Site-Specific Systematic Analysis of Lysine Modification Crosstalk.

Research has revealed that post-translational modifications (PTMs) that occur at lysine (PLMs) can cooperatively regulate various biological processes by crosstalk. However, the trend of the crosstalk between multiple PLMs and the properties of PLM crosstalk require additional investigation. Here, the crosstalk among acetylation, succinylation, and SUMOylation is systematically studied in a site-specific waz. First, crosstalk between SUMOylation is detected and succinylation is found to be underexpressed, whereas succinylation tends to crosstalk with acetylation and SUMOylation on the same lysine residue while PLM crosstalk is tissue-specific across different species. Further analysis reveals that different PLMs tend to occur crosstalk at diverse subcellular compartments and structural regions, and they participate in distinct biological processes and functions. Additionally, short-term evolutionary analysis shows that there is no additional evolutionary pressure on PLMs crosstalk sites, as found by comparison with singly modified sites. Finally, phylogenetic classification reveals that genes with co-occupied lysine crosstalk are more likely to have higher evolutionary similarity and possess a tendency to cluster in the specific branch. The integrated approach reported here has the potential for large-scale prioritization of in situ crosstalk of PLM candidates and provides a profound understanding of the underlying relationship between different lysine modifications.

Computational prediction of species-specific malonylation sites via enhanced characteristic strategy.

MOTIVATION: Protein malonylation is a novel post-translational modification (PTM) which orchestrates a variety of biological processes. Annotation of malonylation in proteomics is the first-crucial step to decipher its physiological roles which are implicated in the pathological processes. Comparing with the expensive and laborious experimental research, computational prediction can provide an accurate and effective approach to the identification of many types of PTMs sites. However, there is still no online predictor for lysine malonylation. RESULTS: By searching from literature and database, a well-prepared up-to-data benchmark datasets were collected in multiple organisms. Data analyses demonstrated that different organisms were preferentially involved in different biological processes and pathways. Meanwhile, unique sequence preferences were observed for each organism. Thus, a novel malonylation site online prediction tool, called MaloPred, which can predict malonylation for three species, was developed by integrating various informative features and via an enhanced feature strategy. On the independent test datasets, AUC (area under the receiver operating characteristic curves) scores are obtained as 0.755, 0.827 and 0.871 for Escherichia coli ( E.coli ), Mus musculus ( M.musculus ) and Homo sapiens ( H.sapiens ), respectively. The satisfying results suggest that MaloPred can provide more instructive guidance for further experimental investigation of protein malonylation. AVAILABILITY AND IMPLEMENTATION: http://bioinfo.ncu.edu.cn/MaloPred.aspx . CONTACT: jdqiu@ncu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Computing Prediction and Functional Analysis of Prokaryotic Propionylation.

Identification and systematic analysis of candidates for protein propionylation are crucial steps for understanding its molecular mechanisms and biological functions. Although several proteome-scale methods have been performed to delineate potential propionylated proteins, the majority of lysine-propionylated substrates and their role in pathological physiology still remain largely unknown. By gathering various databases and literatures, experimental prokaryotic propionylation data were collated to be trained in a support vector machine with various features via a three-step feature selection method. A novel online tool for seeking potential lysine-propionylated sites (PropSeek) ( http://bioinfo.ncu.edu.cn/PropSeek.aspx ) was built. Independent test results of leave-one-out and n-fold cross-validation were similar to each other, showing that PropSeek is a stable and robust predictor with satisfying performance. Meanwhile, analyses of Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathways, and protein-protein interactions implied a potential role of prokaryotic propionylation in protein synthesis and metabolism.

Proteomic analysis and prediction of human phosphorylation sites in subcellular level reveal subcellular specificity.

MOTIVATION: Protein phosphorylation is the most common post-translational modification (PTM) regulating major cellular processes through highly dynamic and complex signaling pathways. Large-scale comparative phosphoproteomic studies have frequently been done on whole cells or organs by conventional bottom-up mass spectrometry approaches, i.e at the phosphopeptide level. Using this approach, there is no way to know from where the phosphopeptide signal originated. Also, as a consequence of the scale of these studies, important information on the localization of phosphorylation sites in subcellular compartments (SCs) is not surveyed. RESULTS: Here, we present a first account of the emerging field of subcellular phosphoproteomics where a support vector machine (SVM) approach was combined with a novel algorithm of discrete wavelet transform (DWT) to facilitate the identification of compartment-specific phosphorylation sites and to unravel the intricate regulation of protein phosphorylation. Our data reveal that the subcellular phosphorylation distribution is compartment type dependent and that the phosphorylation displays site-specific sequence motifs that diverge between SCs. AVAILABILITY AND IMPLEMENTATION: The method and database both are available as a web server at: http://bioinfo.ncu.edu.cn/SubPhos.aspx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SuccFind: a novel succinylation sites online prediction tool via enhanced characteristic strategy.

UNLABELLED: Lysine succinylation orchestrates a variety of biological processes. Annotation of succinylation in proteomes is the first-crucial step to decipher physiological roles of succinylation implicated in the pathological processes. In this work, we developed a novel succinylation site online prediction tool, called SuccFind, which is constructed to predict the lysine succinylation sites based on two major categories of characteristics: sequence-derived features and evolutionary-derived information of sequence and via an enhanced feature strategy for further optimizations. The assessment results obtained from cross-validation suggest that SuccFind can provide more instructive guidance for further experimental investigation of protein succinylation. AVAILABILITY AND IMPLEMENTATION: A user-friendly server is freely available on the web at: http://bioinfo.ncu.edu.cn/SuccFind.aspx. CONTACT: jdqiu@ncu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[Post-translational modification (PTM) bioinformatics in China: progresses and perspectives].

Post-translational modifications (PTMs) are essential for regulating conformational changes, activities and functions of proteins, and are involved in almost all cellular pathways and processes. Identification of protein PTMs is the basis for understanding cellular and molecular mechanisms. In contrast with labor-intensive and time-consuming experiments, the PTM prediction using various bioinformatics approaches can provide accurate, convenient, and efficient strategies and generate valuable information for further experimental consideration. In this review, we summarize the current progresses made by Chineses bioinformaticians in the field of PTM Bioinformatics, including the design and improvement of computational algorithms for predicting PTM substrates and sites, design and maintenance of online and offline tools, establishment of PTM-related databases and resources, and bioinformatics analysis of PTM proteomics data. Through comparing similar studies in China and other countries, we demonstrate both advantages and limitations of current PTM bioinformatics as well as perspectives for future studies in China.

Incorporating key position and amino acid residue features to identify general and species-specific Ubiquitin conjugation sites.

MOTIVATION: Systematic dissection of the ubiquitylation proteome is emerging as an appealing but challenging research topic because of the significant roles ubiquitylation play not only in protein degradation but also in many other cellular functions. High-throughput experimental studies using mass spectrometry have identified many ubiquitylation sites, primarily from eukaryotes. However, the vast majority of ubiquitylation sites remain undiscovered, even in well-studied systems. Because mass spectrometry-based experimental approaches for identifying ubiquitylation events are costly, time-consuming and biased toward abundant proteins and proteotypic peptides, in silico prediction of ubiquitylation sites is a potentially useful alternative strategy for whole proteome annotation. Because of various limitations, current ubiquitylation site prediction tools were not well designed to comprehensively assess proteomes. RESULTS: We present a novel tool known as UbiProber, specifically designed for large-scale predictions of both general and species-specific ubiquitylation sites. We collected proteomics data for ubiquitylation from multiple species from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates the information from key positions and key amino acid residues. Cross-validation tests reveal that UbiProber achieves some improvement over existing tools in predicting species-specific ubiquitylation sites. Moreover, independent tests show that UbiProber improves the areas under receiver operating characteristic curves by ~15% by using the Combined model. AVAILABILITY: The UbiProber server is freely available on the web at http://bioinfo.ncu.edu.cn/UbiProber.aspx. The software system of UbiProber can be downloaded at the same site. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteome-wide analysis of amino acid variations that influence protein lysine acetylation.

Next-generation sequencing (NGS) technologies are yielding ever higher volumes of genetic variation data. Given this large amount of data, it has become both a possibility and a priority to determine what the functional implication of genetic variations is. Considering the essential roles of acetylation in protein functions, it is highly likely that acetylation related genetic variations change protein functions. In this work, we performed a proteome-wide analysis of amino acid variations that could potentially influence protein lysine acetylation characteristics in human variant proteins. Here, we defined the AcetylAAVs as acetylation related amino acid variations that affect acetylation sites or their interacting acetyltransferases, and categorized three types of AcetylAAVs. Using the developed prediction system, named KAcePred, we detected that 50.87% of amino acid variations are potential AcetylAAVs and 12.32% of disease mutations could result in AcetylAAVs. More interestingly, from the statistical analysis, we found that the amino acid variations that directly create new potential lysine acetylation sites have more chance to cause diseases. It can be anticipated that the analysis of AcetylAAVs might be useful to screen important polymorphisms and help to identify the mechanism of genetic diseases. A user-friendly web interface for analysis of AcetylAAVs is now freely available at http://bioinfo.ncu.edu.cn/AcetylAAVs_Home.aspx .

Identify submitochondria and subchloroplast locations with pseudo amino acid composition: approach from the strategy of discrete wavelet transform feature extraction.

It is very challenging and complicated to predict protein locations at the sub-subcellular level. The key to enhancing the prediction quality for protein sub-subcellular locations is to grasp the core features of a protein that can discriminate among proteins with different subcompartment locations. In this study, a different formulation of pseudoamino acid composition by the approach of discrete wavelet transform feature extraction was developed to predict submitochondria and subchloroplast locations. As a result of jackknife cross-validation, with our method, it can efficiently distinguish mitochondrial proteins from chloroplast proteins with total accuracy of 98.8% and obtained a promising total accuracy of 93.38% for predicting submitochondria locations. Especially the predictive accuracy for mitochondrial outer membrane and chloroplast thylakoid lumen were 82.93% and 82.22%, respectively, showing an improvement of 4.88% and 27.22% when other existing methods were compared. The results indicated that the proposed method might be employed as a useful assistant technique for identifying sub-subcellular locations. We have implemented our algorithm as an online service called SubIdent (http://bioinfo.ncu.edu.cn/services.aspx).

PredSulSite: prediction of protein tyrosine sulfation sites with multiple features and analysis.

Tyrosine sulfation is a ubiquitous posttranslational modification that regulates extracellular protein-protein interactions, intracellular protein transportation modulation, and protein proteolytic process. However, identifying tyrosine sulfation sites remains a challenge due to the lability of sulfation sequences. In this study, we developed a method called PredSulSite that incorporates protein secondary structure, physicochemical properties of amino acids, and residue sequence order information based on support vector machine to predict sulfotyrosine sites. Three types of encoding algorithms-secondary structure, grouped weight, and autocorrelation function-were applied to mine features from tyrosine sulfation proteins. The prediction model with multiple features achieved an accuracy of 92.89% in 10-fold cross-validation. Feature analysis showed that the coil structure, acidic amino acids, and residue interactions around the tyrosine sulfation sites all contributed to the sulfation site determination. The detailed feature analysis in this work can help us to understand the sulfation mechanism and provide guidance for the related experimental validation. PredSulSite is available as a community resource at http://www.bioinfo.ncu.edu.cn/inquiries_PredSulSite.aspx.

A method to distinguish between lysine acetylation and lysine methylation from protein sequences.

Lysine acetylation and methylation are two major post-translational modifications of lysine residues. They play vital roles in both biological and pathological processes. Specific lysine residues in H3 histone protein tails appear to be targeted for either acetylation or methylation. Hence it is very challenging to distinguish between acetylated and methylated lysine residues using computational methods. This work presents a method that incorporates protein sequence information, secondary structure and amino acid properties to differentiate acetyl-lysine from methyl-lysine. 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 proposed method achieves an accuracy of 93.3% using a jackknife test. Feature analysis demonstrates that the prediction model with multiple features can take full advantage of the supplementary information from different features to improve classification performance and prediction robustness. Analysis of the characteristics of lysine residues which can be either methylated or acetylated shows that they are more similar to methyl-lysine than to acetyl-lysine.

A homology-based pipeline for global prediction of post-translational modification sites.

The pathways of protein post-translational modifications (PTMs) have been shown to play particularly important roles for almost any biological process. Identification of PTM substrates along with information on the exact sites is fundamental for fully understanding or controlling biological processes. Alternative computational strategies would help to annotate PTMs in a high-throughput manner. Traditional algorithms are suited for identifying the common organisms and tissues that have a complete PTM atlas or extensive experimental data. While annotation of rare PTMs in most organisms is a clear challenge. In this work, to this end we have developed a novel homology-based pipeline named PTMProber that allows identification of potential modification sites for most of the proteomes lacking PTMs data. Cross-promotion E-value (CPE) as stringent benchmark has been used in our pipeline to evaluate homology to known modification sites. Independent-validation tests show that PTMProber achieves over 58.8% recall with high precision by CPE benchmark. Comparisons with other machine-learning tools show that PTMProber pipeline performs better on general predictions. In addition, we developed a web-based tool to integrate this pipeline at http://bioinfo.ncu.edu.cn/PTMProber/index.aspx. In addition to pre-constructed prediction models of PTM, the website provides an extensional functionality to allow users to customize models.

PredHydroxy: computational prediction of protein hydroxylation site locations based on the primary structure.

Compared to well-known and extensively studied protein phosphorylation, protein hydroxylation attracts much less attention and the molecular mechanism of the hydroxylation is still incompletely understood. And yet annotation of hydroxylation in proteomes is a first-critical step toward decoding protein function and understanding their physiological roles that have been implicated in the pathological processes and providing useful information for the drug designs of various diseases related with hydroxylation. In this work, we present a novel method called PredHydroxy to automate the prediction of the proline and lysine hydroxylation sites based on position weight amino acids composition, 8 high-quality amino acid indices and support vector machines. The PredHydroxy achieved a promising performance with an area under the receiver operating characteristic curve (AUC) of 82.72% and a Matthew's correlation coefficient (MCC) of 69.03% for hydroxyproline as well as an AUC of 87.41% and a MCC of 66.68% for hydroxylysine in jackknife cross-validation. The results obtained from both the cross validation and independent tests suggest that the PredHydroxy might be a powerful and complementary tool for further experimental investigation of protein hydroxylation. Feature analyses demonstrate that hydroxylation and non-hydroxylation have distinct location-specific differences; alpha and turn propensity is of importance for the hydroxylation of proline and lysine residues. A user-friendly server is freely available on the web at: .

Systematic Analysis of the Genetic Variability That Impacts SUMO Conjugation and Their Involvement in Human Diseases.

Protein function has been observed to rely on select essential sites instead of requiring all sites to be indispensable. Small ubiquitin-related modifier (SUMO) conjugation or sumoylation, which is a highly dynamic reversible process and its outcomes are extremely diverse, ranging from changes in localization to altered activity and, in some cases, stability of the modified, has shown to be especially valuable in cellular biology. Motivated by the significance of SUMO conjugation in biological processes, we report here on the first exploratory assessment whether sumoylation related genetic variability impacts protein functions as well as the occurrence of diseases related to SUMO. Here, we defined the SUMOAMVR as sumoylation related amino acid variations that affect sumoylation sites or enzymes involved in the process of connectivity, and categorized four types of potential SUMOAMVRs. We detected that 17.13% of amino acid variations are potential SUMOAMVRs and 4.83% of disease mutations could lead to SUMOAMVR with our system. More interestingly, the statistical analysis demonstrates that the amino acid variations that directly create new potential lysine sumoylation sites are more likely to cause diseases. It can be anticipated that our method can provide more instructive guidance to identify the mechanisms of genetic diseases.

Progress and challenges in predicting protein methylation sites.

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

Using support vector machines to identify protein phosphorylation sites in viruses.

Phosphorylation of viral proteins plays important roles in enhancing replication and inhibition of normal host-cell functions. Given its importance in biology, a unique opportunity has arisen to identify viral protein phosphorylation sites. However, experimental methods for identifying phosphorylation sites are resource intensive. Hence, there is significant interest in developing computational methods for reliable prediction of viral phosphorylation sites from amino acid sequences. In this study, a new method based on support vector machine is proposed to identify protein phosphorylation sites in viruses. We apply an encoding scheme based on attribute grouping and position weight amino acid composition to extract physicochemical properties and sequence information of viral proteins around phosphorylation sites. By 10-fold cross-validation, the prediction accuracies for phosphoserine, phosphothreonine and phosphotyrosine with window size of 23 are 88.8%, 95.2% and 97.1%, respectively. Furthermore, compared with the existing methods of Musite and MDD-clustered HMMs, the high sensitivity and accuracy of our presented method demonstrate the predictive effectiveness of the identified phosphorylation sites for viral proteins.

PSEA: Kinase-specific prediction and analysis of human phosphorylation substrates.

Protein phosphorylation catalysed by kinases plays crucial regulatory roles in intracellular signal transduction. With the increasing number of kinase-specific phosphorylation sites and disease-related phosphorylation substrates that have been identified, the desire to explore the regulatory relationship between protein kinases and disease-related phosphorylation substrates is motivated. In this work, we analysed the kinases' characteristic of all disease-related phosphorylation substrates by using our developed Phosphorylation Set Enrichment Analysis (PSEA) method. We evaluated the efficiency of our method with independent test and concluded that our approach is reliable for identifying kinases responsible for phosphorylated substrates. In addition, we found that Mitogen-activated protein kinase (MAPK) and Glycogen synthase kinase (GSK) families are more associated with abnormal phosphorylation. It can be anticipated that our method might be helpful to identify the mechanism of phosphorylation and the relationship between kinase and phosphorylation related diseases. A user-friendly web interface is now freely available at http://bioinfo.ncu.edu.cn/PKPred_Home.aspx.

Lanthanide(III) oxalatophosphonates: syntheses, crystal structures and luminescence properties.

By the introduction of oxalate as the second ligand, five new lanthanide oxalatophosphonate hybrids with a 2D layered structure, namely, [Ln(H2L)(C2O4)(H2O)]·2H2O [Ln = Nd (1), Sm (2), Eu (3), Tb (4), Dy (5), H3L = H2O3PCH2NCH2(CH2CH2OPO2H)], have been hydrothermally synthesized and structurally characterized by X-ray single-crystal diffraction, X-ray powder diffraction, infrared spectroscopy, elemental analysis and thermogravimetric analysis. Compounds 1-5 are isostructural and exhibit a 2D layer formed by the interconnection of a 1D zigzag chain of {Ln(C2O4)}(+) with the phosphonate ligands. The effect of lanthanide contraction induces the decrease of the lattice parameters and crystal size from Nd to Dy. The luminescence properties of compounds 2-5 have been studied.

Systematic analysis and prediction of pupylation sites in prokaryotic proteins.

Prokaryotic ubiquitin-like protein (Pup) is the first identified prokaryotic protein that is functionally analogous to ubiquitin. Recent studies have shed light on the Pup activation and conjugation to target proteins to be a signal for the selective degradation proteins in Mycobacterium tuberculosis (Mtb). By covalently conjugating the Pup, pupylation functions as a critical post-translational modification (PTM) conserved in actinomycetes. Detecting pupylation sites is crucial and fundamental for understanding the molecular mechanisms of Pup. Yet comparative studies with other PTM suggest that the development of accurate and complete repertories of pupylation is still in its early stages. Unbiased screening for pupylation sites by experimental methods is time consuming and expensive; in silico prediction can provide highly potential candidates and reduce the number of potential candidates that require further in vivo or in vitro confirmation. Here, we present an effective classifier of PupPred for predicting pupylation sites, which shows better performance than existing classifiers. Importantly, this work not only investigates the sequential, structural and evolutionary hallmarks around pupylation sites but also compares the differences of pupylation and ubiquitylation from the environmental, conservative and functional characterization of substrates. These prediction and analysis results may be helpful for further experimental investigation of degradation proteins in prokaryotes. Finally, the PupPred server is available at http://bioinfo.ncu.edu.cn/PupPred.aspx.