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Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify key enzymes in the oleaginous yeast Yarrowia lipolytica that correlate with the production of itaconate by integrating a metabolic model with multiomics measurements. To this extent, we quantify the uncertainty for a variety of key parameters, known as flux control coefficients (FCCs), needed to improve the bioproduction of target metabolites and statistically obtain key correlations between the measured enzymes and boundary flux. Based on the top five significant FCCs and five correlated enzymes, our results show phosphoglycerate mutase, acetyl-CoA synthetase (ACSm), carbonic anhydrase (HCO3E), pyrophosphatase (PPAm), and homoserine dehydrogenase (HSDxi) enzymes in rate-limiting reactions that can lead to increased itaconic acid production.

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

Ricin, a toxic protein from the castor plant, is of forensic and biosecurity interest because of its high toxicity and common occurrence in crimes and attempted crimes. Qualitative methods to detect ricin are therefore needed. Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics methods are well suited because of their high specificity. Specificity in LC-MS/MS comes from both the LC and MS components. However, modern untargeted proteomics methods often use nanoflow LC, which has less reproducible retention times than standard-flow LC, making it challenging to use retention time as a point of identification in a forensic assay. We address this challenge by using retention times relative to a standard, namely, the uniformly 15N-labeled ricin A-chain produced recombinantly in a bacterial expression system. This material, added as an internal standard prior to trypsin digestion, produces a stable-isotope-labeled standard for every ricin tryptic peptide in the sample. We show that the MS signals for 15N and natural isotopic abundance ricin peptides are distinct, with mass shifts that correspond to the numbers of nitrogen atoms in each peptide or fragment. We also show that, as expected, labeled and unlabeled peptides coelute, with relative retention time differences of less than 0.2%.

Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify key enzymes in the oleaginous yeast

The discovery of dirigent proteins (DPs) and their functions in plant phenol biochemistry was made over two decades ago with Forsythia × intermedia. Stereo-selective, DP-guided, monolignol-derived radical coupling in vitro was then reported to afford the optically active lignan, (+)-pinoresinol from coniferyl alcohol, provided one-electron oxidase/oxidant capacity was present. It later became evident that DPs have several distinct sub-families. In vascular plants, DPs hypothetically function, along with other essential enzymes/proteins (e.g. oxidases), as part of lignin/lignan forming complexes (LFCs). Herein, we used an integrated bottom-up, top-down, and native mass spectrometry approach to detect potential interacting proteins in a DP-enriched solubilized protein fraction from Forsythia × intermedia, via adaptation of our initial report of DP solubilization and purification. Because this hybrid species lacks a published genome, de novo sequencing was performed using publicly available transcriptome and genomic data from closely related species. We detected and identified two new DP homologs, which appear to form hetero-trimers. Molecular dynamics simulations suggest that similar hetero-trimers were possible between Arabidopsis DP homologs with comparable sequence similarity. Other identified proteins in the DP-enriched preparation were putatively associated with DP function or the cell wall. Although their co-occurrence after extraction and chromatographic separation is suggestive for components of a protein complex in vivo, none were found to form stable complexes with DPs under the specific experimental conditions we have explored. Nevertheless, our integrated mass spectrometry method development helps prepare for future investigations directed to detect hypothetical LFCs and other related complexes isolated from plant biomass fractionation.

Ricin, a protein found in castor seeds, is a lethal toxin that is designated as a category 2 select agent, and cases of attempted ricin poisoning are relatively common. Many methods to detect protein toxins such as ricin use targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify toxin peptides, usually tryptic peptides. The successful use of untargeted methods has also been reported. However, the use of untargeted proteomics methods, including database search, for peptide and protein identification is less common in forensic practice and may be unfamiliar to forensic science practitioners. Here, we propose a method to create spectral libraries of tryptic ricin peptides and use these libraries for ricin identification by spectral library search, which may be more familiar to forensic scientists because of the use of spectral libraries in small molecule identification. Peptide spectral libraries offer a direct comparison to an authentic standard, a key element of forensic analysis, but have not previously been used in a forensic context. To construct these spectral libraries, two pure ricin samples (one from a proposed standard reference material) were digested with trypsin and analyzed using a standard shotgun LC-MS/MS protocol. Spectral libraries were created from resulting tryptic peptides identified from filtered search results from four database search tools. The library was then used in a search using SpectraST on forensically realistic castor seed extracts. These castor seed samples were made using the crude methods commonly encountered in real-world ricin cases. Analysis showed that the spectral library search resulted in more peptides identified from crude castor seed samples compared to MS-GF+ and Sequest plus Percolator database searches. These results, the first published use of spectral library search to detect protein toxins in forensically relevant samples, suggest that computational comparison of putative ricin peptide spectra to library spectra can be an effective method to detect ricin in an unknown sample. Data are available via ProteomeXchange with identifier PXD013711.

Mass spectrometry-based proteomics has been a useful tool for addressing numerous questions in basic biology research for many years. This success, combined with the maturity of mass spectrometric instrumentation, the ever-increasing availability of protein sequence databases derived from genome sequencing, and the growing sophistication of data analysis methods, places proteomics in a position to have an important role in biological forensics. Because proteins contain information about genotype (sequence) and phenotype (expression levels), proteomics methods can both identify biological samples and characterize the conditions that produced them. In addition to serving as a valuable orthogonal method to genomic analyses, proteomics can be used in cases where nucleic acids are absent, degraded, or uninformative. Mass spectrometry provides both broad applicability and exquisite specificity, often without customized detection reagents like primers or antibodies. This review briefly introduces proteomics methods, and surveys a variety of forensic applications (including criminal justice, historical, archaeological, and national security areas). Finally, challenges and crucial areas for further research are addressed.

Proteomics is a useful tool for addressing numerous questions regarding any protein-containing sample. These questions can include indicators of production method or growth environment, such as whether a given microorganism is a wild or laboratory-adapted strain, or whether the growth medium used to culture a specific microorganism contains hydrolyzed milk casein or other components that can be used to narrow down the potential source of a given microbial sample. In addition, proteomics can address not only whether a protein toxin such as ricin is present in a sample but also collect information on other untargeted proteinaceous sample components, providing additional forensic context. This chapter briefly introduces proteomics methods and provides examples of bioforensic applications.

Ricin, a toxic protein from the castor plant, is of forensic and biosecurity interest because of its high toxicity and common occurrence in crimes and attempted crimes. Qualitative methods to detect ricin are therefore needed. Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics methods are well suited because of their high specificity. Specificity in LC-MS/MS comes from both the LC and MS components. However, modern untargeted proteomics methods often use nanoflow LC, which has less reproducible retention times than standard-flow LC, making it challenging to use retention time as a point of identification in a forensic assay. We address this challenge by using retention times relative to a standard, namely, the uniformly

The process of sporulation is vital for the stability and infectious cycle of Bacillus anthracis. The spore is the infectious form of the organism and therefore relevant to biodefense. While the morphological and molecular events occurring during sporulation have been well studied, the influence of growth medium and temperature on the proteins expressed in sporulated cultures is not well understood. Understanding the features of B. anthracis sporulation specific to natural vs. laboratory production will address an important question in microbial forensics. In an effort to bridge this knowledge gap, a system for sporulation on two types of agar-immobilized soils was used for comparison to cultures sporulated on two common types of solid laboratory media, and one liquid sporulation medium. The total number of proteins identified as well as their identity differed between samples generated in each medium and growth temperature, demonstrating that sporulation environment significantly impacts the protein content of the spore. In addition, a subset of proteins common in all of the soil-cultivated samples was distinct from the expression profiles in laboratory medium (and vice versa). These differences included proteins involved in thiamine and phosphate metabolism in the sporulated cultures produced on soils with a notable increase in expression of ATP binding cassette (ABC) transporters annotated to be for phosphate and antimicrobial peptides. A distinct set of ABC transporters for amino acids, sugars and oligopeptides were found in cultures produced on laboratory media as well as increases in carbon and amino acid metabolism-related proteins. These protein expression changes indicate that the sporulation environment impacts the protein profiles in specific ways that are reflected in the metabolic and membrane transporter proteins present in sporulated cultures.

Robust and highly specific methods for the detection of the protein toxin ricin are of interest to the law enforcement community. In previous studies, methods based on liquid chromatography-tandem mass spectrometry shotgun proteomics have been proposed. The successful implementation of this approach relies on specific data evaluation criteria addressing (1) the quality of the mass spectrometric data, (2) the confidence of peptide identifications (peptide-spectrum matches), and (3) the number and sequence specificity of peptides detected. We present such data evaluation criteria and use a novel approach to establish the limit of detection for this ricin assay. Specifically, we use logistic regression to determine the probability of detection for individual ricin peptides at different concentrations. We then apply basic rules from probability theory, combining these individual peptide probabilities into an overall assay limit of detection. This procedure yields an assay limit of detection for ricin at 42.5 ng on column or 21.25 ng/μL for a 2-μL injection. We also show that, despite the conventional wisdom that detergents are deleterious to mass spectrometric analyses, the presence of Tween-20 did not prevent detection of ricin peptides, and indeed assays performed in buffers that included Tween-20 gave better results than assays performed using other buffer formulations with or without detergent removal.

Progress in modern biology is being driven, in part, by the large amounts of freely available data in public resources such as the International Nucleotide Sequence Database Collaboration (INSDC), the world's primary database of biological sequence (and related) information. INSDC and similar databases have dramatically increased the pace of fundamental biological discovery and enabled a host of innovative therapeutic, diagnostic, and forensic applications. However, as high-value, openly shared resources with a high degree of assumed trust, these repositories share compelling similarities to the early days of the Internet. Consequently, as public biological databases continue to increase in size and importance, we expect that they will face the same threats as undefended cyberspace. There is a unique opportunity, before a significant breach and loss of trust occurs, to ensure they evolve with quality and security as a design philosophy rather than costly “retrofitted” mitigations. This Perspective surveys some potential quality assurance and security weaknesses in existing open genomic and proteomic repositories, describes methods to mitigate the likelihood of both intentional and unintentional errors, and offers recommendations for risk mitigation based on lessons learned from cybersecurity.

Metaproteomics has been increasingly utilized for high-throughput molecular characterization in complex environments and has been demonstrated to provide insights into microbial composition and functional roles in soil systems. Despite its potential for the study of microbiomes, significant challenges remain in data analysis, including the creation of a sample-specific protein sequence database as the taxonomic composition of soil is often unknown. Almost all metaproteome analysis tools require this database and their accuracy and sensitivity suffer when the database is incomplete or contains extraneous sequences from organisms which are not present. Here, we leverage a de novo peptide sequencing approach to identify sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data, and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified unsequenced soil isolates directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S ribosomal RNA sequencing of the soil samples and also uncovered several additional species which were strongly represented only in proteomic data. Our pipeline offers an alternative and complementary method for metaproteomic data analysis by creating a protein database directly from proteomic data, thus removing the need for metagenomic sequencing.Significance StatementProteomic characterization of environmental samples, or metaproteomics, reveals microbial activity critical to our understanding of climate, nutrient cycling and human health. Metaproteomic samples originate from diverse environs, such as soil and oceans. One option for data analysis is a de novo interpretation of the mass spectra. Unfortunately, the current generation of de novo algorithms were primarily trained on data originating from human proteins. Therefore, these algorithms struggle with data from environmental samples, limiting our ability to analyze metaproteomics data. To address this challenge, we trained a new algorithm with data from dozens of diverse environmental bacteria and achieved significant improvements in accuracy across a broad range of organisms. This generality opens proteomics to the world of natural isolates and microbiomes.

Bottom-up proteomics is increasingly being used to characterize unknown environmental, clinical, and forensic samples. Proteomics-based bacterial identification typically proceeds by tabulating peptide "hits" (i.e., confidently identified peptides) associated with the organisms in a database; those organisms with enough hits are declared present in the sample. This approach has proven to be successful in laboratory studies; however, important research gaps remain. First, the common-practice reliance on unique peptides for identification is susceptible to a phenomenon known as signal erosion. Second, no general guidelines are available for determining how many hits are needed to make a confident identification. These gaps inhibit the transition of this approach to real-world forensic samples where conditions vary and large databases may be needed. In this work, we propose statistical criteria that overcome the problem of signal erosion and can be applied regardless of the sample quality or data analysis pipeline. These criteria are straightforward, producing a p-value on the result of an organism or toxin identification. We test the proposed criteria on 919 LC-MS/MS data sets originating from 2 toxins and 32 bacterial strains acquired using multiple data collection platforms. Results reveal a95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.

Inactivation of pathogenic microbial samples is often necessary for the protection of researchers and to comply with local and federal regulations. By its nature, biological inactivation causes changes to microbial samples, potentially affecting observed experimental results. While inactivation-induced damage to materials such as DNA has been evaluated, the effect of various inactivation strategies on proteomic data, to our knowledge, has not been discussed. To this end, we inactivated samples of Yersinia pestis and Escherichia coli by autoclave, ethanol, or irradiation treatment to determine how inactivation changes liquid chromatography-tandem mass spectrometry data quality as well as apparent protein content of cells. Proteomic datasets obtained from aliquots of samples inactivated by different methods were highly similar, with Pearson correlation coefficients ranging from 0.822 to 0.985 and 0.816 to 0.985 for E . coli and Y . pestis , respectively, suggesting that inactivation had only slight impacts on the set of proteins identified. In addition, spectral quality metrics such as distributions of various database search algorithm scores remained constant across inactivation methods, indicating that inactivation does not appreciably degrade spectral quality. Though overall changes resulting from inactivation were small, there were detectable trends. For example, one-sided Fischer exact tests determined that periplasmic proteins decrease in observed abundance after sample inactivation by autoclaving (α = 1.71 × 10 − 2 for E . coli , α = 4.97 × 10 − 4 for Y . pestis ) and irradiation (α = 9.43 × 10 − 7 for E . coli , α = 1.21 × 10 − 5 for Y . pestis ) when compared to controls that were not inactivated. Based on our data, if sample inactivation is necessary, we recommend inactivation with ethanol treatment with secondary preference given to irradiation.

Bacterial extracellular metal respiration, as carried out by members of the genus Geobacter, is of interest for applications including microbial fuel cells and bioremediation. Geobacter bemidjiensis is the major species whose growth is stimulated during groundwater amendment with acetate. We have carried out label-free proteomics studies of G. bemidjiensis grown with acetate as the electron donor and either fumarate, ferric citrate, or one of two hydrous ferric oxide mineral types as electron acceptor. The major class of proteins whose expression changes across these conditions is c-type cytochromes, many of which are known to be involved in extracellular metal reduction in other, better-characterized Geobacter species. Some proteins with multiple homologues in G. bemidjiensis (OmcS, OmcB) had different expression patterns than observed for their G. sulfurreducens homologues under similar growth conditions. We also compared the proteome from our study to a prior proteomics study of biomass recovered from an aquifer in Colorado, where the microbial community was dominated by strains closely related to G. bemidjiensis. We detected an increased number of proteins with functions related to motility and chemotaxis in the Colorado field samples compared to the laboratory samples, suggesting the importance of motility for in situ extracellular metal respiration.

The toxic protein ricin (also known as RCA60), found in the seed of the castor plant (Ricinus communis) is frequently encountered in law enforcement investigations. The ability to detect ricin by analyzing its proteolytic (tryptic) peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is well established. However, ricin is just one member of a family of proteins in R. communis with closely related amino acid sequences, including R. communis agglutinin I (RCA120) and other ricin-like proteins (RLPs). Inferring the presence of ricin from its constituent peptides requires an understanding of the specificity, or uniqueness to ricin, of each peptide. Here we describe the set of ricin-derived tryptic peptides that can serve to uniquely identify ricin in distinction to closely-related RLPs and to proteins from other species. Other ricin-derived peptide sequences occur only in the castor plant, and still others are shared with unrelated species. We also characterized the occurrence and relative abundance of ricin and related proteins in an assortment of forensically relevant crude castor seed preparations. We find that whereas ricin and RCA120 are abundant in castor seed extracts, other RLPs are not represented by abundant unique peptides. Therefore, the detection of peptides shared between ricin and RLPs (other than RCA120) in crude castor seed extracts most likely reflects the presence of ricin in the sample.

Integrative structural biology attempts to model the structures of protein complexes that are challenging or intractable by classical structural methods (due to size, dynamics, or heterogeneity) by combining computational structural modeling with data from experimental methods. One such experimental method is chemical crosslinking mass spectrometry (XL-MS), in which protein complexes are crosslinked and characterized using liquid chromatography-mass spectrometry to pinpoint specific amino acid residues in close structural proximity. The commonly used lysine-reactive N-hydroxysuccinimide ester reagents disuccinimidylsuberate (DSS) and bis(sulfosuccinimidyl)suberate (BS3) have a linker arm that is 11.4 A long when fully extended, allowing Cα (alpha carbon of protein backbone) atoms of crosslinked lysine residues to be up to ∼24 A apart. However, XL-MS studies on proteins of known structure frequently report crosslinks that exceed this distance. Typically, a tolerance of ∼3 A is added to the theoretical maximum to account for this observation, with limited justification for the chosen value. We used the Dynameomics database, a repository of high-quality molecular dynamics simulations of 807 proteins representative of diverse protein folds, to investigate the relationship between lysine–lysine distances in experimental starting structures and in simulation ensembles. We conclude that for DSS/BS3, a distance constraint of 26–30 A between Cα atoms is appropriate. This analysis provides a theoretical basis for the widespread practice of adding a tolerance to the crosslinker length when comparing XL-MS results to structures or in modeling. We also discuss the comparison of XL-MS results to MD simulations and known structures as a means to test and validate experimental XL-MS methods.

The rapid pace of bacterial evolution enables organisms to adapt to the laboratory environment with repeated passage and thus diverge from naturally-occurring environmental ("wild") strains. Distinguishing wild and laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to convergent phenotypes, difficulty in detecting certain types of mutations, or perhaps because some adaptive modifications are epigenetic. Monitoring protein abundance, a molecular measure of phenotype, can overcome some of these difficulties. We have assembled a collection of Yersinia pestis proteomics datasets from our own published and unpublished work, and from a proteomics data archive, and demonstrated that protein abundance data can clearly distinguish laboratory-adapted from wild. We developed a lasso logistic regression classifier that uses binary (presence/absence) or quantitative protein abundance measures to predict whether a sample is laboratory-adapted or wild that proved to be ~98% accurate, as judged by replicated 10-fold cross-validation. Protein features selected by the classifier accord well with our previous study of laboratory adaptation in Y. pestis. The input data was derived from a variety of unrelated experiments and contained significant confounding variables. We show that the classifier is robust with respect to these variables. The methodology is able to discover signatures for laboratory facility and culture medium that are largely independent of the signature of laboratory adaptation. Going beyond our previous laboratory evolution study, this work suggests that proteomic differences between laboratory-adapted and wild Y. pestis are general, potentially pointing to a process that could apply to other species as well. Additionally, we show that proteomics datasets (even archived data collected for different purposes) contain the information necessary to distinguish wild and laboratory samples. This work has clear applications in biomarker detection as well as biodefense.

Protein reduction-oxidation (redox) modification is an important mechanism that allows microorganisms to sense environmental changes and initiate cellular responses. We have developed a quantitative chemical probe approach for live cell labeling and imaging of proteins that are sensitive to redox modifications. We utilize this in vivo strategy to identify 176 proteins undergoing ∼5-10-fold dynamic redox change in response to nutrient limitation and subsequent replenishment in the photoautotrophic cyanobacterium Synechococcus sp. PCC 7002. We detect redox changes in as little as 30 s after nutrient perturbation and oscillations in reduction and oxidation for 60 min following the perturbation. Many of the proteins undergoing dynamic redox transformations participate in the major components for the production (photosystems and electron transport chains) or consumption (Calvin-Benson cycle and protein synthesis) of reductant and/or energy in photosynthetic organisms. Thus, our in vivo approach reveals new redox-susceptible proteins and validates those previously identified in vitro.

The post-translational modifications (PTMs) of cysteine residues include oxidation, S-glutathionylation, S-nitrosylation, and succination, all of which modify protein function or turnover in response to a changing intracellular redox environment. Succination is a chemical modification of cysteine in proteins by the Krebs cycle intermediate, fumarate, yielding S-(2-succino)cysteine (2SC). Intracellular fumarate concentration and succination of proteins are increased by hyperpolarization of the inner mitochondrial membrane, in concert with mitochondrial, endoplasmic reticulum (ER) and oxidative stress in 3T3 adipocytes grown in high glucose medium and in adipose tissue in obesity and diabetes in mice. Increased succination of proteins is also detected in the kidney of a fumarase deficient conditional knock-out mouse which develops renal cysts. A wide range of proteins are subject to succination, including enzymes, adipokines, cytoskeletal proteins, and ER chaperones with functional cysteine residues. There is also some overlap between succinated and glutathionylated proteins, suggesting that the same low pKa thiols are targeted by both. Succination of adipocyte proteins in diabetes increases as a result of nutrient excess derived mitochondrial stress and this is inhibited by uncouplers, which discharge the mitochondrial membrane potential (ΔΨm) and relieve the electron transport chain. 2SC therefore serves as a biomarker of mitochondrial stress or dysfunction in chronic diseases, such as obesity, diabetes, and cancer, and recent studies suggest that succination is a mechanistic link between mitochondrial dysfunction, oxidative and ER stress, and cellular progression toward apoptosis. In this article, we review the history of the succinated proteome and the challenges associated with measuring this non-enzymatic PTM of proteins by proteomics approaches.

Multiprotein complexes, rather than individual proteins, make up a large part of the biological macromolecular machinery of a cell. Understanding the structure and organization of these complexes is critical to understanding cellular function. Chemical cross-linking coupled with mass spectrometry is emerging as a complementary technique to traditional structural biology methods and can provide low-resolution structural information for a multitude of purposes, such as distance constraints in computational modeling of protein complexes. In this review, we discuss the experimental considerations for successful application of chemical cross-linking-mass spectrometry in biological studies and highlight three examples of such studies from the recent literature. These examples (as well as many others) illustrate the utility of a chemical cross-linking-mass spectrometry approach in facilitating structural analysis of large and challenging complexes.

The bacterial pathogen Yersinia pestis, the cause of plague in humans and animals, normally has a sylvatic lifestyle, cycling between fleas and mammals. In contrast, laboratory-grown Y. pestis experiences a more constant environment and conditions that it would not normally encounter. The transition from the natural environment to the laboratory results in a vastly different set of selective pressures, and represents what could be considered domestication. Understanding the kinds of adaptations Y. pestis undergoes as it becomes domesticated will contribute to understanding the basic biology of this important pathogen. In this study, we performed a parallel serial passage experiment (PSPE) to explore the mechanisms by which Y. pestis adapts to laboratory conditions, hypothesizing that cells would undergo significant changes in virulence and nutrient acquisition systems. Two wild strains were serially passaged in 12 independent populations each for ~750 generations, after which each population was analyzed using whole-genome sequencing, LC-MS/MS proteomic analysis, and GC/MS metabolomics. We observed considerable parallel evolution in the endpoint populations, detecting multiple independent mutations in ail, pepA, and zwf, suggesting that specific selective pressures are shaping evolutionary responses. Complementary LC-MS/MS proteomic data provide physiological context to the observed mutations, and reveal regulatory changes not necessarily associated with specific mutations, including changes in amino acid metabolism and cell envelope biogenesis. Proteomic data support hypotheses generated by genomic data in addition to suggesting future mechanistic studies, indicating that future whole-genome sequencing studies be designed to leverage proteomics as a critical complement.

The c-type cytochromes play essential roles in many biological activities of both prokaryotic and eukaryotic cells, including electron transfer, enzyme catalysis, and induction of apoptosis. We report a novel enrichment strategy for identifying c-type heme-containing peptides that uses nonactivated IMAC resin. The strategy demonstrated at least 7-fold enrichment for heme-containing peptides digested from a cytochrome c protein standard, and quantitative linear performance was also assessed for heme-containing peptide enrichment. Heme-containing peptides extracted from the periplasmic fraction of Shewanella oneidensis MR-1 were further identified using higher-energy collisional dissociation tandem mass spectrometry. The results demonstrated the applicability of this enrichment strategy to identify c-type heme-containing peptides from a highly complex biological sample and, at the same time, confirmed the periplasmic localization of heme-containing proteins during suboxic respiration activities of S. oneidensis MR-1.

A widely held hypothesis regarding the thermostability ofthermophilic proteins states asserts that, at any giventemperature, thermophilic proteins are more rigid thantheir mesophilic counterparts. Many experimental andcomputational studies have addressed this question withconflicting results. Here, we compare two homologousenzymes, one mesophilic (Escherichia coli FMN-dependentnitroreductase; NTR) and one thermophilic (Thermusthermophilus NADH oxidase; NOX), by multiple molecu-lar dynamics simulations at temperatures from 5 to1008C. We find that the global rigidity/flexibility of thetwo proteins, assessed by a variety of metrics, is similaron the time scale of our simulations. However, the ther-mophilic enzyme retains its native conformation to amuch greater degree at high temperature than does themesophilic enzyme, both globally and within the activesite. The simulations identify the helix F–helix G ‘arm’as the region with the greatest difference in loss of nativecontacts between the two proteins with increasing temp-erature. In particular, a network of electrostatic inter-actions holds helix F to the body of the protein in thethermophilic protein, and this network is absent in themesophilic counterpart.Keywords: corresponding states/flavoproteins/moleculardynamics simulations/nitroreductase/thermophilic proteinsIntroductionProteins derived from thermophilic organisms are moreresistant to thermal denaturation than homologous proteinsfrom organisms adapted to moderate temperatures (meso-philes). The differences in stability, structure and amino acidcomposition between thermophilic and mesophilic proteinshave been the subject of intense research (reviewed in Vieilleand Zeikus, 2001). In general, thermophilic enzymes areoptimally active at high temperatures at which their hostorganisms thrive, and they are relatively inactive at tempera-tures where mesophilic enzyme activity is optimal. It hasbeen proposed that the low activity of thermophilic proteinsat ‘mesophilic’ temperatures is due to their greater confor-mational rigidity at low temperatures relative to mesophilicproteins (Vihinen, 1987;Zavodsky et al., 1998).An early articulation of this idea, known as the ‘corre-sponding states hypothesis’, comes from Vihinen (1987):‘Flexibilities of proteins performing the same catalyticactivity seem to be about the same at their temperatureoptima, but the more rigid thermostable proteins reach theflexibility of the thermolabile proteins at higher tempera-tures’. Vihinen reached this conclusion based on the com-parison of normalized B-factors from a small number ofcrystal structures of proteins for which thermostability datawere also available. Although the validity of his analysismay be called into question, given the sensitivity ofB-factors to the details of the refinement process and tocrystal contacts, the underlying hypothesis has spawnednumerous studies of homologous mesophilic and thermophi-lic proteins.The results of both experimental and computationalstudies of matched mesophilic and thermophilic protein pairshave been mixed. For instance, Fourier-transform infraredspectroscopic (FTIR) hydrogen–deuterium exchange exper-iments on thermophilic and mesophilic isopropylmalatedehydrogenases (Zavodsky et al., 1998) and glyceraldehyde-3-phosphate dehydrogenases (Wrba et al., 1990) showed thatamide hydrogen exchange was less extensive in the thermo-philic protein than in the mesophilic protein at 258C, but thisdifference was greatly reduced when the measurements weremade at the respective optimal activity temperatures of thetwo enzymes. In contrast, a hyperthermophilic zinc-containing rubredoxin (from Pyrococcus furiosus) was foundto have hydrogen exchange rates similar to those of manymesophilic proteins (Hernandez et al., 2000). Neutron scat-tering experiments with live whole cells between 5 and 378C(Tehei et al., 2004) suggest that the overall population ofmacromolecules (predominantly proteins) from hyperthermo-philes and thermophiles have slightly lower mean-squarefluctuations on the 0.1 ns time scale than those from meso-philes. However, neutron scattering studies on dihydrofolatereductase found that the thermophilic protein had higheratomic mean-square fluctuations than the mesophilic homol-ogue (Meinhold et al., 2008). Nuclear magnetic resonance(NMR) spectroscopy studies on the ribonuclease H fromEscherichia coli and Thermus thermophilus indicated localdifferences in dynamics that may be related to thermal stab-ility and/or catalysis, but no global differences. In contrast,Kern and coworkers (Wolf-Watz et al., 2004) used NMR tocharacterize a lid closing/opening conformational change inadenylate kinase from E.coli and the hyperthermophileAquifex aeolicus. This hyperthermophilic enzyme is 9-foldless active than the mesophilic form at 208C. Each protein’slid-opening rate was found to be equal to its k

Recent work suggests that some native conformations of proteins can vary with temperature. To obtain an atomic-level description of this structural and conformational variation, we have performed all-atom, explicit-solvent molecular dynamics simulations of bovine pancreatic ribonuclease A (RNase A) up to its melting temperature (Tm approximately 337 K). RNase A has a thermal pretransition near 320 K [Stelea, S. D., Pancoska, P., Benight, A. S., and Keiderling, T. A. (2001) Protein Sci. 10, 970-978]. Our simulations identify a conformational change that coincides with this pretransition. Between 310 and 320 K, there is a small but significant decrease in the number of native contacts, beta-sheet hydrogen bonding, and deviation of backbone conformation from the starting structure, and an increase in the number of nonnative contacts. Native contacts are lost in beta-sheet regions and in alpha1, partially due to movement of alpha1 away from the beta-sheet core. At 330 and 340 K, a nonnative helical segment of residues 15-20 forms, corresponding to a helix observed in the N-terminal domain-swapped dimer [Liu, Y. S., Hart, P. J., Schulnegger, M. P., and Eisenberg, D. (1998) Proc. Natl. Acad. Sci. U.S.A. 95, 3437-3432]. The conformations observed at the higher temperatures possess nativelike topology and overall conformation, with many native contacts, but they have a disrupted active site. We propose that these conformations may represent the native state at elevated temperature, or the N' state. These simulations show that subtle, functionally important changes in protein conformation can occur below the Tm.

Integrative structural biology attempts to model the structures of protein complexes that are challenging or intractable by classical structural methods (due to size, dynamics, or heterogeneity) by combining computational structural modeling with data from experimental methods. One such experimental method is chemical crosslinking mass spectrometry (XL-MS), in which protein complexes are crosslinked and characterized using liquid chromatography-mass spectrometry to pinpoint specific amino acid residues in close structural proximity. The commonly used lysine-reactive N-hydroxysuccinimide ester reagents disuccinimidylsuberate (DSS) and bis(sulfosuccinimidyl)suberate (BS(3) ) have a linker arm that is 11.4 Å long when fully extended, allowing Cα (alpha carbon of protein backbone) atoms of crosslinked lysine residues to be up to ∼24 Å apart. However, XL-MS studies on proteins of known structure frequently report crosslinks that exceed this distance. Typically, a tolerance of ∼3 Å is added to the theoretical maximum to account for this observation, with limited justification for the chosen value. We used the Dynameomics database, a repository of high-quality molecular dynamics simulations of 807 proteins representative of diverse protein folds, to investigate the relationship between lysine-lysine distances in experimental starting structures and in simulation ensembles. We conclude that for DSS/BS(3), a distance constraint of 26-30 Å between Cα atoms is appropriate. This analysis provides a theoretical basis for the widespread practice of adding a tolerance to the crosslinker length when comparing XL-MS results to structures or in modeling. We also discuss the comparison of XL-MS results to MD simulations and known structures as a means to test and validate experimental XL-MS methods.

The post-translational modifications (PTMs) of cysteine residues include oxidation, S-glutathionylation, S-nitrosylation, and succination, all of which modify protein function or turnover in response to a changing intracellular redox environment. Succination is a chemical modification of cysteine in proteins by the Krebs cycle intermediate, fumarate, yielding S-(2-succino) cysteine (2SC). Intracellular fumarate concentration and succination of proteins are increased by hyperpolarization of the inner mitochondrial membrane, in concert with mitochondrial, endoplasmic reticulum (ER) and oxidative stress in 3T3 adipocytes grown in high glucose medium and in adipose tissue in obesity and diabetes in mice. Increased succination of proteins is also detected in the kidney of a fumarase deficient conditional knock-out mouse which develops renal cysts. A wide range of proteins are subject to succination, including enzymes, adipokines, cytoskeletal proteins, and ER chaperones with functional cysteine residues. There is also some overlap between succinated and glutathionylated proteins, suggesting that the same low pKa thiols are targeted by both. Succination of adipocyte proteins in diabetes increases as a result of nutrient excess derived mitochondrial stress and this is inhibited by uncouplers, which discharge the mitochondrial membrane potential (ΔΨm) and relieve the electron transport chain. 2SC therefore serves as a biomarker of mitochondrial stress or dysfunction in chronic diseases, such as obesity, diabetes, and cancer, and recent studies suggest that succination is a mechanistic link between mitochondrial dysfunction, oxidative and ER stress, and cellular progression toward apoptosis. In this article, we review the history of the succinated proteome and the challenges associated with measuring this non-enzymatic PTM of proteins by proteomics approaches.

Multiheme c-type cytochromes (proteins with covalently attached heme c moieties) play important roles in extracellular metal respiration in dissimilatory metal-reducing bacteria. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) characterization of c-type cytochromes is hindered by the presence of multiple heme groups, since the heme c modified peptides are typically not observed or, if observed, not identified. Using a recently reported histidine affinity chromatography (HAC) procedure, we enriched heme c tryptic peptides from purified bovine heart cytochrome c, two bacterial decaheme cytochromes, and subjected these samples to LC-MS/MS analysis. Enriched bovine cytochrome c samples yielded 3- to 6-fold more confident peptide-spectrum matches to heme c containing peptides than unenriched digests. In unenriched digests of the decaheme cytochrome MtoA from Sideroxydans lithotrophicus ES-1, heme c peptides for 4 of the 10 expected sites were observed by LC-MS/MS; following HAC fractionation, peptides covering 9 out of 10 sites were obtained. Heme c peptide spiked into E. coli lysates at mass ratios as low as 1×10(-4) was detected with good signal-to-noise after HAC and LC-MS/MS analysis. In addition to HAC, we have developed a proteomics database search strategy that takes into account the unique physicochemical properties of heme c peptides. The results suggest that accounting for the double thioether link between heme c and peptide, and the use of the labile heme fragment as a reporter ion, can improve database searching results. The combination of affinity chromatography and heme-specific informatics yielded increases in the number of peptide-spectrum matches of 20-100-fold for bovine cytochrome c.

Chemical cross-linking of proteins followed by proteolysis and mass spectrometric analysis of the resulting cross-linked peptides provides powerful insight into the quaternary structure of protein complexes. Mixed-isotope cross-linking (a method for distinguishing intermolecular cross-links) was coupled with liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS) to provide an additional separation dimension to the traditional cross-linking approach. This method produced multiplet m/z peaks that are aligned in the IMS drift time dimension and serve as signatures of intermolecular cross-linked peptides. We developed an informatics tool to use the amino acid sequence information inherent in the multiplet spacing for accurate identification of the cross-linked peptides. Because of the separation of cross-linked and non-cross-linked peptides in drift time, our LC-IMS-MS approach was able to confidently detect more intermolecular cross-linked peptides than LC-MS alone.

The Gram-negative bacterium Sideroxydans lithotrophicus ES-1 (ES-1) grows on FeCO(3) or FeS at oxic-anoxic interfaces at circumneutral pH, and the ES-1-mediated Fe(II) oxidation occurs extracellularly. However, the molecular mechanisms underlying ES-1's ability to oxidize Fe(II) remain unknown. Survey of the ES-1 genome for candidate genes for microbial extracellular Fe(II) oxidation revealed that it contained a three-gene cluster encoding homologs of Shewanella oneidensis MR-1 (MR-1) MtrA, MtrB, and CymA that are involved in extracellular Fe(III) reduction. Homologs of MtrA and MtrB were also previously shown to be involved in extracellular Fe(II) oxidation by Rhodopseudomonas palustris TIE-1. To distinguish them from those found in MR-1, the identified homologs were named MtoAB and CymA(ES-1). Cloned mtoA partially complemented an MR-1 mutant without MtrA with regards to ferrihydrite reduction. Characterization of purified MtoA showed that it was a decaheme c-type cytochrome and oxidized soluble Fe(II). Oxidation of Fe(II) by MtoA was pH- and Fe(II)-complexing ligand-dependent. Under conditions tested, MtoA oxidized Fe(II) from pH 7 to pH 9 with the optimal rate at pH 9. MtoA oxidized Fe(II) complexed with different ligands at different rates. The reaction rates followed the order Fe(II)Cl(2) Fe(II)-citrate Fe(II)-NTA Fe(II)-EDTA with the second-order rate constants ranging from 6.3 × 10(-3) μM(-1) s(-1) for oxidation of Fe(II)Cl(2) to 1.0 × 10(-3) μM(-1) s(-1) for oxidation of Fe(II)-EDTA. Thermodynamic modeling showed that redox reaction rates for the different Fe(II)-complexes correlated with their respective estimated reaction-free energies. Collectively, these results demonstrate that MtoA is a functional Fe(II)-oxidizing protein that, by working in concert with MtoB and CymA(ES-1), may oxidize Fe(II) at the bacterial surface and transfer released electrons across the bacterial cell envelope to the quinone pool in the inner membrane during extracellular Fe(II) oxidation by ES-1.

Using molecular dynamics simulations and steady-state fluorescence spectroscopy, we have identified a conformational change in the active site of a thermophilic flavoenzyme, NADH oxidase from Thermus thermophilus HB8 (NOX). The enzyme's far-UV circular dichroism spectrum, intrinsic tryptophan fluorescence, and apparent molecular weight measured by dynamic light scattering varied little between 25 and 75°C. However, the fluorescence of the tightly bound FAD cofactor increased approximately fourfold over this temperature range. This effect appears not to be due to aggregation, unfolding, cofactor dissociation, or changes in quaternary structure. We therefore attribute the change in flavin fluorescence to a temperature-dependent conformational change involving the NOX active site. Molecular dynamics simulations and the effects of mutating aromatic residues near the flavin suggest that the change in fluorescence results from a decrease in quenching by electron transfer from tyrosine 137 to the flavin. Proteins 2012. © 2011 Wiley Periodicals, Inc.

The dynamic behavior of proteins is important for an understanding of their function and folding. We have performed molecular dynamics simulations of the native state and unfolding pathways of over 2000 protein/peptide systems (approximately 11,000 independent simulations) representing the majority of folds in globular proteins. These data are stored and organized using an innovative database approach, which can be mined to obtain both general and specific information about the dynamics and folding/unfolding of proteins, relevant subsets thereof, and individual proteins. Here we describe the project in general terms and the type of information contained in the database. Then we provide examples of mining the database for information relevant to protein folding, structure building, the effect of single-nucleotide polymorphisms, and drug design. The native state simulation data and corresponding analyses for the 100 most populated metafolds, together with related resources, are publicly accessible through http://www.dynameomics.org.

International audience; The genus Yersinia includes a large variety of nonpathogenic and life-threatening pathogenic bacteria, which cause a broad spectrum of diseases in humans and animals, such as plague, enteritis, Far East scarlet-like fever (FESLF), and enteric redmouth disease. Like most clinically relevant microorganisms, Yersinia spp. are currently subjected to intense multi-omics investigations whose numbers have increased extensively in recent years, generating massive amounts of data useful for diagnostic and therapeutic developments. The lack of a simple and centralized way to exploit these data led us to design Yersiniomics, a web-based platform allowing straightforward analysis of Yersinia omics data. Yersiniomics contains a curated multi-omics database at its core, gathering 200 genomic, 317 transcriptomic, and 62 proteomic data sets for Yersinia species. It integrates genomic, transcriptomic, and proteomic browsers, a genome viewer, and a heatmap viewer to navigate within genomes and experimental conditions. For streamlined access to structural and functional properties, it directly links each gene to GenBank, the Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, InterPro, IntAct, and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and each experiment to Gene Expression Omnibus (GEO), the European Nucleotide Archive (ENA), or the Proteomics Identifications Database (PRIDE). Yersiniomics provides a powerful tool for microbiologists to assist with investigations ranging from specific gene studies to systems biology studies. IMPORTANCE The expanding genus Yersinia is composed of multiple nonpathogenic species and a few pathogenic species, including the deadly etiologic agent of plague, Yersinia pestis. In 2 decades, the number of genomic, transcriptomic, and proteomic studies on Yersinia grew massively, delivering a wealth of data. We developed Yersiniomics, an interactive web-based platform, to centralize and analyze omics data sets on Yersinia species. The platform allows user-friendly navigation between genomic data, expression data, and experimental conditions. Yersiniomics will be a valuable tool to microbiologists.