Fast and accurate identification of semi-tryptic peptides in shotgun proteomics

An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics

MOTIVATION

Cross-linking tandem mass spectrometry (XL-MS/MS) is an established analytical platform used to determine distance constraints between residues within a protein or from physically interacting proteins, thus improving our understanding of protein structure and function. To aid biological discovery with XL-MS/MS, it is essential that pairs of chemically linked peptides be accurately identified, a process that requires: (i) database search, that creates a ranked list of candidate peptide pairs for each experimental spectrum and (ii) false discovery rate (FDR) estimation, that determines the probability of a false match in a group of top-ranked peptide pairs with scores above a given threshold. Currently, the only available FDR estimation mechanism in XL-MS/MS is the target-decoy approach (TDA). However, despite its simplicity, TDA has both theoretical and practical limitations that impact the estimation accuracy and increase run time over potential decoy-free approaches (DFAs).

RESULTS

We introduce a novel decoy-free framework for FDR estimation in XL-MS/MS. Our approach relies on multi-sample mixtures of skew normal distributions, where the latent components correspond to the scores of correct peptide pairs (both peptides identified correctly), partially incorrect peptide pairs (one peptide identified correctly, the other incorrectly), and incorrect peptide pairs (both peptides identified incorrectly). To learn these components, we exploit the score distributions of first- and second-ranked peptide-spectrum matches for each experimental spectrum and subsequently estimate FDR using a novel expectation-maximization algorithm with constraints. We evaluate the method on ten datasets and provide evidence that the proposed DFA is theoretically sound and a viable alternative to TDA owing to its good performance in terms of accuracy, variance of estimation, and run time.

AVAILABILITY AND IMPLEMENTATION

https://github.com/shawn-peng/xlms.

An Introduction to Analytical Challenges, Approaches, and Applications in Mass Spectrometry-Based Secretomics

The active release of proteins into the extracellular space and the proteolytic cleavage of cell surface proteins are key processes that coordinate and fine-tune a multitude of physiological functions. The entirety of proteins that fulfill these extracellular tasks are referred to as the secretome and are of special interest for the investigation of biomarkers of disease states and physiological processes related to cell-cell communication. LC-MS-based proteomics approaches are a valuable tool for the comprehensive and unbiased characterization of this important subproteome. This review discusses procedures, opportunities, and limitations of mass spectrometry-based secretomics to better understand and navigate the complex analytical landscape for studying protein secretion in biomedical science.

Permafrost preservation reveals proteomic evidence for yak milk consumption in the 13th century

Domesticated yaks endure as iconic symbols of high-altitude frozen landscapes, where herding communities depend on their high-fat milk, transport, dung, and natural fibers. While there is established proteomic evidence for ancient consumption of ruminant and horse milk in the mountains and steppes of northern Eurasia, yak dairy products have yet to be detected. Yak domestication and the species' dispersal from Tibet into the mountainous zones to the north are also poorly resolved due to a paucity of zooarchaeological data. To examine the potential of paleoproteomics to shed light on domesticated yak in Mongolia, we analyzed human dental calculus from Mongol era elite individuals recovered from permafrost burials in Khovsgol province, where people continue to herd yak to this day. We report the first evidence for yak dairy consumption, linked to local resource control. In addition, we confirm a large diversity of recovered whey, curd, tissue, and blood proteins, likely reflecting the excellent preservation conditions found at permafrost sites.

Investigation of Tryptic Protein Digestion in Microdroplets and in Bulk Solution

Recent studies have shown that ultrafast enzymatic digestion of proteins can be achieved in microdroplet within 250 μs. Further investigation of peptides resulting from microdroplet digestion (MD) would be necessary to evaluate it as an alternative to the conventional bulk digestion for bottom-up and biotherapeutic protein characterization. Herein we examined and compared protein tryptic digestion in both MD and bulk solution. In the case of MD of β-lactoglobulin B, the preservation of long peptides was observed due to the short digestion time. In addition, MD is applicable to digest both high- and low-abundance proteins in mixture. In the case of digesting NIST 8671 mAb antibody containing a low level of commonly encountered host cell protein (HCP) PLBL2 (mAb:PLBL2 = 100:1 by weight), MD produced lower levels of digestion-induced chemical modifications of asparagine/glutamine deamidation, compared with overnight digestion. No significant difference between MD and bulk digestion was observed in terms of trypsin digestion specificity based on examination of semi- and unspecific-cleaved peptides. Our study suggests that MD, a fast digestion approach, could be adopted for bottom-up proteomics research and for peptide mapping of mAbs to characterize site-specific deamidation and glycosylation, for the purpose of development of biopharmaceuticals.

High-Performance Thin-Layer Chromatography-Immunostaining as a Technique for the Characterization of Whey Protein Enrichment in Edam Cheese

Whey protein-enriched cheese can be produced by means of a high-temperature treatment of a part of the cheese milk. In this way, the nutritional quality of the resulting cheeses can be increased while resources are conserved. High-performance thin-layer chromatography-immunostaining (HPTLC-IS) using specific β-lactoglobulin (β-LG) antibodies was applied to study the implementation and stability of β-LG in two different sample sets of whey protein-enriched Edam model cheeses, including industrial-scale ones. Two methods were compared for the extraction of the proteins/peptides from the cheese samples. By applying tryptic hydrolysis directly from a suspended cheese sample instead of a supernatant of a centrifuged suspension, a better yield was obtained for the extraction of β-LG. When applying this method, it was found that selected epitopes in the tryptic β-LG peptides remain stable over the ripening period of the cheese. For four of the tryptic β-LG peptides detected by immunostaining, the amino acid sequence was identified using MALDI-TOF-MS/MS. One of the peptides identified was the semi-tryptic peptide VYVEELKPTP. A linear relationship was found between the content of this peptide in cheese and the proportion of high-heated milk in the cheese milk. β-LG enrichment factors of 1.72 (n = 3, sample set I) and 1.33 ± 0.19 (n = 1, sample set II) were determined for the cheese samples containing 30% high-heated milk compared to the non-enriched samples. The relative β-LG contents in the cheese samples with 30% high-heated milk were calculated to be 4.35% ± 0.39% (sample set I) and 9.11% ± 0.29% (sample set II) using a one-point calibration. It can be concluded that the HPTLC-IS method used is a suitable tool for the analysis of whey protein accumulation in cheese, being therefore potentially directly applicable on an industrial scale. For more accurate quantification of the whey protein content in cheese, an enhanced calibration curve needs to be applied.

A Systematic Evaluation of Semispecific Peptide Search Parameter Enables Identification of Previously Undescribed N-Terminal Peptides and Conserved Proteolytic Processing in Cancer Cell Lines

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the most commonly used technique in explorative proteomic research. A variety of open-source tools for peptide-spectrum matching have become available. Most analyses of explorative MS data are performed using conventional settings, such as fully specific enzymatic constraints. Here we evaluated the impact of the fragment mass tolerance in combination with the enzymatic constraints on the performance of three search engines. Three open-source search engines (Myrimatch, X! Tandem, and MSGF+) were evaluated concerning the suitability in semi- and unspecific searches as well as the importance of accurate fragment mass spectra in non-specific peptide searches. We then performed a semispecific reanalysis of the published NCI-60 deep proteome data applying the most suited parameters. Semi- and unspecific LC-MS/MS data analyses particularly benefit from accurate fragment mass spectra while this effect is less pronounced for conventional, fully specific peptide-spectrum matching. Search speed differed notably between the three search engines for semi- and non-specific peptide-spectrum matching. Semispecific reanalysis of NCI-60 proteome data revealed hundreds of previously undescribed N-terminal peptides, including cases of proteolytic processing or likely alternative translation start sites, some of which were ubiquitously present in all cell lines of the reanalyzed panel. Highly accurate MS2 fragment data in combination with modern open-source search algorithms enable the confident identification of semispecific peptides from large proteomic datasets. The identification of previously undescribed N-terminal peptides in published studies highlights the potential of future reanalysis and data mining in proteomic datasets.

An Algorithm to Improve the Speed of Semi and Non-Specific Enzyme Searches in Proteomics

Background

The identification of non-specifically cleaved peptides in proteomics and peptidomics poses a significant computational challenge. Current strategies for the identification of such peptides are typically time consuming and hinder routine data analysis.

Objective

We aimed to design an algorithm that would improve the speed of semi- and non-specific enzyme searches and could be applicable to existing search programs.

Method

We developed a novel search algorithm that leverages fragment-ion redundancy to simultaneously search multiple non-specifically cleaved peptides at once. Briefly, a theoretical peptide tandem mass spectrum is generated using only the fragment-ion series from a single terminus. This spectrum serves as a proxy for several shorter theoretical peptides sharing the same terminus. After database searching, amino acids are removed from the opposing terminus until the observed and theoretical precursor masses match within a given mass tolerance.

Results

The algorithm was implemented in the search program MetaMorpheus and found to perform an order of magnitude faster than the traditional MetaMorpheus search and produce superior results.

Conclusion

We report a speedy non-specific enzyme search algorithm which is open-source and enables search programs to utilize fragment-ion redundancy to achieve a notable increase in search speed.

New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics

MOTIVATION

Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra.

RESULTS

We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms.

AVAILABILITYAND IMPLEMENTATION

https://github.com/shawn-peng/FDR-estimation.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Nonspecific cleavages arising from reconstitution of trypsin under mildly acidic conditions

Tryptic digestion of proteins followed by liquid chromatography with tandem mass spectrometry analysis is an extensively used approach in proteomics research and biopharmaceutical product characterization, owing to the high level of cleavage fidelity produced with this technique. However, nonspecific trypsin cleavages have been frequently reported and shown to be related to a number of digestion conditions and predigestion sample treatments. In this work, we reveal that, for a number of commercial trypsins, reconstitution and storage conditions can have a significant impact on the occurrence of trypsin nonspecific cleavages. We analyzed the tryptic digestion of a variety of biotherapeutics, using trypsins reconstituted under different conditions. The results indicate that, for many commercial trypsins, commonly recommended reconstitution/storage conditions (mildly acidic, e.g., 50 mM acetic acid, 1 mM HCl) can actually promote nonspecific trypsin activities, which are time dependent and can be as high as 20% in total relative abundance. In contrast, using water for reconstitution and storage can effectively limit nonspecific cleavages to 1%. Interestingly, the performances of different commercial trypsins were found to be quite distinct in their levels of nonspecific cleavages and responses to the two reconstitution conditions. Our findings demonstrate the importance of choosing the appropriate trypsin for tryptic digestion and the necessity of assessing the impact of trypsin reconstitution and storage on nonspecific cleavages. We advocate for manufacturers of commercial trypsins to reevaluate manufacturing processes and reconstitution/storage conditions to provide good cleavage specificity.

Evaluation and minimization of nonspecific tryptic cleavages in proteomic sample preparation

High specificity of trypsin is a prerequisite for accurate identification and quantification of proteins in shotgun proteomics. It is important to minimize nonspecific enzymatic cleavages during proteomic sample preparation.

METHODS

In this study, protein extraction and trypsin digestion conditions were extensively evaluated using the less-complex Escherichia coli lysates to improve the sensitivity of detecting low-abundance nonspecific peptides by liquid chromatography/tandem mass spectrometry.

RESULTS

Trypsin digestion buffers and digestion times were proved to have a significant effect on nonspecific cleavages. The triethylammonium bicarbonate buffer induces significantly lower nonspecific cleavages than the other two buffers, but a freshly prepared urea solution does not induce more than sodium dodecyl sulfate. Because prolonged trypsin digestion resulted in a considerable number of nonspecific cleavages, an optimized 2-h protocol was developed with 45.2% less semispecific tryptic peptides but 18.5% more unmodified peptides identified than the commonly used 16-h protocol.

CONCLUSIONS

The significant decrease in nonspecific cleavages and artificial modifications improves the accuracy of protein quantification and the identification of low-abundance proteins, and it is especially useful for studying protein posttranslational modifications. For trypsin digestion, the proposed 2-h protocol can potentially be a replacement for the traditional 16-h protocol.

Metagenome-Guided Proteomic Quantification of Reductive Dehalogenases in the Dehalococcoides mccartyi-Containing Consortium SDC-9

At groundwater sites contaminated with chlorinated ethenes, fermentable substrates are often added to promote reductive dehalogenation by indigenous or augmented microorganisms. Contemporary bioremediation performance monitoring relies on nucleic acid biomarkers of key organohalide-respiring bacteria, such as Dehalococcoides mccartyi (Dhc). Metagenome sequencing of the commercial, Dhc-containing consortium, SDC-9, identified 12 reductive dehalogenase (RDase) genes, including pceA (two copies), vcrA, and tceA, and allowed for specific detection and quantification of RDase peptides using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Shotgun (i.e., untargeted) proteomics applied to the SDC-9 consortium grown with tetrachloroethene (PCE) and lactate identified 143 RDase peptides, and 36 distinct peptides that covered greater than 99% of the protein-coding sequences of the PceA, TceA, and VcrA RDases. Quantification of RDase peptides using multiple reaction monitoring (MRM) assays with ¹³C-/¹⁵N-labeled peptides determined 1.8 × 10³ TceA and 1.2 × 10² VcrA RDase molecules per Dhc cell. The MRM mass spectrometry approach allowed for sensitive detection and accurate quantification of relevant Dhc RDases and has potential utility in bioremediation monitoring regimes.

Assessing the Similarity between Random Copolymer Drug Glatiramer Acetate by Using LC-MS Data Coupling with Hypothesis Testing

The full characterization of nonbiological complex drugs (NBCDs) is not possible, but analytical approaches are of urgent need to evaluate the similarity between different lots and compare with their follow-up versions. Here, we propose a hypothesis testing-based approach to assess the similarity/difference between random amino acid copolymer drugs using liquid chromatography mass spectrometry (LC-MS) analysis. Two glatiramer acetate (GA) drugs, commercially available Copaxone and in-house synthesized SPT, and a negative control were digested by Lys-C and followed by HILIC-MS analysis. After retention time alignment and feature identification, 1627 features matched to m/z values in an elemental composition database were considered as derived from active drug ingredients. A hypothesis testing approach, the sum of squared deviations test, was developed to process high-dimensional data derived from LC-MS spectra. The feasibility of this approach was first demonstrated by testing 5 versus 5 lots of Copaxone and Copaxone versus SPT, which suggested a significant similarity by obtaining the estimated 95th percentile of the distribution of the estimator (ρ̂_(95%)) at 0.0056 (p-value = 0.0026) and 0.0026 (p-value < 0.0001), respectively. In contrast, the ρ̂ was 0.036 (p-value = 1.00), while comparing Copaxone and the negative control, implying a lack of similarity. We further synthesized nine stable isotope-labeled peptides to validate the proposed amino acid sequences in the database, demonstrating the correctness in sequence identification. The quantitation variations in our analytical procedures were determined to be 6.8-7.7%. This approach was found to have a great potential for evaluating the similarity between generic NBCDs and listed reference drugs, as well as to monitor the lot-to-lot variation.

Proteomics Analysis Reveals That Caspase-Like and Metacaspase-Like Activities Are Dispensable for Activation of Proteases Involved in Early Response to Biotic Stress in Triticum aestivum L

Plants, including Triticum aestivum L., are constantly attacked by various pathogens which induce immune responses. Immune processes in plants are tightly regulated by proteases from different families within their degradome. In this study, a wheat degradome was characterized. Using profile hidden Markov model (HMMer) algorithm and Pfam database, comprehensive analysis of the T. aestivum genome revealed a large number of proteases (1544 in total) belonging to the five major protease families: serine, cysteine, threonine, aspartic, and metallo-proteases. Mass-spectrometry analysis revealed a 30% difference between degradomes of distinct wheat cultivars (Khakasskaya and Darya), and infection by biotrophic (Puccinia recondita Rob. ex Desm f. sp. tritici) or necrotrophic (Stagonospora nodorum) pathogens induced drastic changes in the presence of proteolytic enzymes. This study shows that an early immune response to biotic stress is associated with the same core of proteases from the C1, C48, C65, M24, M41, S10, S9, S8, and A1 families. Further liquid chromatography-mass spectrometry (LC-MS) analysis of the detected protease-derived peptides revealed that infection by both pathogens enhances overall proteolytic activity in wheat cells and leads to activation of proteolytic cascades. Moreover, sites of proteolysis were identified within the proteases, which probably represent targets of autocatalytic activation, or hydrolysis by another protease within the proteolytic cascades. Although predicted substrates of metacaspase-like and caspase-like proteases were similar in biotrophic and necrotrophic infections, proteolytic activation of proteases was not found to be associated with metacaspase-like and caspase-like activities. These findings indicate that the response of T. aestivum to biotic stress is regulated by unique mechanisms.

Proteomic evidence of dietary sources in ancient dental calculus

Archaeological dental calculus has emerged as a rich source of ancient biomolecules, including proteins. Previous analyses of proteins extracted from ancient dental calculus revealed the presence of the dietary milk protein β-lactoglobulin, providing direct evidence of dairy consumption in the archaeological record. However, the potential for calculus to preserve other food-related proteins has not yet been systematically explored. Here we analyse shotgun metaproteomic data from 100 archaeological dental calculus samples ranging from the Iron Age to the post-medieval period (eighth century BC to nineteenth century AD) in England, as well as 14 dental calculus samples from contemporary dental patients and recently deceased individuals, to characterize the range and extent of dietary proteins preserved in dental calculus. In addition to milk proteins, we detect proteomic evidence of foodstuffs such as cereals and plant products, as well as the digestive enzyme salivary amylase. We discuss the importance of optimized protein extraction methods, data analysis approaches and authentication strategies in the identification of dietary proteins from archaeological dental calculus. This study demonstrates that proteomic approaches can robustly identify foodstuffs in the archaeological record that are typically under-represented due to their poor macroscopic preservation.

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

Unraveling the Proteome Composition and Immuno-profiling of Western India Russell's Viper Venom for In-Depth Understanding of Its Pharmacological Properties, Clinical Manifestations, and Effective Antivenom Treatment

The proteome composition of western India (WI) Russell's viper venom (RVV) was correlated with pharmacological properties and pathological manifestations of RV envenomation. Proteins in the 5-19 and 100-110 kDa mass ranges were the most predominate (∼35.1%) and least abundant (∼3.4%) components, respectively, of WI RVV. Non-reduced SDS-PAGE indicated the occurrence of multiple subunits, non-covalent oligomers, self-aggregation, and/or interactions among the RVV proteins. A total of 55 proteins belonging to 13 distinct snake venom families were unambiguously identified by ESI-LC-MS/MS analysis. Phospholipase A2 (32.5%) and Kunitz-type serine protease inhibitors (12.5%) represented the most abundant enzymatic and non-enzymatic proteins, respectively. However, ATPase, ADPase, and hyaluronidase, detected by enzyme assays, were not identified by proteomic analysis owing to limitations in protein database deposition. Several biochemical and pharmacological properties of WI RVV were also investigated. Neurological symptoms exhibited by some RV-bite patients in WI may be correlated to the presence of neurotoxic phospholipase A2 enzymes and Kunitz-type serine protease inhibitor complex in this venom. Monovalent antivenom was found to be better than polyvalent antivenom in immuno-recognition and neutralization of the tested pharmacological properties and enzyme activities of WI RVV; nevertheless, both antivenoms demonstrated poor cross-reactivity and neutralization of pharmacological activities shown by low-molecular-mass proteins (<18 kDa) of this venom.

Proteomic analysis to unravel the complex venom proteome of eastern India Naja naja: Correlation of venom composition with its biochemical and pharmacological properties

The complex venom proteome of the eastern India (EI) spectacled cobra (Naja naja) was analyzed using tandem mass spectrometry of cation-exchange venom fractions. About 75% of EI N. naja venom proteins were <18kDa and cationic at physiological pH of blood. SDS-PAGE (non-reduced) analysis indicated that in the native state venom proteins either interacted with each-other or self-aggregated resulting in the formation of higher molecular mass complexes. Proteomic analysis revealed that 43 enzymatic and non-enzymatic proteins in EI N. naja venom with a percent composition of about 28.4% and 71.6% respectively were distributed over 15 venom protein families. The three finger toxins (63.8%) and phospholipase A₂s (11.4%) were the most abundant families of non-enzymatic and enzymatic proteins, respectively. nanoLC-ESI-MS/MS analysis demonstrated the occurrence of acetylcholinesterase, phosphodiesterase, cholinesterase and snake venom serine proteases in N. naja venom previously not detected by proteomic analysis. ATPase, ADPase, hyaluronidase, TAME, and BAEE-esterase activities were detected by biochemical analysis; however, due to a limitation in the protein database depository they were not identified in EI N. naja venom by proteomic analysis. The proteome composition of EI N. naja venom was well correlated with its in vitro and in vivo pharmacological properties in experimental animals and envenomed human.

BIOLOGICAL SIGNIFICANCE

Proteomic analysis reveals the complex and diverse protein profile of EI N. naja venom which collectively contributes to the severe pathophysiological manifestation upon cobra envenomation. The study has also aided in comprehending the compositional variation in venom proteins of N. naja within the Indian sub-continent. In addition, this study has also identified several enzymes in EI N. naja venom which were previously uncharacterized by proteomic analysis of Naja venom.

XLSearch: a Probabilistic Database Search Algorithm for Identifying Cross-Linked Peptides

Chemical cross-linking combined with mass spectrometric analysis has become an important technique for probing protein three-dimensional structure and protein-protein interactions. A key step in this process is the accurate identification and validation of cross-linked peptides from tandem mass spectra. The identification of cross-linked peptides, however, presents challenges related to the expanded nature of the search space (all pairs of peptides in a sequence database) and the fact that some peptide-spectrum matches (PSMs) contain one correct and one incorrect peptide but often receive scores that are comparable to those in which both peptides are correctly identified. To address these problems and improve detection of cross-linked peptides, we propose a new database search algorithm, XLSearch, for identifying cross-linked peptides. Our approach is based on a data-driven scoring scheme that independently estimates the probability of correctly identifying each individual peptide in the cross-link given knowledge of the correct or incorrect identification of the other peptide. These conditional probabilities are subsequently used to estimate the joint posterior probability that both peptides are correctly identified. Using the data from two previous cross-link studies, we show the effectiveness of this scoring scheme, particularly in distinguishing between true identifications and those containing one incorrect peptide. We also provide evidence that XLSearch achieves more identifications than two alternative methods at the same false discovery rate (availability: https://github.com/COL-IU/XLSearch ).

The need to revisit published data: A concept and framework for complementary proteomics

Tandem proteomic strategies based on large-scale and high-resolution mass spectrometry have been widely applied in various biomedical studies. However, protein sequence databases and proteomic software are continuously updated. Proteomic studies should not be ended with a stable list of proteins. It is necessary and beneficial to regularly revise the results. Besides, the original proteomic studies usually focused on a limited aspect of protein information and valuable information may remain undiscovered in the raw spectra. Several studies have reported novel findings by reanalyzing previously published raw data. However, there are still no standard guidelines for comprehensive reanalysis. In the present study, we proposed the concept and draft framework for complementary proteomics, which are aimed to revise protein list or mine new discoveries by revisiting published data.

Proteomic profiling of nipple aspirate fluid (NAF): Exploring the complementarity of different peptide fractionation strategies

NAF is a breast fluid that is closely related to the tumor microenvironment and a valuable sample for studying breast cancer. To perform an in-depth proteomic analysis of this sample, aliquots of a single NAF digest were analyzed by the following peptide-centric fractionation strategies: a) 30-cm reversed-phase (RP) column on-line with an LTQ-Orbitrap XL; b) off-line strong cation-exchange (SCX) column; and c) pI-based OFFGEL fractionation. All fractions from approaches (b) and (c) were further analyzed on a 10-cm RP column hyphenated to the same mass spectrometer. The RP-30cm, SCX/RP-10cm, and OFFGEL/RP-10cm approaches identified 1676, 2930, and 3240 peptides, which corresponded to 193, 390 and 528 proteins, respectively. In our cumulative dataset, 4466 distinct NAF peptides corresponded to a total of 557 proteins, of which only 34% were identified by all three approaches. No exclusive protein identification was associated to the RP-30cm approach, while SCX/RP-10cm and OFFGEL/RP-10cm contributed to 28 and 166 exclusive identifications, respectively. Each approach provided additional information related to energy metabolism (fermentation process/carbohydrate biosynthesis). In conclusion, the pre-fractionation platforms used were complementary for the comprehensive characterization of NAF and our work provides methodological information for future quantitative cancer-related NAF sample studies.

BIOLOGICAL SIGNIFICANCE

High-resolution peptide separation is a sine qua non condition for achieving extensive proteome coverage. Various techniques have been employed to improve peptide fractionation prior to LC-MS/MS, thus allowing a comprehensive characterization of complex biological samples. Although fractionation efficiency is very sample-dependent, this issue is commonly overlooked, and a "cookbook" approach is routinely used during this critical step. The present study provides a systematic comparison of analytical information needed for the successful large-scale differential proteomic analysis of NAF samples from breast cancer patients, a precious and volume-limited biological sample. It reinforces the importance of optimizing sample-specific fractionation protocols for information retrieval from mass spectrometric analysis.

Neddylation inhibition impairs spine development, destabilizes synapses and deteriorates cognition

Neddylation is a ubiquitylation-like pathway that controls cell cycle and proliferation by covalently conjugating Nedd8 to specific targets. However, its role in neurons, nonreplicating postmitotic cells, remains unexplored. Here we report that Nedd8 conjugation increased during postnatal brain development and is active in mature synapses, where many proteins are neddylated. We show that neddylation controls spine development during neuronal maturation and spine stability in mature neurons. We found that neddylated PSD-95 was present in spines and that neddylation on Lys202 of PSD-95 is required for the proactive role of the scaffolding protein in spine maturation and synaptic transmission. Finally, we developed Nae1(CamKIIα-CreERT2) mice, in which neddylation is conditionally ablated in adult excitatory forebrain neurons. These mice showed synaptic loss, impaired neurotransmission and severe cognitive deficits. In summary, our results establish neddylation as an active post-translational modification in the synapse regulating the maturation, stability and function of dendritic spines.

Controlling nonspecific trypsin cleavages in LC-MS/MS-based shotgun proteomics using optimized experimental conditions

The highlight of this study is the efficient control of nonspecific trypsin cleavages in shotgun proteomics and N-glycoproteomics using optimized experimental conditions, which greatly increased the specificity of trypsin.

Improving peptide identification sensitivity in shotgun proteomics by stratification of search space

Because of its high specificity, trypsin is the enzyme of choice in shotgun proteomics. Nonetheless, several publications do report the identification of semitryptic and nontryptic peptides. Many of these peptides are thought to be signaling peptides or to have formed during sample preparation. It is known that only a small fraction of tandem mass spectra from a trypsin-digested protein mixture can be confidently matched to tryptic peptides. If other possibilities such as post-translational modifications and single-amino acid polymorphisms are ignored, this suggests that many unidentified spectra originate from semitryptic and nontryptic peptides. To include them in database searches, however, may not improve overall peptide identification because of the possible sensitivity reduction from search space expansion. To circumvent this issue for E-value-based search methods, we have designed a scheme that categorizes qualified peptides (i.e., peptides whose differences in molecular weight from the parent ion are within a specified error tolerance) into three tiers: tryptic, semitryptic, and nontryptic. This classification allows peptides that belong to different tiers to have different Bonferroni correction factors. Our results show that this scheme can significantly improve retrieval performance compared to those of search strategies that assign equal Bonferroni correction factors to all qualified peptides.

Extending the coverage of spectral libraries: a neighbor-based approach to predicting intensities of peptide fragmentation spectra

Searching spectral libraries in MS/MS is an important new approach to improving the quality of peptide and protein identification. The idea relies on the observation that ion intensities in an MS/MS spectrum of a given peptide are generally reproducible across experiments, and thus, matching between spectra from an experiment and the spectra of previously identified peptides stored in a spectral library can lead to better peptide identification compared to the traditional database search. However, the use of libraries is greatly limited by their coverage of peptide sequences: even for well-studied organisms a large fraction of peptides have not been previously identified. To address this issue, we propose to expand spectral libraries by predicting the MS/MS spectra of peptides based on the spectra of peptides with similar sequences. We first demonstrate that the intensity patterns of dominant fragment ions between similar peptides tend to be similar. In accordance with this observation, we develop a neighbor-based approach that first selects peptides that are likely to have spectra similar to the target peptide and then combines their spectra using a weighted K-nearest neighbor method to accurately predict fragment ion intensities corresponding to the target peptide. This approach has the potential to predict spectra for every peptide in the proteome. When rigorous quality criteria are applied, we estimate that the method increases the coverage of spectral libraries available from the National Institute of Standards and Technology by 20-60%, although the values vary with peptide length and charge state. We find that the overall best search performance is achieved when spectral libraries are supplemented by the high quality predicted spectra.

Computational approaches to protein inference in shotgun proteomics

Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programming and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area.

Using a spike-in experiment to evaluate analysis of LC-MS data

BACKGROUND

Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards.

RESULTS

The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose.

CONCLUSIONS

The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.

Novel amidinating cross-linker for facilitating analyses of protein structures and interactions

A novel bifunctional thioimidate cross-linking reagent (diethyl suberthioimidate) that modifies amines without sacrificing their native basicity is developed. Intermolecular cross-linking of neurotensin and intramolecular cross-linking of cytochrome c under physiological conditions is investigated with this reagent. Because it does not perturb the electrostatic properties of a protein, it is unlikely to lead to artifactual conclusions about native protein structure. The interpeptide cross-links formed with this reagent are easily separated from other tryptic fragments using strong cation exchange chromatography, and they have a readily identified mass spectrometric signature. The use of this novel amidinating protein cross-linking reagent holds great promise for efficient, large-scale structural analysis of complex systems.

The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics

Peptide detectability is defined as the probability that a peptide is identified in an LC-MS/MS experiment and has been useful in providing solutions to protein inference and label-free quantification. Previously, predictors for peptide detectability trained on standard or complex samples were proposed. Although the models trained on complex samples may benefit from the large training data sets, it is unclear to what extent they are affected by the unequal abundances of identified proteins. To address this challenge and improve detectability prediction, we present a new algorithm for the iterative learning of peptide detectability from complex mixtures. We provide evidence that the new method approximates detectability with useful accuracy and, based on its design, can be used to interpret the outcome of other learning strategies. We studied the properties of peptides from the bacterium Deinococcus radiodurans and found that at standard quantities, its tryptic peptides can be roughly classified as either detectable or undetectable, with a relatively small fraction having medium detectability. We extend the concept of detectability from peptides to proteins and apply the model to predict the behavior of a replicate LC-MS/MS experiment from a single analysis. Finally, our study summarizes a theoretical framework for peptide/protein identification and label-free quantification.

Strain identification of commercial influenza vaccines by mass spectrometry

Current influenza vaccine manufacturing and testing timelines require that the constituent hemagglutinin (HA) and neuraminidase (NA) strains be selected each year approximately 10 months before the vaccine becomes available. The threat of a pandemic influenza outbreak requires that more rapid testing methods be found. We have developed a specialized on-filter sample preparation method that uses both trypsin and chymotrypsin to enzymatically digest peptide-N-glycosidase F (PNGase F)-deglycosylated proteins in vaccines. In tandem with replicate liquid chromatography-mass spectrometry (LC-MS) analyses, this approach yields sufficient protein sequencing data (>85% sequence coverage on average) for strain identification of HA and NA components. This has allowed the confirmation, and in some cases the correction, of the identity of the influenza strains in recent commercial vaccines as well as the correction of some ambiguous HA sequence annotations in available databases. This method also allows the identification of low-level contaminant egg proteins produced during the manufacturing process.