PTMSearchPlus: Software Tool for Automated Protein Identification and Post-Translational Modification Characterization by Integrating Accurate Intact Protein Mass and Bottom-Up Mass Spectrometric Data Searches

Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of analytical, biochemical, and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes accompanying plant development, senescence, and response to biotic or abiotic stress. Among individual post-genomic methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. During the recent decades, multiple methodological advances were introduced in different branches of life science, although only some of them were established in seed proteomics so far. Therefore, here we discuss main methodological approaches already employed in seed proteomics, as well as those still waiting for implementation in this field of plant research, with a special emphasis on sample preparation, data acquisition, processing, and post-processing. Thereby, the overall goal of this review is to bring new methodologies emerging in different areas of proteomics research (clinical, food, ecological, microbial, and plant proteomics) to the broad society of seed biologists.

Applications of Infrared Multiple Photon Dissociation (IRMPD) to the Detection of Posttranslational Modifications

Infrared multiple photon dissociation (IRMPD) spectroscopy allows for the derivation of the vibrational fingerprint of molecular ions under tandem mass spectrometry (MS/MS) conditions. It provides insight into the nature and localization of posttranslational modifications (PTMs) affecting single amino acids and peptides. IRMPD spectroscopy, which takes advantage of the high sensitivity and resolution of MS/MS, relies on a wavelength specific fragmentation process occurring on resonance with an IR active vibrational mode of the sampled species and is well suited to reveal the presence of a PTM and its impact in the molecular environment. IRMPD spectroscopy is clearly not a proteomics tool. It is rather a valuable source of information for fixed wavelength IRMPD exploited in dissociation protocols of peptides and proteins. Indeed, from the large variety of model PTM containing amino acids and peptides which have been characterized by IRMPD spectroscopy, specific signatures of PTMs such as phosphorylation or sulfonation can be derived. High throughput workflows relying on the selective fragmentation of modified peptides within a complex mixture have thus been proposed. Sequential fragmentations can be observed upon IR activation, which do not only give rise to rich fragmentation patterns but also overcome low mass cutoff limitations in ion trap mass analyzers. Laser-based vibrational spectroscopy of mass-selected ions holding various PTMs is an increasingly expanding field both in the variety of chemical issues coped with and in the technological advancements and implementations.

Are proteins a redundant ontology? Epistemological limitations in the analysis of multistate species

Advances in proteomics have exponentially increased the numbers of post-translational modifications identified, the resulting volume of data is overwhelming both databases and empiricists. We review methodologies for chemical and functional PTM assignment. Using β-oxidation as a paradigm, we discuss epistemic limitations and conceptual approaches to resolving them combining relational biology, proteomics, and the erosion of "protein" and "metabolite" as distinct ontologies.

PTMTreeSearch: a novel two-stage tree-search algorithm with pruning rules for the identification of post-translational modification of proteins in MS/MS spectra

MOTIVATION

Tandem mass spectrometry has become a standard tool for identifying post-translational modifications (PTMs) of proteins. Algorithmic searches for PTMs from tandem mass spectrum data (MS/MS) tend to be hampered by noisy data as well as by a combinatorial explosion of search space. This leads to high uncertainty and long search-execution times.

RESULTS

To address this issue, we present PTMTreeSearch, a new algorithm that uses a large database of known PTMs to identify PTMs from MS/MS data. For a given peptide sequence, PTMTreeSearch builds a computational tree wherein each path from the root to the leaves is labeled with the amino acids of a peptide sequence. Branches then represent PTMs. Various empirical tree pruning rules have been designed to decrease the search-execution time by eliminating biologically unlikely solutions. PTMTreeSearch first identifies a relatively small set of high confidence PTM types, and in a second stage, performs a more exhaustive search on this restricted set using relaxed search parameter settings. An analysis of experimental data shows that using the same criteria for false discovery, PTMTreeSearch annotates more peptides than the current state-of-the-art methods and PTM identification algorithms, and achieves this at roughly the same execution time. PTMTreeSearch is implemented as a plugable scoring function in the X!Tandem search engine.

AVAILABILITY

The source code of PTMTreeSearch and a demo server application can be found at http://net.icgeb.org/ptmtreesearch

Mass spectrometry-based proteomics approaches applied in cataract research

Cataract, the opacification of the eye lens, is the leading cause of blindness worldwide--it accounts for approximately 42% of all cases. The lens fibers have the highest protein content within the body, more than 35% of their wet weight. Given the eye lens pure composition of highly abundant structural proteins crystallins (up to 90%), it seems to be an ideal proteomic entity to study and might be also hypothesized to model the other protein conformational diseases. Crystallins are extremely long-lived, and there is virtually no protein turnover. This provides great opportunities for post-translational modifications (PTM) to occur and to predispose lens to the cataract formation. Despite recent progress in proteomics, the human lens proteome remains largely unknown. Mass spectrometry hold great promise to determine which crystallin modifications lead to a cataract. Quantitative analysis of PTMs at the peptide level with proteomics is a powerful bioanalytical tool for lens-tissue samples, and provides more comprehensive results. New mass spectrometry-based approaches that are being applied to lens research will be highlighted. Finally, the future directions of proteomics cataract research will be outlined.

Baking a mass-spectrometry data PIE with McMC and simulated annealing: predicting protein post-translational modifications from integrated top-down and bottom-up data

MOTIVATION

Post-translational modifications are vital to the function of proteins, but are hard to study, especially since several modified isoforms of a protein may be present simultaneously. Mass spectrometers are a great tool for investigating modified proteins, but the data they provide is often incomplete, ambiguous and difficult to interpret. Combining data from multiple experimental techniques-especially bottom-up and top-down mass spectrometry-provides complementary information. When integrated with background knowledge this allows a human expert to interpret what modifications are present and where on a protein they are located. However, the process is arduous and for high-throughput applications needs to be automated.

RESULTS

This article explores a data integration methodology based on Markov chain Monte Carlo and simulated annealing. Our software, the Protein Inference Engine (the PIE) applies these algorithms using a modular approach, allowing multiple types of data to be considered simultaneously and for new data types to be added as needed. Even for complicated data representing multiple modifications and several isoforms, the PIE generates accurate modification predictions, including location. When applied to experimental data collected on the L7/L12 ribosomal protein the PIE was able to make predictions consistent with manual interpretation for several different L7/L12 isoforms using a combination of bottom-up data with experimentally identified intact masses.

AVAILABILITY

Software, demo projects and source can be downloaded from http://pie.giddingslab.org/

Automated data integration and determination of posttranslational modifications with the protein inference engine

This chapter describes using the Protein Inference Engine (PIE) to integrate various types of data--especially top down and bottom up mass spectrometer (MS) data--to describe a protein's posttranslational modifications (PTMs). PTMs include cleavage events such as the n-terminal loss of methionine and residue modifications like phosphorylation. Modifications are key elements in many biological processes, but are difficult to study as no single, general method adequately characterizes a protein's PTMs; manually integrating data from several MS experiments is usually required. The PIE is designed to automate this process using a guess and refine process similar to how an expert manually integrates data. The PIE repeatedly "imagines" a possible modification set, evaluates it using available data, and then tries to improve on it. After many rounds of refinement, the resulting modification set is proposed as a candidate answer. Multiple candidate answers are generated to obtain both best and near-best answers. Near-best answers are crucial in allowing for proteins with more than one supported modification pattern (isoforms) and obtaining robust results given incomplete and inconsistent data.The goal of this chapter is to walk the reader through installing and using the downloadable version of PIE, both in general and by means of a specific, detailed example. The example integrates several types of experimental and background (prior) data. It is not a "perfect-world" scenario, but has been designed to illustrate several real-world difficulties that may be encountered when trying to analyze imperfect data.