MeMo: a web tool for prediction of protein methylation modifications

Impact of Missense Variants on the Structure and Function of Polycystic Ovary Syndrome-Associated HSD17B1 Gene

HSD17B1 regulates estrogen availability in the ovary, and its dysregulation is linked to cyst formation in polycystic ovary syndrome. This study aimed to understand the role of missense variants in HSD17B1 dysfunction. Bioinformatic tools (sift, polyphen2, panther, snps & go, phd-snp, pmut, snap and revel score) were used to identify the deleterious missense variants of HSD17B1 gene. InterPro and Pfam tools were utilized to predict the functional domain of these deleterious variants, and its impact on structure and stability of HSD17B1 was analyzed by Hope, MutPred2, I-Mutant 2.0, and Mupro. Further, the AutoDock was used to determine the binding affinity of NADP to HSD17B1 protein. Finally, the HSD17B1 expression in clinical samples was analyzed using qRT-PCR. Of the 355 missense variants identified, five (rs202173252, rs149630844, rs146159533, rs200202791, and rs138503851) variants were deleterious, pocketed at the NADP binding domain and located at the conserved region of HSD17B1. Further, series of in silico prediction and molecular docking shows only the variant T191I (rs138503851) has an adverse effect on HSD17B1 function because of increased unfavorable bonds and non-interaction of adenine and nicotinamide of NADP to the Glycine94 and Leucine93 of HSD17B1. Additionally, a two-fold reduced expression of HSD17B1 in peripheral blood of PCOS subjects was observed, compared to healthy individuals. Overall, these results indicate that rs138503851 genetic variant of HSD17B1 may affect estrogen synthesis in PCOS. However, further clinical studies are warranted to validate the presence of rs138503851 genetic variant to identify the potential females who are predisposal to the development of PCOS.

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/.

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.

G9a Inhibition Promotes Neuroprotection through GMFB Regulation in Alzheimer's Disease

Epigenetic alterations are a fundamental pathological hallmark of Alzheimer's disease (AD). Herein, we show the upregulation of G9a and H3K9me2 in the brains of AD patients. Interestingly, treatment with a G9a inhibitor (G9ai) in SAMP8 mice reversed the high levels of H3K9me2 and rescued cognitive decline. A transcriptional profile analysis after G9ai treatment revealed increased gene expression of glia maturation factor β (GMFB) in SAMP8 mice. Besides, a H3K9me2 ChIP-seq analysis after G9a inhibition treatment showed the enrichment of gene promoters associated with neural functions. We observed the induction of neuronal plasticity and a reduction of neuroinflammation after G9ai treatment, and more strikingly, these neuroprotective effects were reverted by the pharmacological inhibition of GMFB in mice and cell cultures; this was also validated by the RNAi approach generating the knockdown of GMFB/Y507A.10 in Caenorhabditis elegans. Importantly, we present evidence that GMFB activity is controlled by G9a-mediated lysine methylation as well as we identified that G9a directly bound GMFB and catalyzed the methylation at lysine (K) 20 and K25 in vitro. Furthermore, we found that the neurodegenerative role of G9a as a GMFB suppressor would mainly rely on methylation of the K25 position of GMFB, and thus G9a pharmacological inhibition removes this methylation promoting neuroprotective effects. Then, our findings confirm an undescribed mechanism by which G9a inhibition acts at two levels, increasing GMFB and regulating its function to promote neuroprotective effects in age-related cognitive decline.

PRMxAI: protein arginine methylation sites prediction based on amino acid spatial distribution using explainable artificial intelligence

BACKGROUND

Protein methylation, a post-translational modification, is crucial in regulating various cellular functions. Arginine methylation is required to understand crucial biochemical activities and biological functions, like gene regulation, signal transduction, etc. However, some experimental methods, including Chip-Chip, mass spectrometry, and methylation-specific antibodies, exist for the prediction of methylated proteins. These experimental methods are expensive and tedious. As a result, computational methods based on machine learning play an efficient role in predicting arginine methylation sites.

RESULTS

In this research, a novel method called PRMxAI has been proposed to predict arginine methylation sites. The proposed PRMxAI extract sequence-based features, such as dipeptide composition, physicochemical properties, amino acid composition, and information theory-based features (Arimoto, Havrda-Charvat, Renyi, and Shannon entropy), to represent the protein sequences into numerical format. Various machine learning algorithms are implemented to select the better classifier, such as Decision trees, Naive Bayes, Random Forest, Support vector machines, and K-nearest neighbors. The random forest algorithm is selected as the underlying classifier for the PRMxAI model. The performance of PRMxAI is evaluated by employing 10-fold cross-validation, and it yields 87.17% and 90.40% accuracy on mono-methylarginine and di-methylarginine data sets, respectively. This research also examines the impact of various features on both data sets using explainable artificial intelligence.

CONCLUSIONS

The proposed PRMxAI shows the effectiveness of the features for predicting arginine methylation sites. Additionally, the SHapley Additive exPlanation method is used to interpret the predictive mechanism of the proposed model. The results indicate that the proposed PRMxAI model outperforms other state-of-the-art predictors.

Immune thrombotic thrombocytopenic purpura: pathogenesis and novel therapies: a narrative review

Background and Objectives

Immune thrombotic thrombocytopenic purpura (iTTP) is a rare, but potentially fatal blood disease, resulting from autoantibodies against A Disintegrin and Metalloprotease with ThromboSpondin Type 1 Repeats, 13 (ADAMTS13). While major progress has been made in past decades concerning early diagnosis and management of iTTP, the mechanisms underlying the formation and the mechanism of action of these autoantibodies against ADMATS13 are still unknown. This review will provide a narrative review of pathogenesis and novel therapeutics of iTTP.

Methods

We did PubMed literature search using a combination of thrombotic thrombocytopenic purpura and treatment or pathogenesis from 1955 to November 2022. A total of 4,767 articles with full text were found and only relevant articles in English were further reviewed and summarized.

Key Content and Findings

We found that the primary mechanism underlying severe ADAMTS13 deficiency in patients with iTTP is autoantibody-mediated inhibition and/or accelerated clearance of ADAMTS13 metalloprotease. Other factors including allosteric regulation and post-translational modifications (i.e., glycosylation and citrullination, and arginine methylation, etc.) may affect ADAMTS13 secretion and function and also contribute to the pathogenesis of iTTP. The standard of care for iTTP today consists of therapeutic plasma exchange, anti-von Willebrand factor (vWF) caplacizumab, and immunosuppressives (e.g., corticosteroids and rituximab), known as the triple therapy, which has significantly reduced exacerbation and mortality rates.

Conclusions

We hope that the information provided in the review article helps better understand the pathogenesis of iTTP, which may guide design novel and more effective therapeutics for this potentially fatal disorder.

JMJD6 protects against isoproterenol-induced cardiac hypertrophy via inhibition of NF-κB activation by demethylating R149 of the p65 subunit

Histone modification plays an important role in pathological cardiac hypertrophy and heart failure. In this study we investigated the role of a histone arginine demethylase, Jumonji C domain-containing protein 6 (JMJD6) in pathological cardiac hypertrophy. Cardiac hypertrophy was induced in rats by subcutaneous injection of isoproterenol (ISO, 1.2 mg·kg-1·d-1) for a week. At the end of the experiment, the rats underwent echocardiography, followed by euthanasia and heart collection. We found that JMJD6 levels were compensatorily increased in ISO-induced hypertrophic cardiac tissues, but reduced in patients with heart failure with reduced ejection fraction (HFrEF). Furthermore, we demonstrated that JMJD6 overexpression significantly attenuated ISO-induced hypertrophy in neonatal rat cardiomyocytes (NRCMs) evidenced by the decreased cardiomyocyte surface area and hypertrophic genes expression. Cardiac-specific JMJD6 overexpression in rats protected the hearts against ISO-induced cardiac hypertrophy and fibrosis, and rescued cardiac function. Conversely, depletion of JMJD6 by single-guide RNA (sgRNA) exacerbated ISO-induced hypertrophic responses in NRCMs. We revealed that JMJD6 interacted with NF-κB p65 in cytoplasm and reduced nuclear levels of p65 under hypertrophic stimulation in vivo and in vitro. Mechanistically, JMJD6 bound to p65 and demethylated p65 at the R149 residue to inhibit the nuclear translocation of p65, thus inactivating NF-κB signaling and protecting against pathological cardiac hypertrophy. In addition, we found that JMJD6 demethylated histone H3R8, which might be a new histone substrate of JMJD6. These results suggest that JMJD6 may be a potential target for therapeutic interventions in cardiac hypertrophy and heart failure.

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.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence

Protein arginine methylation (PRme), as one post-translational modification, plays a critical role in numerous cellular processes and regulates critical cellular functions. Though several in silico models for predicting PRme sites have been reported, new models may be required to develop due to the significant increase of identified PRme sites. In this study, we constructed multiple machine-learning and deep-learning models. The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe. CNNArginineMe performed best in AUC scoring metrics in comparisons with several reported predictors. Additionally, we employed CNNArginineMe to predict arginine methylation proteome and performed functional analysis. The arginine methylated proteome is significantly enriched in the amyotrophic lateral sclerosis (ALS) pathway. CNNArginineMe is freely available at https://github.com/guoyangzou/CNNArginineMe.

Arginine methylation of MTHFD1 by PRMT5 enhances anoikis resistance and cancer metastasis

Metastasis accounts for the major cause of cancer-related mortality. How disseminated tumor cells survive under suspension conditions and avoid anoikis is largely unknown. Here, using a metabolic enzyme-centered CRISPR-Cas9 genetic screen, we identified methylenetetrahydrofolate dehydrogenase, cyclohydrolase and formyltetrahydrofolate synthetase 1 (MTHFD1) as a novel suppressor of anoikis. MTHFD1 depletion obviously restrained the capacity of cellular antioxidant defense and inhibited tumor distant metastasis. Mechanistically, MTHFD1 was found to bind the protein arginine methyltransferase 5 (PRMT5) and then undergo symmetric dimethylation on R173 by PRMT5. Under suspension conditions, the interaction between MTHFD1 and PRMT5 was strengthened, which increased the symmetric dimethylation of MTHFD1. The elevated methylation of MTHFD1 largely augmented its metabolic activity to generate NADPH, therefore leading to anoikis resistance and distant organ metastasis. Therapeutically, genetic depletion or pharmacological inhibition of PRMT5 declined tumor distant metastasis. And R173 symmetric dimethylation status was associated with metastasis and prognosis of ESCC patients. In conclusion, our study uncovered a novel regulatory role and therapeutic implications of PRMT5/MTHFD1 axis in facilitating anoikis resistance and cancer metastasis.

An atlas of posttranslational modifications on RNA binding proteins

RNA structure and function are intimately tied to RNA binding protein recognition and regulation. Posttranslational modifications are chemical modifications which can control protein biology. The role of PTMs in the regulation RBPs is not well understood, in part due to a lacking analysis of PTM deposition on RBPs. Herein, we present an analysis of posttranslational modifications (PTMs) on RNA binding proteins (RBPs; a PTM RBP Atlas). We curate published datasets and primary literature to understand the landscape of PTMs and use protein-protein interaction data to understand and potentially provide a framework for understanding which enzymes are controlling PTM deposition and removal on the RBP landscape. Intersection of our data with The Cancer Genome Atlas also provides researchers understanding of mutations that would alter PTM deposition. Additional characterization of the RNA-protein interface provided from in-cell UV crosslinking experiments provides a framework for hypotheses about which PTMs could be regulating RNA binding and thus RBP function. Finally, we provide an online database for our data that is easy to use for the community. It is our hope our efforts will provide researchers will an invaluable tool to test the function of PTMs controlling RBP function and thus RNA biology.

Inactivating NHLH2 variants cause idiopathic hypogonadotropic hypogonadism and obesity in humans

Metabolism has a role in determining the time of pubertal development and fertility. Nonetheless, molecular/cellular pathways linking metabolism/body weight to puberty/reproduction are unknown. The KNDy (Kisspeptin/Neurokinin B/Dynorphin) neurons in the arcuate nucleus of the hypothalamus constitute the GnRH (gonadotropin-releasing hormone) pulse generator. We previously created a mouse model with a whole-body targeted deletion of nescient helix-loop-helix 2 (Nhlh2; N2KO), a class II member of the basic helix-loop-helix family of transcription factors. As this mouse model features pubertal failure and late-onset obesity, we wanted to study whether NHLH2 represents a candidate molecule to link metabolism and puberty in the hypothalamus. Exome sequencing of a large Idiopathic Hypogonadotropic Hypogonadism cohort revealed obese patients with rare sequence variants in NHLH2, which were characterized by in-silico protein analysis, chromatin immunoprecipitation, and luciferase reporter assays. In vitro heterologous expression studies demonstrated that the variant p.R79C impairs Nhlh2 binding to the Mc4r promoter. Furthermore, p.R79C and other variants show impaired transactivation of the human KISS1 promoter. These are the first inactivating human variants that support NHLH2's critical role in human puberty and body weight control. Failure to carry out this function results in the absence of pubertal development and late-onset obesity in humans.

Protein arginine methyltransferase 3 promotes glycolysis and hepatocellular carcinoma growth by enhancing arginine methylation of lactate dehydrogenase A

BACKGROUND

Protein arginine methylation has emerged a pivotal role in cancer progression. However, the role of protein arginine methyltransferase 3 (PRMT3) in hepatocellular carcinoma (HCC) remains unknown.

METHODS

The expression pattern of PRMT3 in HCC was analysed using quantitative real-time-polymerase chain reaction (qRT-PCR), Western blotting and immunohistochemistry assays. Loss- and gain-of-function experiments were carried out to determine the oncogenic role of PRMT3 in HCC. Glucose consumption and lactate production assays, seahorse bioscience, mass spectrometry, co-immunoprecipitation, metabonomic analysis and site-specific mutation experiments were used to explore the underlying molecular mechanisms. Furthermore, a xenograft mouse model was established to investigate the effects of PRMT3 and its inhibitor, SGC707, treatment on tumour growth in vivo.

RESULTS

The expression of PRMT3 was significantly upregulated in HCC, with high expression of which correlated with poor prognosis. PRMT3 knockdown led to the decrease in proliferation, glycolysis of HCC cells and tumour growth, whilst its overexpression showed opposite results. The catalytic activity of PRMT3 was important in mediating these biological processes. Mechanistically, our data showed that PRMT3 interacted with and mediated asymmetric dimethylarginine (ADMA) modification of lactate dehydrogenase A (LDHA) at arginine 112 (R112). Compared with LDHA-wild-type (LDHA-WT) cells, LDHA-R112K-mutant-expressing HCC cells exhibited a decrease in lactate dehydrogenase (LDH) activity, HCC cell glycolysis and proliferation. Furthermore, the administration of SGC707, a selective inhibitor of PRMT3, disrupted the PRMT3-mediated LDHA methylation and abolished PRMT3-induced HCC glycolysis and tumour growth.

CONCLUSIONS

Our results suggested a novel oncogenic role of PRMT3 in HCC, and it could be a promising therapeutic target for HCC by linking post-translational modification and cancer metabolism.

A comprehensive tool for accurate identification of methyl-Glutamine sites

Methylation is a biochemical process involved in nearly all of the human body functions. Glutamine is considered an indispensable amino acid that is susceptible to methylation via post-translational modification (PTM). Modern research has proved that methylation plays a momentous role in the progression of most types of cancers. Therefore, there is a need for an effective method to predict glutamine sites vulnerable to methylation accurately and inexpensively. The motive of this study is the formulation of an accurate method that could predict such sites with high accuracy. Various computationally intelligent classifiers were employed for their formulation and evaluation. Rigorous validations prove that deep learning performs best as compared to other classifiers. The accuracy (ACC) and the area under the receiver operating curve (AUC) obtained by 10-fold cross-validation was 0.962 and 0.981, while with the jackknife testing, it was 0.968 and 0.980, respectively. From these results, it is concluded that the proposed methodology works sufficiently well for the prediction of methyl-glutamine sites. The webserver's code, developed for the prediction of methyl-glutamine sites, is freely available at https://github.com/s20181080001/WebServer.git. The code can easily be set up by any intermediate-level Python user.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

The PRMT5/HURP axis retards Golgi repositioning by stabilizing acetyl-tubulin and Golgi apparatus during cell migration

The Golgi apparatus (GA) translocates to the cell leading end during directional migration, thereby determining cell polarity and transporting essential factors to the migration apparatus. The study provides mechanistic insights into how GA repositioning (GR) is regulated. We show that the methyltransferase PRMT5 methylates the microtubule regulator HURP at R122. The HURP methylation mimicking mutant 122F impairs GR and cell migration. Mechanistic studies revealed that HURP 122F or endogenous methylated HURP, that is, HURP m122, interacts with acetyl-tubulin. Overexpression of HURP 122F stabilizes the bundling pattern of acetyl-tubulin by decreasing the sensitivity of the latter to a microtubule disrupting agent nocodazole. HURP 122F also rigidifies GA via desensitizing the organelle to several GA disrupting chemicals. Similarly, the acetyl-tubulin mimicking mutant 40Q or tubulin acetyltransferase αTAT1 can rigidify GA, impair GR, and retard cell migration. Reversal of HURP 122F-induced GA rigidification, by knocking down GA assembly factors such as GRASP65 or GM130, attenuates 122F-triggered GR and cell migration. Remarkably, PRMT5 is found downregulated and the level of HURP m122 is decreased during the early hours of wound healing-based cell migration, collectively implying that the PRMT5-HURP-acetyl-tubulin axis plays the role of brake, preventing GR and cell migration before cells reach empty space.

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.

DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins

Methylation, which is one of the most prominent post-translational modifications on proteins, regulates many important cellular functions. Though several model-based methylation site predictors have been reported, all existing methods employ machine learning strategies, such as support vector machines and random forest, to predict sites of methylation based on a set of "hand-selected" features. As a consequence, the subsequent models may be biased toward one set of features. Moreover, due to the large number of features, model development can often be computationally expensive. In this paper, we propose an alternative approach based on deep learning to predict arginine methylation sites. Our model, which we termed DeepRMethylSite, is computationally less expensive than traditional feature-based methods while eliminating potential biases that can arise through features selection. Based on independent testing on our dataset, DeepRMethylSite achieved efficiency scores of 68%, 82% and 0.51 with respect to sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC), respectively. Importantly, in side-by-side comparisons with other state-of-the-art methylation site predictors, our method performs on par or better in all scoring metrics tested.

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.

FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

BACKGROUND

Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.

RESULTS

We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.

CONCLUSIONS

FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision-ideal for bench researchers with limited experience in handling computational tools.

Methylation-dependent SUMOylation of the architectural transcription factor HMGA2

High mobility group A2 (HMGA2) is a chromatin-associated protein involved in the regulation of stem cell function, embryogenesis and cancer development. Although the protein does not contain a consensus SUMOylation site, it is shown to be SUMOylated. In this study, we demonstrate that the first lysine residue in the reported K₆₆KAE SUMOylation motif in HMGA2 can be methylated in vitro and in vivo by the Set7/9 methyltransferase. By editing the lysine, the increased hydrophobicity of the resulting 6-N-methyl-lysine transforms the sequence into a consensus SUMO motif. This post-translational editing dramatically increases the subsequent SUMOylation of this site. Furthermore, similar putative methylation-dependent SUMO motifs are found in a number of other chromatin factors, and we confirm methylation-dependent SUMOylation of a site in one such protein, the Polyhomeotic complex 1 homolog (PHC1). Together, these results suggest that crosstalk between methylation and SUMOylation is a general mode for regulation of chromatin function.

Pontin arginine methylation by CARM1 is crucial for epigenetic regulation of autophagy

Autophagy is a catabolic process through which cytoplasmic components are degraded and recycled in response to various stresses including starvation. Recently, transcriptional and epigenetic regulations of autophagy have emerged as essential mechanisms for maintaining homeostasis. Here, we identify that coactivator-associated arginine methyltransferase 1 (CARM1) methylates Pontin chromatin-remodeling factor under glucose starvation, and methylated Pontin binds Forkhead Box O 3a (FOXO3a). Genome-wide analyses and biochemical studies reveal that methylated Pontin functions as a platform for recruiting Tip60 histone acetyltransferase with increased H4 acetylation and subsequent activation of autophagy genes regulated by FOXO3a. Surprisingly, CARM1-Pontin-FOXO3a signaling axis can work in the distal regions and activate autophagy genes through enhancer activation. Together, our findings provide a signaling axis of CARM1-Pontin-FOXO3a and further expand the role of CARM1 in nuclear regulation of autophagy.

Epigenetic regulations of autophagy have emerged as mechanisms for maintaining cellular homeostasis. Here the authors reveal that the CARM1-Pontin-FOXO3a signaling axis can activate autophagy related genes through enhancer activation.

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.

An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation

Butyrylation plays a crucial role in the cellular processes. Due to limit of techniques, it is a challenging task to identify histone butyrylation sites on a large scale. To fill the gap, we propose an approach based on information entropy and machine learning for computationally identifying histone butyrylation sites. The proposed method achieves 0.92 of area under the receiver operating characteristic (ROC) curve over the training set by 3-fold cross validation and 0.80 over the testing set by independent test. Feature analysis implies that amino acid residues in the down/upstream of butyrylation sites would exhibit specific sequence motif to a certain extent. Functional analysis suggests that histone butyrylation was most possibly associated with four pathways (systemic lupus erythematosus, alcoholism, viral carcinogenesis and transcriptional misregulation in cancer), was involved in binding with other molecules, processes of biosynthesis, assembly, arrangement or disassembly and was located in such complex as consists of DNA, RNA, protein, etc. The proposed method is useful to predict histone butyrylation sites. Analysis of feature and function improves understanding of histone butyrylation and increases knowledge of functions of butyrylated histones.

Hypothesis: protein and RNA attributes are continuously optimized over time

Background

Little is known why proteins and RNAs exhibit half-lives varying over several magnitudes. Despite many efforts, a conclusive link between half-lives and gene function could not be established suggesting that other determinants may influence these molecular attributes.

Results

Here, I find that with increasing gene age there is a gradual and significant increase of protein and RNA half-lives, protein structure, and other molecular attributes that tend to affect protein abundance. These observations are accommodated in a hypothesis which posits that new genes at ‘birth’ are not optimized and thus their products exhibit low half-lives and less structure but continuous mutagenesis eventually improves these attributes. Thus, the protein and RNA products of the oldest genes obtained their high degrees of stability and structure only after billions of years while the products of younger genes had less time to be optimized and are therefore less stable and structured. Because more stable proteins with lower turnover require less transcription to maintain the same level of abundance, reduced transcription-associated mutagenesis (TAM) would fixate the changes by increasing gene conservation.

Conclusions

Consequently, the currently observed diversity of molecular attributes is a snapshot of gene products being at different stages along their temporal path of optimization.

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.

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.

GlyStruct: glycation prediction using structural properties of amino acid residues

Background

Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs.

Results

We developed a classifier, GlyStruct based on support vector machine, to predict glycated and non-glycated lysine residues using structural properties of amino acid residues. The features used were secondary structure, accessible surface area and the local backbone torsion angles. For this work, a benchmark dataset was extracted containing 235 glycated and 303 non-glycated lysine residues. GlyStruct demonstrated improved performance of approximately 10% in comparison to benchmark method of Gly-PseAAC. The performance for GlyStruct on the metrics, sensitivity, specificity, accuracy and Mathew’s correlation coefficient were 0.7013, 0.7989, 0.7562, and 0.5065, respectively for 10-fold cross-validation.

Conclusion

Glycation has emerged to be one of the clinically important PTM of proteins in recent times. Therefore, the development of computational tools become necessary to predict glycation, which could help medical professionals administer drugs and manage patients more effectively. The proposed predictor manages to classify glycated and non-glycated lysine residues with promising results consistently on various cross-validation schemes and outperforms other state of the art methods.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2547-x) contains supplementary material, which is available to authorized users.

Web-Based Computational Tools for the Prediction and Analysis of Posttranslational Modifications of Proteins

The increase in the number of Web-based resources on posttranslational modification sites (PTMSs) in proteins is accelerating. This chapter presents a set of computational protocols describing how to work with the Internet resources when dealing with PTMSs. The protocols are intended for querying in PTMS-related databases, search of the PTMSs in the protein sequences and structures, and calculating the pI and molecular mass of the PTM isoforms. Thus, the modern bioinformatics prediction tools make it feasible to express protein modification in broader quantitative terms.

Multiple Arginine Residues Are Methylated in Drosophila Mre11 and Required for Survival Following Ionizing Radiation

Mre11 is a key player for DNA double strand break repair. Previous studies have shown that mammalian Mre11 is methylated at multiple arginines in its C-terminal Glycine-Arginine-Rich motif (GAR) by protein arginine methyltransferase PRMT1. Here, we found that the Drosophila Mre11 is methylated at arginines 559, 563, 565, and 569 in the GAR motif by DART1, the Drosophila homolog of PRMT1. Mre11 interacts with DART1 in S2 cells, and this interaction does not require the GAR motif. Arginines methylated Mre11 localizes exclusively in the nucleus as soluble nuclear protein or chromatin-binding protein. To study the in vivo functions of methylation, we generated the single Arg-Ala and all Arginines mutated flies. We found these mutants were sensitive to ionizing radiation. Furthermore, Arg-Ala mutated flies had no irradiation induced G2/M checkpoint defect in wing disc and eye disc. Thus, we provided evidence that arginines in Drosophila Mre11 are methylated by DART1 methytransferase and flies loss of arginine methylation are sensitive to irradiation.

Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins

Protein methylation is an essential posttranslational modification (PTM) mostly occurs at lysine and arginine residues, and regulates a variety of cellular processes. Owing to the rapid progresses in the large-scale identification of methylation sites, the available data set was dramatically expanded, and more attention has been paid on the identification of specific methylation types of modification residues. Here, we briefly summarized the current progresses in computational prediction of methylation sites, which provided an accurate, rapid and efficient approach in contrast with labor-intensive experiments. We collected 5421 methyllysines and methylarginines in 2592 proteins from the literature, and classified most of the sites into different types. Data analyses demonstrated that different types of methylated proteins were preferentially involved in different biological processes and pathways, whereas a unique sequence preference was observed for each type of methylation sites. Thus, we developed a predictor of GPS-MSP, which can predict mono-, di- and tri-methylation types for specific lysines, and mono-, symmetric di- and asymmetrical di-methylation types for specific arginines. We critically evaluated the performance of GPS-MSP, and compared it with other existing tools. The satisfying results exhibited that the classification of methylation sites into different types for training can considerably improve the prediction accuracy. Taken together, we anticipate that our study provides a new lead for future computational analysis of protein methylation, and the prediction of methylation types of covalently modified lysine and arginine residues can generate more useful information for further experimental manipulation.

ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives

Motivation

Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry-based methods. Existing prediction methods use randomly selected non-validated sites as non-ubiquitination sites to train ubiquitination site prediction models.

Results

We propose an evolutionary screening algorithm (ESA) to select effective negatives among non-validated sites and an ESA-based prediction method, ESA-UbiSite, to identify human ubiquitination sites. The ESA selects non-validated sites least likely to be ubiquitination sites as training negatives. Moreover, the ESA and ESA-UbiSite use a set of well-selected physicochemical properties together with a support vector machine for accurate prediction. Experimental results show that ESA-UbiSite with effective negatives achieved 0.92 test accuracy and a Matthews's correlation coefficient of 0.48, better than existing prediction methods. The ESA increased ESA-UbiSite's test accuracy from 0.75 to 0.92 and can improve other post-translational modification site prediction methods.

Availability and Implementation

An ESA-UbiSite-based web server has been established at http://iclab.life.nctu.edu.tw/iclab_webtools/ESAUbiSite/ .

Contact

syho@mail.nctu.edu.tw.

Supplementary information

Supplementary data are available at Bioinformatics online.

Haplotype analysis of the germacrene A synthase gene and association with cynaropicrin content and biological activities in Cynara cardunculus

Cynara cardunculus: L. represents a natural source of terpenic compounds, with the predominant molecule being cynaropicrin. Cynaropicrin is gaining interest since it has been correlated to anti-hyperlipidaemia, antispasmodic and cytotoxicity activity against leukocyte cancer cells. The objective of this work was to screen a collection of C. cardunculus, from different origins, for new allelic variants in germacrene A synthase (GAS) gene involved in the cynaropicrin biosynthesis and correlate them with improved cynaropicrin content and biological activities. Using high-resolution melting, nine haplotypes were identified. The putative impact of the identified allelic variants in GAS protein was evaluated by bioinformatic tools and polymorphisms that putatively lead to protein conformational changes were described. Additionally, cynaropicrin and main pentacyclic triterpenes contents, and antithrombin, antimicrobial and antiproliferative activities were also determined in C. cardunculus leaf lipophilic-derived extracts. In this work we identified allelic variants with putative impact on GAS protein, which are significantly associated with cynaropicrin content and antiproliferative activity. The results obtained suggest that the identified polymorphisms should be explored as putative genetic markers correlated with biological properties in Cynara cardunculus.

PRmePRed: A protein arginine methylation prediction tool

Protein methylation is an important Post-Translational Modification (PTMs) of proteins. Arginine methylation carries out and regulates several important biological functions, including gene regulation and signal transduction. Experimental identification of arginine methylation site is a daunting task as it is costly as well as time and labour intensive. Hence reliable prediction tools play an important task in rapid screening and identification of possible methylation sites in proteomes. Our preliminary assessment using the available prediction methods on collected data yielded unimpressive results. This motivated us to perform a comprehensive data analysis and appraisal of features relevant in the context of biological significance, that led to the development of a prediction tool PRmePRed with better performance. The PRmePRed perform reasonably well with an accuracy of 84.10%, 82.38% sensitivity, 83.77% specificity, and Matthew’s correlation coefficient of 66.20% in 10-fold cross-validation. PRmePRed is freely available at http://bioinfo.icgeb.res.in/PRmePRed/

KSHV encoded ORF59 modulates histone arginine methylation of the viral genome to promote viral reactivation

Kaposi's sarcoma associated herpesvirus (KSHV) persists in a highly-ordered chromatin structure inside latently infected cells with the majority of the viral genome having repressive marks. However, upon reactivation the viral chromatin landscape changes into 'open' chromatin through the involvement of lysine demethylases and methyltransferases. Besides methylation of lysine residues of histone H3, arginine methylation of histone H4 plays an important role in controlling the compactness of the chromatin. Symmetric methylation of histone H4 at arginine 3 (H4R3me2s) negatively affects the methylation of histone H3 at lysine 4 (H3K4me3), an active epigenetic mark deposited on the viral chromatin during reactivation. We identified a novel binding partner to KSHV viral DNA processivity factor, ORF59-a protein arginine methyl transferase 5 (PRMT5). PRMT5 is an arginine methyltransferase that dimethylates arginine 3 (R3) of histone H4 in a symmetric manner, one hallmark of condensed chromatin. Our ChIP-seq data of symmetrically methylated H4 arginine 3 showed a significant decrease in H4R3me2s on the viral genome of reactivated cells as compared to the latent cells. Reduction in arginine methylation correlated with the binding of ORF59 on the viral chromatin and disruption of PRMT5 from its adapter protein, COPR5 (cooperator of PRMT5). Binding of PRMT5 through COPR5 is important for symmetric methylation of H4R3 and the expression of ORF59 competitively reduces the association of PRMT5 with COPR5, leading to a reduction in PRMT5 mediated arginine methylation. This ultimately resulted in a reduced level of symmetrically methylated H4R3 and increased levels of H3K4me3 marks, contributing to the formation of an open chromatin for transcription and DNA replication. Depletion of PRMT5 levels led to a decrease in symmetric methylation and increase in viral gene transcription confirming the role of PRMT5 in viral reactivation. In conclusion, ORF59 modulates histone-modifying enzymes to alter the chromatin structure during lytic reactivation.

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.

Impact of novel SNPs identified in Cynara cardunculus genes on functionality of proteins regulating phenylpropanoid pathway and their association with biological activities

BACKGROUND

Cynara cardunculus L. offers a natural source of phenolic compounds with the predominant molecule being chlorogenic acid. Chlorogenic acid is gaining interest due to its involvement in various biological properties such as, antibacterial, antifungal, antioxidant, hepatoprotective, and anticarcinogenic activities.

RESULTS

In this work we screened a Cynara cardunculus collection for new allelic variants in key genes involved in the chlorogenic acid biosynthesis pathway. The target genes encode p-coumaroyl ester 3'-hydroxylase (C3'H) and hydroxycinnamoyl-CoA: quinate hydroxycinnamoyl transferase (HQT), both participating in the synthesis of chlorogenic acid. Using high-resolution melting, the C3'H gene proved to be highly conserved with only 4 haplotypes while, for HQT, 17 haplotypes were identified de novo. The putative influence of the identified polymorphisms in C3'H and HQT proteins was further evaluated using bioinformatics tools. We could identify some polymorphisms that may lead to protein conformational changes. Chlorogenic acid content, antioxidant and antithrombin activities were also evaluated in Cc leaf extracts and an association analysis was performed to assess a putative correlation between these traits and the identified polymorphisms.

CONCLUSION

In this work we identified allelic variants with putative impact on C3'H and HQT proteins which are significantly associated with chlorogenic acid content and antioxidant activity. Further study of these alleles should be explored to assess putative relevance as genetic markers correlating with Cynara cardunculus biological properties with further confirmation by functional analysis.

Strategies for large-scale analysis of non-histone protein methylation by LC-MS/MS

Protein methylation is an important post-translational modification (PTM) that plays crucial roles in the regulation of diverse biological processes.

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.

Asymmetric arginine dimethylation of RelA provides a repressive mark to modulate TNFα/NF-κB response

Nuclear factor kappa B (NF-κB) is an inducible transcription factor that plays critical roles in immune and stress responses and is often implicated in pathologies, including chronic inflammation and cancer. Although much has been learned about NF-κB-activating pathways, the specific repression of NF-κB is far less well understood. Here we identified the type I protein arginine methyltransferase 1 (PRMT1) as a restrictive factor controlling TNFα-induced activation of NF-κB. PRMT1 forms a cellular complex with NF-κB through direct interaction with the Rel homology domain of RelA. We demonstrate that PRMT1 methylates RelA at evolutionary conserved R30, located in the DNA-binding L1 loop, which is a critical residue required for DNA binding. Asymmetric R30 dimethylation inhibits the binding of RelA to DNA and represses NF-κB target genes in response to TNFα. Molecular dynamics simulations of the DNA-bound RelA:p50 predicted structural changes in RelA caused by R30 methylation or a mutation that interferes with the stability of the DNA-NF-κB complex. Our findings provide evidence for the asymmetric arginine dimethylation of RelA and unveil a unique mechanism controlling TNFα/NF-κB signaling.

An in silico approach to investigate the source of the controversial interpretations about the phenotypic results of the human AhR-gene G1661A polymorphism

Aryl hydrocarbon receptor (AhR) acts as an enhancer binding ligand-activated intracellular receptor. Chromatin remodeling components and general transcription factors such as TATA-binding protein (TBP) are evoked on AhR-target genes by interaction with its flexible transactivation domain (TAD). AhR-G1661A single nucleotide polymorphism (SNP: rs2066853) causes an arginine to lysine substitution in the acidic sub-domain of TAD at position 554 (R554K). Although, numerous studies associate the SNP with some abnormalities such as cancer, other reliable investigations refuse the associations. Consequently, the interpretation of the phenotypic results of G1661A-transition has been controversial. In this study, an in silico analysis were performed to investigate the possible effects of the transition on AhR-mRNA, protein structure, interaction properties and modifications. The analysis revealed that the R554K substitution affects secondary structure and solvent accessibility of adjacent residues. Also, it causes to decreasing of the AhR stability; altering the hydropathy features of the local sequence and changing the pattern of the residues at the binding site of the TAD-acidic sub-domain. Generating of new sites for ubiquitination and acetylation for AhR-K554 variant respectively at positions 544 and 560 was predicted. Our findings intensify the idea that the AhR-G1661A transition may affects AhR-TAD interactions, especially with the TBP, which influence AhR-target genes expression. However, the previously reported flexibility of the modular TAD could act as an intervening factor, moderate the SNP effects and causes distinct outcomes in different individuals and tissues.

AthMethPre: a web server for the prediction and query of mRNA m6A sites in Arabidopsis thaliana

The first web server that provides a user-friendly tool for the prediction and query of A. thaliana mRNA m⁶A sites.