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.     

Prediction and analysis of protein hydroxyproline and hydroxylysine

Background

Hydroxylation is an important post-translational modification and closely related to various diseases. Besides the biotechnology experiments, in silico prediction methods are alternative ways to identify the potential hydroxylation sites.

Methodology/Principal Findings

In this study, we developed a novel sequence-based method for identifying the two main types of hydroxylation sites – hydroxyproline and hydroxylysine. First, feature selection was made on three kinds of features consisting of amino acid indices (AAindex) which includes various physicochemical properties and biochemical properties of amino acids, Position-Specific Scoring Matrices (PSSM) which represent evolution information of amino acids and structural disorder of amino acids in the sliding window with length of 13 amino acids, then the prediction model were built using incremental feature selection method. As a result, the prediction accuracies are 76.0% and 82.1%, evaluated by jackknife cross-validation on the hydroxyproline dataset and hydroxylysine dataset, respectively. Feature analysis suggested that physicochemical properties and biochemical properties and evolution information of amino acids contribute much to the identification of the protein hydroxylation sites, while structural disorder had little relation to protein hydroxylation. It was also found that the amino acid adjacent to the hydroxylation site tends to exert more influence than other sites on hydroxylation determination.

Conclusions/Significance

These findings may provide useful insights for exploiting the mechanisms of hydroxylation.     

Readers of histone methylarginine marks

Arginine methylation is a common posttranslational modification (PTM) that alters roughly 0.5% of all arginine residues in the cells. There are three types of arginine methylation: monomethylarginine (MMA), asymmetric dimethylarginine (ADMA), and symmetric dimethylarginine (SDMA). These three PTMs are enriched on RNA-binding proteins and on histones, and also impact signal transduction cascades. To date, over thirty arginine methylation sites have been cataloged on the different core histones. These modifications alter protein structure, impact interactions with DNA, and also generate docking sites for effector molecules. The primary “readers” of methylarginine marks are Tudor domain-containing proteins. The complete family of thirty-six Tudor domain-containing proteins has yet to be fully characterized, but at least ten bind methyllysine motifs and eight bind methylarginine motifs. In this review, we will highlight the biological roles of the Tudor domains that interact with arginine methylated motifs, and also address other types of interactions that are regulated by these particular PTMs. This article is part of a Special Issue entitled: Molecular mechanisms of histone modification function.     

Eukaryote-Conserved Methylarginine Is Absent in Diplomonads and Functionally Compensated in Giardia

Methylation is a common posttranslational modification of arginine and lysine in eukaryotic proteins. Methylproteomes are best characterized for higher eukaryotes, where they are functionally expanded and evolved complex regulation. However, this is not the case for protist species evolved from the earliest eukaryotic lineages. Here, we integrated bioinformatic, proteomic, and drug-screening data sets to comprehensively explore the methylproteome of Giardia duodenalis—a deeply branching parasitic protist. We demonstrate that Giardia and related diplomonads lack arginine-methyltransferases and have remodeled conserved RGG/RG motifs targeted by these enzymes. We also provide experimental evidence for methylarginine absence in proteomes of Giardia but readily detect methyllysine. We bioinformatically infer 11 lysine-methyltransferases in Giardia, including highly diverged Su(var)3-9, Enhancer-of-zeste and Trithorax proteins with reduced domain architectures, and novel annotations demonstrating conserved methyllysine regulation of eukaryotic elongation factor 1 alpha. Using mass spectrometry, we identify more than 200 methyllysine sites in Giardia, including in species-specific gene families involved in cytoskeletal regulation, enriched in coiled-coil features. Finally, we use known methylation inhibitors to show that methylation plays key roles in replication and cyst formation in this parasite. This study highlights reduced methylation enzymes, sites, and functions early in eukaryote evolution, including absent methylarginine networks in the Diplomonadida. These results challenge the view that arginine methylation is eukaryote conserved and demonstrate that functional compensation of methylarginine was possible preceding expansion and diversification of these key networks in higher eukaryotes.     

Prediction of protein quaternary structure with feature selection and analysis based on protein biological features

Information of protein quaternary structure can help to understand the biological functions of proteins. Because wet-lab experiments are both time-consuming and costly, we adopt a novel computational approach to assign proteins into 10 kinds of quaternary structures. By coding each protein using its biochemical and physicochemical properties, feature selection was carried out using Incremental Feature Selection (IFS) method. The thus obtained optimal feature set consisted of 97 features, with which the prediction model was built. As a result, the overall prediction success rate is 74.90% evaluated by Jackknife test, much higher than the overall correct rate of a random guess 10% (1/10). The further feature analysis indicates that protein secondary structure is the most contributed feature in the prediction of protein quaternary structure.     

Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches

S-nitrosylation (SNO) is one of the most important and universal post-translational modifications (PTMs) which regulates various cellular functions and signaling events. Identification of the exact S-nitrosylation sites in proteins may facilitate the understanding of the molecular mechanisms and biological function of S-nitrosylation. Unfortunately, traditional experimental approaches used for detecting S-nitrosylation sites are often laborious and time-consuming. However, computational methods could overcome this demerit. In this work, we developed a novel predictor based on nearest neighbor algorithm (NNA) with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The features of physicochemical/biochemical properties, sequence conservation, residual disorder, amino acid occurrence frequency, second structure and the solvent accessibility were utilized to represent the peptides concerned. Feature analysis showed that the features except residual disorder affected identification of the S-nitrosylation sites. It was also shown via the site-specific feature analysis that the features of sites away from the central cysteine might contribute to the S-nitrosylation site determination through a subtle manner. It is anticipated that our prediction method may become a useful tool for identifying the protein S-nitrosylation sites and that the features analysis described in this paper may provide useful insights for in-depth investigation into the mechanism of S-nitrosylation.     

Predicting DNA-binding sites of proteins based on sequential and 3D structural information

Protein–DNA interactions play important roles in many biological processes. To understand the molecular mechanisms of protein–DNA interaction, it is necessary to identify the DNA-binding sites in DNA-binding proteins. In the last decade, computational approaches have been developed to predict protein–DNA-binding sites based solely on protein sequences. In this study, we developed a novel predictor based on support vector machine algorithm coupled with the maximum relevance minimum redundancy method followed by incremental feature selection. We incorporated not only features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure, solvent accessibility, but also five three-dimensional (3D) structural features calculated from PDB data to predict the protein–DNA interaction sites. Feature analysis showed that 3D structural features indeed contributed to the prediction of DNA-binding site and it was demonstrated that the prediction performance was better with 3D structural features than without them. It was also shown via analysis of features from each site that the features of DNA-binding site itself contribute the most to the prediction. Our prediction method may become a useful tool for identifying the DNA-binding sites and the feature analysis described in this paper may provide useful insights for in-depth investigations into the mechanisms of protein–DNA interaction.     

Prediction of tyrosine sulfation with mRMR feature selection and analysis

Protein tyrosine sulfation is a ubiquitous post-translational modification (PTM) of secreted and transmembrane proteins that pass through the Golgi apparatus. In this study, we developed a new method for protein tyrosine sulfation prediction based on a nearest neighbor algorithm with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). We incorporated features of sequence conservation, residual disorder, and amino acid factor, 229 features in total, to predict tyrosine sulfation sites. From these 229 features, 145 features were selected and deemed as the optimized features for the prediction. The prediction model achieved a prediction accuracy of 90.01% using the optimal 145-feature set. Feature analysis showed that conservation, disorder, and physicochemical/biochemical properties of amino acids all contributed to the sulfation process. Site-specific feature analysis showed that the features derived from its surrounding sites contributed profoundly to sulfation site determination in addition to features derived from the sulfation site itself. The detailed feature analysis in this paper might help understand more of the sulfation mechanism and guide the related experimental validation.     

Deciphering arginine methylation: Tudor tells the tale

Proteins can be modified by post-translational modifications such as phosphorylation, methylation, acetylation and ubiquitylation, creating binding sites for specific protein domains. Methylation has pivotal roles in the formation of complexes that are involved in cellular regulation, including in the generation of small RNAs. Arginine methylation was discovered half a century ago, but the ability of methylarginine sites to serve as binding motifs for members of the Tudor protein family, and the functional significance of the protein-protein interactions that are mediated by Tudor domains, has only recently been appreciated. Tudor proteins are now known to be present in PIWI complexes, where they are thought to interact with methylated PIWI proteins and regulate the PIWI-interacting RNA (piRNA) pathway in the germ line.     

Methylations of 70,000-Da heat shock proteins in 3T3 cells: alterations by arsenite treatment, by different stages of growth and by virus transformation.

We have characterized the basic amino acid methylation of three members of the 70,000-Da heat shock protein superfamily, hsp68, hsc70, and BiP, in Balb/c 3T3 cells. It appears that a lysyl residue is the only methylation site in BiP and that both lysyl and arginyl residues are methylated in hsp68 and hsc70. In all cases, epsilon-N-trimethyllysine is the predominant methyllysine species. Both NG-monomethylarginine and NG,NG-dimethylarginine are identified as the methylarginine species. The stoichiometry of the methylation is indirectly determined by using the amount of actin methylation as a reference. Three, four, and four methyl groups are incorporated into lysyl residues of hsp68, hsc70, and BiP, respectively. The level of lysyl methylation in hsc70 remains unchanged under different growth conditions. On the other hand, the arginyl methylation in hsc70 varies considerably. In confluent Balb/c 3T3 cells, there are 1.8 and 1.3 methyl groups in dimethylarginine and monomethyl-arginine, respectively. In nonconfluent cells, the amount of monomethylarginine is similar to that in confluent cells, but dimethylarginine is not detectable. Furthermore, in both confluent and nonconfluent cells, the level of monomethylarginine is reduced 5- to 10-fold after arsenite treatment. However, in 3T3 cells transformed by Rous sarcoma virus (SR-RSV 3T3 cells), the level of arginine methylation is constitutively lower and cannot be reduced further by arsenite.     

Prediction of protein domain with mRMR feature selection and analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28-40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.     

Prediction of methylated CpGs in DNA sequences using a support vector machine

DNA methylation plays a key role in the regulation of gene expression. The most common type of DNA modification consists of the methylation of cytosine in the CpG dinucleotide. At the present time, there is no method available for the prediction of DNA methylation sites. Therefore, in this study we have developed a support vector machine (SVM)-based method for the prediction of cytosine methylation in CpG dinucleotides. Initially a SVM module was developed from human data for the prediction of human-specific methylation sites. This module achieved a MCC and AUC of 0.501 and 0.814, respectively, when evaluated using a 5-fold cross-validation. The performance of this SVM-based module was better than the classifiers built using alternative machine learning and statistical algorithms including artificial neural networks, Bayesian statistics, and decision trees. Additional SVM modules were also developed based on mammalian- and vertebrate-specific methylation patterns. The SVM module based on human methylation patterns was used for genome-wide analysis of methylation sites. This analysis demonstrated that the percentage of methylated CpGs is higher in UTRs as compared to exonic and intronic regions of human genes. This method is available on line for public use under the name of Methylator at http://bio.dfci.harvard.edu/Methylator/.     

Arabidopsis DEMETER-LIKE proteins DML2 and DML3 are required for appropriate distribution of DNA methylation marks

Cytosine DNA methylation is a stable epigenetic mark for maintenance of gene silencing across cellular divisions, but it is a reversible modification. Genetic and biochemical studies have revealed that the Arabidopsis DNA glycosylase domain-containing proteins ROS1 (REPRESSOR OF SILENCING 1) and DME (DEMETER) initiate erasure of 5-methylcytosine through a base excision repair process. The Arabidopsis genome encodes two paralogs of ROS1 and DME, referred to as DEMETER-LIKE proteins DML2 and DML3. We have found that DML2 and DML3 are 5-methylcytosine DNA glycosylases that are expressed in a wide range of plant organs. We analyzed the distribution of methylation marks at two methylated loci in wild-type and dml mutant plants. Mutations in DML2 and/or DML3 lead to hypermethylation of cytosine residues that are unmethylated or weakly methylated in wild-type plants. In contrast, sites that are heavily methylated in wild-type plants are hypomethylated in mutants. These results suggest that DML2 and DML3 are required not only for removing DNA methylation marks from improperly-methylated cytosines, but also for maintenance of high methylation levels in properly targeted sites.     

Surface accessibility of protein post-translational modifications

Protein post-translational modifications are crucial to the function of many proteins. In this study, we have investigated the structural environment of 8378 incidences of 44 types of post-translational modifications with 19 different approaches. We show that modified amino acids likely to be involved in protein-protein interactions, such as ester-linked phosphorylation, methylarginine, acetyllysine, sulfotyrosine, hydroxyproline, and hydroxylysine, are clearly surface associated. Other modifications, including O-GlcNAc, phosphohistidine, 4-aspartylphosphate, methyllysine, and ADP-ribosylarginine, are either not surface associated or are in a protein's core. Artifactual modifications were found to be randomly distributed throughout the protein. We discuss how the surface accessibility of post-translational modifications can be important for protein-protein interactivity.     

Predictive QM/MM Modeling of Modulations in Protein–Protein Binding by Lysine Methylation

Lysine methylation is a key regulator of protein–protein binding. The amine group of lysine can accept up to three methyl groups, and experiments show that protein–protein binding free energies are sensitive to the extent of methylation. These sensitivities have been rationalized in terms of chemical and structural features present in the binding pockets of methyllysine binding domains. However, understanding their specific roles requires an energetic analysis. Here we propose a theoretical framework to combine quantum and molecular mechanics methods, and compute the effect of methylation on protein–protein binding free energies. The advantages of this approach are that it derives contributions from all local non-trivial effects of methylation on induction, polarizability and dispersion directly from self-consistent electron densities, and at the same time determines contributions from well-characterized hydration effects using a computationally efficient classical mean field method. Limitations of the approach are discussed, and we note that predicted free energies of fourteen out of the sixteen cases agree with experiment. Critical assessment of these cases leads to the following overarching principles that drive methylation-state recognition by protein domains. Methylation typically reduces the pairwise interaction between proteins. This biases binding toward lower methylated states. Simultaneously, however, methylation also makes it easier to partially dehydrate proteins and place them in protein–protein complexes. This latter effect biases binding in favor of higher methylated states. The overall effect of methylation on protein–protein binding depends ultimately on the balance between these two effects, which is observed to be tuned via several combinations of local features.     

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

Background

Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.

Methods/Principal Findings

To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.

Conclusion/Significance

Our results indicate that the network prediction system thus established is quite promising and encouraging.     

Conversion of a bacterial warm sensor to a cold sensor by methylation of a single residue in the presence of an attractant

The aspartate chemoreceptor (Tar) of Escherichia coli also serves as a thermosensor, and it is very amenable to genetic and biochemical analysis of the thermosensing mechanism. Its thermosensing properties are controlled by reversible methylation of the cytoplasmic signalling/adaptation domain of the protein. The unmethylated and the fully methylated (aspartate-bound) receptors sense, as attractant stimuli, increases (warm sensor) and decreases (cold sensor) in temperature respectively. To learn more about the mechanism of thermosensing, we replaced the four methyl-accepting glutamyl residues with non-methylatable aspartyl residues in all possible combinations. In a strain defective in both methyltransferase (CheR) and methylesterase (CheB) activities, all of the mutant Tar proteins functioned as warm sensors. To create a situation in which all of the remaining glutamyl residues were methylated, we expressed the mutant proteins in a CheB-defective, CheR-overproducing strain. The fully glutamyl-methylated proteins were designed to mimic the full range of methylation states possible for wild-type Tar. Almost all of the methylated mutant receptors, including those with single glutamyl residues, were cold sensors in the presence of aspartate. Thus, binding of aspartate to Tar and methylation of its single glutamyl residue can invert its temperature-dependent signalling properties.     

Nuclear proteins damage by alkylating agents with different degrees of carcinogenicity

We have tried to establish a correlation between the carcinogenic potency of two alkylating compounds and specific target sites in chromatin. We have therefore compared the nuclear metabolism of radioactively-labelled methylmethanesulfonate (MMS), a relatively weak carcinogen and N-methylnitrosourea (MNU), a highly potent carcinogen in cultured primary hepatocytes which have, high microsomal drug-metabolizing activity and in V79 Chinese hamster cells which have low microsomal enzymatic activity. The modification of specific amino acid residues in acid-soluble nuclear proteins (H) and non-histone nuclear proteins (NH) was studied after exposing the cells to various doses of alkylating agents overnight. We found that at all doses, mainly the cysteine (Cys), but also to a lower extent the histidine (His) residues are methylated in both H and NH protein fractions by MMS. At high doses of MMS, traces of methylarginine and methylated lysines could be detected. MNU predominantly methylates lysine and arginine residues, the former being found mostly in H, the latter in NH. Although both hepatocytes and V79 cells metabolized radioactively-labelled carcinogen, a higher percentage of counts were incorporated by the hepatocytes; 'unusually' methylated amino acids were detectable in the hepatocyte proteins with relatively low doses of the alkylating agents but not in V79 cells. In the presence of exogenous microsomes, during exposure of V79 cells to the alkylating agents, the amount of amino acid methylation is qualitatively and quantitatively similar to that found in hepatocytes. Our data suggest a specific mechanism of protein methylation, at the level of target amino acids, for carcinogens with different potencies similar to what has been found for DNA bases. A component of the microsomal fraction (S9) may be able to enhance this effect.     

An overview of the analysis of DNA methylation in mammalian genomes

DNA methylation at position C5 of the pyrimidine ring of cytosine in mammalian genomes has received a great deal of research interest due to its importance in many biological phenomena. It is associated with events such as epigenetic gene silencing and the maintenance of genome integrity. Aberrant DNA methylation, particularly that of chromosomal regions called CpG islands, is an important step in carcinogenesis. In order to elucidate methylation profiling of complex genomes, various methods have been developed. Many of these methods are based on the differential reactivity of cytosine and 5-methylcytosine to various chemicals. The combined use of these chemical reactions and other preexisting methods has enabled the discrimination of cytosine and 5-methylcytosine in complex genomes. The use of proteins that preferentially bind to methylated DNA has also successfully been used to discriminate between methylated and unmethylated sites. The chemical and structural dissection of the in vivo processes of enzymatic methylation and the binding of methyl-CpG binding proteins provides evidence for the complex mechanisms that nature has acquired. In this review we summarize the methods available for the discrimination between cytosine and 5-methylcytosine in complex genomes.