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.     

The Roles of Post-translational Modifications in the Context of Protein Interaction Networks

Among other effects, post-translational modifications (PTMs) have been shown to exert their function via the modulation of protein-protein interactions. For twelve different main PTM-types and associated subtypes and across 9 diverse species, we investigated whether particular PTM-types are associated with proteins with specific and possibly “strategic” placements in the network of all protein interactions by determining informative network-theoretic properties. Proteins undergoing a PTM were observed to engage in more interactions and positioned in more central locations than non-PTM proteins. Among the twelve considered PTM-types, phosphorylated proteins were identified most consistently as being situated in central network locations and with the broadest interaction spectrum to proteins carrying other PTM-types, while glycosylated proteins are preferentially located at the network periphery. For the human interactome, proteins undergoing sumoylation or proteolytic cleavage were found with the most characteristic network properties. PTM-type-specific protein interaction network (PIN) properties can be rationalized with regard to the function of the respective PTM-carrying proteins. For example, glycosylation sites were found enriched in proteins with plasma membrane localizations and transporter or receptor activity, which generally have fewer interacting partners. The involvement in disease processes of human proteins undergoing PTMs was also found associated with characteristic PIN properties. By integrating global protein interaction networks and specific PTMs, our study offers a novel approach to unraveling the role of PTMs in cellular processes.     

Discriminating between Lysine Sumoylation and Lysine Acetylation Using mRMR Feature Selection and Analysis

Post-translational modifications (PTMs) are crucial steps in protein synthesis and are important factors contributing to protein diversity. PTMs play important roles in the regulation of gene expression, protein stability and metabolism. Lysine residues in protein sequences have been found to be targeted for both types of PTMs: sumoylations and acetylations; however, each PTM has a different cellular role. As experimental approaches are often laborious and time consuming, it is challenging to distinguish the two types of PTMs on lysine residues using computational methods. In this study, we developed a method to discriminate between sumoylated lysine residues and acetylated residues. The method incorporated several features: PSSM conservation scores, amino acid factors, secondary structures, solvent accessibilities and disorder scores. By using the mRMR (Maximum Relevance Minimum Redundancy) method and the IFS (Incremental Feature Selection) method, an optimal feature set was selected from all of the incorporated features, with which the classifier achieved 92.14% accuracy with an MCC value of 0.7322. Analysis of the optimal feature set revealed some differences between acetylation and sumoylation. The results from our study also supported the previous finding that there exist different consensus motifs for the two types of PTMs. The results could suggest possible dominant factors governing the acetylation and sumoylation of lysine residues, shedding some light on the modification dynamics and molecular mechanisms of the two types of PTMs, and provide guidelines for experimental validations.     

Systematic assessment of the benefits and caveats in mining microbial post-translational modifications from shotgun proteomic data: the response of Shewanella oneidensis to chromate exposure

Microbes are known to regulate both gene expression and protein activity through the use of post-translational modifications (PTMs). Common PTMs involved in cellular signaling and gene control include methylations, acetylations, and phosphorylations, whereas oxidations have been implicated as an indicator of stress. Shewanella oneidensis MR-1 is a Gram-negative bacterium that demonstrates both respiratory versatility and the ability to sense and adapt to diverse environmental conditions. The data set used in this study consisted of tandem mass spectra derived from midlog phase aerobic cultures of S. oneidensis either native or shocked with 1 mM chromate [Cr(VI)]. In this study, three algorithms (DBDigger, Sequest, and InsPecT) were evaluated for their ability to scrutinize shotgun proteomic data for evidence of PTMs. The use of conservative scoring filters for peptides or proteins versus creating a subdatabase first from a nonmodification search was evaluated with DBDigger. The use of higher-scoring filters for peptide identifications was found to result in optimal identifications of PTM peptides with a 2% false discovery rate (FDR) for the total data set using the DBDigger algorithm. However, the FDR climbs to unacceptably high levels when only PTM peptides are considered. Sequest was evaluated as a method for confirming PTM peptides putatively identified using DBDigger; however, there was a low identification rate ( approximately 25%) for the searched spectra. InsPecT was found to have a much lower, and thus more acceptable, FDR than DBDigger for PTM peptides. Comparisons between InsPecT and DBDigger were made with respect to both the FDR and PTM peptide identifications. As a demonstration of this approach, a number of S. oneidensis chemotaxis proteins as well as low-abundance signal transduction proteins were identified as being post-translationally modified in response to chromate challenge.     

Systematic functional prioritization of protein posttranslational modifications

Protein function is often regulated by posttranslational modifications (PTMs), and recent advances in mass spectrometry have resulted in an exponential increase in PTM identification. However, the functional significance of the vast majority of these modifications remains unknown. To address this problem, we compiled nearly 200,000 phosphorylation, acetylation, and ubiquitination sites from 11 eukaryotic species, including 2,500 newly identified ubiquitylation sites for Saccharomyces cerevisiae. We developed methods to prioritize the functional relevance of these PTMs by predicting those that likely participate in cross-regulatory events, regulate domain activity, or mediate protein-protein interactions. PTM conservation within domain families identifies regulatory "hot spots" that overlap with functionally important regions, a concept that we experimentally validated on the HSP70 domain family. Finally, our analysis of the evolution of PTM regulation highlights potential routes for neutral drift in regulatory interactions and suggests that only a fraction of modification sites are likely to have a significant biological role.     

Structural Analysis of PTM Hotspots (SAPH-ire) – A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families

Predicting the biological function potential of post-translational modifications (PTMs) is becoming increasingly important in light of the exponential increase in available PTM data from high-throughput proteomics. We developed structural analysis of PTM hotspots (SAPH-ire)—a quantitative PTM ranking method that integrates experimental PTM observations, sequence conservation, protein structure, and interaction data to allow rank order comparisons within or between protein families. Here, we applied SAPH-ire to the study of PTMs in diverse G protein families, a conserved and ubiquitous class of proteins essential for maintenance of intracellular structure (tubulins) and signal transduction (large and small Ras-like G proteins). A total of 1728 experimentally verified PTMs from eight unique G protein families were clustered into 451 unique hotspots, 51 of which have a known and cited biological function or response. Using customized software, the hotspots were analyzed in the context of 598 unique protein structures. By comparing distributions of hotspots with known versus unknown function, we show that SAPH-ire analysis is predictive for PTM biological function. Notably, SAPH-ire revealed high-ranking hotspots for which a functional impact has not yet been determined, including phosphorylation hotspots in the N-terminal tails of G protein gamma subunits—conserved protein structures never before reported as regulators of G protein coupled receptor signaling. To validate this prediction we used the yeast model system for G protein coupled receptor signaling, revealing that gamma subunit–N-terminal tail phosphorylation is activated in response to G protein coupled receptor stimulation and regulates protein stability in vivo. These results demonstrate the utility of integrating protein structural and sequence features into PTM prioritization schemes that can improve the analysis and functional power of modification-specific proteomics data.Post-translational modifications (PTMs)¹ are a rapidly expanding and important class of protein feature that broaden the functional diversity of proteins in a proteome. By definition, PTMs change protein structure and therefore have the potential to affect protein function by altering protein interactions, protein stability or catalytic activity (1, 2). As they have been found to occur on nearly every protein in the eukaryotic proteome, PTMs broadly impact nearly all known cellular processes. Over 300 different types of PTM are known, ranging from single atom modifications (e.g. oxide) to small protein modifiers (e.g. ubiquitin), which can occur on all but five amino acid residues resulting from enzymatic or nonenzymatic processes (3). Over 220,000 distinct PTM sites have been experimentally identified across ∼77,000 different proteins to date (dbPTM; http://dbptm.mbc.nctu.edu.tw/statistics.php) – numbers that continue to grow exponentially because of improved methods for high throughput detection by mass spectrometry (MS). By virtue of how they are detected, most PTM data are sequence-linked and lack structural context.The function of most PTMs is unknown because the rate of PTM detection far surpasses the rate at which any one modification can be studied empirically. Moreover, the functional impact of every PTM is likely not equivalent (4). For example, computational analysis of phosphorylation sites in yeast and human proteomes indicate that well-conserved phosphosites are more likely to have a functional consequence compared with poorly conserved sites, yet only a fraction of phosphosites are well conserved (5, 6). Consequently, the development of tools that provide functional prioritization of PTMs could have a broad impact on our understanding of protein regulation, biological mechanism, and molecular evolution.The emerging need for methods that predict the functional impact of a PTM has not yet been met. Longstanding methods capitalize predominantly on the sequence context of PTMs and have been used to predict sites of modification (expasy.org/proteomics/post-translational_modification) and to compare enzyme/substrate interactions (7–9). More recently, studies aimed at expanding the parameters associated with functional PTMs have emerged. In these cases, a set of common features correlated with functional importance are derived from the analysis of PTMs within and between organisms including: number of PTM observations at a multiple sequence alignment position (i.e. hotspots), measures of co-occurrence between different PTMs (e.g. distance between phosphorylation and ubiquitination sites), biological dynamics (up or down-regulation), and protein–protein interaction influence (7, 10–12). Recent efforts to provide structural context by linking individual PTMs to three-dimensional structures in the protein data bank (PDB) have also been described (13, 14). However, these resources are extensions of existing PTM databases that allow visualization of single instances of modification onto individual proteins, but do not provide quantitative or analytical value.In principle, combining PTM hotspot and structural analysis would offer multiple advantages over any one approach used in isolation. Sequence homology provides protein family membership—thereby clustering PTMs into hotspots for groups of proteins to provide information about: (1) the evolutionary conservation and (2) observation frequencies of PTMs within the family. A primary consequence of their sequence homology is that members of a protein family will exhibit similar structures and protein interactions—features that dictate the function of protein systems. A secondary consequence is that PTM hotspots generated by alignment can be projected onto family-representative protein structures, which places each PTM hotspot into a three-dimensional context that can be visualized for each family. The structural context enabled by this projection can also provide spatial information about the PTM site that can supplement the sequence characteristics of the hotspot, namely: (3) solvent accessibility, which provides an estimate of whether a modification could occur on the folded protein; and (4) protein interface residence, which indicates the potential of the PTM to disrupt protein–protein interactions. Despite the theoretical advantages, no single tool has been developed that exploits the quantitative output from both sequence and structural data to evaluate the function potential of PTMs.Here we describe a new analytical method – Structural Analysis of PTM Hotspots (SAPH-ire), which ranks PTM hotspots by their potential to impact biological function for distinct protein families (Fig. 1). We demonstrate the application of SAPH-ire to the complete set of PTMs for eight distinct protein families including large heterotrimeric G proteins—revealing high-ranking hotspots for which a biological function has not yet been determined. In particular, SAPH-ire revealed the N-terminal tail (Nt) of G protein gamma (Gγ) subunits as one of the highest ranking PTM hotspots for heterotrimeric G proteins (Gα, Gβ, and Gγ). We tested this prediction by monitoring the phosphorylation state and mutation effects of phosphorylation sites in the N terminus of the yeast Gγ subunit (Ste18). Consistent with SAPH-ire predictions, we found that phosphorylation of Ste18-Nt is biologically responsive to a GPCR stimulus and that phospho-null or phospho-mimic mutation of these sites controls protein abundance in an opposite manner in vivo. Thus, SAPH-ire is a powerful new method for predicting the function potential of PTM hotspots, which can guide empirical research toward the discovery of new protein regulatory elements based on high-throughput proteomics.Open in a separate windowFig. 1.Schematic diagram of the SAPH-ire method. A, SAPH-ire integrates InterPro, the Protein Data bank (PDB) and a customized database of experimentally validated PTMs. Uniprot entries with PTMs that belong to specific InterPro-classified protein families undergo multiple-sequence alignment (MSA) and PTM hotspot analysis (HSA), which layers all PTMs for a given alignment position in the MSA. The total PTMs observed in each hotspot and the conservation of a modifiable residue (e.g. conservation lysine at a ubiquitination hotspot) at the hotspot are quantified. B, PTM hotspots within the protein family are then projected onto all known crystal structures for the family using the Structural Projection of PTMs (SPoP) tool. From the structural topology of PTM hotspots generated by SPoP, the solvent accessible surface area (SASA) and protein interface residence is quantified for each hotspot. C, PTM Function Potential Calculator (FPC) integrates the output from HSA and SPoP, resulting in PTM function potential scores for each hotspot. The function potential score can be used to rank PTM hotspots within or between protein families – prioritizing hotspots with the greatest potential to be biologically regulated and/or effect a biological function for the protein family of interest.     

Characterization of Protein Methyltransferases Rkm1, Rkm4, Efm4, Efm7, Set5 and Hmt1 Reveals Extensive Post-Translational Modification

Protein methylation is one of the major post-translational modifications (PTMs) in the cell. In Saccharomyces cerevisiae, over 20 protein methyltransferases (MTases) and their respective substrates have been identified. However, the way in which these MTases are modified and potentially subject to regulation remains poorly understood. Here, we investigated six overexpressed S. cerevisiae protein MTases (Rkm1, Rkm4, Efm4, Efm7, Set5 and Hmt1) to identify PTMs of potential functional relevance. We identified 48 PTM sites across the six MTases, including phosphorylation, acetylation and methylation. Forty-two sites are novel. We contextualized the PTM sites in structural models of the MTases and revealed that many fell in catalytic pockets or enzyme–substrate interfaces. These may regulate MTase activity. Finally, we compared PTMs on Hmt1 with those on its human homologs PRMT1, PRMT3, CARM1, PRMT6 and PRMT8. This revealed that several PTMs are conserved from yeast to human, whereas others are only found in Hmt1. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD006767.     

Diurnal changes in concerted plant protein phosphorylation and acetylation in Arabidopsis organs and seedlings

Protein phosphorylation and acetylation are the two most abundant post‐translational modifications (PTMs) that regulate protein functions in eukaryotes. In plants, these PTMs have been investigated individually; however, their co‐occurrence and dynamics on proteins is currently unknown. Using Arabidopsis thaliana, we quantified changes in protein phosphorylation, acetylation and protein abundance in leaf rosettes, roots, flowers, siliques and seedlings at the end of day (ED) and at the end of night (EN). This identified 2549 phosphorylated and 909 acetylated proteins, of which 1724 phosphorylated and 536 acetylated proteins were also quantified for changes in PTM abundance between ED and EN. Using a sequential dual‐PTM workflow, we identified significant PTM changes and intersections in these organs and plant developmental stages. In particular, cellular process‐, pathway‐ and protein‐level analyses reveal that the phosphoproteome and acetylome predominantly intersect at the pathway‐ and cellular process‐level at ED versus EN. We found 134 proteins involved in core plant cell processes, such as light harvesting and photosynthesis, translation, metabolism and cellular transport, that were both phosphorylated and acetylated. Our results establish connections between PTM motifs, PTM catalyzing enzymes and putative substrate networks. We also identified PTM motifs for further characterization of the regulatory mechanisms that control cellular processes during the diurnal cycle in different Arabidopsis organs and seedlings. The sequential dual‐PTM analysis expands our understanding of diurnal plant cell regulation by PTMs and provides a useful resource for future analyses, while emphasizing the importance of analyzing multiple PTMs simultaneously to elucidate when, where and how they are involved in plant cell regulation.     

Investigation and identification of functional post-translational modification sites associated with drug binding and protein-protein interactions

Background

Protein post-translational modification (PTM) plays an essential role in various cellular processes that modulates the physical and chemical properties, folding, conformation, stability and activity of proteins, thereby modifying the functions of proteins. The improved throughput of mass spectrometry (MS) or MS/MS technology has not only brought about a surge in proteome-scale studies, but also contributed to a fruitful list of identified PTMs. However, with the increase in the number of identified PTMs, perhaps the more crucial question is what kind of biological mechanisms these PTMs are involved in. This is particularly important in light of the fact that most protein-based pharmaceuticals deliver their therapeutic effects through some form of PTM. Yet, our understanding is still limited with respect to the local effects and frequency of PTM sites near pharmaceutical binding sites and the interfaces of protein-protein interaction (PPI). Understanding PTM’s function is critical to our ability to manipulate the biological mechanisms of protein.

Results

In this study, to understand the regulation of protein functions by PTMs, we mapped 25,835 PTM sites to proteins with available three-dimensional (3D) structural information in the Protein Data Bank (PDB), including 1785 modified PTM sites on the 3D structure. Based on the acquired structural PTM sites, we proposed to use five properties for the structural characterization of PTM substrate sites: the spatial composition of amino acids, residues and side-chain orientations surrounding the PTM substrate sites, as well as the secondary structure, division of acidity and alkaline residues, and solvent-accessible surface area. We further mapped the structural PTM sites to the structures of drug binding and PPI sites, identifying a total of 1917 PTM sites that may affect PPI and 3951 PTM sites associated with drug-target binding. An integrated analytical platform (CruxPTM), with a variety of methods and online molecular docking tools for exploring the structural characteristics of PTMs, is presented. In addition, all tertiary structures of PTM sites on proteins can be visualized using the JSmol program.

Conclusion

Resolving the function of PTM sites is important for understanding the role that proteins play in biological mechanisms. Our work attempted to delineate the structural correlation between PTM sites and PPI or drug-target binding. CurxPTM could help scientists narrow the scope of their PTM research and enhance the efficiency of PTM identification in the face of big proteome data. CruxPTM is now available at http://csb.cse.yzu.edu.tw/CruxPTM/.

     

Examining post‐translational modification‐mediated protein–protein interactions using a chemical proteomics approach

Post‐translational modifications (PTM) of proteins can control complex and dynamic cellular processes via regulating interactions between key proteins. To understand these regulatory mechanisms, it is critical that we can profile the PTM‐dependent protein–protein interactions. However, identifying these interactions can be very difficult using available approaches, as PTMs can be dynamic and often mediate relatively weak protein–protein interactions. We have recently developed CLASPI (cross‐linking‐assisted and stable isotope labeling in cell culture‐based protein identification), a chemical proteomics approach to examine protein–protein interactions mediated by methylation in human cell lysates. Here, we report three extensions of the CLASPI approach. First, we show that CLASPI can be used to analyze methylation‐dependent protein–protein interactions in lysates of fission yeast, a genetically tractable model organism. For these studies, we examined trimethylated histone H3 lysine‐9 (H3K9Me₃)‐dependent protein–protein interactions. Second, we demonstrate that CLASPI can be used to examine phosphorylation‐dependent protein–protein interactions. In particular, we profile proteins recognizing phosphorylated histone H3 threonine‐3 (H3T3‐Phos), a mitotic histone “mark” appearing exclusively during cell division. Our approach identified survivin, the only known H3T3‐Phos‐binding protein, as well as other proteins, such as MCAK and KIF2A, that are likely to be involved in weak but selective interactions with this histone phosphorylation “mark”. Finally, we demonstrate that the CLASPI approach can be used to study the interplay between histone H3T3‐Phos and trimethylation on the adjacent residue lysine 4 (H3K4Me₃). Together, our findings indicate the CLASPI approach can be broadly applied to profile protein–protein interactions mediated by PTMs.     

An emerging field: Post-translational modification in microbiome

Post-translational modifications (PTMs) play an essential role in most biological processes. PTMs on human proteins have been extensively studied. Studies on bacterial PTMs are emerging, which demonstrate that bacterial PTMs are different from human PTMs in their types, mechanisms and functions. Few PTM studies have been done on the microbiome. Here, we reviewed several studied PTMs in bacteria including phosphorylation, acetylation, succinylation, glycosylation, and proteases. We discussed the enzymes responsible for each PTM and their functions. We also summarized the current methods used to study microbiome PTMs and the observations demonstrating the roles of PTM in the microbe-microbe interactions within the microbiome and their interactions with the environment or host. Although new methods and tools for PTM studies are still needed, the existing technologies have made great progress enabling a deeper understanding of the functional regulation of the microbiome. Large-scale application of these microbiome-wide PTM studies will provide a better understanding of the microbiome and its roles in the development of human diseases.     

Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease

Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable body of potential regulatory elements that impact hundreds of different biological processes important in eukaryotic biology and human health.Since the discovery of phosphorylation in 1954 (1), post-translational modifications (PTMs)¹ have emerged as a broad class of protein feature that expand the functional proteome in eukaryotes. Improvements in the detection of PTMs by mass spectrometry have resulted in an exponential increase in our knowledge of the number and type of PTMs that make up the landscape of a modified eukaryotic proteome. As a result, the rate at which PTMs are discovered now far surpasses the rate at which they can be experimentally tested for biological function - a characteristic that is specific for each PTM and likely not equivalent between all PTMs that have been observed (2–4). Thus, effective methods of prioritization are essential for quantifying the likelihood of a site to be regulatory and/or impactful on biological function, which we refer to as the function potential of a PTM.Several unique features have been identified as predictors of biological impact for any given PTM - the determination of which relies on placing each PTM in the context of a multiple sequence alignment for a discrete protein or domain family, which we refer to as a Modified Alignment Position (MAP). For example, MAPs that are evolutionarily well conserved are more likely to exhibit biological function (3, 4). Similarly, functional PTMs are more commonly found within MAPs that exhibit a higher PTM observation frequency, are dynamic with respect to biological condition, located at protein interaction interfaces, and more solvent-accessible within a folded protein structure (5–7). Although efforts to elucidate the features associated with functional PTMs are relatively longstanding, few if any have established an integrative approach to quantitatively prioritize the function potential of PTMs beyond the use of single features.Previous evidence from our lab first demonstrated that multiple feature integration can improve functional prioritization. To accomplish this, we built Structural Analysis of PTM Hotspots (SAPH-ire)—an algorithm through which multiple predictors of PTM function are integrated to produce a single, quantitative function potential (FP) score that rank orders each hotspot within or between protein families (6) (Fig. 1). Previously, we used SAPH-ire to predict novel PTM regulatory elements in G protein families—including heterotrimeric G proteins—for which we discovered and experimentally confirmed a novel PTM regulatory element that is critical for cell signaling (6, 8). We propose that similar analysis of PTMs across the entire eukaryotic proteome is likely to result in the discovery of several novel regulatory elements that have yet to be realized.Open in a separate windowFig. 1.Schematic diagram of SAPH-ire. A, A theoretical segment of the multiple sequence alignment for a protein family (IPR000276; G protein-coupled receptor, rhodopsin-like) used here for illustrating the concept of SAPH-ire. Circled amino acid residues represent PTM sites experimentally observed on respective protein family members. Circle and arrow color represents the PTM observation frequency at each aligned position, called a MAP (modified alignment position), where green indicates 1 observation, blue for 2, orange for 3, and red for 5 or more. B, Cartoon rendering of bovine rhodopsin ({"type":"entrez-protein","attrs":{"text":"P02699","term_id":"129204","term_text":"P02699"}}P02699, RHO; PDB 2PED, chain A) showing side chains with projected PTM hotspots colored according to the number of observations within the family at each position aligned with the structural sequence. PDB coordinate data from the structurally projected PTM hotspots is used for calculation of solvent accessible surface area (SASA) and determination of protein interface residence (PPI). C, Hotspot features derived from the sequence and structural data are extracted for each protein family, where each hotspot corresponds to a precise family alignment position containing at least one PTM observation. D, Comparison of the comprehensive and SAPH-ire datasets representing all known experimental PTM data versus PTM data included in this study, respectively. E, Values calculated and derived from extracted hotspot features are analyzed by logistic regression or neural network models to produce probability scores for each hotspot.Here we apply SAPH-ire to protein families for which PTMs and protein structure are currently available, resulting in function potential prediction for 50,839 experimental PTM sites distributed across 31,747 MAPs. Using a neural network model (SAPH-ire NN) trained to predict the identity of embedded known-function MAPs, we derived a probability score that allows rank ordering for the likelihood of function for all MAPs including those with unknown function. We show that the SAPH-ire NN model significantly outperforms all other single or multi-feature predictive models and exhibits a proportional increase in predictive power for known function hotspots that have been more frequently studied (and therefore published). Using a strictly conservative probability threshold, we characterized the top-ranked 1092 MAPs corresponding to “function potential hotspots,” revealing 267 with truly unknown function - a striking fraction of which are also found mutated in human disease irrespective of whether the human protein, specifically, contains an observed PTM.     

Post-translational modification of cellular proteins during Leishmania donovani differentiation

The pathogenic intracellular parasites Leishmania donovani cycle between sand fly gut and the human macrophage phagolysosome, differentiating from extracellular promastigotes to intracellular amastigote forms. Using isobaric tagging for relative and absolute quantifications (iTRAQ/LC-MS/MS) proteomic methodology, we recently described the ordered gene expression changes during this process. While protein abundance changes in Leishmania were documented, little is known about their PTMs. Here we used iTRAQ to detect protein phosphorylation, methylation, acetylation, and glycosylation sites throughout differentiation. We found methylation of arginines, aspartic acids, glutamic acids, asparagines, and histidines. Detected acetylation sites included serines and protein N-terminal acetylations on methionines, serines, alanines, and threonines. Phosphorylations were detected on serines and threonines, but not tyrosines. iTRAQ identified novel fucosylation sites as well as hexosylations. We observed quantity changes in some modifications during differentiation, suggesting a role in L. donovani intracellular development. This study is the first high-throughput analysis of PTM sites dynamics during an intracellular parasitic development.     

Development and Validation of a Method for Profiling Post-Translational Modification Activities Using Protein Microarrays

Background

Post-translational modifications (PTMs) impact on the stability, cellular location, and function of a protein thereby achieving a greater functional diversity of the proteome. To fully appreciate how PTMs modulate signaling networks, proteome-wide studies are necessary. However, the evaluation of PTMs on a proteome-wide scale has proven to be technically difficult. To facilitate these analyses we have developed a protein microarray-based assay that is capable of profiling PTM activities in complex biological mixtures such as whole-cell extracts and pathological specimens.

Methodology/Principal Findings

In our assay, protein microarrays serve as a substrate platform for in vitro enzymatic reactions in which a recombinant ligase, or extracts prepared from whole cells or a pathological specimen is overlaid. The reactions include labeled modifiers (e.g., ubiquitin, SUMO1, or NEDD8), ATP regenerating system, and other required components (depending on the assay) that support the conjugation of the modifier. In this report, we apply this methodology to profile three molecularly complex PTMs (ubiquitylation, SUMOylation, and NEDDylation) using purified ligase enzymes and extracts prepared from cultured cell lines and pathological specimens. We further validate this approach by confirming the in vivo modification of several novel PTM substrates identified by our assay.

Conclusions/Significance

This methodology offers several advantages over currently used PTM detection methods including ease of use, rapidity, scale, and sample source diversity. Furthermore, by allowing for the intrinsic enzymatic activities of cell populations or pathological states to be directly compared, this methodology could have widespread applications for the study of PTMs in human diseases and has the potential to be directly applied to most, if not all, basic PTM research.     

Proteomic analysis of phosphorylation, oxidation and nitrosylation in signal transduction

Signal transduction pathways control cell fate, survival and function. They are organized as intricate biochemical networks which enable biochemical protein activities, crosstalk and subcellular localization to be integrated and tuned to produce highly specific biological responses in a robust and reproducible manner. Post translational Modifications (PTMs) play major roles in regulating these processes through a wide variety of mechanisms that include changes in protein activities, interactions, and subcellular localizations. Determining and analyzing PTMs poses enormous challenges. Recent progress in mass spectrometry (MS) based proteomics have enhanced our capability to map and identify many PTMs. Here we review the current state of proteomic PTM analysis relevant for signal transduction research, focusing on two areas: phosphorylation, which is well established as a widespread key regulator of signal transduction; and oxidative modifications, which from being primarily viewed as protein damage now start to emerge as important regulatory mechanisms.     

In vivo quantification of protein-protein interactions in Saccharomyces cerevisiae using bimolecular fluorescence complementation assay

Most of the biological processes are carried out and regulated by dynamic networks of protein-protein interactions. In this study, we demonstrate the feasibility of the bimolecular fluorescence complementation (BiFC) assay for in vivo quantitative analysis of protein-protein interactions in Saccharomyces cerevisiae. We show that the BiFC assay can be used to quantify not only the amount but also the cell-to-cell variation of protein-protein interactions in S. cerevisiae. In addition, we show that protein sumoylation and condition-specific protein-protein interactions can be quantitatively analyzed by using the BiFC assay. Taken together, our results validate that the BiFC assay is a very effective method for quantitative analysis of protein-protein interactions in living yeast cells and has a great potential as a versatile tool for the study of protein function.