Identification and characterization of lysine-methylated sites on histones and non-histone proteins

Predicting lysine methylation sites using a convolutional neural network

Protein lysine methylation is a particular type of post translational modification that plays an important role in both histone and non-histone function regulation in proteins. Deregulation caused by lysine methyltransferases has been identified as the cause of several diseases including cancer as well as both mental and developmental disorders. Identifying lysine methylation sites is a critical step in both early diagnosis and drug design. This study proposes a new Machine Learning method called CNN-Meth for predicting lysine methylation sites using a convolutional neural network (CNN). Our model is trained using evolutionary, structural, and physicochemical-based presentation along with binary encoding. Unlike previous studies, instead of extracting handcrafted features, we use CNN to automatically extract features from different presentations of amino acids to avoid information loss. Automated feature extraction from these representations of amino acids as well as CNN as a classifier have never been used for this problem. Our results demonstrate that CNN-Meth can significantly outperform previous methods for predicting methylation sites. It achieves 96.0%, 85.1%, 96.4%, and 0.65 in terms of Accuracy, Sensitivity, Specificity, and Matthew's Correlation Coefficient (MCC), respectively. CNN-Meth and its source code are publicly available at https://github.com/MLBC-lab/CNN-Meth.

The Role of Protein Methyltransferases in Immunity

The immune system protects our body from bacteria, viruses, and toxins and removes malignant cells. Activation of immune cells requires the onset of a network of important signaling proteins. Methylation of these proteins affects their structure and biological function. Under stimulation, T cells, B cells, and other immune cells undergo activation, development, proliferation, differentiation, and manufacture of cytokines and antibodies. Methyltransferases alter the above processes and lead to diverse outcomes depending on the degree and type of methylation. In the previous two decades, methyltransferases have been reported to mediate a great variety of immune stages. Elucidating the roles of methylation in immunity not only contributes to understanding the immune mechanism but is helpful in the development of new immunotherapeutic strategies. Hence, we review herein the studies on methylation in immunity, aiming to provide ideas for new approaches.

Protein Methylation in Diabetic Kidney Disease

Chronic kidney disease (CKD) is defined by persistent urine aberrations, structural abnormalities, or impaired excretory renal function. Diabetes is the leading cause of CKD. Their common pathological manifestation is renal fibrosis. Approximately half of all patients with type 2 diabetes and one-third with type 1 diabetes will develop CKD. However, renal fibrosis mechanisms are still poorly understood, especially post-transcriptional and epigenetic regulation. And an unmet need remains for innovative treatment strategies for preventing, arresting, treating, and reversing diabetic kidney disease (DKD). People believe that protein methylation, including histone and non-histone, is an essential type of post-translational modification (PTM). However, prevalent reviews mainly focus on the causes such as DNA methylation. This review will take insights into the protein part. Furthermore, by emphasizing the close relationship between protein methylation and DKD, we will summarize the clinical research status and foresee the application prospect of protein methyltransferase (PMT) inhibitors in DKD treatment. In a nutshell, our review will contribute to a more profound understanding of DKD’s molecular mechanism and inspire people to dig into this field.

SSMFN: a fused spatial and sequential deep learning model for methylation site prediction

BACKGROUND

Conventional in vivo methods for post-translational modification site prediction such as spectrophotometry, Western blotting, and chromatin immune precipitation can be very expensive and time-consuming. Neural networks (NN) are one of the computational approaches that can predict effectively the post-translational modification site. We developed a neural network model, namely the Sequential and Spatial Methylation Fusion Network (SSMFN), to predict possible methylation sites on protein sequences.

METHOD

We designed our model to be able to extract spatial and sequential information from amino acid sequences. Convolutional neural networks (CNN) is applied to harness spatial information, while long short-term memory (LSTM) is applied for sequential data. The latent representation of the CNN and LSTM branch are then fused. Afterwards, we compared the performance of our proposed model to the state-of-the-art methylation site prediction models on the balanced and imbalanced dataset.

RESULTS

Our model appeared to be better in almost all measurement when trained on the balanced training dataset. On the imbalanced training dataset, all of the models gave better performance since they are trained on more data. In several metrics, our model also surpasses the PRMePred model, which requires a laborious effort for feature extraction and selection.

CONCLUSION

Our models achieved the best performance across different environments in almost all measurements. Also, our result suggests that the NN model trained on a balanced training dataset and tested on an imbalanced dataset will offer high specificity and low sensitivity. Thus, the NN model for methylation site prediction should be trained on an imbalanced dataset. Since in the actual application, there are far more negative samples than positive samples.

A global map of associations between types of protein posttranslational modifications and human genetic diseases

There are >200 types of protein posttranslational modifications (PTMs) described in eukaryotes, each with unique proteome coverage and functions. We hypothesized that some genetic diseases may be caused by the removal of a specific type of PTMs by genomic variants and the consequent deregulation of particular functions. We collected >320,000 human PTMs representing 59 types and crossed them with >4M nonsynonymous DNA variants annotated with predicted pathogenicity and disease associations. We report >1.74M PTM-variant co-occurrences that an enrichment analysis distributed into 215 pairwise associations between 18 PTM types and 148 genetic diseases. Of them, 42% were not previously described. Removal of lysine acetylation exerts the most pronounced effect, and less studied PTM types such as S-glutathionylation or S-nitrosylation show relevance. Using pathogenicity predictions, we identified PTM sites that may produce particular diseases if prevented. Our results provide evidence of a substantial impact of PTM-specific removal on the pathogenesis of genetic diseases and phenotypes.

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There is an enrichment of disease-associated nsSNVs preventing certain types of PTMs

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We report 215 pairwise associations between 18 PTM types and 148 genetic diseases

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The removal of lysine acetylation exerts the most pronounced effect

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We report a set of PTM sites that may produce particular diseases if prevented

Proteome-wide Prediction of Lysine Methylation Leads to Identification of H2BK43 Methylation and Outlines the Potential Methyllysine Proteome

Protein Lys methylation plays a critical role in numerous cellular processes, but it is challenging to identify Lys methylation in a systematic manner. Here we present an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We develop MethylSight, a program that predicts Kme events solely on the physicochemical properties of residues surrounding the putative methylation sites, which then requires validation by targeted MS. Using this approach, we identify 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts that Lys methylation is a prevalent post-translational modification in the human proteome. Our work provides a useful resource for guiding systematic exploration of the role of Lys methylation in human health and disease.

Comprehensive in silico mutational-sensitivity analysis of PTEN establishes signature regions implicated in pathogenesis of Autism Spectrum Disorders

An extensively studied cancer and Autism Spectrum Disorders (ASD) gene like PTEN provided an exclusive opportunity to map its mutational-landscape, compare and establish plausible genotypic predictors of ASD-associated phenotypic outcomes. Our exhaustive in silico analysis on 4252 SNPs using >30 tools identified increased mutational-density in exon7. Phosphatase domain, although evolutionarily conserved, had the most nsSNPs localised within signature regions. The evolutionarily variable C-terminal side contained the highest truncating-SNPs outside signature regions of C2 domain and most PTMs within C-tail site which displayed maximum intolerance to polymorphisms, and permitted benign but destabilising nsSNPs that enhanced its intrinsically-disordered nature. ASD-associated SNPs localised within ATP-binding motifs and Nuclear-Localising-Sequences were the most potent triggers of ASD manifestation. These, along with variations within P, WPD and TI loops, M1 within phosphatase domain, M2 and MoRFs of C2 domain, caused severe long-range conformational fluctuations altering PTEN's dynamic stability- not observed in variations outside signature regions. 3'UTR-SNPs affected 44 strong miRNA brain-specific targets; several 5' UTR-SNPs targeted transcription-factor POLR2A and 10 pathogenic Splice-Affecting-Variants were identified.

Two-Level Protein Methylation Prediction using structure model-based features

Protein methylation plays a vital role in cell processing. Many novel methods try to predict methylation sites from protein sequence by sequence information or predicted structural information, but none of them use protein tertiary structure information in prediction. In particular, most of them do not build models for predicting methylation types (mono-, di-, tri-methylation). To address these problems, we propose a novel method, Met-predictor, to predict methylation sites and methylation types using a support vector machine-based network. Met-predictor combines a variety of sequence-based features that are derived from protein sequences with structure model-based features, which are geometric information extracted from predicted protein tertiary structure models, and are firstly used in methylation prediction. Met-predictor was tested on two independent test sets, where the addition of structure model-based features improved AUC from 0.611 and 0.520 to 0.655 and 0.566 for lysine and from 0.723 and 0.640 to 0.734 and 0.643 for arginine. When compared with other state-of-the-art methods, Met-predictor had 13.1% (3.9%) and 8.5% (16.4%) higher accuracy than the best of other methods for methyllysine and methylarginine prediction on the independent test set I (II). Furthermore, Met-predictor also attains excellent performance for predicting methylation types.

Protein lysine methylation in the regulation of anoxia tolerance in the red eared slider turtle, Trachemys scripta elegans

The red eared slider turtle (Trachemys scripta elegans) is a champion vertebrate facultative anaerobe, capable of surviving for several months under conditions of exceptionally low oxygen availability. The ability of the turtle to facilitate this impressive tolerance to oxygen restriction is accomplished through a dramatic reduction in non-essential cellular processes. This is done in an attempt to conserve limited ATP stores and match demand in the anoxic state, with ATP supplied primarily through anaerobic glycolysis. Determining both the non-essential and the essential cellular processes that are deemed to be anoxia-responsive in the turtle has been an intense area of study over the past few decades. As a result, recent advancements have established the influence of global metabolic controls, such as post-transcriptional and post-translational regulation of gene expression in anoxia adaptation. A remaining question is whether or not epigenetic-level regulatory mechanisms are also utilized to allow for local control over gene expression. Recently, research has begun to document lysine methylation as an anoxia-responsive post-translational histone modification, as the activities of a number of methyl-lysine regulatory enzymes are extraordinarily sensitive to oxygen availability. As a result, oxygen-dependent methyl-lysine regulatory enzymes have been of particular interest to several recent studies of animal oxygen sensitivity, including the freshwater turtle. This review will introduce the concept of lysine methylation as an oxygen-sensitive protein modification as well as a prospectus on how this modification may contribute to anoxia tolerance in the turtle.

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.

Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method

Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an increasing number of site-specific succinylated peptides obtained from high-throughput mass spectrometry (MS), various tools have been developed for computationally identifying succinylated sites on proteins. However, most of these tools predict succinylation sites based on traditional machine learning methods. Hence, this work aimed to carry out the succinylation site prediction based on a deep learning model. The abundance of MS-verified succinylated peptides enabled the investigation of substrate site specificity of succinylation sites through sequence-based attributes, such as position-specific amino acid composition, the composition of k-spaced amino acid pairs (CKSAAP), and position-specific scoring matrix (PSSM). Additionally, the maximal dependence decomposition (MDD) was adopted to detect the substrate signatures of lysine succinylation sites by dividing all succinylated sequences into several groups with conserved substrate motifs. According to the results of ten-fold cross-validation, the deep learning model trained using PSSM and informative CKSAAP attributes can reach the best predictive performance and also perform better than traditional machine-learning methods. Moreover, an independent testing dataset that truly did not exist in the training dataset was used to compare the proposed method with six existing prediction tools. The testing dataset comprised of 218 positive and 2621 negative instances, and the proposed model could yield a promising performance with 84.40% sensitivity, 86.99% specificity, 86.79% accuracy, and an MCC value of 0.489. Finally, the proposed method has been implemented as a web-based prediction tool (CNN-SuccSite), which is now freely accessible at http://csb.cse.yzu.edu.tw/CNN-SuccSite/.

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.

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.

Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization

As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation.

AVAILABILITY AND IMPLEMENTATION

The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

Position-specific prediction of methylation sites from sequence conservation based on information theory

Protein methylation plays vital roles in many biological processes and has been implicated in various human diseases. To fully understand the mechanisms underlying methylation for use in drug design and work in methylation-related diseases, an initial but crucial step is to identify methylation sites. The use of high-throughput bioinformatics methods has become imperative to predict methylation sites. In this study, we developed a novel method that is based only on sequence conservation to predict protein methylation sites. Conservation difference profiles between methylated and non-methylated peptides were constructed by the information entropy (IE) in a wider neighbor interval around the methylation sites that fully incorporated all of the environmental information. Then, the distinctive neighbor residues were identified by the importance scores of information gain (IG). The most representative model was constructed by support vector machine (SVM) for Arginine and Lysine methylation, respectively. This model yielded a promising result on both the benchmark dataset and independent test set. The model was used to screen the entire human proteome, and many unknown substrates were identified. These results indicate that our method can serve as a useful supplement to elucidate the mechanism of protein methylation and facilitate hypothesis-driven experimental design and validation.

Progress and challenges in predicting protein methylation sites

We review the progress in the prediction of protein methylation sites in the past 10 years and discuss the challenges that are faced while developing novel predictors in the future.

Non-histone protein methylation as a regulator of cellular signalling and function

Methylation of Lys and Arg residues on non-histone proteins has emerged as a prevalent post-translational modification and as an important regulator of cellular signal transduction mediated by the MAPK, WNT, BMP, Hippo and JAK-STAT signalling pathways. Crosstalk between methylation and other types of post-translational modifications, and between histone and non-histone protein methylation frequently occurs and affects cellular functions such as chromatin remodelling, gene transcription, protein synthesis, signal transduction and DNA repair. With recent advances in proteomic techniques, in particular mass spectrometry, the stage is now set to decode the methylproteome and define its functions in health and disease.

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.