Protein Methylation Sites Research Articles

RMSxAI: arginine methylation sites prediction from protein sequences using machine learning algorithms and explainable artificial intelligence

Protein methylation is a vital regulator of many biological processes at the post-translational level, and accurate prediction of protein methylation sites is essential for research and drug discovery. In this paper, we present a new method, namely RMSxAI, to predict the arginine methylation sites from primary sequences using machine learning algorithms and describe the predictions using explainable artificial intelligence (XAI) techniques. Leveraging experimentally validated methylated and unmethylated protein sequences from diverse organisms, we deduced several sequence features, encompassing physicochemical properties, amino acid composition, and evolutionary insights. Our results show that the proposed RMSxAI can predict protein methylation sites with high accuracy, bringing the F1 score up to 0.88 and overall accuracy up to 88.4%. We use various XAI methods to explain the output results. These include key features, partial occupancy maps, and local variation models that provide insight into key features and interactions that lead to predictions. Overall, our approach is relevant to research and drug discovery, and our results demonstrate the potential of machine learning algorithms and XAI methods to provide accurate and meaningful prediction of arginine methylation sites.

Hybrid Bayesian Optimization-Based Graphical Discovery for Methylation Sites Prediction.

Protein methylation is one of the most important reversible post-translational modifications (PTMs), playing a vital role in the regulation of gene expression. Protein methylation sites serve as biomarkers in cardiovascular and pulmonary diseases, influencing various aspects of normal cell biology and pathogenesis. Nonetheless, the majority of existing computational methods for predicting protein methylation sites (PMSP) have been constructed based on protein sequences, with few methods leveraging the topological information of proteins. To address this issue, we propose an innovative framework for predicting Methylation Sites using Graphs (GraphMethySite) that employs graph convolution network in conjunction with Bayesian Optimization (BO) to automatically discover the graphical structure surrounding a candidate site and improve the predictive accuracy. In order to extract the most optimal subgraphs associated with methylation sites, we extend GraphMethySite by coupling it with a hybrid Bayesian optimization (together named GraphMethySite +) to determine and visualize the topological relevance among amino-acid residues. We evaluated our framework on two extended protein methylation datasets, and empirical results demonstrate that it outperforms existing state-of-the-art methylation prediction methods.

DeepPRMS: advanced deep learning model to predict protein arginine methylation sites.

Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research and drug discovery. Some experimental techniques, such as methyl-specific antibodies, chromatin immune precipitation and mass spectrometry, exist for predicting protein methylation sites, but these techniques are time-consuming and costly. The ability to predict methylation sites using in silico techniques may help researchers identify potential candidate sites for future examination and make it easier to carry out site-specific investigations and downstream characterizations. In this research, we proposed a novel deep learning-based predictor, named DeepPRMS, to identify protein methylation sites in primary sequences. The DeepPRMS utilizes the gated recurrent unit (GRU) and convolutional neural network (CNN) algorithms to extract the sequential and spatial information from the primary sequences. GRU is used to extract sequential information, while CNN is used for spatial information. We combined the latent representation of GRU and CNN models to have a better interaction among them. Based on the independent test data set, DeepPRMS obtained an accuracy of 85.32%, a specificity of 84.94%, Matthew's correlation coefficient of 0.71 and a sensitivity of 85.80%. The results indicate that DeepPRMS can predict protein methylation sites with high accuracy and outperform the state-of-the-art models. The DeepPRMS is expected to effectively guide future research experiments for identifying potential methylated protein sites. The web server is available at http://deepprms.nitsri.ac.in/.

SVM-Based Prediction of Protein Methylation Sites: A Comprehensive Analysis of 553 Properties from the AAindex Database

SVM-Based Prediction of Protein Methylation Sites: A Comprehensive Analysis of 553 Properties from the AAindex Database

Multifactorial feature extraction and site prognosis model for protein methylation data.

Integrated studies (multi-omics studies) comprising genetic, proteomic and epigenetic data analyses have become an emerging topic in biomedical research. Protein methylation is a posttranslational modification that plays an essential role in various cellular activities. The prediction of methylation sites (arginine and lysine) is vital to understand the molecular processes of protein methylation. However, current experimental techniques used for methylation site predictions are tedious and expensive. Hence, computational techniques for predicting methylation sites in proteins are necessary. For predicting methylation sites, various computational methods have been proposed in recent years. Most existing methods require structural and evolutionary information for retrieving features, acquiring this information is not always convenient. Thus, we proposed a novel method, called multi-factorial feature extraction and site prognosis model (MufeSPM), for the prediction of protein methylation sites based on information theory features (Renyi, Shannon, Havrda-Charvat and Arimoto entropy), amino acid composition and physicochemical properties acquired from protein methylation data. A random forest algorithm was used to predict methylation sites in protein sequences. This paper also studied the impact of different features and classifiers on arginine and lysine methylation data sets. For the R methylation data set, MufeSPM yielded 82.45%($\pm $ 3.47) accuracy, and for the K methylation data set, it provided an average accuracy of 71.94%($\pm $ 2.12). Additionally, the area under the receiver operating characteristic curve for different classifiers in predicting methylation site was provided. The experimental results signify that MufeSPM performs better than the state-of-the-art predictors.

Review of Progress in Predicting Protein Methylation Sites

Protein methylation is an important and reversible post-translational modification that regulates many biological processes in cells. It occurs mainly on lysine and arginine residues and involves many important biological processes, including transcriptional activity, signal transduction, and the regulation of gene expression. Protein methylation and its regulatory enzymes are related to a variety of human diseases, so improved identification of methylation sites is useful for designing drugs for a variety of related diseases. In this review, we systematically summarize and analyze the tools used for the prediction of protein methylation sites on arginine and lysine residues over the last decade.

Direct Imaging of Protein-Specific Methylation in Mammalian Cells.

Abundant post-translational modification through methylation alters the function, stability, and/or localization of a protein. Malfunctions in post-translational modification are associated with severe diseases. To unravel protein methylation sites and their biological functions, chemical methylation reporters have been developed. However, until now, their usage was limited to cell lysates. Herein, we present the first generally applicable approach for imaging methylation of individual proteins in human cells, which is based on a combination of chemical reporter strategies, bioorthogonal ligation reactions, and FRET detected by means of fluorescence lifetime imaging microscopy. Through this approach, methylation of histone 4 and the non-histone proteins tumor suppressor p53, kinase Akt1, and transcription factor Foxo1 in two human cell lines has been successfully imaged. To further demonstrate its potential, the localization-dependent methylation state of Foxo1 in the cellular context has been visualized.

MethylQuant: A Tool for Sensitive Validation of Enzyme-Mediated Protein Methylation Sites from Heavy-Methyl SILAC Data.

The study of post-translational methylation is hampered by the fact that large-scale LC-MS/MS experiments produce high methylpeptide false discovery rates (FDRs). The use of heavy-methyl stable isotope labeling by amino acids in cell culture (heavy-methyl SILAC) can drastically reduce these FDRs; however, this approach is limited by a lack of heavy-methyl SILAC compatible software. To fill this gap, we recently developed MethylQuant. Here, using an updated version of MethylQuant, we demonstrate its methylpeptide validation and quantification capabilities and provide guidelines for its best use. Using reference heavy-methyl SILAC data sets, we show that MethylQuant predicts with statistical significance the true or false positive status of methylpeptides in samples of varying complexity, degree of methylpeptide enrichment, and heavy to light mixing ratios. We introduce methylpeptide confidence indicators, MethylQuant Confidence and MethylQuant Score, and demonstrate their strong performance in complex samples characterized by a lack of methylpeptide enrichment. For these challenging data sets, MethylQuant identifies 882 of 1165 true positive methylpeptide spectrum matches (i.e., >75% sensitivity) at high specificity (<2% FDR) and achieves near-perfect specificity at 41% sensitivity. We also demonstrate that MethylQuant produces high accuracy relative quantification data that are tolerant of interference from coeluting peptide ions. Together MethylQuant's capabilities provide a path toward routine, accurate characterizations of the methylproteome using heavy-methyl SILAC.

Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique.

Protein methylation, an important post-translational modification, plays crucial roles in many cellular processes. The accurate prediction of protein methylation sites is fundamentally important for revealing the molecular mechanisms undergoing methylation. In recent years, computational prediction based on machine learning algorithms has emerged as a powerful and robust approach for identifying methylation sites, and much progress has been made in predictive performance improvement. However, the predictive performance of existing methods is not satisfactory in terms of overall accuracy. Motivated by this, we propose a novel random-forest-based predictor called MePred-RF, integrating several discriminative sequence-based feature descriptors and improving feature representation capability using a powerful feature selection technique. Importantly, unlike other methods based on multiple, complex information inputs, our proposed MePred-RF is based on sequence information alone. Comparative studies on benchmark datasets via vigorous jackknife tests indicate that our proposed MePred-RF method remarkably outperforms other state-of-the-art predictors, leading by a 4.5 percent average in terms of overall accuracy. A user-friendly webserver that implements the proposed method has been established for researchers' convenience, and is now freely available for public use through http://server.malab.cn/MePred-RF. We anticipate our research tool to be useful for the large-scale prediction and analysis of protein methylation sites.

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.

Pedican: an online gene resource for pediatric cancers with literature evidence.

Pediatric cancer (PC), that is cancer occurring in children, is the leading cause of death among children worldwide, with an incidence of 175,000 per year. Elucidating the genetic abnormalities and underlying cellular mechanisms may provide less toxic curative treatments. Therefore, it is important to understand the pathology of pediatric cancer at the genetic, genomic and epigenetic level. To unveil the cellular complexity of PC, we have developed a database of pediatric cancers (Pedican), the first literature-based pediatric gene data resource by comprehensive literature curation and data integration. In the current release, Pedican contains 735 human genes, 88 gene fusion and 24 chromosome abnormal events curated from 2245 PubMed abstracts. Pedican provides detailed annotations for each gene, such as Entrez gene information, involved pathways, protein–protein interactions, mutations, gene expression, methylation sites, TF regulation, and post-translational modification. Additionally Pedican has a user-friendly web interface, which allows sophisticated text query, sequence searches, and browsing by highlighted literature evidence and hundreds of cancer types. Overall, our curated pediatric cancer-related gene list maps the genomic and cellular landscape for various pediatric cancers, providing a valuable resource for further experiment design. The Pedican is available at http://pedican.bioinfo-minzhao.org/.

Progress and challenges in predicting protein methylation sites.

Protein methylation catalyzed by methyltransferases carries many important biological functions. Methylation and their regulatory enzymes are involved in a variety of human disease states, raising the possibility that abnormally methylated proteins can be disease markers and methyltransferases are potential therapeutic targets. Identification of methylation sites is a prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles that have been implicated in the pathological processes. Due to various limitations of experimental methods, in silico approaches for identifying novel methylation sites have become increasingly popular. In this review, we summarize the progress in the prediction of protein methylation sites from the dataset, feature representation, prediction algorithm and online resources in the past ten years. We also discuss the challenges that are faced while developing novel predictors in the future. The development and application of methylation site prediction is a promising field of systematic biology, provided that protein methyltransferases, species and functional information will be taken into account.

Quantifying in vivo, site-specific changes in protein methylation with SILAC.

Interest in protein methylation has grown rapidly in recent years. Mass spectrometry-based proteomics is ideally suited to characterize protein modifications, but the multiplicity of methylated residues and the lack of efficient methods to enrich methylated proteins have limited the proteomic identification of protein methylation sites. In this protocol, we compare two metabolic labeling approaches, stable isotope labeling by amino acids in cell culture (SILAC) and its variant heavy methyl SILAC, for studying protein methylation. Instead of heavy lysine and arginine in the typical SILAC experiment, heavy methyl SILAC uses (13)C, (2)H methionine as the labeling amino acid. As cells convert methionine to S-adenosylmethionine, heavy methyl SILAC encodes a 4 Da mass tag for each methyl group, distinguishing between degrees of methylation is possible from mass difference alone. We provide a protocol for SILAC-based analyses of protein methylation and highlight the strengths and weaknesses of each method for targeted and proteomic analyses.

IMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach

Before becoming the native proteins during the biosynthesis, their polypeptide chains created by ribosome's translating mRNA will undergo a series of “product-forming” steps, such as cutting, folding, and posttranslational modification (PTM). Knowledge of PTMs in proteins is crucial for dynamic proteome analysis of various human diseases and epigenetic inheritance. One of the most important PTMs is the Arg- or Lys-methylation that occurs on arginine or lysine, respectively. Given a protein, which site of its Arg (or Lys) can be methylated, and which site cannot? This is the first important problem for understanding the methylation mechanism and drug development in depth. With the avalanche of protein sequences generated in the postgenomic age, its urgency has become self-evident. To address this problem, we proposed a new predictor, called iMethyl-PseAAC. In the prediction system, a peptide sample was formulated by a 346-dimensional vector, formed by incorporating its physicochemical, sequence evolution, biochemical, and structural disorder information into the general form of pseudo amino acid composition. It was observed by the rigorous jackknife test and independent dataset test that iMethyl-PseAAC was superior to any of the existing predictors in this area.

Prediction of Protein Methylation Sites Using Conditional Random Field

Prediction of Protein Methylation Sites Using Conditional Random Field

Prediction of Protein Methylation Sites Using Conditional Random Field

Protein methylation is an important and reversible post-translational modification which regulates diverse protein properties. Many methylation sites on arginine and lysine have been identification through experiments. However, experimental identification without prior knowledge is laborious and costly. Hence, there is interest in the development of computational methods for reliable prediction of methylation sites. Prediction of methylation sites may provide researches with useful information for further productivity in methylation candidate sites discovery. This work proposes Methcrf, a computational predictor based on conditional random field (CRF) for predicting protein methylation sites limit to lysine and arginine residues due to the absence of enough experimentally verified data for other residues. The approach is developed to consider combining protein sequence features with structural information such as solvent accessibility of amino acids that surround the methylation sites. In 10-fold cross validation Methcrf can achieve the area under receiver operating characteristic curve (AUC) of 0.85 and 0.80 for arginine and lysine, respectively. The proposed method has comparable performance with previous methods for accurately predicting methylation sites.

Prediction of Protein Methylation Sites Using Conditional Random Field

Prediction of Protein Methylation Sites Using Conditional Random Field

PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme

Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew’s correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew’s correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx.

PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features

Post-translational lysine methylation and acetylation are two major modifications of lysine residues. They play critical roles in various biological processes, especially in gene regulation. Identification of protein methylation and acetylation sites would be a foundation for understanding their modification dynamics and molecular mechanism. This work presents a method called PLMLA that incorporates protein sequence information, secondary structure and amino acid properties to predict methylation and acetylation of lysine residues in whole protein sequences. We apply an encoding scheme based on grouped weight and position weight amino acid composition to extract sequence information and physicochemical properties around lysine sites. The prediction accuracy for methyllysine and acetyllysine are 83.02% and 83.08%, respectively. Feature analysis reveals that methyllysine is likely to occur at the coil region and acetyllysine prefers to occur at the helix region of protein. The upstream residues away from the central site may be close to methylated lysine in three-dimensional structure and have a significant influence on methyllysine, while the positively charged residues may have a significant influence on acetyllysine. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PLMLA.aspx.

Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine

Protein methylation is involved in dozens of biological processes and plays an important role in adjusting protein physicochemical properties, conformation and function. However, with the rapid increase of protein sequence entering into databanks, the gap between the number of known sequence and the number of known methylation annotation is widening rapidly. Therefore, it is vitally significant to develop a computational method for quick and accurate identification of methylation sites. In this study, a novel predictor (Methy_SVMIACO) based on support vector machine (SVM) and improved ant colony optimization algorithm (IACO) is developed to identify methylation sites. The IACO is utilized to find the optimal feature subset and parameter of SVM, while SVM is employed to perform the identification of methylation sites. Comparison of the IACO with conventional ACO shows that the IACO converges quickly toward the global optimal solution and it is more useful tool for feature selection and SVM parameter optimization. The performance of Methy_SVMIACO is evaluated with a sensitivity of 85.71%, a specificity of 86.67%, an accuracy of 86.19% and a Matthew's correlation coefficient ( MCC) of 0.7238 for lysine as well as a sensitivity of 89.08%, a specificity of 94.07%, an accuracy of 91.56% and a MCC of 0.8323 for arginine in 10-fold cross-validation test. It is shown through the analysis of the optimal feature subset that some upstream and downstream residues play important role in the methylation of arginine and lysine. Compared with other existing methods, the Methy_SVMIACO provides higher Acc, Sen and Spe, indicating that the current method may serve as a powerful complementary tool to other existing approaches in this area. The Methy_SVMIACO can be acquired freely on request from the authors.