Predicting and analyzing protein phosphorylation sites in plants using musite

Unveiling orphan receptor-like kinases in plants: novel client discovery using high-confidence library predictions in the Kinase-Client (KiC) assay

Plants are remarkable in their ability to adapt to changing environments, with receptor-like kinases (RLKs) playing a pivotal role in perceiving and transmitting environmental cues into cellular responses. Despite extensive research on RLKs from the plant kingdom, the function and activity of many kinases, i.e., their substrates or "clients", remain uncharted. To validate a novel client prediction workflow and learn more about an important RLK, this study focuses on P2K1 (DORN1), which acts as a receptor for extracellular ATP (eATP), playing a crucial role in plant stress resistance and immunity. We designed a Kinase-Client (KiC) assay library of 225 synthetic peptides, incorporating previously identified P2K phosphorylated peptides and novel predictions from a deep-learning phosphorylation site prediction model (MUsite) and a trained hidden Markov model (HMM) based tool, HMMER. Screening the library against purified P2K1 cytosolic domain (CD), we identified 46 putative substrates, including 34 novel clients, 27 of which may be novel peptides, not previously identified experimentally. Gene Ontology (GO) analysis among phosphopeptide candidates revealed proteins associated with important biological processes in metabolism, structure development, and response to stress, as well as molecular functions of kinase activity, catalytic activity, and transferase activity. We offer selection criteria for efficient further in vivo experiments to confirm these discoveries. This approach not only expands our knowledge of P2K1's substrates and functions but also highlights effective prediction algorithms for identifying additional potential substrates. Overall, the results support use of the KiC assay as a valuable tool in unraveling the complexities of plant phosphorylation and provide a foundation for predicting the phosphorylation landscape of plant species based on peptide library results.

Regulation of PaRBOH1-mediated ROS production in Norway spruce by Ca2+ binding and phosphorylation

Plant respiratory burst oxidase homologs (RBOHs) are plasma membrane-localized NADPH oxidases that generate superoxide anion radicals, which then dismutate to H₂O₂, into the apoplast using cytoplasmic NADPH as an electron donor. PaRBOH1 is the most highly expressed RBOH gene in developing xylem as well as in a lignin-forming cell culture of Norway spruce (Picea abies L. Karst.). Since no previous information about regulation of gymnosperm RBOHs exist, our aim was to resolve how PaRBOH1 is regulated with a focus on phosphorylation. The N-terminal part of PaRBOH1 was found to contain several putative phosphorylation sites and a four-times repeated motif with similarities to the Botrytis-induced kinase 1 target site in Arabidopsis AtRBOHD. Phosphorylation was indicated for six of the sites in in vitro kinase assays using 15 amino-acid-long peptides for each of the predicted phosphotarget site in the presence of protein extracts of developing xylem. Serine and threonine residues showing positive response in the peptide assays were individually mutated to alanine (kinase-inactive) or to aspartate (phosphomimic), and the wild type PaRBOH1 and the mutated constructs transfected to human kidney embryogenic (HEK293T) cells with a low endogenous level of extracellular ROS production. ROS-producing assays with HEK cells showed that Ca²⁺ and phosphorylation synergistically activate the enzyme and identified several serine and threonine residues that are likely to be phosphorylated including a novel phosphorylation site not characterized in other plant species. These were further investigated with a phosphoproteomic study. Results of Norway spruce, the first gymnosperm species studied in relation to RBOH regulation, show that regulation of RBOH activity is conserved among seed plants.

A Pretrained ELECTRA Model for Kinase-Specific Phosphorylation Site Prediction

Phosphorylation plays a vital role in signal transduction and cell cycle. Identifying and understanding phosphorylation through machine-learning methods has a long history. However, existing methods only learn representations of a protein sequence segment from a labeled dataset itself, which could result in biased or incomplete features, especially for kinase-specific phosphorylation site prediction in which training data are typically sparse. To learn a comprehensive contextual representation of a protein sequence segment for kinase-specific phosphorylation site prediction, we pretrained our model from over 24 million unlabeled sequence fragments using ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately). The pretrained model was applied to kinase-specific site prediction of kinases CDK, PKA, CK2, MAPK, and PKC. The pretrained ELECTRA model achieves 9.02% improvement over BERT and 11.10% improvement over MusiteDeep in the area under the precision-recall curve on the benchmark data.

Disruption of AtHAK/KT/KUP9 enhances plant cesium accumulation under low potassium supply

Understanding the molecular mechanisms that underlie cesium (Cs⁺ ) transport in plants is important to limit the entry of its radioisotopes from contaminated areas into the food chain. The potentially toxic element Cs⁺ , which is not involved in any biological process, is chemically closed to the macronutrient potassium (K⁺ ). Among the multiple K⁺ carriers, the high-affinity K⁺ transporters family HAK/KT/KUP is thought to be relevant in mediating opportunistic Cs⁺ transport. Of the 13 KUP identified in A. thaliana, only HAK5, the major contributor to root K⁺ acquisition under low K⁺ supply, has been functionally demonstrated to be involved in Cs⁺ uptake in planta. In the present study, we showed that accumulation of Cs⁺ increased by up to 30% in two A. thaliana mutant lines lacking KUP9 and grown under low K⁺ supply. Since further experiments revealed that Cs⁺ release from contaminated plants to the external medium is proportionally lower in the two kup9 mutant alleles, we proposed that KUP9 disruption could impair Cs⁺ efflux. By contrast, K⁺ status in kup9 mutants is not affected, suggesting that KUP9 disruption does not alter substantially K⁺ transport in experimental conditions used. The putative primary role of KUP9 in plants is further discussed.

Evolutionary Variation in MADS Box Dimerization Affects Floral Development and Protein Abundance in Maize

Interactions between MADS box transcription factors are critical in the regulation of floral development, and shifting MADS box protein-protein interactions are predicted to have influenced floral evolution. However, precisely how evolutionary variation in protein-protein interactions affects MADS box protein function remains unknown. To assess the impact of changing MADS box protein-protein interactions on transcription factor function, we turned to the grasses, where interactions between B-class MADS box proteins vary. We tested the functional consequences of this evolutionary variability using maize (Zea mays) as an experimental system. We found that differential B-class dimerization was associated with subtle, quantitative differences in stamen shape. In contrast, differential dimerization resulted in large-scale changes to downstream gene expression. Differential dimerization also affected B-class complex composition and abundance, independent of transcript levels. This indicates that differential B-class dimerization affects protein degradation, revealing an important consequence for evolutionary variability in MADS box interactions. Our results highlight complexity in the evolution of developmental gene networks: changing protein-protein interactions could affect not only the composition of transcription factor complexes but also their degradation and persistence in developing flowers. Our results also show how coding change in a pleiotropic master regulator could have small, quantitative effects on development.

Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation

Acetylation is one of post-translational modification (PTM), which often reacts with acetic acid and brings an acetyl radical to an organic compound. It is helpful to identify acetylation protein correctly for understanding the mechanism of acetylation in biological systems. Although many acetylation sites have been identified by high throughput experimental studies via mass spectrometry, there still are lots of acetylation sites need to be discovered. Computational methods have showed their power for identifying acetylation sites with informatics techniques which usually reduce experiment cost and improve the effectiveness and efficiency. In fact, if there is an approach can distinguish the acetylated proteins from the non-acetylated ones, it is no doubt a very meaningful and effective method for this issue. Here, we proposed a novel computational method for identifying acetylation proteins by extracting features from the conservation information of sequence via gray system model and KNN scores based on the information of functional domain annotation and subcellular localization. The authors have performed the 5-fold cross-validation on three datasets along with much analysis of features and the Relief feature selection algorithm. The obtained accuracies are all satisfactory, as the mean performance, the accuracy is 77.10%, the Matthew's correlation coefficient is 0.5457, and the AUC value is 0.8389. These works might provide useful insights for the related experimental validation, and further studies of other PTM process. For the convenience of related researchers, the web-server named “iACetyP” was established and is accessible at http://www.jci-bioinfo.cn/iAcetyP.

Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy

Protein phosphorylation is a reversible and ubiquitous post-translational modification that primarily occurs at serine, threonine and tyrosine residues and regulates a variety of biological processes. In this paper, we first briefly summarized the current progresses in computational prediction of eukaryotic protein phosphorylation sites, which mainly focused on animals and plants, especially on human, with a less extent on fungi. Since the number of identified fungi phosphorylation sites has greatly increased in a wide variety of organisms and their roles in pathological physiology still remain largely unknown, more attention has been paid on the identification of fungi-specific phosphorylation. Here, experimental fungi phosphorylation sites data were collected and most of the sites were classified into different types to be encoded with various features and trained via a two-step feature optimization method. A novel method for prediction of species-specific fungi phosphorylation-PreSSFP was developed, which can identify fungi phosphorylation in seven species for specific serine, threonine and tyrosine residues (http://computbiol.ncu.edu.cn/PreSSFP). Meanwhile, we critically evaluated the performance of PreSSFP and compared it with other existing tools. The satisfying results showed that PreSSFP is a robust predictor. Feature analyses exhibited that there have some significant differences among seven species. The species-specific prediction via two-step feature optimization method to mine important features for training could considerably improve the prediction performance. We anticipate that our study provides a new lead for future computational analysis of fungi phosphorylation.

Monitoring of Plant Protein Post-translational Modifications Using Targeted Proteomics

Protein post-translational modifications (PTMs) are among the fastest and earliest of plant responses to changes in the environment, making the mechanisms and dynamics of PTMs an important area of plant science. One of the most studied PTMs is protein phosphorylation. This review summarizes the use of targeted proteomics for the elucidation of the biological functioning of plant PTMs, and focuses primarily on phosphorylation. Since phosphorylated peptides have a low abundance, usually complex enrichment protocols are required for their research. Initial identification is usually performed with discovery phosphoproteomics, using high sensitivity mass spectrometers, where as many phosphopeptides are measured as possible. Once a PTM site is identified, biological characterization can be addressed with targeted proteomics. In targeted proteomics, Selected/Multiple Reaction Monitoring (S/MRM) is traditionally coupled to simple, standard protein digestion protocols, often omitting the enrichment step, and relying on triple-quadruple mass spectrometer. The use of synthetic peptides as internal standards allows accurate identification, avoiding cross-reactivity typical for some antibody based approaches. Importantly, internal standards allow absolute peptide quantitation, reported down to 0.1 femtomoles, also useful for determination of phospho-site occupancy. S/MRM is advantageous in situations where monitoring and diagnostics of peptide PTM status is needed for many samples, as it has faster sample processing times, higher throughput than other approaches, and excellent quantitation and reproducibility. Furthermore, the number of publicly available data-bases with plant PTM discovery data is growing, facilitating selection of modified peptides and design of targeted proteomics workflows. Recent instrument developments result in faster scanning times, inclusion of ion-trap instruments leading to parallel reaction monitoring- which further facilitates S/MRM experimental design. Finally, recent combination of data independent and data dependent spectra acquisition means that in addition to anticipated targeted data, spectra can now be queried for unanticipated information. The potential for future applications in plant biology is outlined.

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.

iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier

Protein phosphorylation is a posttranslational modification (PTM or PTLM), where a phosphoryl group is added to the residue(s) of a protein molecule. The most commonly phosphorylated amino acids occur at serine (S), threonine (T), and tyrosine (Y). Protein phosphorylation plays a significant role in a wide range of cellular processes; meanwhile its dysregulation is also involved with many diseases. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of S, T, or Y, which ones can be phosphorylated, and which ones cannot? To address this problem, we have developed a predictor called iPhos-PseEn by fusing four different pseudo component approaches (amino acids’ disorder scores, nearest neighbor scores, occurrence frequencies, and position weights) into an ensemble classifier via a voting system. Rigorous cross-validations indicated that the proposed predictor remarkably outperformed its existing counterparts. For the convenience of most experimental scientists, a user-friendly web-server for iPhos-PseEn has been established at http://www.jci-bioinfo.cn/iPhos-PseEn, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.

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

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

Chlorophyte aspartyl aminopeptidases: Ancient origins, expanded families, new locations, and secondary functions

M18 aspartyl aminopeptidases (DAPs) are well characterized in microbes and animals with likely functions in peptide processing and vesicle trafficking. In contrast, there is a dearth of knowledge on plant aminopeptidases with a preference for proteins and peptides with N-terminal acidic residues. During evolution of the Plantae, there was an expansion and diversification of the M18 DAPs. After divergence of the ancestral green algae from red and glaucophyte algae, a duplication yielded the DAP1 and DAP2 lineages. Subsequently DAP1 genes were lost in chlorophyte algae. A duplication of DAP2-related genes occurred early in green plant evolution. DAP2 genes were retained in land plants and picoeukaryotic algae and lost in green algae. In contrast, DAP2-like genes persisted in picoeukaryotic and green algae, while this lineage was lost in land plants. Consistent with this evolutionary path, Arabidopsis thaliana has two DAP gene lineages (AtDAP1 and AtDAP2). Similar to animal and yeast DAPs, AtDAP1 is localized to the cytosol or vacuole; while AtDAP2 harbors an N-terminal transit peptide and is chloroplast localized. His₆-DAP1 and His₆-DAP2 expressed in Escherichia coli were enzymatically active and dodecameric with masses exceeding 600 kDa. His₆-DAP1 and His₆-DAP2 preferentially hydrolyzed Asp-p-nitroanilide and Glu-p-nitroanilide. AtDAPs are highly conserved metallopeptidases activated by MnCl₂ and inhibited by ZnCl₂ and divalent ion chelators. The protease inhibitor PMSF inhibited and DTT stimulated both His₆-DAP1 and His₆-DAP2 activities suggesting a role for thiols in the AtDAP catalytic mechanism. The enzymes had distinct pH and temperature optima, as well as distinct kinetic parameters. Both enzymes had high catalytic efficiencies (k_cat/K_m) exceeding 1.0 x 10⁷ M^-1 sec^-1. Using established molecular chaperone assays, AtDAP1 and AtDAP2 prevented thermal denaturation. AtDAP1 also prevented protein aggregation and promoted protein refolding. Collectively, these data indicate that plant DAPs have a complex evolutionary history and have evolved new biochemical features that may enable their role in vivo.

Bioinformatics Analysis of Protein Phosphorylation in Plant Systems Biology Using P3DB

Protein phosphorylation is one of the most pervasive protein post-translational modification events in plant cells. It is involved in many plant biological processes, such as plant growth, organ development, and plant immunology, by regulating or switching signaling and metabolic pathways. High-throughput experimental methods like mass spectrometry can easily characterize hundreds to thousands of phosphorylation events in a single experiment. With the increasing volume of the data sets, Plant Protein Phosphorylation DataBase (P3DB, http://p3db.org ) provides a comprehensive, systematic, and interactive online platform to deposit, query, analyze, and visualize these phosphorylation events in many plant species. It stores the protein phosphorylation sites in the context of identified mass spectra, phosphopeptides, and phosphoproteins contributed from various plant proteome studies. In addition, P3DB associates these plant phosphorylation sites to protein physicochemical information in the protein charts and tertiary structures, while various protein annotations from hierarchical kinase phosphatase families, protein domains, and gene ontology are also added into the database. P3DB not only provides rich information, but also interconnects and provides visualization of the data in networks, in systems biology context. Currently, P3DB includes the KiC (Kinase Client) assay network, the protein-protein interaction network, the kinase-substrate network, the phosphatase-substrate network, and the protein domain co-occurrence network. All of these are available to query for and visualize existing phosphorylation events. Although P3DB only hosts experimentally identified phosphorylation data, it provides a plant phosphorylation prediction model for any unknown queries on the fly. P3DB is an entry point to the plant phosphorylation community to deposit and visualize any customized data sets within this systems biology framework. Nowadays, P3DB has become one of the major bioinformatics platforms of protein phosphorylation in plant biology.

Content of intrinsic disorder influences the outcome of cell-free protein synthesis

Cell-free protein synthesis is used to produce proteins with various structural traits. Recent bioinformatics analyses indicate that more than half of eukaryotic proteins possess long intrinsically disordered regions. However, no systematic study concerning the connection between intrinsic disorder and expression success of cell-free protein synthesis has been presented until now. To address this issue, we examined correlations of the experimentally observed cell-free protein expression yields with the contents of intrinsic disorder bioinformatically predicted in the expressed sequences. This analysis revealed strong relationships between intrinsic disorder and protein amenability to heterologous cell-free expression. On the one hand, elevated disorder content was associated with the increased ratio of soluble expression. On the other hand, overall propensity for detectable protein expression decreased with disorder content. We further demonstrated that these tendencies are rooted in some distinct features of intrinsically disordered regions, such as low hydrophobicity, elevated surface accessibility and high abundance of sequence motifs for proteolytic degradation, including sites of ubiquitination and PEST sequences. Our findings suggest that identification of intrinsically disordered regions in the expressed amino acid sequences can be of practical use for predicting expression success and optimizing cell-free protein synthesis.

In Silico Analysis of Correlations between Protein Disorder and Post-Translational Modifications in Algae

Recent proteome analyses have reported that intrinsically disordered regions (IDRs) of proteins play important roles in biological processes. In higher plants whose genomes have been sequenced, the correlation between IDRs and post-translational modifications (PTMs) has been reported. The genomes of various eukaryotic algae as common ancestors of plants have also been sequenced. However, no analysis of the relationship to protein properties such as structure and PTMs in algae has been reported. Here, we describe correlations between IDR content and the number of PTM sites for phosphorylation, glycosylation, and ubiquitination, and between IDR content and regions rich in proline, glutamic acid, serine, and threonine (PEST) and transmembrane helices in the sequences of 20 algae proteomes. Phosphorylation, O-glycosylation, ubiquitination, and PEST preferentially occurred in disordered regions. In contrast, transmembrane helices were favored in ordered regions. N-glycosylation tended to occur in ordered regions in most of the studied algae; however, it correlated positively with disordered protein content in diatoms. Additionally, we observed that disordered protein content and the number of PTM sites were significantly increased in the species-specific protein clusters compared to common protein clusters among the algae. Moreover, there were specific relationships between IDRs and PTMs among the algae from different groups.

Phosphorylation of Serine422 increases the stability and transactivation activities of human Osterix

Osterix (Osx) is an essential regulator for osteoblast differentiation and bone formation. Although phosphorylation has been reported to be involved in the regulation of Osx activity, the precise underlying mechanisms remain to be elucidated. Here we identified S422 as a novel phosphorylation site of Osx and demonstrated that GSK-3β interacted and co-localized with Osx. GSK-3β increased the stability and transactivation activity of Osx through phosphorylation of the newly identified site. These findings expanded our understanding of the mechanisms of posttranslational regulation of Osx and the role of GSK-3β in the control of Osx transactivation activity.

Recent advances and challenges in plant phosphoproteomics

Plants are sessile organisms that need to respond to environmental changes quickly and efficiently. They can accomplish this by triggering specialized signaling pathways often mediated by protein phosphorylation and dephosphorylation. Phosphorylation is a fast response that can switch on or off a myriad of biological pathways and processes. Proteomics and MS are the main tools employed in the study of protein phosphorylation. Advances in the technologies allow simultaneous identification and quantification of thousands of phosphopeptides and proteins that are essential to understanding the sophisticated biological systems and regulations. In this review, we summarize the advances in phosphopeptide enrichment and quantitation, MS for phosphorylation site mapping and new data acquisition methods, databases and informatics, interpretation of biological insights and crosstalk with other PTMs, as well as future directions and challenges in the field of phosphoproteomics.

Phosphorylation site prediction in plants

Protein phosphorylation events on serine, threonine, and tyrosine residues are the most pervasive protein covalent bond modifications in plant signaling. Both low and high throughput studies reveal the importance of phosphorylation in plant molecular biology. Although becoming more and more common, the proteome-wide screening on phosphorylation by experiments remains time consuming and costly. Therefore, in silico prediction methods are proposed as a complementary analysis tool to enhance the phosphorylation site identification, develop biological hypothesis, or help experimental design. These methods build statistical models based on the experimental data, and they do not have some of the technical-specific bias, which may have advantage in proteome-wide analysis. More importantly computational methods are very fast and cheap to run, which makes large-scale phosphorylation identifications very practical for any types of biological study. Thus, the phosphorylation prediction tools become more and more popular. In this chapter, we will focus on plant specific phosphorylation site prediction tools, with essential illustration of technical details and application guidelines. We will use Musite, PhosPhAt and PlantPhos as the representative tools. We will present the results on the prediction of the Arabidopsis protein phosphorylation events to give users a general idea of the performance range of the three tools, together with their strengths and limitations. We believe these prediction tools will contribute more and more to the plant phosphorylation research community.

Plant-PrAS: a database of physicochemical and structural properties and novel functional regions in plant proteomes

Arabidopsis thaliana is an important model species for studies of plant gene functions. Research on Arabidopsis has resulted in the generation of high-quality genome sequences, annotations and related post-genomic studies. The amount of annotation, such as gene-coding regions and structures, is steadily growing in the field of plant research. In contrast to the genomics resource of animals and microorganisms, there are still some difficulties with characterization of some gene functions in plant genomics studies. The acquisition of information on protein structure can help elucidate the corresponding gene function because proteins encoded in the genome possess highly specific structures and functions. In this study, we calculated multiple physicochemical and secondary structural parameters of protein sequences, including length, hydrophobicity, the amount of secondary structure, the number of intrinsically disordered regions (IDRs) and the predicted presence of transmembrane helices and signal peptides, using a total of 208,333 protein sequences from the genomes of six representative plant species, Arabidopsis thaliana, Glycine max (soybean), Populus trichocarpa (poplar), Oryza sativa (rice), Physcomitrella patens (moss) and Cyanidioschyzon merolae (alga). Using the PASS tool and the Rosetta Stone method, we annotated the presence of novel functional regions in 1,732 protein sequences that included unannotated sequences from the Arabidopsis and rice proteomes. These results were organized into the Plant Protein Annotation Suite database (Plant-PrAS), which can be freely accessed online at http://plant-pras.riken.jp/.

Intrinsically disordered proteins and multicellular organisms

Intrinsically disordered proteins (IDPs) and IDP regions lack stable tertiary structure yet carry out numerous biological functions, especially those associated with signaling, transcription regulation, DNA condensation, cell division, and cellular differentiation. Both post-translational modifications (PTMs) and alternative splicing (AS) expand the functional repertoire of IDPs. Here we propose that an "IDP-based developmental toolkit," which is comprised of IDP regions, PTMs, especially multiple PTMs, within these IDP regions, and AS events within segments of pre-mRNA that code for these same IDP regions, allows functional diversification and environmental responsiveness for molecules that direct the development of complex metazoans.

Correlations between predicted protein disorder and post-translational modifications in plants

MOTIVATION

Protein structural research in plants lags behind that in animal and bacterial species. This lag concerns both the structural analysis of individual proteins and the proteome-wide characterization of structure-related properties. Until now, no systematic study concerning the relationships between protein disorder and multiple post-translational modifications (PTMs) in plants has been presented.

RESULTS

In this work, we calculated the global degree of intrinsic disorder in the complete proteomes of eight typical monocotyledonous and dicotyledonous plant species. We further predicted multiple sites for phosphorylation, glycosylation, acetylation and methylation and examined the correlations of protein disorder with the presence of the predicted PTM sites. It was found that phosphorylation, acetylation and O-glycosylation displayed a clear preference for occurrence in disordered regions of plant proteins. In contrast, methylation tended to avoid disordered sequence, whereas N-glycosylation did not show a universal structural preference in monocotyledonous and dicotyledonous plants. In addition, the analysis performed revealed significant differences between the integral characteristics of monocot and dicot proteomes. They included elevated disorder degree, increased rate of O-glycosylation and R-methylation, decreased rate of N-glycosylation, K-acetylation and K-methylation in monocotyledonous plant species, as compared with dicotyledonous species. Altogether, our study provides the most compelling evidence so far for the connection between protein disorder and multiple PTMs in plants.

CONTACT

tokmak@phoenix.kobe-u.ac.jp or tetsuya.sakurai@riken.jp Supplementary information: Supplementary data are available at Bioinformatics online.

P³DB 3.0: From plant phosphorylation sites to protein networks

In the past few years, the Plant Protein Phosphorylation Database (P³DB, http://p3db.org) has become one of the most significant in vivo data resources for studying plant phosphoproteomics. We have substantially updated P³DB with respect to format, new datasets and analytic tools. In the P³DB 3.0, there are altogether 47 923 phosphosites in 16 477 phosphoproteins curated across nine plant organisms from 32 studies, which have met our multiple quality standards for acquisition of in vivo phosphorylation site data. Centralized by these phosphorylation data, multiple related data and annotations are provided, including protein–protein interaction (PPI), gene ontology, protein tertiary structures, orthologous sequences, kinase/phosphatase classification and Kinase Client Assay (KiC Assay) data—all of which provides context for the phosphorylation event. In addition, P³DB 3.0 incorporates multiple network viewers for the above features, such as PPI network, kinase-substrate network, phosphatase-substrate network, and domain co-occurrence network to help study phosphorylation from a systems point of view. Furthermore, the new P³DB reflects a community-based design through which users can share datasets and automate data depository processes for publication purposes. Each of these new features supports the goal of making P³DB a comprehensive, systematic and interactive platform for phosphoproteomics research.

Multifarious roles of intrinsic disorder in proteins illustrate its broad impact on plant biology

Intrinsically disordered proteins (IDPs) are highly abundant in eukaryotic proteomes. Plant IDPs play critical roles in plant biology and often act as integrators of signals from multiple plant regulatory and environmental inputs. Binding promiscuity and plasticity allow IDPs to interact with multiple partners in protein interaction networks and provide important functional advantages in molecular recognition through transient protein-protein interactions. Short interaction-prone segments within IDPs, termed molecular recognition features, represent potential binding sites that can undergo disorder-to-order transition upon binding to their partners. In this review, we summarize the evidence for the importance of IDPs in plant biology and evaluate the functions associated with intrinsic disorder in five different types of plant protein families experimentally confirmed as IDPs. Functional studies of these proteins illustrate the broad impact of disorder on many areas of plant biology, including abiotic stress, transcriptional regulation, light perception, and development. Based on the roles of disorder in the protein-protein interactions, we propose various modes of action for plant IDPs that may provide insight for future experimental approaches aimed at understanding the molecular basis of protein function within important plant pathways.