SysPTM: a systematic resource for proteomic research on post-translational modifications

Charge block-driven liquid-liquid phase separation - mechanism and biological roles

Liquid-liquid phase separation (LLPS) has increasingly been found to play pivotal roles in a number of intracellular events and reactions, and has introduced a new paradigm in cell biology to explain protein-protein and enzyme-ligand interactions beyond conventional molecular and biochemical theories. LLPS is driven by the cumulative effects of weak and promiscuous interactions, including electrostatic, hydrophobic and cation-π interactions, among polypeptides containing intrinsically disordered regions (IDRs) and describes the macroscopic behaviours of IDR-containing proteins in an intracellular milieu. Recent studies have revealed that interactions between 'charge blocks' - clusters of like charges along the polypeptide chain - strongly induce LLPS and play fundamental roles in its spatiotemporal regulation. Introducing a new parameter, termed 'charge blockiness', into physicochemical models of disordered polypeptides has yielded a better understanding of how the intrinsic amino acid sequence of a polypeptide determines the spatiotemporal occurrence of LLPS within a cell. Charge blockiness might also explain why some post-translational modifications segregate within IDRs and how they regulate LLPS. In this Review, we summarise recent progress towards understanding the mechanism and biological roles of charge block-driven LLPS and discuss how this new characteristic parameter of polypeptides offers new possibilities in the fields of structural biology and cell biology.

Charge block-driven liquid-liquid phase separation: A mechanism of how phosphorylation regulates phase behavior of disordered proteins

Phosphorylation regulates protein function by modulating stereospecific interactions between protein-protein or enzyme-ligand. On the other hand, many bioinformatics studies have demonstrated that phosphorylation preferably occurs in intrinsically disordered regions (IDRs), which do not have any secondary and tertiary structures. Although studies have demonstrated that phosphorylation changes the phase behavior of IDRs, the mechanism, which is distinct from the "stereospecific" effect, had not been elucidated. Here, we describe how phosphorylation in IDRs regulates the protein function by modulating phase behavior. Mitotic phosphorylation in the IDRs of Ki-67 and NPM1 promotes or suppresses liquid-liquid phase separation, respectively, by altering the "charge blockiness" along the polypeptide chain. The phosphorylation-mediated regulation of liquid-liquid phase separation by enhancing or suppressing "charge blockiness," rather than by modulating stereospecific interactions, may provide one of the general mechanisms of protein regulation by posttranslational modifications and the role of multiple phosphorylations.

A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction

Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.

Applications of deep learning in understanding gene regulation

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.

Evolutionary Divergence of Phosphorylation to Regulate Interactive Protein Networks in Lower and Higher Species

The phosphorylation of proteins affects their functions in extensively documented circumstances. However, the role of phosphorylation in many interactive networks of proteins remains very elusive due to the experimental limits of exploring the transient interaction in a large complex of assembled proteins induced by stimulation. Previous studies have suggested that phosphorylation is a recent evolutionary process that differently regulates ortholog proteins in numerous lineages of living organisms to create new functions. Despite the fact that numerous phospho-proteins have been compared between species, little is known about the organization of the full phospho-proteome, the role of phosphorylation to orchestrate large interactive networks of proteins, and the intertwined phospho-landscape in these networks. In this report, we aimed to investigate the acquired role of phosphate addition in the phenomenon of protein networking in different orders of living organisms. Our data highlighted the acquired status of phosphorylation in organizing large, connected assemblages in Homo sapiens. The protein networking guided by phosphorylation turned out to be prominent in humans, chaotic in yeast, and weak in flies. Furthermore, the molecular functions of GO annotation enrichment regulated by phosphorylation were found to be drastically different between flies, yeast, and humans, suggesting an evolutionary drift specific to each species.

Crotonylation versus acetylation in petunia corollas with reduced acetyl-CoA due to PaACL silencing

Protein acetylation and crotonylation are important posttranslational modifications of lysine. In animal cells, the correlation of acetylation and crotonylation has been well characterized and the lysines of some proteins are acetylated or crotonylated depending on the relative concentrations of acetyl-CoA and crotonyl-CoA. However, in plants, the correlation of acetylation and crotonylation and the effects of the relative intracellular concentrations of crotonyl-CoA and acetyl-CoA on protein crotonylation and acetylation are not well known. In our previous study, PaACL silencing changed the content of acetyl-CoA in petunia (Petunia hybrida) corollas, and the effect of PaACL silencing on the global acetylation proteome in petunia was analyzed. In the present study, we found that PaACL silencing did not significantly alter the content of crotonyl-CoA. We performed a global crotonylation proteome analysis of the corollas of PaACL-silenced and control petunia plants; we found that protein crotonylation was closely related to protein acetylation and that proteins with more crotonylation sites often had more acetylation sites. Crotonylated proteins and acetylated proteins were enriched in many common Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. However, PaACL silencing resulted in different KEGG pathway enrichments of proteins with different levels of crotonylation sites and acetylation sites. PaACLB1-B2 silencing did not led to changes in the opposite direction in crotonylation and acetylation levels at the same lysine site in cytoplasmic proteins, which indicated that cytoplasmic lysine acetylation and crotonylation might not depend on the relative concentrations of acetyl-CoA and crotonyl-CoA. Moreover, the global crotonylome and acetylome were weakly positively correlated in the corollas of PaACL-silenced and control plants.

Cell cycle-specific phase separation regulated by protein charge blockiness

Dynamic morphological changes of intracellular organelles are often regulated by protein phosphorylation or dephosphorylation^1–6. Phosphorylation modulates stereospecific interactions among structured proteins, but how it controls molecular interactions among unstructured proteins and regulates their macroscopic behaviours remains unknown. Here we determined the cell cycle-specific behaviour of Ki-67, which localizes to the nucleoli during interphase and relocates to the chromosome periphery during mitosis. Mitotic hyperphosphorylation of disordered repeat domains of Ki-67 generates alternating charge blocks in these domains and increases their propensity for liquid–liquid phase separation (LLPS). A phosphomimetic sequence and the sequences with enhanced charge blockiness underwent strong LLPS in vitro and induced chromosome periphery formation in vivo. Conversely, mitotic hyperphosphorylation of NPM1 diminished a charge block and suppressed LLPS, resulting in nucleolar dissolution. Cell cycle-specific phase separation can be modulated via phosphorylation by enhancing or reducing the charge blockiness of disordered regions, rather than by attaching phosphate groups to specific sites.

Yamazaki et al. show that cell cycle-regulated changes in hyperphosphorylation of Ki-67 and NPM1 modulate alternating charge blocks in these proteins, which defines their propensity for liquid–liquid phase separation at chromatin.

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.

KANPHOS: A Database of Kinase-Associated Neural Protein Phosphorylation in the Brain

Protein phosphorylation plays critical roles in a variety of intracellular signaling pathways and physiological functions that are controlled by neurotransmitters and neuromodulators in the brain. Dysregulation of these signaling pathways has been implicated in neurodevelopmental disorders, including autism spectrum disorder, attention deficit hyperactivity disorder and schizophrenia. While recent advances in mass spectrometry-based proteomics have allowed us to identify approximately 280,000 phosphorylation sites, it remains largely unknown which sites are phosphorylated by which kinases. To overcome this issue, previously, we developed methods for comprehensive screening of the target substrates of given kinases, such as PKA and Rho-kinase, upon stimulation by extracellular signals and identified many candidate substrates for specific kinases and their phosphorylation sites. Here, we developed a novel online database to provide information about the phosphorylation signals identified by our methods, as well as those previously reported in the literature. The "KANPHOS" (Kinase-Associated Neural Phospho-Signaling) database and its web portal were built based on a next-generation XooNIps neuroinformatics tool. To explore the functionality of the KANPHOS database, we obtained phosphoproteomics data for adenosine-A2A-receptor signaling and its downstream MAPK-mediated signaling in the striatum/nucleus accumbens, registered them in KANPHOS, and analyzed the related pathways.

Posttranslational modifications in systems biology

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.

Recent Advances in Predicting Protein S-Nitrosylation Sites

Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.

Insights into Bioinformatic Applications for Glycosylation: Instigating an Awakening towards Applying Glycoinformatic Resources for Cancer Diagnosis and Therapy

Glycosylation plays a crucial role in various diseases and their etiology. This has led to a clear understanding on the functions of carbohydrates in cell communication, which eventually will result in novel therapeutic approaches for treatment of various disease. Glycomics has now become one among the top ten technologies that will change the future. The direct implication of glycosylation as a hallmark of cancer and for cancer therapy is well established. As in proteomics, where bioinformatics tools have led to revolutionary achievements, bioinformatics resources for glycosylation have improved its practical implication. Bioinformatics tools, algorithms and databases are a mandatory requirement to manage and successfully analyze large amount of glycobiological data generated from glycosylation studies. This review consolidates all the available tools and their applications in glycosylation research. The achievements made through the use of bioinformatics into glycosylation studies are also presented. The importance of glycosylation in cancer diagnosis and therapy is discussed and the gap in the application of widely available glyco-informatic tools for cancer research is highlighted. This review is expected to bring an awakening amongst glyco-informaticians as well as cancer biologists to bridge this gap, to exploit the available glyco-informatic tools for cancer.

DeepPhos: prediction of protein phosphorylation sites with deep learning

Motivation

Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites.

Results

In this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers. DeepPhos can also be used for kinase-specific prediction varying from group, family, subfamily and individual kinase level. The experimental results demonstrated that DeepPhos outperforms competitive predictors in general and kinase-specific phosphorylation site prediction.

Availability and implementation

The source code of DeepPhos is publicly deposited at https://github.com/USTCHIlab/DeepPhos.

Supplementary information

Supplementary data are available at Bioinformatics online.

Towards Computational Models of Identifying Protein Ubiquitination Sites

Ubiquitination is an important post-translational modification (PTM) process for the regulation of protein functions, which is associated with cancer, cardiovascular and other diseases. Recent initiatives have focused on the detection of potential ubiquitination sites with the aid of physicochemical test approaches in conjunction with the application of computational methods. The identification of ubiquitination sites using laboratory tests is especially susceptible to the temporality and reversibility of the ubiquitination processes, and is also costly and time-consuming. It has been demonstrated that computational methods are effective in extracting potential rules or inferences from biological sequence collections. Up to the present, the computational strategy has been one of the critical research approaches that have been applied for the identification of ubiquitination sites, and currently, there are numerous state-of-the-art computational methods that have been developed from machine learning and statistical analysis to undertake such work. In the present study, the construction of benchmark datasets is summarized, together with feature representation methods, feature selection approaches and the classifiers involved in several previous publications. In an attempt to explore pertinent development trends for the identification of ubiquitination sites, an independent test dataset was constructed and the predicting results obtained from five prediction tools are reported here, together with some related discussions.

PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact

Post-translational modifications (PTMs) play very important roles in various cell signaling pathways and biological process. Due to PTMs' extremely important roles, many major PTMs have been studied, while the functional and mechanical characterization of major PTMs is well documented in several databases. However, most currently available databases mainly focus on protein sequences, while the real 3D structures of PTMs have been largely ignored. Therefore, studies of PTMs 3D structural signatures have been severely limited by the deficiency of the data. Here, we develop PRISMOID, a novel publicly available and free 3D structure database for a wide range of PTMs. PRISMOID represents an up-to-date and interactive online knowledge base with specific focus on 3D structural contexts of PTMs sites and mutations that occur on PTMs and in the close proximity of PTM sites with functional impact. The first version of PRISMOID encompasses 17 145 non-redundant modification sites on 3919 related protein 3D structure entries pertaining to 37 different types of PTMs. Our entry web page is organized in a comprehensive manner, including detailed PTM annotation on the 3D structure and biological information in terms of mutations affecting PTMs, secondary structure features and per-residue solvent accessibility features of PTM sites, domain context, predicted natively disordered regions and sequence alignments. In addition, high-definition JavaScript packages are employed to enhance information visualization in PRISMOID. PRISMOID equips a variety of interactive and customizable search options and data browsing functions; these capabilities allow users to access data via keyword, ID and advanced options combination search in an efficient and user-friendly way. A download page is also provided to enable users to download the SQL file, computational structural features and PTM sites' data. We anticipate PRISMOID will swiftly become an invaluable online resource, assisting both biologists and bioinformaticians to conduct experiments and develop applications supporting discovery efforts in the sequence-structural-functional relationship of PTMs and providing important insight into mutations and PTM sites interaction mechanisms. The PRISMOID database is freely accessible at http://prismoid.erc.monash.edu/. The database and web interface are implemented in MySQL, JSP, JavaScript and HTML with all major browsers supported.

dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications

The dbPTM (http://dbPTM.mbc.nctu.edu.tw/) has been maintained for over 10 years with the aim to provide functional and structural analyses for post-translational modifications (PTMs). In this update, dbPTM not only integrates more experimentally validated PTMs from available databases and through manual curation of literature but also provides PTM-disease associations based on non-synonymous single nucleotide polymorphisms (nsSNPs). The high-throughput deep sequencing technology has led to a surge in the data generated through analysis of association between SNPs and diseases, both in terms of growth amount and scope. This update thus integrated disease-associated nsSNPs from dbSNP based on genome-wide association studies. The PTM substrate sites located at a specified distance in terms of the amino acids encoded from nsSNPs were deemed to have an association with the involved diseases. In recent years, increasing evidence for crosstalk between PTMs has been reported. Although mass spectrometry-based proteomics has substantially improved our knowledge about substrate site specificity of single PTMs, the fact that the crosstalk of combinatorial PTMs may act in concert with the regulation of protein function and activity is neglected. Because of the relatively limited information about concurrent frequency and functional relevance of PTM crosstalk, in this update, the PTM sites neighboring other PTM sites in a specified window length were subjected to motif discovery and functional enrichment analysis. This update highlights the current challenges in PTM crosstalk investigation and breaks the bottleneck of how proteomics may contribute to understanding PTM codes, revealing the next level of data complexity and proteomic limitation in prospective PTM research.

EPSD: a well-annotated data resource of protein phosphorylation sites in eukaryotes

As an important post-translational modification (PTM), protein phosphorylation is involved in the regulation of almost all of biological processes in eukaryotes. Due to the rapid progress in mass spectrometry-based phosphoproteomics, a large number of phosphorylation sites (p-sites) have been characterized but remain to be curated. Here, we briefly summarized the current progresses in the development of data resources for the collection, curation, integration and annotation of p-sites in eukaryotic proteins. Also, we designed the eukaryotic phosphorylation site database (EPSD), which contained 1 616 804 experimentally identified p-sites in 209 326 phosphoproteins from 68 eukaryotic species. In EPSD, we not only collected 1 451 629 newly identified p-sites from high-throughput (HTP) phosphoproteomic studies, but also integrated known p-sites from 13 additional databases. Moreover, we carefully annotated the phosphoproteins and p-sites of eight model organisms by integrating the knowledge from 100 additional resources that covered 15 aspects, including phosphorylation regulator, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein-protein interaction, drug-target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, protein expression/proteomics and subcellular localization. We anticipate that the EPSD can serve as a useful resource for further analysis of eukaryotic phosphorylation. With a data volume of 14.1 GB, EPSD is free for all users at http://epsd.biocuckoo.cn/.

The sequential phosphorylation of PHF10 subunit of the PBAF chromatin-remodeling complex determines different properties of the PHF10 isoforms

The mammalian PBAF subfamily of SWI/SNF chromatin remodeling complexes plays a wide role in the regulation of gene expression. PHF10 is a subunit of the signature module of PBAF, responsible for its interaction with chromatin. PHF10 is represented by four different isoforms, which are alternatively incorporated in the complex. Two of PHF10 isoforms lacking C-terminal PHD domains contain a cluster of phosphorylated serine residues, designated as X-cluster. In the present study, we explore the phosphorylation of the X-cluster in detail. We identified additional phosphorylated serine residues and designated them as either frequently or rarely phosphorylated. The X-cluster consists of two independently phosphorylated subclusters. Phosphorylation of the second subcluster depends on phosphorylation of a primary serine 327. These two subclusters surround a sequence, which is predicted to be a nuclear localization sequence (NLS3). The NLS3 does not affect localization of PHF10 isoforms. However, it is essential for X-cluster phosphorylation and increased stability of isoforms that lack PHD. Conversely, the presence of NLS3 signal in isoforms that contain C-terminal PHD domains reduces their stability. Thus, phosphorylation of PHF10 isoforms regulates their cell level, determining the rate of incorporation in PBAF. This may alter the pattern of PBAF regulated genes.

Summary: The sequential phosphorylation of the linker domain of PHF10 subunit of PBAF chromatin remodeling complex is triggered by two primarily phosphorylated serines and determines the different properties of PHF10 isoforms.

Protein Post-Translational Modification Crosstalk in Acute Myeloid Leukemia Calls for Action

BACKGROUND

Post-translational modification (PTM) crosstalk is a young research field. However, there is now evidence of the extraordinary characterization of the different proteoforms and their interactions in a biological environment that PTM crosstalk studies can describe. Besides gene expression and phosphorylation profiling of acute myeloid leukemia (AML) samples, the functional combination of several PTMs that might contribute to a better understanding of the complexity of the AML proteome remains to be discovered.

OBJECTIVE

By reviewing current workflows for the simultaneous enrichment of several PTMs and bioinformatics tools to analyze mass spectrometry (MS)-based data, our major objective is to introduce the PTM crosstalk field to the AML research community.

RESULTS

After an introduction to PTMs and PTM crosstalk, this review introduces several protocols for the simultaneous enrichment of PTMs. Two of them allow a simultaneous enrichment of at least three PTMs when using 0.5-2 mg of cell lysate. We have reviewed many of the bioinformatics tools used for PTM crosstalk discovery as its complex data analysis, mainly generated from MS, becomes challenging for most AML researchers. We have presented several non-AML PTM crosstalk studies throughout the review in order to show how important the characterization of PTM crosstalk becomes for the selection of disease biomarkers and therapeutic targets.

CONCLUSION

Herein, we have reviewed the advances and pitfalls of the emerging PTM crosstalk field and its potential contribution to unravel the heterogeneity of AML. The complexity of sample preparation and bioinformatics workflows demands a good interaction between experts of several areas.

TCellXTalk facilitates the detection of co-modified peptides for the study of protein post-translational modification cross-talk in T cells

MOTIVATION

Protein function is regulated by post-translational modifications (PTMs) that may act individually or interact with others in a phenomenon termed PTM cross-talk. Multiple databases have been dedicated to PTMs, including recent initiatives oriented towards the in silico prediction of PTM interactions. The study of PTM cross-talk ultimately requires experimental evidence about whether certain PTMs coexist in a single protein molecule. However, available resources do not assist researchers in the experimental detection of co-modified peptides.

RESULTS

Herein, we present TCellXTalk, a comprehensive database of phosphorylation, ubiquitination and acetylation sites in human T cells that supports the experimental detection of co-modified peptides using targeted or directed mass spectrometry. We demonstrate the efficacy of TCellXTalk and the strategy presented here in a proof of concept experiment that enabled the identification and quantification of 15 co-modified (phosphorylated and ubiquitinated) peptides from CD3 proteins of the T-cell receptor complex. To our knowledge, these are the first co-modified peptide sequences described in this widely studied cell type. Furthermore, quantitative data showed distinct dynamics for co-modified peptides upon T cell activation, demonstrating differential regulation of co-occurring PTMs in this biological context. Overall, TCellXTalk facilitates the experimental detection of co-modified peptides in human T cells and puts forward a novel and generic strategy for the study of PTM cross-talk.

AVAILABILITY AND IMPLEMENTATION

TCellXTalk is available at https://www.tcellxtalk.org. Source Code is available at https://bitbucket.org/lp-csic-uab/tcellxtalk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Succinyl-proteome profiling of Pyricularia oryzae, a devastating phytopathogenic fungus that causes rice blast disease

Pyricularia oryzae is the pathogen for rice blast disease, which is a devastating threat to rice production worldwide. Lysine succinylation, a newly identified post-translational modification, is associated with various cellular processes. Here, liquid chromatography tandem-mass spectrometry combined with a high-efficiency succinyl-lysine antibody was used to identify the succinylated peptides in P. oryzae. In total, 2109 lysine succinylation sites in 714 proteins were identified. Ten conserved succinylation sequence patterns were identified, among which, K*******Ksuc, and K**Ksuc, were two most preferred ones. The frequency of lysine succinylation sites, however, greatly varied among organisms, including plants, animals, and microbes. Interestingly, the numbers of succinylation site in each protein of P. oryzae were significantly greater than that of most previous published organisms. Gene ontology and KEGG analysis showed that these succinylated peptides are associated with a wide range of cellular functions, from metabolic processes to stimuli responses. Further analyses determined that lysine succinylation occurs on several key enzymes of the tricarboxylic acid cycle and glycolysis pathway, indicating that succinylation may play important roles in the regulation of basal metabolism in P. oryzae. Furthermore, more than 40 pathogenicity-related proteins were identified as succinylated proteins, suggesting an involvement of succinylation in pathogenicity. Our results provide the first comprehensive view of the P. oryzae succinylome and may aid to find potential pathogenicity-related proteins to control the rice blast disease. Significance Plant pathogens represent a great threat to world food security, and enormous reduction in the global yield of rice was caused by P. oryzae infection. Here, the succinylated proteins in P. oryzae were identified. Furthermore, comparison of succinylation sites among various species, indicating that different degrees of succinylation may be involved in the regulation of basal metabolism. This data facilitates our understanding of the metabolic pathways and proteins that are associated with pathogenicity.

Research progress in protein posttranslational modification site prediction

Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.

Quantitative Global Proteome and Lysine Succinylome Analyses Reveal the Effects of Energy Metabolism in Renal Cell Carcinoma

In light of the increasing incidence of renal cell carcinoma (RCC), its molecular mechanisms have been comprehensively explored in numerous recent studies. However, few studies focus on the influence of multi-factor interactions during the occurrence and development of RCC. This study aims to investigate the quantitative global proteome and the changes in lysine succinylation in related proteins, seeking to facilitate a better understanding of the molecular mechanisms underlying RCC. LC-MS/MS combined with bioinformatics analysis are used to quantitatively detect the perspectives at the global protein level. IP and WB analysis were conducted to further verify the alternations of related proteins and lysine succinylation. A total of 3,217 proteins and 1,238 lysine succinylation sites are quantified in RCC tissues, and 668 differentially expressed proteins and 161 differentially expressed lysine succinylation sites are identified. Besides, expressions of PGK1 and PKM2 at protein and lysine, succinylation levels are significantly altered in RCC tissues. Bioinformatics analysis indicates that the glycolysis pathway is a potential mechanism of RCC progression and lysine succinylation may plays a potential role in energy metabolism. These results can provide a new direction for exploring the molecular mechanism of RCC tumorigenesis.

MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction

Motivation

Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction.

Results

We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data.

Availability and implementation

MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep.

Contact

xudong@missouri.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Splice-Aware Multiple Sequence Alignment of Protein Isoforms

Multiple sequence alignment (MSA) is a classic problem in computational genomics. In typical use, MSA software is expected to align a collection of homologous genes, such as orthologs from multiple species or duplication-induced paralogs within a species. Recent focus on the importance of alternatively-spliced isoforms in disease and cell biology has highlighted the need to create MSAs that more effectively accommodate isoforms. MSAs are traditionally constructed using scoring criteria that prefer alignments with occasional mismatches over alignments with long gaps. Alternatively spliced protein isoforms effectively contain exon-length insertions or deletions (indels) relative to each other, and demand an alternative approach. Some improvements can be achieved by making indel penalties much smaller, but this is merely a patchwork solution. In this work we present Mirage, a novel MSA software package for the alignment of alternatively spliced protein isoforms. Mirage aligns isoforms to each other by first mapping each protein sequence to its encoding genomic sequence, and then aligning isoforms to one another based on the relative genomic coordinates of their constitutive codons. Mirage is highly effective at mapping proteins back to their encoding exons, and these protein-genome mappings lead to extremely accurate intra-species alignments; splice site information in these alignments is used to improve the accuracy of inter-species alignments of isoforms. Mirage alignments have also revealed the ubiquity of dual-coding exons, in which an exon conditionally encodes multiple open reading frames as overlapping spliced segments of frame-shifted genomic sequence.

PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile

Protein post-translational modifications (PTMs) are chemical modifications of a protein after its translation. Owing to its play an important role in deep understanding of various biological processes and the development of effective drugs, PTM site prediction have become a hot topic in bioinformatics. Recently, many online tools are developed to prediction various types of PTM sites, most of which are based on local sequence and some biological information. However, few of existing tools consider the relations between different PTMs for their prediction task. Here, we develop a web server called PTM-ssMP to predict PTM site, which adopts site-specific modification profile (ssMP) to efficiently extract and encode the information of both proximal PTMs and local sequence simultaneously. In PTM-ssMP we provide efficient prediction of multiple types of PTM site including phosphorylation, lysine acetylation, ubiquitination, sumoylation, methylation, O-GalNAc, O-GlcNAc, sulfation and proteolytic cleavage. To assess the performance of PTM-ssMP, a large number of experimentally verified PTM sites are collected from several sources and used to train and test the prediction models. Our results suggest that ssMP consistently contributes to remarkable improvement of prediction performance. In addition, results of independent tests demonstrate that PTM-ssMP compares favorably with other existing tools for different PTM types. PTM-ssMP is implemented as an online web server with user-friendly interface, which is freely available at http://bioinformatics.ustc.edu.cn/PTM-ssMP/index/.

TDP-43 post-translational modifications in health and disease

INTRODUCTION

Nuclear factor TDP-43 is a ubiquitously expressed RNA binding protein that plays a key causative role in several neurodegenerative diseases, especially in the ALS/FTD spectrum. In addition, its aberrant aggregation and expression has been recently observed in other type of diseases, such as myopathies and Niemann-Pick C, a lysosomal storage disease. Areas covered: This review aims to specifically cover the post-translational modifications (PTMs) that can affect TDP-43 function and cellular status both in health and disease. To this date, these include phosphorylation, formation of C-terminal fragments, disulfide bridge formation, ubiquitination, acetylation, and sumoylation. Recently published articles on these subjects have been reviewed in this manuscript. Expert opinion: Targeting aberrant TDP-43 expression in neurodegenerative diseases is a very challenging task due to the fact that both its overexpression and downregulation are considerably toxic to cells. This characteristic makes it difficult to therapeutically target this protein in a generalized manner. An alternative approach could be the identification of specific aberrant PTMs that promote its aggregation or toxicity, and developing novel therapeutic approaches toward their selective modification.

BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery

Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited in scope and/or content. BioMuta, a database of cancer-associated single-nucleotide variations, and BioXpress, a database of cancer-associated differentially expressed genes and microRNAs, differ from other disease-associated variation and expression databases primarily through the aggregation of data across many studies into a single source with a unified representation and annotation of functional attributes. Early versions of these resources were initiated by pilot funding for specific research applications, but newly awarded funds have enabled hardening of these databases to production-level quality and will allow for sustained development of these resources for the next few years. Because both resources were developed using a similar methodology of integration, curation, unification, and annotation, we present BioMuta and BioXpress as allied databases that will facilitate a more comprehensive view of gene associations in cancer. BioMuta and BioXpress are hosted on the High-performance Integrated Virtual Environment (HIVE) server at the George Washington University at https://hive.biochemistry.gwu.edu/biomuta and https://hive.biochemistry.gwu.edu/bioxpress, respectively.

PTMscape: an open source tool to predict generic post-translational modifications and map modification crosstalk in protein domains and biological processes

PTMscape predicts PTM sites using descriptors of sequence and physico-chemical microenvironment, and tests enrichment of single or pairs of PTMs in protein domains.

While tandem mass spectrometry can detect post-translational modifications (PTM) at the proteome scale, reported PTM sites are often incomplete and include false positives. Computational approaches can complement these datasets by additional predictions, but most available tools use prediction models pre-trained for single PTM type by the developers and it remains a difficult task to perform large-scale batch prediction for multiple PTMs with flexible user control, including the choice of training data. We developed an R package called PTMscape which predicts PTM sites across the proteome based on a unified and comprehensive set of descriptors of the physico-chemical microenvironment of modified sites, with additional downstream analysis modules to test enrichment of individual or pairs of PTMs in protein domains. PTMscape is flexible in the ability to process any major modifications, such as phosphorylation and ubiquitination, while achieving the sensitivity and specificity comparable to single-PTM methods and outperforming other multi-PTM tools. Applying this framework, we expanded proteome-wide coverage of five major PTMs affecting different residues by prediction, especially for lysine and arginine modifications. Using a combination of experimentally acquired sites (PSP) and newly predicted sites, we discovered that the crosstalk among multiple PTMs occur more frequently than by random chance in key protein domains such as histone, protein kinase, and RNA recognition motifs, spanning various biological processes such as RNA processing, DNA damage response, signal transduction, and regulation of cell cycle. These results provide a proteome-scale analysis of crosstalk among major PTMs and can be easily extended to other types of PTM.

Src family kinase phosphorylation of the motor domain of the human kinesin-5, Eg5

Spindle formation in mammalian cells requires precise spatial and temporal regulation of the kinesin-5, Eg5, which generates outward force to establish spindle bipolarity. Our results demonstrate that Eg5 is phosphorylated in cultured cells by Src family kinases (SFKs) at three sites in the motor head: Y125, Y211, and Y231. Mutation of these sites diminishes motor activity in vitro, and replacement of endogenous Eg5 with phosphomimetic Y211 in LLC-Pk1 cells results in monopolar spindles, consistent with loss of Eg5 activity. Cells treated with SFK inhibitors show defects in spindle formation, similar to those in cells expressing the nonphosphorylatable Y211 mutant, and distinct from inhibition of other mitotic kinases. We propose that this phosphoregulatory mechanism tunes Eg5 enzymatic activity for optimal spindle morphology.

Technological advances for interrogating the human kinome

There is increasing appreciation among researchers and clinicians of the value of investigating biology and pathobiology at the level of cellular kinase (kinome) activity. Kinome analysis provides valuable opportunity to gain insights into complex biology (including disease pathology), identify biomarkers of critical phenotypes (including disease prognosis and evaluation of therapeutic efficacy), and identify targets for therapeutic intervention through kinase inhibitors. The growing interest in kinome analysis has fueled efforts to develop and optimize technologies that enable characterization of phosphorylation-mediated signaling events in a cost-effective, high-throughput manner. In this review, we highlight recent advances to the central technologies currently available for kinome profiling and offer our perspectives on the key challenges remaining to be addressed.

Prediction of post-translational modification sites using multiple kernel support vector machine

Protein post-translational modification (PTM) is an important mechanism that is involved in the regulation of protein function. Considering the high-cost and labor-intensive of experimental identification, many computational prediction methods are currently available for the prediction of PTM sites by using protein local sequence information in the context of conserved motif. Here we proposed a novel computational method by using the combination of multiple kernel support vector machines (SVM) for predicting PTM sites including phosphorylation, O-linked glycosylation, acetylation, sulfation and nitration. To largely make use of local sequence information and site-modification relationships, we developed a local sequence kernel and Gaussian interaction profile kernel, respectively. Multiple kernels were further combined to train SVM for efficiently leveraging kernel information to boost predictive performance. We compared the proposed method with existing PTM prediction methods. The experimental results revealed that the proposed method performed comparable or better performance than the existing prediction methods, suggesting the feasibility of the developed kernels and the usefulness of the proposed method in PTM sites prediction.

Mapping Biological Networks from Quantitative Data-Independent Acquisition Mass Spectrometry: Data to Knowledge Pipelines

Data-independent acquisition mass spectrometry (DIA-MS) strategies and applications provide unique advantages for qualitative and quantitative proteome probing of a biological sample allowing constant sensitivity and reproducibility across large sample sets. These advantages in LC-MS/MS are being realized in fundamental research laboratories and for clinical research applications. However, the ability to translate high-throughput raw LC-MS/MS proteomic data into biological knowledge is a complex and difficult task requiring the use of many algorithms and tools for which there is no widely accepted standard and best practices are slowly being implemented. Today a single tool or approach inherently fails to capture the full interpretation that proteomics uniquely supplies, including the dynamics of quickly reversible chemically modified states of proteins, irreversible amino acid modifications, signaling truncation events, and, finally, determining the presence of protein from allele-specific transcripts. This chapter highlights key steps and publicly available algorithms required to translate DIA-MS data into knowledge.

The current state of the art of quantitative phosphoproteomics and its applications to diabetes research

Protein phosphorylation is a fundamental regulatory mechanism in many cellular processes and aberrant perturbation of phosphorylation has been implicated in various human diseases. Kinases and their cognate inhibitors have been considered as hotspots for drug development. Therefore, the emerging tools, which enable a system-wide quantitative profiling of phosphoproteome, would offer a powerful impetus in unveiling novel signaling pathways, drug targets and/or biomarkers for diseases of interest. This review highlights recent advances in phosphoproteomics, the current state of the art of the technologies and the challenges and future perspectives of this research area. Finally, some exemplary applications of phosphoproteomics in diabetes research are underscored.

GlycoMinestruct: a new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features

Glycosylation plays an important role in cell-cell adhesion, ligand-binding and subcellular recognition. Current approaches for predicting protein glycosylation are primarily based on sequence-derived features, while little work has been done to systematically assess the importance of structural features to glycosylation prediction. Here, we propose a novel bioinformatics method called GlycoMine^struct(http://glycomine.erc.monash.edu/Lab/GlycoMine_Struct/) for improved prediction of human N- and O-linked glycosylation sites by combining sequence and structural features in an integrated computational framework with a two-step feature-selection strategy. Experiments indicated that GlycoMine^struct outperformed NGlycPred, the only predictor that incorporated both sequence and structure features, achieving AUC values of 0.941 and 0.922 for N- and O-linked glycosylation, respectively, on an independent test dataset. We applied GlycoMine^struct to screen the human structural proteome and obtained high-confidence predictions for N- and O-linked glycosylation sites. GlycoMine^struct can be used as a powerful tool to expedite the discovery of glycosylation events and substrates to facilitate hypothesis-driven experimental studies.

A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regions

Background

One very important functional domain of proteins is the protein-protein interacting region (PPIR), which forms the binding interface between interacting polypeptide chains. Post-translational modifications (PTMs) that occur in the PPIR can either interfere with or facilitate the interaction between proteins. The ability to predict whether sites of protein modifications are inside or outside of PPIRs would be useful in further elucidating the regulatory mechanisms by which modifications of specific proteins regulate their cellular functions.

Results

Using two of the comprehensive databases for protein-protein interaction and protein modification site data (PDB and PhosphoSitePlus, respectively), we created new databases that map PTMs to their locations inside or outside of PPIRs. The mapped PTMs represented only 5 % of all known PTMs. Thus, in order to predict localization within or outside of PPIRs for the vast majority of PTMs, a machine learning strategy was used to generate predictive models from these mapped databases. For the three mapped PTM databases which had sufficient numbers of modification sites for generating models (acetylation, phosphorylation, and ubiquitylation), the resulting models yielded high overall predictive performance as judged by a combined performance score (CPS). Among the multiple properties of amino acids that were used in the classification tasks, hydrophobicity was found to contribute substantially to the performance of the final predictive models. Compared to the other classifiers we also evaluated, the SVM provided the best performance overall.

Conclusions

These models are the first to predict whether PTMs are located inside or outside of PPIRs, as demonstrated by their high predictive performance. The models and data presented here should be useful in prioritizing both known and newly identified PTMs for further studies to determine the functional relationship between specific PTMs and protein-protein interactions. The implemented R package is available online (http://sysbio.chula.ac.th/PtmPPIR).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1165-8) contains supplementary material, which is available to authorized users.

A novel method for predicting post-translational modifications on serine and threonine sites by using site-modification network profiles

Post-translational modifications (PTMs) regulate many aspects of biological behaviours including protein-protein interactions and cellular processes. Identification of PTM sites is helpful for understanding the PTM regulatory mechanisms. The PTMs on serine and threonine sites include phosphorylation, O-linked glycosylation and acetylation. Although a lot of computational approaches have been developed for PTM site prediction, currently most of them generate the predictive models by employing only local sequence information and few of them consider the relationship between different PTMs. In this paper, by adopting the site-modification network (SMNet) profiles that efficiently incorporate in situ PTM information, we develop a novel method to predict PTM sites on serine and threonine. PTM data are collected from various PTM databases and the SMNet is built to reflect the relationship between multiple PTMs, from which SMNet profiles are extracted to train predictive models based on SVM. Performance analysis of the SVM models shows that the SMNet profiles play an important role in accurately predicting PTM sites on serine and threonine. Furthermore, the proposed method is compared with existing PTM prediction approaches. The results from 10-fold cross-validation demonstrate that the proposed method with SMNet profiles performs remarkably better than existing methods, suggesting the power of SMNet profiles in identifying PTM sites.

dbPAF: an integrative database of protein phosphorylation in animals and fungi

Protein phosphorylation is one of the most important post-translational modifications (PTMs) and regulates a broad spectrum of biological processes. Recent progresses in phosphoproteomic identifications have generated a flood of phosphorylation sites, while the integration of these sites is an urgent need. In this work, we developed a curated database of dbPAF, containing known phosphorylation sites in H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, S. pombe and S. cerevisiae. From the scientific literature and public databases, we totally collected and integrated 54,148 phosphoproteins with 483,001 phosphorylation sites. Multiple options were provided for accessing the data, while original references and other annotations were also present for each phosphoprotein. Based on the new data set, we computationally detected significantly over-represented sequence motifs around phosphorylation sites, predicted potential kinases that are responsible for the modification of collected phospho-sites, and evolutionarily analyzed phosphorylation conservation states across different species. Besides to be largely consistent with previous reports, our results also proposed new features of phospho-regulation. Taken together, our database can be useful for further analyses of protein phosphorylation in human and other model organisms. The dbPAF database was implemented in PHP + MySQL and freely available at http://dbpaf.biocuckoo.org.

Global Proteome Analyses of Lysine Acetylation and Succinylation Reveal the Widespread Involvement of both Modification in Metabolism in the Embryo of Germinating Rice Seed

Regulation of rice seed germination has been shown to mainly occur at post-transcriptional levels, of which the changes on proteome status is a major one. Lysine acetylation and succinylation are two prevalent protein post-translational modifications (PTMs) involved in multiple biological processes, especially for metabolism regulation. To investigate the potential mechanism controlling metabolism regulation in rice seed germination, we performed the lysine acetylation and succinylation analyses simultaneously. Using high-accuracy nano-LC-MS/MS in combination with the enrichment of lysine acetylated or succinylated peptides from digested embryonic proteins of 24 h after imbibition (HAI) rice seed, a total of 699 acetylated sites from 389 proteins and 665 succinylated sites from 261 proteins were identified. Among these modified lysine sites, 133 sites on 78 proteins were commonly modified by two PTMs. The overlapped PTM sites were more likely to be in polar acidic/basic amino acid regions and exposed on the protein surface. Both of the acetylated and succinylated proteins cover nearly all aspects of cellular functions. Ribosome complex and glycolysis/gluconeogenesis-related proteins were significantly enriched in both acetylated and succinylated protein profiles through KEGG enrichment and protein-protein interaction network analyses. The acetyl-CoA and succinyl-CoA metabolism-related enzymes were found to be extensively modified by both modifications, implying the functional interaction between the two PTMs. This study provides a rich resource to examine the modulation of the two PTMs on the metabolism pathway and other biological processes in germinating rice seed.

The role of Plk3 in oncogenesis

The polo-like kinases (Plks) encompass a family of five serine/threonine protein kinases that play essential roles in many cellular processes involved in the control of the cell cycle, including entry into mitosis, DNA replication and the response to different types of stress. Plk1, which has been validated as a cancer target, came into the focus of many pharmaceutical companies for the development of small-molecule inhibitors as anticancer agents. Recently, FDA (Food and Drug Administration) has granted a breakthrough therapy designation to the Plk inhibitor BI 6727 (volasertib), which provided a survival benefit for patients suffering from acute myeloid leukemia. However, the various ATP-competitive inhibitors of Plk1 that are currently in clinical development also inhibit the activities of Plk2 and Plk3, which are considered as tumor suppressors. Plk3 contributes to the control and progression of the cell cycle while acting as a mediator of apoptosis and various types of cellular stress. The aberrant expression of Plk3 was found in different types of tumors. Recent progress has improved our understanding of Plk3 in regulating stress signaling and tumorigenesis. When using ATP-competitive Plk1 inhibitors, the biological roles of Plk1-related family members like Plk3 in cancer cells need to be considered carefully to improve treatment strategies against cancer.

dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins

Owing to the importance of the post-translational modifications (PTMs) of proteins in regulating biological processes, the dbPTM (http://dbPTM.mbc.nctu.edu.tw/) was developed as a comprehensive database of experimentally verified PTMs from several databases with annotations of potential PTMs for all UniProtKB protein entries. For this 10th anniversary of dbPTM, the updated resource provides not only a comprehensive dataset of experimentally verified PTMs, supported by the literature, but also an integrative interface for accessing all available databases and tools that are associated with PTM analysis. As well as collecting experimental PTM data from 14 public databases, this update manually curates over 12 000 modified peptides, including the emerging S-nitrosylation, S-glutathionylation and succinylation, from approximately 500 research articles, which were retrieved by text mining. As the number of available PTM prediction methods increases, this work compiles a non-homologous benchmark dataset to evaluate the predictive power of online PTM prediction tools. An increasing interest in the structural investigation of PTM substrate sites motivated the mapping of all experimental PTM peptides to protein entries of Protein Data Bank (PDB) based on database identifier and sequence identity, which enables users to examine spatially neighboring amino acids, solvent-accessible surface area and side-chain orientations for PTM substrate sites on tertiary structures. Since drug binding in PDB is annotated, this update identified over 1100 PTM sites that are associated with drug binding. The update also integrates metabolic pathways and protein-protein interactions to support the PTM network analysis for a group of proteins. Finally, the web interface is redesigned and enhanced to facilitate access to this resource.

A Phosphotyrosine Switch Controls the Association of Histone Mark Readers with Methylated Proteins

Although histone post-translational modifications play a paramount role in controlling access to genetic information, our understanding of the precise mechanisms regulating chromatin signaling remains superficial. For instance, histone H3 trimethylated on lysine 9 (H3K9(me3)) favors the association of chromodomain proteins such as heterochromatin protein 1α (HP1α) with chromatin. However, HP1α and other such chromatin proteins are not covering all specific histone marks at all times. Thus, how are these reader-histone interactions regulated? We propose tyrosine phosphorylation within the aromatic cage of histone mark readers as a molecular switch that can either turn ON or OFF and even alter the specificity of reader-histone interactions. We have identified tyrosine phosphorylation events on the chromatin proteins HP1α and M-phase phosphoprotein 8 that regulate their association with methylated histones in vitro (synthetic peptides, calf thymus purified histones, and nucleosomes), but also in cells, thus controlling access to genetic information.

Cell Pluripotency Levels Associated with Imprinted Genes in Human

Pluripotent stem cells are exhibited similarly in the morphology, gene expression, growth properties, and epigenetic modification with embryonic stem cells (ESCs). However, it is still controversial that the pluripotency of induced pluripotent stem cell (iPSC) is much inferior to ESC, and the differentiation capacity of iPSC and ESC can also be separated by transcriptome and epigenetics. miRNAs, which act in posttranscriptional regulation of gene expression and are involved in many basic cellular processes, may reveal the answer. In this paper, we focused on identifying the hidden relationship between miRNAs and imprinted genes in cell pluripotency. Total miRNA expression patterns in iPSC and ES cells were comprehensively analysed and linked with human imprinted genes, which show a global picture of their potential function in pluripotent level. A new CPA4-KLF14 region which locates in chromosomal homologous segments (CHSs) within mammals and include both imprinted genes and significantly expressed miRNAs was first identified. Molecular network analysis showed genes interacted with imprinted genes closely and enriched in modules such as cancer, cell death and survival, and tumor morphology. This imprinted region may provide a new look for those who are interested in cell pluripotency of hiPSCs and hESCs.

Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks

Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.

Features of S-nitrosylation based on statistical analysis and molecular dynamics simulation: cysteine acidity, surrounding basicity, steric hindrance and local flexibility

S-Nitrosylation is involved in protein functional regulation and cellular signal transduction. Although intensive efforts have been made, the molecular mechanisms of S-nitrosylation have not yet been fully understood. In this work, we carried out a survey on 213 protein structures with S-nitrosylated cysteine sites and molecular dynamic simulations of hemoglobin as a case study. It was observed that the S-nitrosylated cysteines showed a lower pKa, a higher population of basic residues, a lower population of big-volume residues in the neighborhood, and relatively higher flexibility. The case study of hemoglobin showed that, compared to that in the T-state, Cysβ93 in the R-state hemoglobin possessed the above structural features, in agreement with the previous report that the R-state was more reactive in S-nitrosylation. Moreover, basic residues moved closer to the Cysβ93 in the dep-R-state hemoglobin, while big-volume residues approached the Cysβ93 in the dep-T-state. Using the four characteristics, i.e. cysteine acidity, surrounding basicity, steric hindrance, and local flexibility, a 3-dimensional model of S-nitrosylation was constructed to explain 61.9% of the S-nitrosylated and 58.1% of the non-S-nitrosylated cysteines. Our study suggests that cysteine deprotonation is a prerequisite for protein S-nitrosylation, and these characteristics might be useful in identifying specificity of protein S-nitrosylation.

Regulation of translesion DNA synthesis: Posttranslational modification of lysine residues in key proteins

Posttranslational modification of proteins often controls various aspects of their cellular function. Indeed, over the past decade or so, it has been discovered that posttranslational modification of lysine residues plays a major role in regulating translesion DNA synthesis (TLS) and perhaps the most appreciated lysine modification is that of ubiquitination. Much of the recent interest in ubiquitination stems from the fact that proliferating cell nuclear antigen (PCNA) was previously shown to be specifically ubiquitinated at K164 and that such ubiquitination plays a key role in regulating TLS. In addition, TLS polymerases themselves are now known to be ubiquitinated. In the case of human polymerase η, ubiquitination at four lysine residues in its C-terminus appears to regulate its ability to interact with PCNA and modulate TLS. Within the past few years, advances in global proteomic research have revealed that many proteins involved in TLS are, in fact, subject to a previously underappreciated number of lysine modifications. In this review, we will summarize the known lysine modifications of several key proteins involved in TLS; PCNA and Y-family polymerases η, ι, κ and Rev1 and we will discuss the potential regulatory effects of such modification in controlling TLS in vivo.

PEIMAN 1.0: Post-translational modification Enrichment, Integration and Matching ANalysis

Conventional proteomics has discovered a wide gap between protein sequences and biological functions. The third generation of proteomics was provoked to bridge this gap. Targeted and untargeted post-translational modification (PTM) studies are the most important parts of today’s proteomics. Considering the expensive and time-consuming nature of experimental methods, computational methods are developed to study, analyze, predict, count and compute the PTM annotations on proteins. The enrichment analysis softwares are among the common computational biology and bioinformatic software packages. The focus of such softwares is to find the probability of occurrence of the desired biological features in any arbitrary list of genes/proteins. We introduce Post-translational modification Enrichment Integration and Matching Analysis (PEIMAN) software to explore more probable and enriched PTMs on proteins. Here, we also represent the statistics of detected PTM terms used in enrichment analysis in PEIMAN software based on the latest released version of UniProtKB/Swiss-Prot. These results, in addition to giving insight to any given list of proteins, could be useful to design targeted PTM studies for identification and characterization of special chemical groups.

Database URL:http://bs.ipm.ir/softwares/PEIMAN/

Large-scale determination of absolute phosphorylation stoichiometries in human cells by motif-targeting quantitative proteomics

Our ability to model the dynamics of signal transduction networks will depend on accurate methods to quantify levels of protein phosphorylation on a global scale. Here we describe a motif-targeting quantitation method for phosphorylation stoichiometry typing. Proteome-wide phosphorylation stoichiometry can be obtained by a simple phosphoproteomic workflow integrating dephosphorylation and isotope tagging with enzymatic kinase reaction. Proof-of-concept experiments using CK2-, MAPK- and EGFR-targeting assays in lung cancer cells demonstrate the advantage of kinase-targeted complexity reduction, resulting in deeper phosphoproteome quantification. We measure the phosphorylation stoichiometry of >1,000 phosphorylation sites including 366 low-abundance tyrosine phosphorylation sites, with high reproducibility and using small sample sizes. Comparing drug-resistant and sensitive lung cancer cells, we reveal that post-translational phosphorylation changes are significantly more dramatic than those at the protein and messenger RNA levels, and suggest potential drug targets within the kinase-substrate network associated with acquired drug resistance.