Estimating the Distribution of Protein Post-Translational Modification States by Mass Spectrometry

MSModDetector: a tool for detecting mass shifts and post-translational modifications in individual ion mass spectrometry data

MOTIVATION

Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs on the same protein molecule, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even PTMs at distant sites to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein.

RESULTS

We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I2MS) data. I2MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I2MS data and experimental I2MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein's PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/marjanfaizi/MSModDetector.

A semi-automated hybrid HPLC-MS approach for in-depth characterization of intact non-covalent heterodimer glycoforms of gonadotropin biopharmaceuticals

BACKGROUND

Gonadotropins are a class of heavily glycosylated protein hormones, thus extremely challenging to characterize by mass spectrometry. As biopharmaceuticals, gonadotropins are prescribed for the treatment of infertility and are derived from different sources: either from pooled urine of pregnant women or upon production in genetically modified Chinese Hamster Ovary cells. Human chorionic gonadotropin (hCG) is sold as a biopharmaceutical under the name Pregnyl® (urinary hCG, u-hCG) and Ovitrelle® (recombinant hCG, r-hCG), and recombinant human follicle stimulating hormone (r-hFSH) is marketed as Gonal-f®. Recently, we reported the exhaustive characterization of r-hCG at different structural levels.

RESULTS

We implement size exclusion (SE) HPLC-MS to automatize the acquisition of native mass spectra of r-hCG dimer, but also u-hCG and r-hFSH, comparing the drug products up to intact heterodimer level. A hybrid HPLC-MS approach was employed for the characterization of r-hCG, u-hCG and r-hFSH drug products at different structural levels. Released glycans were analyzed by porous graphitized carbon (PGC)-HPLC-MS/MS, glycopeptides by reversed-phase (RP)-HPLC-MS/MS, subunits by RP-HPLC-MS and finally the intact native heterodimers by semi-automated online buffer exchange SE-HPLC-MS. The data were integrated using bioinformatic tools, to finally unravel the composition of 1481 co-existing dimeric glycoforms for r-hCG, 1167 glycoforms for u-hCG, and 1440 glycoforms for r-hFSH, and to compare critical quality attributes of the different drug products such as their degree of sialylation and O-glycosylation.

SIGNIFICANCE AND NOVELTY

The strong alliance of bioanalytics and bioinformatics data integration at the different structural levels allowed the identification of more than thousand different glycoforms of r-hCG, u-hCG, and r-hFSH. The results showed that these biopharmaceuticals differ considerably in their glycosylation patterns and highlight the importance of in-depth characterization of biopharmaceuticals for quality control. © 2017 Elsevier Inc. All rights reserved.

MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data

Motivation

Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even widely separated PTMs to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein.

Results

We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I MS) data. I MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I MS data and experimental I MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein's PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes.

Availability

The source code is available at https://github.com/marjanfaizi/MSModDetector together with the scripts used for analyses and to generate the figures presented in this study.

Enhanced Characterization of Histones Using 193 nm Ultraviolet Photodissociation and Proton Transfer Charge Reduction

Top-down characterization of histones, proteins that are critical participants in an array of DNA-dependent processes, offers the potential to examine the relationship between histone structure and mechanisms of genetic regulation. Mapping patterns of post-translational modifications (PTMs) of histones requires extensive backbone cleavages to bracket the sites of mass shifts corresponding to specific PTMs. Ultraviolet photodissociation (UVPD) causes substantial fragmentation of proteins, which is well-suited for PTM localization, but the resulting spectra are congested with fragment ions that may have overlapping isotopic distributions that confound deconvolution. Gas-phase proton transfer charge reduction (PTCR) decreases the charge states of highly charged ions, thus alleviating this congestion and facilitating the identification of additional sequence-determining and PTM-localizing fragment ions. By integrating UVPD with PTCR for histone proteoform analyses, sequence coverages up to 91% were achieved for calf thymus histone H4 containing acetylation marks at the N-terminus and Lys12 as well as a dimethylation at Arg3. UVPD-PTCR exhibited large gains in characterization for other histones, such as histone H2A, increasing the sequence coverage from 59 to 77% for monoacetylated H2A.

Advanced Strategies for Proton-Transfer Reactions Coupled with Parallel Ion Parking on a 21 T FT-ICR MS for Intact Protein Analysis

Proton-transfer reactions (PTRs) have emerged as a powerful tool for the study of intact proteins. When coupled with m/z-selective kinetic excitation, such as parallel ion parking (PIP), one can exert exquisite control over rates of reaction with a high degree of specificity. This allows one to "concentrate", in the gas phase, nearly all the signals from an intact protein charge state envelope into a single charge state, improving the signal-to-noise ratio (S/N) by 10× or more. While this approach has been previously reported, here we show that implementing these technologies on a 21 T FT-ICR MS provides a tremendous advantage for intact protein analysis. Advanced strategies for performing PTR with PIP were developed to complement this unique instrument, including subjecting all analyte ions entering the mass spectrometer to PTR and PIP. This experiment, which we call "PTR-MS¹-PIP", generates a pseudo-MS¹ spectrum derived from ions that are exposed to the PTR reagent and PIP waveforms but have not undergone any prior true mass filtering or ion isolation. The result is an extremely rapid and significant improvement in the spectral S/N of intact proteins. This permits the observation of many more proteoforms and reduces ion injection periods for subsequent tandem mass spectrometry characterization. Additionally, the product ion parking waveform has been optimized to enhance the PTR rate without compromise to the parking efficiency. We demonstrate that this process, called "rapid park", can improve reaction rates by 5-10× and explore critical factors discovered to influence this process. Finally, we demonstrate how coupling PTR-MS¹ and rapid park provides a 10-fold reduction in ion injection time, improving the rate of tandem MS sequencing.

Profiling Dopamine-Induced Oxidized Proteoforms of β-synuclein by Top-Down Mass Spectrometry

The formation of multiple proteoforms by post-translational modifications (PTMs) enables a single protein to acquire distinct functional roles in its biological context. Oxidation of methionine residues (Met) is a common PTM, involved in physiological (e.g., signaling) and pathological (e.g., oxidative stress) states. This PTM typically maps at multiple protein sites, generating a heterogeneous population of proteoforms with specific biophysical and biochemical properties. The identification and quantitation of the variety of oxidized proteoforms originated under a given condition is required to assess the exact molecular nature of the species responsible for the process under investigation. In this work, the binding and oxidation of human β-synuclein (BS) by dopamine (DA) has been explored. Native mass spectrometry (MS) has been employed to analyze the interaction of BS with DA. In a second step, top-down fragmentation of the intact protein from denaturing conditions has been performed to identify and quantify the distinct proteoforms generated by DA-induced oxidation. The analysis of isobaric proteoforms is approached by a combination of electron-transfer dissociation (ETD) at each extent of modification, quantitation of methionine-containing fragments and combinatorial analysis of the fragmentation products by multiple linear regression. This procedure represents a promising approach to systematic assessment of proteoforms variety and their relative abundance. The method can be adapted, in principle, to any protein containing any number of methionine residues, allowing for a full structural characterization of the protein oxidation states.

What's in a mass?

This short essay pretends to make the reader reflect on the concept of biological mass and on the added value that the determination of this molecular property of a protein brings to the interpretation of evolutionary and translational snake venomics research. Starting from the premise that the amino acid sequence is the most distinctive primary molecular characteristics of any protein, the thesis underlying the first part of this essay is that the isotopic distribution of a protein's molecular mass serves to unambiguously differentiate it from any other of an organism's proteome. In the second part of the essay, we discuss examples of collaborative projects among our laboratories, where mass profiling of snake venom PLA2 across conspecific populations played a key role revealing dispersal routes that determined the current phylogeographic pattern of the species.

Posttranslational modifications in systems biology

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.

Decoding the protein composition of whole nucleosomes with Nuc-MS

Current proteomic approaches disassemble and digest nucleosome particles, blurring readouts of the 'histone code'. To preserve nucleosome-level information, we developed Nuc-MS, which displays the landscape of histone variants and their post-translational modifications (PTMs) in a single mass spectrum. Combined with immunoprecipitation, Nuc-MS quantified nucleosome co-occupancy of histone H3.3 with variant H2A.Z (sixfold over bulk) and the co-occurrence of oncogenic H3.3K27M with euchromatic marks (for example, a >15-fold enrichment of dimethylated H3K79me2). Nuc-MS is highly concordant with chromatin immunoprecipitation-sequencing (ChIP-seq) and offers a new readout of nucleosome-level biology.

[Size exclusion-reverse liquid column chromatography-mass spectrometry and its application in the identification of post-translationally modified proteins in rat kidney]

Proteomics is an emerging field that has been shown to play a crucial role in unveiling the mechanisms underlying physiological and pathological processes, and liquid chromatography-mass spectrometry (LC-MS) is one of the most important methods employed in this field. However, in complex biological systems, such as eukaryotes, it is challenging to perform a comprehensive and unbiased proteome analysis due to the high complexity of biological samples and enormous differences in sample contents. For example, post-translational modifications (PTMs) in proteins are imperative for cell signaling, but post-translationally modified proteins account for about 1% of the total proteins in a single cell, making their identification extremely difficult. Therefore, chromatographic separation methods based on different principles are generally applied to reduce the complexity of biological samples and enrich trace proteins for their identification through mass spectrometry (MS). In this study, we developed a new proteomics method by combining size exclusion chromatography (SEC) and reversed-phase chromatography (RPLC), to separate and identify trace proteins in complex systems. SEC was used to separate and enrich kidney-specific proteins. After optimization of the method, it was found that 30 mmol/L of ammonium acetate could efficiently separate rat kidney proteins from the total protein fraction so that they could be eluted based on their relative molecular mass. Sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis and LC-MS results showed that our SEC separation method not only refined the protein composition of the biological sample but also enhanced the relative contents of trace proteins through multiple injections. The collected protein fractions were further concentrated through ultrafiltration centrifugation followed by freeze-drying, which further improved the recovery of trace proteins by approximately 90% and largely decreased the time required with the use of freeze-drying alone. Thereafter, five protein fractions were separately digested using trypsin, and the resultant peptides were further analyzed by reverse phase chromatography-MS analysis. In the RPLC column, the peptides were isolated mainly based on their hydrophobicity. As a result, by combining SEC and RPLC, 23621 peptides and 1345 proteins were identified from the kidney, with an increase in numbers by 69% and 27%, respectively, when compared to those obtained using the common 2D strong cation exchange (SCX)-RPLC-MS method. However, no significant difference was observed in the pI and grand average of hydropathicity (GRAVY) values. Gene ontology (GO) analysis revealed an increase in the number of proteins in each cell component, especially the membrane. Furthermore, identification of a higher rate of identified peptides than proteins suggested that the protein coverage was also improved, thereby facilitating the detection of PTM proteins. Consequently, five common PTMs in biological processes, including methylation, acetylation, carbamylation, oxidation, and phosphorylation, were examined and compared between the two methods. As expected, the number of post-translationally modified peptides identified using SEC-RPLC-MS were 1.7-1.9 times more than those determined using the SCX-RPLC-MS method. Especially for the identification of phosphorylated peptides, we could achieve the level of the targeted enrichment strategy; however no significant difference was observed in the extents of phosphorylation among serine, threonine, and tyrosine. These results further indicate that upon combining SEC and RPLC, high efficiency could be achieved by decreasing the complexity of the protein sample, and the identification was unbiased. Finally, the phosphorylation of some kidney proteins, such as spectrin, L-lactate dehydrogenase, and ATPases, was found, which is critical for their functions. In summary, the SEC-RPLC-MS approach was developed for the identification of rat kidney proteins and is especially applicable for the identification of PTM proteins. Using this method, the identification efficiency for PTM peptides increased significantly. Therefore, this method has potential for better understanding the impact of PTM on kidney proteins and further elucidating the potential mechanisms underlying its physiological and pathological functions.

DeepPSP: A Global-Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites

Identification of phosphorylation sites is an important step in the function study and drug design of proteins. In recent years, there have been increasing applications of the computational method in the identification of phosphorylation sites because of its low cost and high speed. Most of the currently available methods focus on using local information around potential phosphorylation sites for prediction and do not take the global information of the protein sequence into consideration. Here, we demonstrated that the global information of protein sequences may be also critical for phosphorylation site prediction. In this paper, a new deep neural network model, called DeepPSP, was proposed for the prediction of protein phosphorylation sites. In the DeepPSP model, two parallel modules were introduced to extract both local and global features from protein sequences. Two squeeze-and-excitation blocks and one bidirectional long short-term memory block were introduced into each module to capture effective representations of the sequences. Comparative studies were carried out to evaluate the performance of DeepPSP, and four other prediction methods using public data sets The F1-score, area under receiver operating characteristic curves (AUROC), and area under precision-recall curves (AUPRC) of DeepPSP were found to be 0.4819, 0.82, and 0.50, respectively, for S/T general site prediction and 0.4206, 0.73, and 0.39, respectively, for Y general site prediction. Compared with the MusiteDeep method, the F1-score, AUROC, and AUPRC of DeepPSP were found to increase by 8.6, 2.5, and 8.7%, respectively, for S/T general site prediction and by 20.6, 5.8, and 18.2%, respectively, for Y general site prediction. Among the tested methods, the developed DeepPSP method was also found to produce best results for different kinase-specific site predictions including CDK, mitogen-activated protein kinase, CAMK, AGC, and CMGC. Taken together, the developed DeepPSP method may offer a more accurate phosphorylation site prediction by including global information. It may serve as an alternative model with better performance and interpretability for protein phosphorylation site prediction.

Comparative Proteomic Analysis of Wild Type and Mutant Lacking an SCF E3 Ligase F-Box Protein in Magnaporthe oryzae

Magnaporthe oryzae (M. oryzae) is a pathogenic, filamentous fungus that is a primary cause of rice blast disease. The M. oryzae protein MGG_13065, SCF E3 ubiquitin ligase complex F-box protein, has been identified as playing a crucial role in the infection process, specifically, as part of the ubiquitin mediated proteolysis pathway. Proteins targeted by MGG_13065 E3 ligase are first phosphorylated and then ubiquitinated by E3 ligase. In this study, we used a label-free quantitative global proteomics technique to probe the role of ubiquitination and phosphorylation in the mechanism of how E3 ligase regulates change in virulence of M. oryzae. To do this, we compared the WT M. oryzae 70-15 strain with a gene knock out (E3 ligase KO) strain. After applying a ≥ 5 normalized spectral count cutoff, a total of 4432 unique proteins were identified comprised of 4360 and 4372 in the WT and E3 ligase KO samples, respectively. Eighty proteins drastically increased in abundance, while 65 proteins decreased in abundance in the E3 ligase KO strain. Proteins (59) were identified only in the WT strain; 13 of these proteins had both phosphorylation and ubiquitination post-translational modifications. Proteins (71) were revealed to be only in the E3 ligase KO strain; 23 of the proteins have both phosphorylation and ubiquitination post-translational modifications. Several of these proteins were associated with key biological processes. These data greatly assist in the selection of future genes for functional studies and enable mechanistic insight related to virulence.

Aspartic Acid Isomerization Characterized by High Definition Mass Spectrometry Significantly Alters the Bioactivity of a Novel Toxin from Poecilotheria

Research in toxinology has created a pharmacological paradox. With an estimated 220,000 venomous animals worldwide, the study of peptidyl toxins provides a vast number of effector molecules. However, due to the complexity of the protein-protein interactions, there are fewer than ten venom-derived molecules on the market. Structural characterization and identification of post-translational modifications are essential to develop biological lead structures into pharmaceuticals. Utilizing advancements in mass spectrometry, we have created a high definition approach that fuses conventional high-resolution MS-MS with ion mobility spectrometry (HDMSE) to elucidate these primary structure characteristics. We investigated venom from ten species of "tiger" spider (Genus: Poecilotheria) and discovered they contain isobaric conformers originating from non-enzymatic Asp isomerization. One conformer pair conserved in five of ten species examined, denominated PcaTX-1a and PcaTX-1b, was found to be a 36-residue peptide with a cysteine knot, an amidated C-terminus, and isoAsp33Asp substitution. Although the isomerization of Asp has been implicated in many pathologies, this is the first characterization of Asp isomerization in a toxin and demonstrates the isomerized product's diminished physiological effects. This study establishes the value of a HDMSE approach to toxin screening and characterization.

Thorough Performance Evaluation of 213 nm Ultraviolet Photodissociation for Top-down Proteomics

Top-down proteomics studies intact proteoform mixtures and offers important advantages over more common bottom-up proteomics technologies, as it avoids the protein inference problem. However, achieving complete molecular characterization of investigated proteoforms using existing technologies remains a fundamental challenge for top-down proteomics. Here, we benchmark the performance of ultraviolet photodissociation (UVPD) using 213 nm photons generated by a solid-state laser applied to the study of intact proteoforms from three organisms. Notably, the described UVPD setup applies multiple laser pulses to induce ion dissociation, and this feature can be used to optimize the fragmentation outcome based on the molecular weight of the analyzed biomolecule. When applied to complex proteoform mixtures in high-throughput top-down proteomics, 213 nm UVPD demonstrated a high degree of complementarity with the most employed fragmentation method in proteomics studies, higher-energy collisional dissociation (HCD). UVPD at 213 nm offered higher average proteoform sequence coverage and degree of proteoform characterization (including localization of post-translational modifications) than HCD. However, previous studies have shown limitations in applying database search strategies developed for HCD fragmentation to UVPD spectra which contains up to nine fragment ion types. We therefore performed an analysis of the different UVPD product ion type frequencies. From these data, we developed an ad hoc fragment matching strategy and determined the influence of each possible ion type on search outcomes. By paring down the number of ion types considered in high-throughput UVPD searches from all types down to the four most abundant, we were ultimately able to achieve deeper proteome characterization with UVPD. Lastly, our detailed product ion analysis also revealed UVPD cleavage propensities and determined the presence of a product ion produced specifically by 213 nm photons. All together, these observations could be used to better elucidate UVPD dissociation mechanisms and improve the utility of the technique for proteomic applications.

Proteome-Wide Identification of Lysine Propionylation in the Conidial and Mycelial Stages of Trichophyton rubrum

Posttranslational modifications (PTMs) exist in a wide variety of organisms and play key roles in regulating various essential biological processes. Lysine propionylation is a newly discovered PTM that has rarely been identified in fungi. Trichophyton rubrum (T. rubrum) is one of the most common fungal pathogens in the world and has been studied as an important model organism of anthropic pathogenic filamentous fungi. In this study, we performed a proteome-wide propionylation analysis in the conidial and mycelial stages of T. rubrum. A total of 157 propionylated sites on 115 proteins were identified, and the high confidence of propionylation identification was validated by parallel reaction monitoring (PRM) assay. The results show that the propionylated proteins were mostly involved in various metabolic pathways. Histones and 15 pathogenicity-related proteins were also targets for propionylation modification, suggesting their roles in epigenetic regulation and pathogenicity. A comparison of the conidial and mycelial stages revealed that most propionylated proteins and sites were growth-stage specific and independent of protein abundance. Based on the function classifications, the propionylated proteins had a similar distribution in both stages; however, some differences were also identified. Furthermore, our results show that the concentration of propionyl-CoA had a significant influence on the propionylation level. In addition to the acetylation, succinylation and propionylation identified in T. rubrum, 26 other PTMs were also found to exist in this fungus. Overall, our study provides the first global propionylation profile of a pathogenic fungus. These results would be a foundation for further research on the regulation mechanism of propionylation in T. rubrum, which will enhance our understanding of the physiological features of T. rubrum and provide some clues for the exploration of improved therapies to treat this medically important fungus.

Coenzyme A-aptamer-facilitated label-free electrochemical stripping strategy for sensitive detection of histone acetyltransferase activity

Abnormal histone acetyltransferases (HAT) activity gives rise to all kinds of cellular diseases. Herein, we first report a coenzyme A (CoA)-aptamer-facilitated label-free electrochemical stripping biosensor for sensitive detection of HAT activity via square wave voltammetry (SWV) technique. The presence of HAT can lead to the transfer of the acetyl group from acetyl coenzyme A (Ac-CoA) to lysine residues of substrate peptide, thus generating CoA molecule. Later, CoA, which acts as an initiator, can embrace its aptamer via the typical target-aptamer interaction, then arousing deoxynucleotide terminal transferase (TdT)-induced silver nanoclusters (AgNCs) as signal output. Under optimized conditions, the resultant aptasensor shows obvious electrochemical stripping signal and is employed for HAT p300 analysis in a wide concentration range from 0.01 to 100 nM with a very low detection limit of 0.0028 nM (3δ/slope). The good analytical performances of the biosensor depend on the strong interaction of CoA and its aptamer and abundant stripping resource rooted from AgNCs. Next, the proposed biosensor is used for screening HAT's inhibitors and the practical HAT detection with satisfactory results. Therefore, the new, simple and sensitive HAT biosensor presents a promising direction for HAT-targeted drug discovery and epigenetic research.

Proton Transfer Charge Reduction Enables High-Throughput Top-Down Analysis of Large Proteoforms

Despite the recent technological advances in Fourier transform mass spectrometry (FTMS) instrumentation, top-down proteomics (TDP) is currently mostly applied to the characterization of proteoforms <30 kDa due to the poor performance of high-resolution FTMS for the analysis of larger proteoforms and the high complexity of intact proteomes in the 30-60 kDa mass range. Here, we propose a novel data acquisition method based on ion-ion proton transfer, herein termed proton transfer charge reduction (PTCR), to investigate large proteoforms of Pseudomonas aeruginosa in a high-throughput fashion. We designed a targeted data acquisition strategy, named tPTCR, which applies two consecutive gas phase fractionation steps for obtaining intact precursor masses: first, a narrow (1.5 m/z-wide) quadrupole filter m/z transmission window is used to select a subset of charge states from all ionized proteoform cations; second, this aliquot of protein cations is subjected to PTCR in order to reduce their average charge state: upon m/z analysis in an Orbitrap, proteoform mass spectra with minimal m/z peak overlap and easy-to-interpret charge state distributions are obtained, simplifying the proteoform mass calculation. Subsequently, the same quadrupole-selected narrow m/z region of analytes is subjected to collisional dissociation to obtain proteoform sequence information, which used in combination with intact mass information leads to proteoform identification through an off-line database search. The newly proposed method was benchmarked against the previously developed "medium/high" data-dependent acquisition strategy and doubled the number of UniProt entries and proteoforms >30 kDa identified on the liquid chromatography time scale.

Ultrasensitive mushroom-like electrochemical immunosensor for probing the activity of histone acetyltransferase

A novel mushroom-like electrochemical immunoassay for the ultrasensitive detection of histone acetyltransferase activity (HAT p300) has been established on account of the new composite graphene oxide (GO) nanolayer. The immunoassay involves immobilization of substrate peptide onto Au electrode, acetylation in lysine of substrate peptide, and the interaction between acetyl group of lysine and acetyl-antibody (Ab_Ac) of the GO nanolayer. The GO nanolayer comprises large amounts of methylene blue molecules (MB), giving rise to large signal amplification. Only in the presence of HAT p300, an obvious electrochemical signal appears and the peak linear current is proportion to the HAT p300 concentrations ranging from 0.01 to 150 nM with a detection limit of 0.0036 nM. The great enhancement on sensitivity of the proposed mushroom-like immunosensor derives from both the constructed Faraday cage and the extended outer Helmholtz plane (OHP). Further, the immunosensor with excellent sensitivity and selectivity can be applied for the HAT p300 activity detection in Hela cell lysates, serum and urine, hinting an improved and splendid analytical performance. Briefly, this stable, simple and ultrasensitive electrochemical immunoassay has considerable promise for further applications in the HATs-interrelated epigenetic studies and drug development.