Prediction of Posttranslational Modifications Using Intact-Protein Mass Spectrometric Data

Direct Identification of Intact Proteins Using a Low-Resolution Mass Spectrometer with CIDn/ETnoD

Over the past decades, proteomics has become increasingly important and a heavily discussed topic. The identification of intact proteins remains a major focus in this field. While most intact proteins are analyzed using high-resolution mass spectrometry, identifying them through low-resolution mass spectrometry continues to pose challenges. In our study, we investigated the capability of identifying various intact proteins using collision-induced dissociation (CID) and electron transfer without dissociation (ETnoD). Using myoglobin as our test protein, stable product ions were generated with CID, and the identities of the product ions were identified with ETnoD. ETnoD uses a short activation time (AcT, 5 ms) to create sequential charge-reduced precursor ion (CRI). The charges of the fragments and their sequences were determined with corresponding CRI. The product ions can be selected for subsequent CID (termed CIDn) combined with ETnoD for further sequence identification and validation. We refer to this method as CIDn/ETnoD. The use of a multistage CID activation (CIDn) and ETnoD protocol has been applied to several intact proteins to obtain multiple sequence identifications.

Defining intact protein primary structures from saliva: a step toward the human proteome project

Top-down mass spectrometry has been used to investigate structural diversity within some abundant salivary protein families. In this study, we report the identification of two isoforms of protein II-2 which differed in mass by less than 1 Da, the determination of a sequence for protein IB8a that was best satisfied by including a mutation and a covalent modification in the C-terminal part, and the assignment of a sequence of a previously unreported protein of mass 10433 Da. The final characterization of Peptide P-J was achieved, and the discovery of a truncated form of this peptide was reported. The first sequence assignment was done at low resolution using a hybrid quadrupole time-of-flight instrument to quickly identify and characterize proteins, and data acquisition was switched to Fourier-transform ion cyclotron resonance (FTICR) for proteins that required additional sequence coverage and certainty of assignment. High-resolution and high mass accuracy mass spectrometry on a FTICR-mass spectrometry (MS) instrument combined with electron-capture dissociation (ECD) provided the most informative data sets, with the more frequent presence of "unique" ions that unambiguously define the primary structure. A mixture of predictable and unusual post-translational modifications in the protein sequence precluded the use of shotgun-annotated databases at this stage, requiring manual iterations of sequence refinement in many cases. This led us to propose guidelines for an iterative processing workflow of MS and MSMS data sets that allow researchers to completely assign the identity and the structure of a protein.

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/

Intact mass detection, interpretation, and visualization to automate Top-Down proteomics on a large scale

Applying high-throughput Top-Down MS to an entire proteome requires a yet-to-be-established model for data processing. Since Top-Down is becoming possible on a large scale, we report our latest software pipeline dedicated to capturing the full value of intact protein data in automated fashion. For intact mass detection, we combine algorithms for processing MS1 data from both isotopically resolved (FT) and charge-state resolved (ion trap) LC-MS data, which are then linked to their fragment ions for database searching using ProSight. Automated determination of human keratin and tubulin isoforms is one result. Optimized for the intricacies of whole proteins, new software modules visualize proteome-scale data based on the LC retention time and intensity of intact masses and enable selective detection of PTMs to automatically screen for acetylation, phosphorylation, and methylation. Software functionality was demonstrated using comparative LC-MS data from yeast strains in addition to human cells undergoing chemical stress. We further these advances as a key aspect of realizing Top-Down MS on a proteomic scale.

In silico prediction of post-translational modifications

Methods for predicting protein post-translational modifications have been developed extensively. In this chapter, we review major post-translational modification prediction strategies, with a particular focus on statistical and machine learning approaches. We present the workflow of the methods and summarize the advantages and disadvantages of the methods.

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.

Mining Phosphopeptide Signals in Liquid Chromatography−Mass Spectrometry Data for Protein Phosphorylation Analysis

Protein phosphorylation is a key post-translational modification that governs biological processes. Despite the fact that a number of analytical strategies have been exploited for the characterization of protein phosphorylation, the identification of protein phosphorylation sites is still challenging. We proposed here an alternative approach to mine phosphopeptide signals generated from a mixture of proteins when liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis is involved. The approach combined dephosphorylation reaction, accurate mass measurements from a quadrupole/time-of-flight mass spectrometer, and a computing algorithm to differentiate possible phosphopeptide signals obtained from the LC-MS analyses by taking advantage of the mass shift generated by alkaline phosphatase treatment. The retention times and m/z values of these selected LC-MS signals were used to facilitate subsequent LC-MS/MS experiments for phosphorylation site determination. Unlike commonly used neutral loss scan experiments for phosphopeptide detection, this strategy may not bias against tyrosine-phosphorylated peptides. We have demonstrated the applicability of this strategy to sequence more, in comparison with conventional data-dependent LC-MS/MS experiments, phosphopeptides in a mixture of alpha- and beta-caseins. The analytical scheme was applied to characterize the nasopharyngeal carcinoma (NPC) cellular phosphoproteome and yielded 221 distinct phosphorylation sites. Our data presented in this paper demonstrated the merits of computation in mining phosphopeptide signals from a complex mass spectrometric data set.

Development of a Multibed Sorption Trap, Comprehensive Two-Dimensional Gas Chromatography, and Time-of-Flight Mass Spectrometry System for the Analysis of Volatile Organic Compounds in Human Breath

A method for the determination of volatile organic compounds (VOCs) at sub-trace levels in breath samples based on a multibed sorption trap for the collection and concentration of VOCs, a comprehensive multidimensional gas chromatograph (GCxGC) for the separation of complex mixtures, and a time-of-flight mass spectrometer detector is designed and developed. The good performance of the trap tube device developed for the concentration together with the high sensitivity and separation power of the GCxGC results in a powerful system. In the analysis of samples, more than 100 different compounds are detected of which between 65 and 85 are clearly identified. A total of approximately 250 different compounds are observed in all the samples evaluated of which 142 are identified. A preliminary study to evaluate breath biomarkers for active smoking is performed. The levels of previously described biomarkers are found to be strongly time-dependent with amounts found approximately 1 h after smoking returning to the levels found in nonsmoking volunteers. However, 2,5-dimethylfuran, 2-methylfuran, and furan are found to be effective biomarkers given that they were only found in samples taken from smokers and could still be detected more than 2 h after smoking.

Integration of Multidimensional Chromatographic Protein Separations with a Combined “Top-Down” and “Bottom-Up” Proteomic Strategy

In this paper, we present a combined top-down/bottom-up proteomic analysis workflow for the characterization of proteomic samples. This workflow combines protein fractionation (multidimensional chromatographic separation) with parallel online ESI-TOF-MS intact protein analysis, and fraction collection. Collected fractions were digested and protein identifications were produced using MALDI Q-TOF-MS analysis. These identifications were then linked with corresponding ESI-TOF-MS intact protein mass data to permit full protein characterization. This methodology was applied to an E. coli cytosolic protein fraction, and enabled the identification and characterization of proteins exhibiting co-translational processing, post-translational modification, and proteolytic processing events. The approach also provided the ability to distinguish between closely related protein isoforms. The summary of results from this study indicated that roughly one-third of all detected components generated corresponding data from both top-down and bottom-up analyses, and that significant and novel information can be derived from this application of the hybrid analytical methodology.

Characterization of the 70S Ribosome from Rhodopseudomonas palustris Using an Integrated “Top-Down” and “Bottom-Up” Mass Spectrometric Approach

We present a comprehensive mass spectrometric approach that integrates intact protein molecular mass measurement ("top-down") and proteolytic fragment identification ("bottom-up") to characterize the 70S ribosome from Rhodopseudomonas palustris. Forty-two intact protein identifications were obtained by the top-down approach and 53 out of the 54 orthologs to Escherichia coli ribosomal proteins were identified from bottom-up analysis. This integrated approach simplified the assignment of post-translational modifications by increasing the confidence of identifications, distinguishing between isoforms, and identifying the amino acid positions at which particular post-translational modifications occurred. Our combined mass spectrometry data also allowed us to check and validate the gene annotations for three ribosomal proteins predicted to possess extended C-termini. In particular, we identified a highly repetitive C-terminal "alanine tail" on L25. This type of low complexity sequence, common to eukaryotic proteins, has previously not been reported in prokaryotic proteins. To our knowledge, this is the most comprehensive protein complex analysis to date that integrates two MS techniques.