Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics.

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 de novo peptide sequencing approach to identify the 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 organisms from soil isolates and synthetic communities 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 as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification.

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.

Constructing a Tandem Mass Spectral Library for Forensic Ricin Identification.

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.

Applications and challenges of forensic proteomics.

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 Goes to Court: A Statistical Foundation for Forensic Toxin/Organism Identification Using Bottom-Up Proteomics.

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 a > 95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.

Ricin-like proteins from the castor plant do not influence liquid chromatography-mass spectrometry detection of ricin in forensically relevant samples.

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.

The succinated proteome.

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.

Mixed-isotope labeling with LC-IMS-MS for characterization of protein-protein interactions by chemical cross-linking.

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.

Detection and identification of heme c-modified peptides by histidine affinity chromatography, high-performance liquid chromatography-mass spectrometry, and database searching.

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.