ac*F, I*K^ac, and L*K^ac, were identified in this bacterium. Protein interaction network analysis indicated that all kinds of interactions are modulated by protein lysine acetylation. Overall, 12 acetylated proteins were related to the virulence of B. nigrifluens.

Analysis and prediction of human acetylation using a cascade classifier based on support vector machine

Background

Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in proteins accurately. Computational methods are popular because they are more convenient and faster than experimental methods. In this study, we proposed a new computational method to predict acetylation sites in human by combining sequence features and structural features including physicochemical property (PCP), position specific score matrix (PSSM), auto covariation (AC), residue composition (RC), secondary structure (SS) and accessible surface area (ASA), which can well characterize the information of acetylated lysine sites. Besides, a two-step feature selection was applied, which combined mRMR and IFS. It finally trained a cascade classifier based on SVM, which successfully solved the imbalance between positive samples and negative samples and covered all negative sample information.

Results

The performance of this method is measured with a specificity of 72.19% and a sensibility of 76.71% on independent dataset which shows that a cascade SVM classifier outperforms single SVM classifier.

Conclusions

In addition to the analysis of experimental results, we also made a systematic and comprehensive analysis of the acetylation data.

Computational Prediction of Protein O-GlcNAc Modification

Protein O-GlcNAcylation on serine and threonine residues is a significant posttranslational modification. Experimental techniques can uncover only a small portion of O-GlcNAcylation sites. Several computational algorithms have been proposed as necessary auxiliary tools to identify potential O-GlcNAcylation sites. This chapter discusses the metrics and procedures used to assess prediction tools and surveys six computational tools for the prediction of protein O-GlcNAcylation sites. Analyses of these tools using an independent test dataset indicated the advantages and disadvantages of the six existing prediction methods. We also discuss the challenges that may be faced while developing novel predictors in the future.

A deep learning method to more accurately recall known lysine acetylation sites

Background

Lysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding of regulatory mechanism of lysine acetylation, the key is to identify lysine acetylation sites. Previously, several shallow machine learning algorithms had been applied to predict lysine modification sites in proteins. However, shallow machine learning has some disadvantages. For instance, it is not as effective as deep learning for processing big data.

Results

In this work, a novel predictor named DeepAcet was developed to predict acetylation sites. Six encoding schemes were adopted, including a one-hot, BLOSUM62 matrix, a composition of K-space amino acid pairs, information gain, physicochemical properties, and a position specific scoring matrix to represent the modified residues. A multilayer perceptron (MLP) was utilized to construct a model to predict lysine acetylation sites in proteins with many different features. We also integrated all features and implemented the feature selection method to select a feature set that contained 2199 features. As a result, the best prediction achieved 84.95% accuracy, 83.45% specificity, 86.44% sensitivity, 0.8540 AUC, and 0.6993 MCC in a 10-fold cross-validation. For an independent test set, the prediction achieved 84.87% accuracy, 83.46% specificity, 86.28% sensitivity, 0.8407 AUC, and 0.6977 MCC.

Conclusion

The predictive performance of our DeepAcet is better than that of other existing methods. DeepAcet can be freely downloaded from https://github.com/Sunmile/DeepAcet.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2632-9) contains supplementary material, which is available to authorized users.

LAIPT: Lysine Acetylation Site Identification with Polynomial Tree

Post-translational modification plays a key role in the field of biology. Experimental identification methods are time-consuming and expensive. Therefore, computational methods to deal with such issues overcome these shortcomings and limitations. In this article, we propose a lysine acetylation site identification with polynomial tree method (LAIPT), making use of the polynomial style to demonstrate amino-acid residue relationships in peptide segments. This polynomial style was enriched by the physical and chemical properties of amino-acid residues. Then, these reconstructed features were input into the employed classification model, named the flexible neural tree. Finally, some effect evaluation measurements were employed to test the model’s performance.

Research progress in protein posttranslational modification site prediction

Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.

Prediction of Protein Acetylation Sites using Kernel Naive Bayes Classifier Based on Protein Sequences Profiling

Lysine acetylation is one of the decisive categories of protein post-translational modification (PTM), it is convoluted in many significant cellular developments and severe diseases in the biological system. The experimental identification of protein-acetylated sites is painstaking, time-consuming and expensive. Hence, there is significant interest in the development of computational approaches for consistent prediction of acetylation sites using protein sequences. Features selection from protein sequences plays a significant role for acetylation sites prediction. We describe an improved feature selection approach for acetylation sites prediction based on kernel naive Bayes classifier (KNBC). We have shown that KNBC generated from selected features by a new feature selection method outperforms than the existing methods for identification of acetylation sites. The sensitivity, specificity, ACC (Accuracy), MCC (Matthews Correlation Coefficient) and AUC (Area under Curve of ROC) in our proposed method are as follows 80.71%, 93.39%, 76.73%, 41.37% and 83.0% with the optimum window size is 47. Thus the kernel naive Bayes classifier finds application in acetylation site prediction.

PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile

Protein post-translational modifications (PTMs) are chemical modifications of a protein after its translation. Owing to its play an important role in deep understanding of various biological processes and the development of effective drugs, PTM site prediction have become a hot topic in bioinformatics. Recently, many online tools are developed to prediction various types of PTM sites, most of which are based on local sequence and some biological information. However, few of existing tools consider the relations between different PTMs for their prediction task. Here, we develop a web server called PTM-ssMP to predict PTM site, which adopts site-specific modification profile (ssMP) to efficiently extract and encode the information of both proximal PTMs and local sequence simultaneously. In PTM-ssMP we provide efficient prediction of multiple types of PTM site including phosphorylation, lysine acetylation, ubiquitination, sumoylation, methylation, O-GalNAc, O-GlcNAc, sulfation and proteolytic cleavage. To assess the performance of PTM-ssMP, a large number of experimentally verified PTM sites are collected from several sources and used to train and test the prediction models. Our results suggest that ssMP consistently contributes to remarkable improvement of prediction performance. In addition, results of independent tests demonstrate that PTM-ssMP compares favorably with other existing tools for different PTM types. PTM-ssMP is implemented as an online web server with user-friendly interface, which is freely available at http://bioinformatics.ustc.edu.cn/PTM-ssMP/index/.

Comprehensive Proteomic Analysis of Lysine Acetylation in the Foodborne Pathogen Trichinella spiralis

Lysine acetylation is a dynamic and highly conserved post-translational modification that plays a critical role in regulating diverse cellular processes. Trichinella spiralis is a foodborne parasite with a considerable socio-economic impact. However, to date, little is known regarding the role of lysine acetylation in this parasitic nematode. In this study, we utilized a proteomic approach involving anti-acetyl lysine-based enrichment and highly sensitive mass spectrometry to identify the global acetylated proteome and investigate lysine acetylation in T. spiralis. In total, 3872 lysine modification sites were identified in 1592 proteins that are involved in a wide variety of biological processes. Consistent with the results of previous studies, a large number of the acetylated proteins appear to be involved in metabolic and biosynthetic processes. Interestingly, according to the functional enrichment analysis, 29 acetylated proteins were associated with phagocytosis, suggesting an important role of lysine acetylation in this process. Among the identified proteins, 15 putative acetylation motifs were detected. The presence of serine downstream of the lysine acetylation site was commonly observed in the regions surrounding the sites. Moreover, protein interaction network analysis revealed that various interactions are regulated by protein acetylation. These data represent the first report of the acetylome of T. spiralis and provide an important resource for further explorations of the role of lysine acetylation in this foodborne pathogen.

Proteome-wide lysine acetylation identification in developing rice (Oryza sativa) seeds and protein co-modification by acetylation, succinylation, ubiquitination, and phosphorylation

Protein lysine acetylation is a highly conserved post-translational modification with various biological functions. However, only a limited number of acetylation sites have been reported in plants, especially in cereals, and the function of non-histone protein acetylation is still largely unknown. In this report, we identified 1003 lysine acetylation sites in 692 proteins of developing rice seeds, which greatly extended the number of known acetylated sites in plants. Seven distinguished motifs were detected flanking acetylated lysines. Functional annotation analyses indicated diverse biological processes and pathways engaged in lysine acetylation. Remarkably, we found that several key enzymes in storage starch synthesis pathway and the main storage proteins were heavily acetylated. A comprehensive comparison of the rice acetylome, succinylome, ubiquitome and phosphorylome with available published data was conducted. A large number of proteins carrying multiple kinds of modifications were identified and many of these proteins are known to be key enzymes of vital metabolic pathways. Our study provides extending knowledge of protein acetylation. It will have critical reference value for understanding the mechanisms underlying PTM mediated multiple signal integration in the regulation of metabolism and development in plants.

Regulation, Function, and Detection of Protein Acetylation in Bacteria

N^ε-Lysine acetylation is now recognized as an abundant posttranslational modification (PTM) that influences many essential biological pathways. Advancements in mass spectrometry-based proteomics have led to the discovery that bacteria contain hundreds of acetylated proteins, contrary to the prior notion of acetylation events being rare in bacteria. Although the mechanisms that regulate protein acetylation are still not fully defined, it is understood that this modification is finely tuned via both enzymatic and nonenzymatic mechanisms. The opposing actions of Gcn5-related N-acetyltransferases (GNATs) and deacetylases, including sirtuins, provide the enzymatic control of lysine acetylation. A nonenzymatic mechanism of acetylation has also been demonstrated and proven to be prominent in bacteria, as well as in mitochondria. The functional consequences of the vast majority of the identified acetylation sites remain unknown. From studies in mammalian systems, acetylation of critical lysine residues was shown to impact protein function by altering its structure, subcellular localization, and interactions. It is becoming apparent that the same diversity of functions can be found in bacteria. Here, we review current knowledge of the mechanisms and the functional consequences of acetylation in bacteria. Additionally, we discuss the methods available for detecting acetylation sites, including quantitative mass spectrometry-based methods, which promise to promote this field of research. We conclude with possible future directions and broader implications of the study of protein acetylation in bacteria.

Protein post-translational modifications: In silico prediction tools and molecular modeling

Post-translational modifications (PTMs) occur in almost all proteins and play an important role in numerous biological processes by significantly affecting proteins' structure and dynamics. Several computational approaches have been developed to study PTMs (e.g., phosphorylation, sumoylation or palmitoylation) showing the importance of these techniques in predicting modified sites that can be further investigated with experimental approaches. In this review, we summarize some of the available online platforms and their contribution in the study of PTMs. Moreover, we discuss the emerging capabilities of molecular modeling and simulation that are able to complement these bioinformatics methods, providing deeper molecular insights into the biological function of post-translational modified proteins.

Global Insight into Lysine Acetylation Events and Their Links to Biological Aspects in Beauveria bassiana, a Fungal Insect Pathogen

Lysine acetylation (Kac) events in filamentous fungi are poorly explored. Here we show a lysine acetylome generated by LC-MS/MS analysis of immunoaffinity-based Kac peptides from normal hyphal cells of Beauveria bassiana, a fungal entomopathogen. The acetylome comprised 283 Kac proteins and 464 Kac sites. These proteins were enriched to eight molecular functions, 20 cellular components, 27 biological processes, 20 KEGG pathways and 12 subcellular localizations. All Kac sites were characterized as six Kac motifs, including a novel motif (KacW) for 26 Kac sites of 17 unknown proteins. Many Kac sites were predicted to be multifunctional, largely expanding the fungal Kac events. Biological importance of identified Kac sites was confirmed through functional analysis of Kac sites on Pmt1 and Pmt4, two O-mannosyltransferases. Singular site mutations (K88R and K482R) of Pmt1 resulted in impaired conidiation, attenuated virulence and decreased tolerance to oxidation and cell wall perturbation. These defects were close to or more severe than those caused by the deletion of pmt1. The Pmt4 K360R mutation facilitated colony growth under normal and stressful conditions and enhanced the fungal virulence. Our findings provide the first insight into the Kac events of B. bassiana and their links to the fungal potential against insect pests.

GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences

Protein acetylation catalyzed by specific histone acetyltransferases (HATs) is an essential post-translational modification (PTM) and involved in the regulation a broad spectrum of biological processes in eukaryotes. Although several ten thousands of acetylation sites have been experimentally identified, the upstream HATs for most of the sites are unclear. Thus, the identification of HAT-specific acetylation sites is fundamental for understanding the regulatory mechanisms of protein acetylation. In this work, we first collected 702 known HAT-specific acetylation sites of 205 proteins from the literature and public data resources, and a motif-based analysis demonstrated that different types of HATs exhibit similar but considerably distinct sequence preferences for substrate recognition. Using 544 human HAT-specific sites for training, we constructed a highly useful tool of GPS-PAIL for the prediction of HAT-specific sites for up to seven HATs, including CREBBP, EP300, HAT1, KAT2A, KAT2B, KAT5 and KAT8. The prediction accuracy of GPS-PAIL was critically evaluated, with a satisfying performance. Using GPS-PAIL, we also performed a large-scale prediction of potential HATs for known acetylation sites identified from high-throughput experiments in nine eukaryotes. Both online service and local packages were implemented, and GPS-PAIL is freely available at: http://pail.biocuckoo.org.

Proteome-wide analysis of lysine acetylation in the plant pathogen Botrytis cinerea

Lysine acetylation is a dynamic and reversible post-translational modification that plays an important role in diverse cellular processes. Botrytis cinerea is the most thoroughly studied necrotrophic species due to its broad host range and huge economic impact. However, to date, little is known about the functions of lysine acetylation in this plant pathogen. In this study, we determined the lysine acetylome of B. cinerea through the combination of affinity enrichment and high-resolution LC-MS/MS analysis. Overall, 1582 lysine acetylation sites in 954 proteins were identified. Bioinformatics analysis shows that the acetylated proteins are involved in diverse biological functions and show multiple cellular localizations. Several particular amino acids preferred near acetylation sites, including K^acY, K^acH, K^ac***R, K^acF, FK^ac and K^ac***K, were identified in this organism. Protein interaction network analysis demonstrates that a variety of interactions are modulated by protein acetylation. Interestingly, 6 proteins involved in virulence of B. cinerea, including 3 key components of the high-osmolarity glycerol pathway, were found to be acetylated, suggesting that lysine acetylation plays regulatory roles in pathogenesis. These data provides the first comprehensive view of the acetylome of B. cinerea and serves as a rich resource for functional analysis of lysine acetylation in this plant pathogen.

Systematic Analysis of the Lysine Acetylome in Candida albicans

Candida albicans (C. albicans) is a worldwide cause of fungal infectious diseases. As a general post-translational modification (PTM), lysine acetylation of proteins play an important regulatory role in almost every cell. In our research, we used a high-resolution proteomic technique (LC-MS/MS) to present the comprehensive analysis of the acetylome in C. albicans. In general, we detected 477 acetylated proteins among all 9038 proteins (5.28%) in C. albicans, which had 1073 specific acetylated sites. The bioinformatics analysis of the acetylome showed a significant role in the regulation of metabolism. To be more precise, proteins involved in carbon metabolism and biosynthesis were the underlying objectives of acetylation. Besides, through the study of the acetylome, we found a universal rule in acetylated motifs: the +4, +5, or +6 position, which is an alkaline residue with a long side chain (K or R), and the +1 or +2 position, which is a residue with a long side chain (Y, H, W, or F). To the best of our knowledge, all screening acetylated histone sites of this study have not been previously reported. Moreover, protein-protein interaction network (PPI) study demonstrated that a variety of connections in glycolysis/gluconeogenesis, oxidative phosphorylation, and the ribosome were modulated by acetylation and phosphorylation, but the phosphorylated proteins in oxidative phosphorylation PPI network were not abundant, which indicated that acetylation may have a more significant effect than phosphorylation on oxidative phosphorylation. This is the first study of the acetylome in human pathogenic fungi, providing an important starting point for the in-depth discovery of the functional analysis of acetylated proteins in such fungal pathogens.

Temporal Regulation of the Bacillus subtilis Acetylome and Evidence for a Role of MreB Acetylation in Cell Wall Growth

The past decade highlighted N^ε-lysine acetylation as a prevalent posttranslational modification in bacteria. However, knowledge regarding the physiological importance and temporal regulation of acetylation has remained limited. To uncover potential regulatory roles for acetylation, we analyzed how acetylation patterns and abundances change between growth phases in B. subtilis. To demonstrate that the identification of cell growth-dependent modifications can point to critical regulatory acetylation events, we further characterized MreB, the cell shape-determining protein. Our findings led us to propose a role for MreB acetylation in controlling cell width by restricting cell wall growth.

N^ε-Lysine acetylation has been recognized as a ubiquitous regulatory posttranslational modification that influences a variety of important biological processes in eukaryotic cells. Recently, it has been realized that acetylation is also prevalent in bacteria. Bacteria contain hundreds of acetylated proteins, with functions affecting diverse cellular pathways. Still, little is known about the regulation or biological relevance of nearly all of these modifications. Here we characterize the cellular growth-associated regulation of the Bacillus subtilis acetylome. Using acetylation enrichment and quantitative mass spectrometry, we investigate the logarithmic and stationary growth phases, identifying over 2,300 unique acetylation sites on proteins that function in essential cellular pathways. We determine an acetylation motif, EK(ac)(D/Y/E), which resembles the eukaryotic mitochondrial acetylation signature, and a distinct stationary-phase-enriched motif. By comparing the changes in acetylation with protein abundances, we discover a subset of critical acetylation events that are temporally regulated during cell growth. We functionally characterize the stationary-phase-enriched acetylation on the essential shape-determining protein MreB. Using bioinformatics, mutational analysis, and fluorescence microscopy, we define a potential role for the temporal acetylation of MreB in restricting cell wall growth and cell diameter.

IMPORTANCE The past decade highlighted N^ε-lysine acetylation as a prevalent posttranslational modification in bacteria. However, knowledge regarding the physiological importance and temporal regulation of acetylation has remained limited. To uncover potential regulatory roles for acetylation, we analyzed how acetylation patterns and abundances change between growth phases in B. subtilis. To demonstrate that the identification of cell growth-dependent modifications can point to critical regulatory acetylation events, we further characterized MreB, the cell shape-determining protein. Our findings led us to propose a role for MreB acetylation in controlling cell width by restricting cell wall growth.

Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set

Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor.

A comprehensive catalog of the lysine-acetylation targets in rice (Oryza sativa) based on proteomic analyses

Lysine acetylation is a dynamic and reversible post-translational modification that plays an important role in the gene transcription regulation. Here, we report high quality proteome-scale data for lysine-acetylation (Kac) sites and Kac proteins in rice (Oryza sativa). A total of 1337 Kac sites in 716 Kac proteins with diverse biological functions and subcellular localizations were identified in rice seedlings. About 42% of the sites were predicted to be localized in the chloroplast. Seven putative acetylation motifs were detected. Phenylalanine, located in both the upstream and downstream of the Kac sites, is the most conserved amino acid surrounding the regions. In addition, protein interaction network analysis revealed that a variety of signaling pathways are modulated by protein acetylation. KEGG pathway category enrichment analysis indicated that glyoxylate and dicarboxylate metabolism, carbon metabolism, and photosynthesis pathways are significantly enriched. Our results provide an in-depth understanding of the acetylome in rice seedlings, and the method described here will facilitate the systematic study of how Kac functions in growth, development, and abiotic and biotic stress responses in rice and other plants.

BIOLOGICAL SIGNIFICANCE

Rice is one of the most important crops consumption and is a model monocot for research. In this study, we combined a highly sensitive immune-affinity purification method (used pan anti-acetyl-lysine antibody conjugated agarose for immunoaffinity acetylated peptide enrichment) with high-resolution LC-MS/MS. In total, we identified 1337 Kac sites on 716 Kac proteins in rice cells. Bioinformatic analysis of the acetylome revealed that the acetylated proteins are involved in a variety of cellular functions and have diverse subcellular localizations. We also identified seven putative acetylation motifs in the acetylated proteins of rice. In addition, protein interaction network analysis revealed that a variety of signaling pathways were modulated by protein acetylation. KEGG pathway category enrichment analysis indicated that glyoxylate and dicarboxylate metabolism, carbon metabolism, and photosynthesis pathways were significantly enriched. To our knowledge, the number of Kac sites we identified was 23-times greater and the number of Kac proteins was 16-times greater than in a previous report. Our results provide an in-depth understanding of the acetylome in rice seedlings, and the method described here will facilitate the systematic study of how Kac functions in growth, development and responses to abiotic and biotic stresses in rice or other plants.

Evidence supporting a critical contribution of intrinsically disordered regions to the biochemical behavior of full-length human HP1γ

HP1γ, a non-histone chromatin protein, has elicited significant attention because of its role in gene silencing, elongation, splicing, DNA repair, cell growth, differentiation, and many other cancer-associated processes, including therapy resistance. These characteristics make it an ideal target for developing small drugs for both mechanistic experimentation and potential therapies. While high-resolution structures of the two globular regions of HP1γ, the chromo- and chromoshadow domains, have been solved, little is currently known about the conformational behavior of the full-length protein. Consequently, in the current study, we use threading, homology-based molecular modeling, molecular mechanics calculations, and molecular dynamics simulations to develop models that allow us to infer properties of full-length HP1γ at an atomic resolution level. HP1γ appears as an elongated molecule in which three Intrinsically Disordered Regions (IDRs, 1, 2, and 3) endow this protein with dynamic flexibility, intermolecular recognition properties, and the ability to integrate signals from various intracellular pathways. Our modeling also suggests that the dynamic flexibility imparted to HP1γ by the three IDRs is important for linking nucleosomes with PXVXL motif-containing proteins, in a chromatin environment. The importance of the IDRs in intermolecular recognition is illustrated by the building and study of both IDR2 HP1γ−importin-α and IDR1 and IDR2 HP1γ−DNA complexes. The ability of the three IDRs for integrating cell signals is demonstrated by combined linear motif analyses and molecular dynamics simulations showing that posttranslational modifications can generate a histone mimetic sequence within the IDR2 of HP1γ, which when bound by the chromodomain can lead to an autoinhibited state. Combined, these data underscore the importance of IDRs 1, 2, and 3 in defining the structural and dynamic properties of HP1γ, discoveries that have both mechanistic and potentially biomedical relevance.

In silico prediction and characterization of protein post-translational modifications

This review outlines the computational approaches and procedures for predicting post translational modification (PTM)-induced changes in protein conformation and their influence on protein function(s), the latter being assessed as differential affinity in interaction with either low (ligands for receptors or transporters, substrates for enzymes) or high molecular mass molecules (proteins or nucleic acids in supramolecular assemblies). The scope for an in silico approach is discussed against a summary of the in vitro evidence on the structural and functional outcome of protein PTM.

SIRT1 contributes to aldose reductase expression through modulating NFAT5 under osmotic stress: In vitro and in silico insights

So far, a myriad of molecules were characterized to modulate NFAT5 and its downstream targets. Among these NFAT5 modifiers, SIRT1 was proposed to have a promising role in NFAT5 dependent events, yet the exact underlying mechanism still remains obscure. Hence, the link between SIRT1 and NFAT5-aldose reductase (AR) axis under osmotic stress, was aimed to be delineated in this study. A unique osmotic stress model was generated and its mechanistic components were deciphered in U937 monocytes. In this model, AR expression and nuclear NFAT5 stabilization were revealed to be positively regulated by SIRT1 through utilization of pharmacological modulators. Overexpression and co-transfection studies of NFAT5 and SIRT1 further validated the contribution of SIRT1 to AR and NFAT5. The involvement of SIRT1 activity in these events was mediated via modification of DNA binding of NFAT5 to AR ORE region. Besides, NFAT5 and SIRT1 were also shown to co-immunoprecipitate under isosmotic conditions and this interaction was disrupted by osmotic stress. Further in silico experiments were conducted to investigate if SIRT1 directly targets NFAT5. In this regard, certain lysine residues of NFAT5, when kept deacetylated, were found to contribute to its DNA binding and SIRT1 was shown to directly bind K282 of NFAT5. Based on these in vitro and in silico findings, SIRT1 was identified, for the first time, as a novel positive regulator of NFAT5 dependent AR expression under osmotic stress in U937 monocytes.

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique

As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/.

Evidence supporting the existence of a NUPR1-like family of helix-loop-helix chromatin proteins related to, yet distinct from, AT hook-containing HMG proteins

NUPR1, a small chromatin protein, plays a critical role in cancer development, progression, and resistance to therapy. Here, using a combination of structural bioinformatics and molecular modeling methods, we report several novel findings that enhance our understanding of the biochemical function of this protein. We find that NUPR1 has been conserved throughout evolution, and over time it has undergone duplications and transpositions to form other transcriptional regulators. Using threading, homology-based molecular modeling, molecular mechanics calculations, and molecular dynamics simulations, we generated structural models for four of these proteins: NUPR1a, NUPR1b, NUPR2, and the NUPR-like domain of GTF2-I. Comparative analyses of these models combined with extensive linear motif identification reveal that these four proteins, though similar in their propensities for folding, differ in size, surface changes, and sites amenable for posttranslational modification. Lastly, taking NUPR1a as the paradigm for this family, we built models of a NUPR–DNA complex. Additional structural comparisons revealed that NUPR1 defines a new family of small-groove-binding proteins that share structural features with, yet are distinct from, helix-loop-helix AT-hook-containing HMG proteins. These models and inferences should lead to a better understanding of the function of this group of chromatin proteins, which play a critical role in the development of human malignant diseases.

An intelligent system for identifying acetylated lysine on histones and nonhistone proteins

Lysine acetylation is an important and ubiquitous posttranslational modification conserved in prokaryotes and eukaryotes. This process, which is dynamically and temporally regulated by histone acetyltransferases and deacetylases, is crucial for numerous essential biological processes such as transcriptional regulation, cellular signaling, and stress response. Since the experimental identification of lysine acetylation sites within proteins is time-consuming and laboratory-intensive, several computational approaches have been developed to identify candidates for experimental validation. In this work, acetylated protein data collected from UniProtKB were categorized into histone or nonhistone proteins. Support vector machines (SVMs) were applied to build predictive models by using amino acid pair composition (AAPC) as a feature in a histone model. We combined BLOSUM62 and AAPC features in a nonhistone model. Furthermore, using maximal dependence decomposition (MDD) clustering can enhance the performance of the model on a fivefold cross-validation evaluation to yield a sensitivity of 0.863, specificity of 0.885, accuracy of 0.880, and MCC of 0.706. Additionally, the proposed method is evaluated using independent test sets resulting in a predictive accuracy of 74%. This indicates that the performance of our method is comparable with that of other acetylation prediction methods.

Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features

Lysine acetylation is a reversible post-translational modification, playing an important role in cytokine signaling, transcriptional regulation, and apoptosis. To fully understand acetylation mechanisms, identification of substrates and specific acetylation sites is crucial. Experimental identification is often time-consuming and expensive. Alternative bioinformatics methods are cost-effective and can be used in a high-throughput manner to generate relatively precise predictions. Here we develop a method termed as SSPKA for species-specific lysine acetylation prediction, using random forest classifiers that combine sequence-derived and functional features with two-step feature selection. Feature importance analysis indicates functional features, applied for lysine acetylation site prediction for the first time, significantly improve the predictive performance. We apply the SSPKA model to screen the entire human proteome and identify many high-confidence putative substrates that are not previously identified. The results along with the implemented Java tool, serve as useful resources to elucidate the mechanism of lysine acetylation and facilitate hypothesis-driven experimental design and validation.

Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou's pseudo amino acid composition

Protein S-nitrosylation is a reversible post-translational modification by covalent modification on the thiol group of cysteine residues by nitric oxide. Growing evidence shows that protein S-nitrosylation plays an important role in normal cellular function as well as in various pathophysiologic conditions. Because of the inherent chemical instability of the S-NO bond and the low abundance of endogenous S-nitrosylated proteins, the unambiguous identification of S-nitrosylation sites by commonly used proteomic approaches remains challenging. Therefore, computational prediction of S-nitrosylation sites has been considered as a powerful auxiliary tool. In this work, we mainly adopted an adapted normal distribution bi-profile Bayes (ANBPB) feature extraction model to characterize the distinction of position-specific amino acids in 784 S-nitrosylated and 1568 non-S-nitrosylated peptide sequences. We developed a support vector machine prediction model, iSNO-ANBPB, by incorporating ANBPB with the Chou’s pseudo amino acid composition. In jackknife cross-validation experiments, iSNO-ANBPB yielded an accuracy of 65.39% and a Matthew’s correlation coefficient (MCC) of 0.3014. When tested on an independent dataset, iSNO-ANBPB achieved an accuracy of 63.41% and a MCC of 0.2984, which are much higher than the values achieved by the existing predictors SNOSite, iSNO-PseAAC, the Li et al. algorithm, and iSNO-AAPair. On another training dataset, iSNO-ANBPB also outperformed GPS-SNO and iSNO-PseAAC in the 10-fold crossvalidation test.

An integrated perspective and functional impact of the mitochondrial acetylome

Growing evidence suggests that a range of reversible protein post-translational modifications such as acetylation regulates mitochondria signalling, impacting cellular homeostasis. However, the extent of this type of regulation in the control of mitochondria functionality is just beginning to be discovered, aided by the availability of high-resolution mass spectrometers and bioinformatic tools. Data mining from literature on protein acetylation profiling focused on mitochondria isolated from tissues retrieved more than 1395 distinct proteins, corresponding to more than 4858 acetylation sites. ClueGo analysis of identified proteins highlighted oxidative phosphorylation, tricarboxylic acid cycle, fatty acid oxidation and amino acid metabolism as the biological processes more prone to regulation through acetylation. This review also examines the physiological relevance of protein acetylation on the molecular pathways harbored in mitochondria under distinct pathophysiological conditions as caloric restriction and alcohol-induced liver damage. This integrative perspective will certainly help to envisage future studies targeting the regulation of mitochondrial functionality.