Software eyes for protein post-translational modifications.

Identification of post-translational modifications (PTMs) is important to understanding the biological functions of proteins. MS/MS is a useful tool to identify PTMs. Most existing search tools are restricted to take only a few types of PTMs as input. Here we describe a new algorithm, called MOD(i) (pronounced "mod eye"), that rapidly searches for all known types of PTMs at once without limiting a multitude of modified sites in a peptide. MOD(i) introduces the notion of a tag chain, a combination structure made from multiple sequence tags, that effectively localizes modified regions within a spectrum and overcomes de novo sequencing errors common in tag-based approaches. MOD(i) showed its performance competence by identifying various types of PTMs in analysis of PTM-rich proteins such as glyceraldehyde-3-phosphate dehydrogenase and lens protein. We demonstrated that MOD(i) innovatively manages the computational complexity of identifying multiple PTMs in a peptide, which may exist in a greater variety than usually expected. In addition, it is suggested that MOD(i) has great potential to discover novel modifications.

Most tandem mass spectrometry (MS/MS) database search algorithms perform a restrictive search that takes into account only a few types of post-translational modifications (PTMs) and ignores all others. We describe an unrestrictive PTM search algorithm, MS-Alignment, that searches for all types of PTMs at once in a blind mode, that is, without knowing which PTMs exist in nature. Blind PTM identification makes it possible to study the extent and frequency of different types of PTMs, still an open problem in proteomics. Application of this approach to lens proteins resulted in the largest set of PTMs reported in human crystallins so far. Our analysis of various MS/MS data sets implies that the biological phenomenon of modification is much more widespread than previously thought. We also argue that MS-Alignment reveals some uncharacterized modifications that warrant further experimental validation.

Redox-active cysteine, a highly reactive sulfhydryl, is one of the major targets of ROS. Formation of disulfide bonds and other oxidative derivatives of cysteine including sulfenic, sulfinic, and sulfonic acids, regulates the biological function of various proteins. We identified novel low-abundant cysteine modifications in cellular GAPDH purified on 2-dimensional gel electrophoresis (2D-PAGE) by employing selectively excluded mass screening analysis for nano ultraperformance liquid chromatography-electrospray-quadrupole-time of flight tandem mass spectrometry, in conjunction with MODi and MODmap algorithm. We observed unexpected mass shifts (Δm=-16, -34, +64, +87, and +103 Da) at redox-active cysteine residue in cellular GAPDH purified on 2D-PAGE, in oxidized NDP kinase A, peroxiredoxin 6, and in various mitochondrial proteins. Mass differences of -16, -34, and +64 Da are presumed to reflect the conversion of cysteine to serine, dehydroalanine (DHA), and Cys-SO2-SH respectively. To determine the plausible pathways to the formation of these products, we prepared model compounds and examined the hydrolysis and hydration of thiosulfonate (Cys-S-SO2-Cys) either to DHA (Δm=-34 Da) or serine along with Cys-SO2-SH (Δm=+64 Da). We also detected acrylamide adducts of sulfenic and sulfinic acids (+87 and +103 Da). These findings suggest that oxidations take place at redox-active cysteine residues in cellular proteins, with the formation of thiosulfonate, Cys-SO2-SH, and DHA, and conversion of cysteine to serine, in addition to sulfenic, sulfinic and sulfonic acids of reactive cysteine.

Post-translational modifications (PTMs) enable proteins carryout multiple biological functions. PTMs facilitate and regulate various protein functions by changing the chemical and physical characteristics of a protein, which affect its stability, cellular localization, and its interaction with other proteins as well as non-protein molecules which endow the protein-protein interactions, cellular localization and with new or altered biological activities. Understanding how and to what extent cellular proteins are post-translationally modified, at which sites on the protein, how the modifications affect the stoichiometry of the protein sequence, and what the functional consequences of each of the modifications are, therefore becomes indispensible for understanding cellular function and regulation of a protein. Recent advances that facilitated combining affinity-based enrichment of low abundant and novel PTMs with peptide sequencing with tandem mass spectrometric analysis have increased its speed and sensitivity of analytical approaches. Although it has been demonstrated that mass spectrometry can be an ideal tool for both qualitative and quantitative analysis of protein modifications, comprehensive identification of PTMs using mass spectrometry (MS) in a high-throughput manner remains a highly challenging task because of the diversity, dynamic complexities, low abundance and heterogeneity of PTMs. In addition, difficulties in interpreting tandem MS spectra for peptide sequencing, poor peptide fragmentation, and surprising appearance of totally unexpected modifications, among others, have compounded this challenge and limited the application of mass spectrometry to the identification of a few types of PTMs. We will describe in this chapter, a strategy we have been employing successfully for rapid, efficient and sensitive identification of diverse and novel PTMs occurring in mammalian biological systems in vivo. Our strategy includes: separation of unmodified and modified proteins on 2-dimensional gel electrophoresis (2D-PAGE); and detection of low abundant peptide modifications employing a selectively excluded mass screening analysis (SEMSA) of unmodified peptides during LC-ESI-q-TOF MS/MS through replicated runs (Seo et al., 2008), in conjunction with a searching algorithm MODi (Kim et al., 2006; Na et al., 2008) and

Proteins through post translational modifications perform their biological process and cellular functions. Post-translational modifications play important roles in various biological process and cell functions. Identifying the PTMs sites in proteins is very significant to basic research and drug design. Experimental technique to identify post translational modifications is laborious. Computational identification of post translational modifications is a complementary way for its convenience. This review gives the processing to predict post-translational modification sites in proteins including feature construction, algorithms, evaluation measurement, and online webserver. There are two types of post translational modification in proteins. In the prediction of single PTM sites, we transformed it into binary classification learning. While in the prediction of crosstalk PTM sites, we transformed it into multi-label learning. This review summarized the steps on the two issues.

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.

Rice (Oryza sativa L.) is a foremost staple food for approximately half the world’s population. The components of rice starch, amylose, and amylopectin are synthesized by a series of enzymes, which are responsible for rice starch properties and functionality, and then affect rice cooking and eating quality. Recently, proteomics technology has been applied to the establishment of the differentially expressed starch biosynthesis-related proteins and the identification of posttranslational modifications (PTMs) target starch biosynthesis proteins as well. It is necessary to summarize the recent studies in proteomics and PTMs in rice endosperm to deepen our understanding of starch biosynthesis protein expression and regulation, which will provide useful information to rice breeding programs and industrial starch applications. The review provides a comprehensive summary of proteins and PTMs involved in starch biosynthesis based on proteomic studies of rice developing seeds. Starch biosynthesis proteins in rice seeds were differentially expressed in the developing seeds at different developmental stages. All the proteins involving in starch biosynthesis were identified using proteomics methods. Most starch biosynthesis-related proteins are basically increased at 6–20 days after flowering (DAF) and decreased upon the high-temperature conditions. A total of 10, 14, 2, 17, and 7 starch biosynthesis related proteins were identified to be targeted by phosphorylation, lysine acetylation, succinylation, lysine 2-hydroxyisobutyrylation, and malonylation, respectively. The phosphoglucomutase is commonly targeted by five PTMs types. Research on the function of phosphorylation in multiple enzyme complex formation in endosperm starch biosynthesis is underway, while the functions of other PTMs in starch biosynthesis are necessary to be conducted in the near future.

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.

In computational proteomics, the identification of peptides with an unlimited number of post-translational modification (PTM) types is a challenging task. The computational cost associated with database search increases exponentially with respect to the number of modified amino acids and linearly with respect to the number of potential PTM types at each amino acid. The problem becomes intractable very quickly if we want to enumerate all possible PTM patterns. To address this issue, one group of methods named restricted tools (including Mascot, Comet, and MS-GF+) only allow a small number of PTM types in database search process. Alternatively, the other group of methods named unrestricted tools (including MS-Alignment, ProteinProspector, and MODa) avoids enumerating PTM patterns with an alignment-based approach to localizing and characterizing modified amino acids. However, because of the large search space and PTM localization issue, the sensitivity of these unrestricted tools is low. This paper proposes a novel method named PIPI to achieve PTM-invariant peptide identification. PIPI belongs to the category of unrestricted tools. It first codes peptide sequences into Boolean vectors and codes experimental spectra into real-valued vectors. For each coded spectrum, it then searches the coded sequence database to find the top scored peptide sequences as candidates. After that, PIPI uses dynamic programming to localize and characterize modified amino acids in each candidate. We used simulation experiments and real data experiments to evaluate the performance in comparison with restricted tools (i.e., Mascot, Comet, and MS-GF+) and unrestricted tools (i.e., Mascot with error tolerant search, MS-Alignment, ProteinProspector, and MODa). Comparison with restricted tools shows that PIPI has a close sensitivity and running speed. Comparison with unrestricted tools shows that PIPI has the highest sensitivity except for Mascot with error tolerant search and ProteinProspector. These two tools simplify the task by only considering up to one modified amino acid in each peptide, which results in a higher sensitivity but has difficulty in dealing with multiple modified amino acids. The simulation experiments also show that PIPI has the lowest false discovery proportion, the highest PTM characterization accuracy, and the shortest running time among the unrestricted tools.

Most naturally occurring mammalian cancers and immortalized tissue culture cell lines share a common characteristic, the overexpression of full-length HMGA1 (high mobility group A1) proteins. The HMGA1 protooncogene codes for two closely related isoform proteins, HMGA1a and HMGA1b, and causes cancerous cellular transformation when overexpressed in either transgenic mice or "normal" cultured cell lines. Previous work has suggested that the in vivo types and patterns of the HMGA1 post-translational modifications (PTMs) differ between normal and malignant cells. The present study focuses on the important question of whether HMGA1a and HMGA1b proteins isolated from the same cell type have identical or different PTM patterns and also whether these isoform patterns differ between non-malignant and malignant cells. Two independent mass spectrometry methods were used to identify the types of PTMs found on specific amino acid residues on the endogenous HMGA1a and HMGA1b proteins isolated from a non-metastatic human mammary epithelial cell line, MCF-7, and a malignant metastatic cell line derived from MCF-7 cells that overexpressed the transgenic HMGA1a protein. Although some of the PTMs were the same on both the HMGA1a and HMGA1b proteins isolated from a given cell type, many other modifications were present on one but not the other isoform. Furthermore, we demonstrate that both HMGA1 isoforms are di-methylated on arginine and lysine residues. Most importantly, however, the PTM patterns on the endogenous HMGA1a and HMGA1b proteins isolated from non-metastatic and metastatic cells were consistently different, suggesting that the isoforms likely exhibit differences in their biological functions/activities in these cell types.

Rapid high-throughput antibody analysis using microwave-assisted digestion.

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 17145 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.

Genomic comparisons of Rhizobium species using in silico AFLP-PCR, endonuclease restriction, and AMPylating enzymes

Post-translational modifications (PTMs) of histones play an important role in many cellular processes, notably gene regulation. Using a combination of mass spectrometric and immunobiochemical approaches, we show that the PTM profile of histone H3 differs significantly among the various model organisms examined. Unicellular eukaryotes, such as Saccharomyces cerevisiae (yeast) and Tetrahymena thermophila (Tet), for example, contain more activation than silencing marks as compared with mammalian cells (mouse and human), which are generally enriched in PTMs more often associated with gene silencing. Close examination reveals that many of the better-known modified lysines (Lys) can be either methylated or acetylated and that the overall modification patterns become more complex from unicellular eukaryotes to mammals. Additionally, novel species-specific H3 PTMs from wild-type asynchronously grown cells are also detected by mass spectrometry. Our results suggest that some PTMs are more conserved than previously thought, including H3K9me1 and H4K20me2 in yeast and H3K27me1, -me2, and -me3 in Tet. On histone H4, methylation at Lys-20 showed a similar pattern as H3 methylation at Lys-9, with mammals containing more methylation than the unicellular organisms. Additionally, modification profiles of H4 acetylation were very similar among the organisms examined.

Protein 3D structures, determined by their amino acid sequences, are the support of major crucial biological functions. Post-translational modifications (PTMs) play an essential role in regulating these functions by altering the physicochemical properties of proteins. By virtue of their importance, several PTM databases have been developed and released in decades, but very few of these databases incorporate real 3D structural data. Since PTMs influence the function of the protein and their aberrant states are frequently implicated in human diseases, providing structural insights to understand the influence and dynamics of PTMs is crucial for unraveling the underlying processes. This review is dedicated to the current status of databases providing 3D structural data on PTM sites in proteins. Some of these databases are general, covering multiple types of PTMs in different organisms, while others are specific to one particular type of PTM, class of proteins or organism. The importance of these databases is illustrated with two major types of in silico applications: predicting PTM sites in proteins using machine learning approaches and investigating protein structure-function relationships involving PTMs. Finally, these databases suffer from multiple problems and care must be taken when analyzing the PTMs data.