Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium
© Ansong et al; licensee BioMed Central Ltd. 2011
Received: 7 March 2011
Accepted: 25 August 2011
Published: 25 August 2011
Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function.
We experimentally annotated the bacterial pathogen Salmonella Typhimurium 14028, using "shotgun" proteomics to accurately uncover the translational landscape and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in Salmonella and suggest revisions to several genes that appear to have incorrectly assigned translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the Salmonella genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function.
This work highlights several ways in which application of proteomics data can improve the quality of genome annotations to facilitate novel biological insights and provides a comprehensive proteome map of Salmonella as a resource for systems analysis.
Many aspects of modern biological research are dependent on accurate identification of the protein-coding genes in each genome, as well as the nature of the mature functional protein products, a process commonly referred to as genome annotation. With the exponential increase in the number of sequenced prokaryotic genomes afforded by advances in genome sequencing technologies over the last decade, present day prokaryotic genome annotation is essentially an automated high-throughput process that relies heavily on de novo gene prediction programs [1–3].
While de novo gene prediction programs have significantly improved for prokaryotic genomes considerable challenges remain , such as determining the precise start and stop site of a gene, accurately predicting short genes, and determining a stop codon that represents an alternative amino acid rather than a true stop site. As efforts to sequence more branches of the tree of life expand, the level of accuracy for current gene prediction programs trained on proteobacteria datasets will markedly decrease, leading to an increase in incorrect predictions of protein-coding genes . Compounding the issue is the lack of experimental evidence in support of predicted protein-coding regions for the overwhelming majority of annotated genomes. Where available, experimental evidence is typically based on expressed RNA sequences, such as from microarray or RNA Seq experiments. However, these genome-centric analyses do not independently and unequivocally determine whether a predicted protein-coding gene is translated into a protein or importantly provide any reliable information on post-translational processing.
Bottom-up proteomics offers the ability to directly measure peptides arising from expressed proteins representing the current best option for independently and unambiguously identifying at least an important subset of the protein-coding genes in a genome and can be used to experimentally validate gene annotations [4–9]. In a bottom up approach, proteins within a complex mixture are typically digested with a protease, after which the resulting peptides are separated by chromatographic methods and then analyzed using tandem mass spectrometry (MS/MS) [10, 11]. Each MS/MS spectrum is a measure of fragment masses, ideally from a single peptide sequence of ~ 6-50 amino acids. This set of mass values is analogous to a 'fingerprint' that identifies the peptide. Interpretation of MS/MS peptide spectra is accomplished 1) by using algorithms such as X!Tandem , SEQUEST , or Mascot  to compare measured masses against a set of theoretical masses of possible protein sequences or 2) less commonly, by de novo analysis, which does not depend on any prior knowledge of the possible sequences [15, 16]. Similar to searching MS/MS spectra against a set of predicted protein sequences, it is also possible (and feasible for simple genomes) to identify the protein-coding genes in a genome by searching MS/MS spectra against a six-frame translation of the genomic DNA sequence, thereby precluding the inherent biases derived from gene prediction methods. We note that scaling up to an exponentially larger database, as a result of six-frame translation of a genomic DNA sequence, makes searches slower by potentially orders of magnitude. Additionally, it also results in increased possibility for false-positive identifications as the false discovery rate scales with the increasing database size, greatly decreasing sensitivity; twin challenges that can only be feasibly met with dedicated/sophisticated computing resources precluding routine use at present.
Bottom-up proteomics also provides an avenue for obtaining biologically relevant information about post-translational modifications. Even for relatively simple biological systems such as prokaryotes, post-translational modification (PTM) events are increasingly being recognized as important, but are poorly characterized with regard to sites or function. While PTM information gained from genome-scale MS/MS datasets stands to benefit biological understanding of bacterial organisms, significant technological challenges hamper the routine inclusion of this valuable information in primary annotations. This situation is exemplified by only a single report of genome-scale MS/MS datasets being used to comprehensively annotate post-translational modifications events in a genome sequence . However, the low resolution of the MS/MS datasets makes assignment of modifications, including crucially the site of modification, less confident and ill suited for routine use. For example, the 0.036 Dalton mass difference between Gln (Q) and Lys (K) cannot be resolved in low resolution MS/MS spectra, which increases the number of possible peptide candidates to be matched and reduces confidence in assignments. On the other hand, the sequencing precision afforded by high resolution MS/MS spectra can resolve residues with small mass differences like Gln and Lys, which reduces the search space and thus the computational burden, and increases confidence in assignments.
In this study, we employed a bottom-up proteomics approach supplemented with a recently described de novo sequencing methodology using high resolution and high mass measurement accuracy MS/MS data [18, 19] to accurately uncover the translational landscape and post-translational features of the bacterial pathogen Salmonella enterica serovar Typhimurium (STM) 14028. Salmonella Typhimurium is a leading cause of bacterial gastroenteritis and is widely used as a model to investigate basic genetic mechanisms as well as the interaction between bacterial pathogens and mammalian hosts. In spite of the clinical and basic science relevance of Salmonella Typhimurium there has been no comprehensive analysis undertaken to provide experimental support of its in silico-based genome annotation to facilitate systems-level analysis. Our data provides protein-level experimental validation for approximately half of the predicted protein-coding genes in STM 14028 and suggests revisions to 47 genes assigned incorrect translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the STM 14028 genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function.
Contrary to the overwhelming majority of proteogenomics analyses that utilize proteomics data to improve the quality and completeness of previously annotated genomes, this study represents one of the first to utilize proteomics data "as part of a largely automated, high-throughput annotation process directly at the primary stage of genome annotation" .
Results and discussion
General features of S. Typhimurium 14028 genome
To confirm and correct STM 14028 annotations and in particular to identify proteins specified by non-annotated genes, we searched low resolution MS/MS spectra from 330 individual LC-MS/MS analyses against a six-frame translation of the STM 14028 genome sequence. These data comprise STM 14028 samples from different cell culture conditions designed to mimic various aspects of the non-infectious and infectious environments experienced by Salmonella Typhimurium. Briefly, these included Luria-Bertani (LB) logarithmic and LB stationary phases, and two acidic minimal medium (AMM) conditions, AMM1 and AMM2 [21, 22]. The mass spectrometry datasets employed in this proteogenomic annotation study have been previously described, detailing the annotated Salmonella Typhimurium proteome response to the above growth conditions [21, 23, 24]. Peptides with either one or two tryptic ends were filtered according to the parameters described in the Methods section. As an additional confidence metric we only retained those filter-passing peptides that mapped to unique locations (i.e. a unique ORF) in the six-frame translation. Peptides mapping to multiple locations (i.e. different ORFs) in the six-frame translation introduce a potential source of ambiguity making it difficult to confidently validate individual ORFs or correct annotation errors. A total of 23889 unique peptides that passed the above criteria were identified with a false discovery rate of < 1%. Of these, 23097 peptides were assigned as being generated by two tryptic ends and 791, with one tryptic end.
Experimental validation of predicted genes at the protein level
For the majority of genes annotated as part of sequencing efforts there is no direct experimental evidence that the gene is translated into a protein. In cases where experimental evidence is available it is typically based on expressed RNA sequences, such as from microarray or RNASeq experiments. It is clear however that expressed RNA sequences cannot independently and unequivocally determine whether a predicted protein-coding gene is translated into a protein. Thus knowing that a gene or part of it in the relevant frame is being made (i.e. translated into protein) is useful information.
The ~44% level of proteome coverage is in line with, and in most published cases exceeds, results obtained in both typical proteomics experiments [25–27] and previous proteogenomic analyses [17, 28, 29]. Typical coverage is in the 30% range for the annotated ORFs. However, multiple factors potentially prevent 100% coverage, including extensive post-transcriptional regulation that makes transcripts rather poor guides for protein expression [21, 30]. Here we have used four differing growth conditions including typical laboratory conditions and an infection-like environment. However, not all possible growth conditions for Salmonella Typhimurium can be efficiently sampled and it is likely that certain ORFs require specific conditions for expression, such as intracellular growth in host cells . In addition, various technical aspects associated with our experiments may have precluded detection, such as incompatibility with buffers for soluble protein extraction. The two peptide criterion may also negatively impact coverage of small proteins present in low concentrations. However it is possible that application of additional protein and peptide separation methods could enable improved coverage of low abundance proteins.
Refinement of predicted gene structures: Start sites
Determining the correct start position of a gene remains a challenge for current gene prediction algorithms. In a recent re-analysis of 143 annotated prokaryotic genomes, Nielsen and Krogh  observed that in some genomes up to 60% of the genes may have been annotated with a wrong start codon, especially in GC-rich genomes. Accurate start site predictions better define intergenic spaces that may encode promoters and regulatory binding sites, which are critical elements in studies of transcriptional regulation. Cellular localization signals also are contained in start sites, which makes accurate start site predictions important for accurately determining the localization of proteins within a cell.
Peptides that map to genomic regions within 200 bp upstream of previously annotated genes and in the same translational reading frame represent evidence supporting extensions of their predicted start sites. We note that this approach has been described in detail in a number of recent publications [17, 29, 33]. Additionally peptides that span (i.e., partially overlap) the start site of previously annotated genes also represent evidence supporting extensions of their predicted start sites. To this end, we examined the identified peptides for experimental evidence supporting extension of start sites in each of the predicted genes, and where possible proposed a new start codon. 51 peptides spanned the start site of previously annotated genes, and 36 peptides mapped to genomic regions within 200bp upstream of previously annotated genes. Overall, 87 of the 23,889 peptides mapped to genomic regions that served as experimental evidence supporting extension of start sites. We restricted our correction of start sites to predicted genes confirmed at the protein level (see preceding section), which yielded a final candidate list of 75 peptides that correspond to 47 genes requiring N-terminal extension/start site correction (Additional file 1 Table S3A). New start sites were largely defined by the first Methionine amino acid and/or start codon encountered upstream of peptides mapped to the genomic region within 200 bp upstream of their previously annotated start sites.
Identification of novel genes
The identification of protein-coding genes (structural annotation) in eukaryotic genomes is complicated by the high frequency of alternative splicing in most eukaryotic genes. Additionally the small fraction of protein-coding genes that comprise eukaryotic genomes (< 25% in worms and < 5% in humans) make the identification of coding sequences against the ubiquitous background of non-coding sequences difficult . In contrast, the usual absence of introns and the compact nature of prokaryotic genomes make the identification of all possible ORFs longer than a chosen threshold in a DNA sequence a relatively straightforward computational exercise. This view, in particular of bacterial genomes, has lead to the assumption that computationally derived coding sequences completely describe the entire coding capacity of a bacterial genome. However, structural annotation of prokaryotic genome sequences by predicting coding sequences is far from being a trivial matter. A number of recent studies [7, 17, 37] used proteomics data to identify novel protein-coding genes in prokaryotic genomes that had been missed by de-novo gene finding programs.
Peptides that map to genomic regions outside the boundaries of predicted genes are evidence suggestive of the presence of novel genes missed by gene finding programs. By matching the 23,889 identified peptides to the 4817 predicted chromosomal genes in the STM 14028 genome sequence, we detected 130 peptides mapped to regions falling outside the boundaries of known protein coding genes. We further refined the list of peptides by excluding the 36 peptides that mapped to genomic regions within 200 bp upstream of predicted genes, i.e., peptides that are indicative of N-terminal extensions (see previous section), which yielded a final candidate list of 92 peptides (Additional file 1 Table S4A). These peptides represent experimental evidence for the presence of novel genes missed by gene finding programs. Using the set of 92 intergenic peptides, we defined 12 novel genes that had been missed by gene prediction methods. Note that novel genes were confirmed as detected and expressed at the protein level only when a minimum of two peptides mapped to that particular novel gene.
Assessing the performance of proteogenomic annotation
Manual curation has become a luxury reserved for a few organisms and the majority of newly sequenced genomes only receive a single round of computational annotation with no additional manual refinement, therefore the utility of proteogenomics lies in its application at the primary stage of genome annotation to improve the quality and completeness of the automated genome annotation. A version of the STM14028 genome annotated using a combination of computational methods and human annotators, i.e. refined annotation, released while this manuscript was in preparation  allowed us to assess the performance of proteogenomic annotation.
A comparison of the new start sites for the 47 genes proposed by the proteomics data to the start sites for the same 47 genes in the refined STM14028 genome annotation revealed an overlap of 34 genes where start sites matched exactly (Additional file 1 Table S3B). Of the remaining 13 genes, 10 had proteomics-suggested start sites that were downstream of those determined by refined annotation. While the proteomics data suggest a shorter ORF than indicated in the refined annotation, homology analysis (BLASTP) supports the refined annotation. Note that deeper proteome coverage may improve matches to the refined annotation. The remaining three genes had proteomics-suggested start sites that were upstream of start sites in the refined annotation, which is tangible experimental evidence that suggests a longer ORF than indicated in the refined annotation, i.e., correcting the refined annotation. In summary 44 of 47 start site corrections proposed by our proteogenomic analysis in the primary annotation reported in this study were in line with the refined annotation. The remaining three start site corrections suggested represent corrections to both the automated (this study) and refined annotation .
Our proteogenomic analysis also identified 12 novel genes missed by gene prediction methods. A comparison of the 12 novel genes proposed by proteomics data to the refined STM14028 genome annotation revealed 9 of the 12 novel genes were also identified and annotated similarly in the refined annotation (Additional file 1 Table S4B). This result highlights the power of proteomics data to identify relevant novel protein-coding genes. The three remaining proteomics-identified genes missed by de novo gene finding programs and human annotators represent a correction and improvement to both the current automated (this study) and refined  annotations.
Annotation of complex post-translational chemical modifications
While a number of groups have used genome-scale MS/MS data to confirm predicted bacterial genes at the protein level, as well as identify new genes and correct gene prediction [37, 42–44], there is only a single report of genome-scale MS/MS data being used for comprehensive "unrestricted" analysis and annotation of post-translational chemical modifications (PTCMs) in a bacterial system . We note however a number of recent studies focused on characterizing ribosomal protein modifications [45, 46]. As relatively little is known about PTCMs in bacteria, even for intensively studied model organisms such as E. coli and Salmonella, any PTCM information gained from genome-scale MS/MS data would aid biological understanding of bacterial organisms.
We have recently described a de novo sequencing approach (de novo-UStags), using high resolution and high mass measurement accuracy MS/MS data, for the accurate discovery of unknown or unexpected PTCMs of proteins [18, 19]. Here we apply the de novo-UStags approach to analyze 60 high resolution LC-MS/MS datasets for PTCMs in the STM 14028 genome. These datasets represented samples that had been grown in a variety of cell culture conditions.
Using the approach illustrated above, we determined mass shifts for UStags in 4144 MS/MS spectra that represented 675 proteins. We estimate the false discovery rate (FDR) of PTCM analysis to be < 1% (see Methods). Utilizing in-house developed software and a list of 450 UNIMOD modifications, including SNP substitutions, http://www.unimod.org (Additional file 3 Table S5F) we inferred hypothetical explanations for observed mass shifts in ~92% of the spectra in which modifications were detected (i.e. 3826 MS/MS spectra), several of which had multiple potential chemical explanations (Additional file 3 Table S5D). Where applicable observation of "differential" fragments were used to resolve ambiguity of assignments between SNPs and other modifications of similar mass defect as described below and in the Methods and Additional file 2. The remaining spectra (~8%, i.e. 318 MS/MS spectra) in which modifications were detected but where no plausible modification combination of up to two of the selected 450 UNIMOD modifications could explain observed mass shift for prefix or suffix sequence represent novel previously un-described PTCMs and are listed in Additional file 3 Table S5E. To obtain lower limit estimates on the number of distinct PTCMs, we binned different mass shifts and inferred a total of 70 distinct modifications mass-shifts, each with multiple potential chemical explanations according to UNIMOD (Additional file 3 Table S5A). This represents a much larger and complex repertoire of chemical modifications than previously thought existed in bacteria. Note that this only considered mass-shifts for which a potential chemical explanation was found in UNIMOD, thus most likely an underestimate. Nevertheless this estimate represents a much larger number of modification types than can be considered by commonly used, but often restrictive PTCM search algorithms such as Sequest, X!Tandem, and Mascot [12–14]. The 3826 modification containing MS/MS spectra with hypothetical explanations for the observed mass shifts were ranked using a peptide identification scoring function (see Methods and Additional file 2) and based on the identification scores we obtained unambiguous explanations, with regards to type, number and site of modification, for observed mass shifts in 1273 MS/MS spectra (i.e., ~31% of modification containing spectra, see Additional file 3 Table S5B). We also established confident assignment of number and type(s) of modification to explain observed mass shifts in an additional 239 MS/MS spectra (i.e., ~6% of modification-containing spectra); however site(s) of modification could not be unambiguously derived from spectral evidence (Additional file 3 Table S5C).
Among the modification types observed were those known to result from sample preparation, including carbamylation and carbamidomethyl, as well as those that can occur both in vitro and in vivo, such as methionine oxidation [47, 48] and asparagine deamidation [49–51]. As no methods are currently available for distinguishing between in vitro and in vivo modifications, to identify PTCMs in STM that are biologically relevant we assumed such modifications would be conserved across closely related organisms. Using this approach we report a number of PTCMs of biological significance previously unappreciated in STM. Methylation of ribosomal proteins has been suggested to modulate the intra- or intermolecular interactions of the methylated ribosomal proteins or affects their affinity for RNA, and, thus, influences various cell processes, including ribosome assembly and translation accuracy [52, 53]. Single methylation of the E. coli ribosomal protein L7/12 has been reported  and localized to K82 , while the ribosomal protein L3 has been reported as being methylated at Q150 in E. coli. In the present study we observe methylation of the E. coli ribosomal protein L7/12 homolog in STM14028 ORF04348 at the same location, K82, and methylation of the ribosomal protein L3 ortholog in STM14028 ORF03631 at the exact same position, Q150, suggesting a similar regulatory and/or structural role in STM14028 as in E. coli (Additional file 4 Figure S1; 3 methylated residues shown in red). Methylation of the translation elongation factor Tu (tufB) at position K56 has been reported, and suggested as a mechanism for 'fine tuning' of tufB-tRNA inter-molecular interactions . We observed the same modification at K57 of the STM14028 protein ORF03636, the homologous position to K56 of the E. coli protein, suggesting a similar functional role in STM14028 as in E. coli (Additional file 4 Figure S1; modified residue shown in red).
Annotation of post-translational proteolytic events
Proteolytic cleavage plays an essential role in the control of numerous biological processes, including protein localization, fate and activity as well as the processing of cellular information. However current high throughput genome annotation pipelines are blind to this information, any amount of which would clearly improve the quality of the genome annotation. The low throughput and labor-intensive nature of Edman degradation and two dimensional gel electrophoresis approaches make them incompatible with state of the art high throughput annotation pipelines. Using the high precision MS/MS data generated above, we highlight the use of proteomics data to identify a subset of post-translational proteolytic events in a high throughput label-free manner and include this additional layer of information to improve the quality of the genome annotation.
Data were searched against the predicted genes in the STM 14028 genome sequence using X!Tandem  and included 10336 unique fully tryptic peptides (i.e., two inferred tryptic ends), 1756 with one tryptic end (partially-tryptic), and 20 with no tryptic ends (non-tryptic), identified at < 1% FDR. In light of the high specificity of trypsin , it is likely that peptides with either one or no tryptic termini are representative of proteolytic events. We note that these peptides may also be generated by degradation of fully tryptic peptides due to hydrolysis during sample processing or to in-source decay during instrumental analysis, both of which introduce a potential source of ambiguity. To address this concern, we employed a two step filtering procedure. First, we only considered non- or partially-tryptic peptides that were not contained within a longer observed tryptic peptide, which reduced the non- or partially-tryptic peptide candidate list to 1656 peptides. Second, we removed non- or partially-tryptic peptides contained within any other peptide, which further reduced the candidate peptide list for examining proteolytic events to 754 peptides (Additional file 5 Table S6). While conservative, this two step filtering approach ensured we considered biologically generated non- and partially-tryptic peptides rather than experimental or analytical artifacts.
In this study, we reported the genomic sequence of the bacterial pathogen Salmonella Typhimurium (STM) 14028 and demonstrated the use of MS-based proteomics to guide accurate primary genome annotation. Our proteomics data provided experimental confirmation for > 40% of the predicted protein-coding genes, and further improved the genome annotation with 47 start site corrections and the identification of 12 novel genes missed by gene finding programs, importantly these include some that appear to play a role in Salmonella pathogenesis.
Comprehensive analysis of post-translational processing events in STM 14028 identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function and also revealed a large and complex repertoire of post-translational chemical modifications, including those known to influences various cell processes and several novel chemical modifications.
A major impact of this work on genome annotation efforts is in demonstrating the utility of proteogenomics for high-throughput protein-level experimental annotation, validating and augmenting the in silico primary annotation. Even for a well annotated organism like Salmonella with a high degree of homology to E. coli, we still uncovered a number of annotation errors as well as novel genes. An additional impact of this work is further highlighted in the unique capability of proteogenomics to experimentally annotate in vivo post-translational processing events, increasingly recognized to play important roles in prokaryotic biology.
As more distantly related organism are sequenced in efforts to sample more branches of the tree of life, the level of accuracy for current gene prediction programs trained on proteobacteria datasets is expected to markedly decrease, and depending on the GC content of a genome annotation methods suffer accordingly. Thus, the ability of proteogenomics to provide direct protein-level evidence for a significant fraction of predicted protein coding genes will be of significant benefit as a complementary tool in any genome annotation effort. In addition, the data used for proteogenomic annotations may already be generated as part of existing proteomics experiments and then only requires a different analysis method to incorporate the results.
The genome of Salmonella Typhimurium strain 14028s was decoded using 454 FLX pyrosequencing technology (Roche) in combination with conventional chain-terminator sequencing at the Genome Center in St. Louis, MO http://genome.wustl.edu. The pyrosequencing efforts resulted in 565,427 reads of 249.3 bases average length, corresponding to 28.8× sequence coverage. These data were complemented with Sanger sequencing reads approximating 4.4× coverage. De novo assembly yielded seven contigs (Clifton, unpublished data). A sequence for a different isolate of this strain has recently been deposited at GenBank under accession number CP001363. This sequence is identical to the sequence in this study except for point mutations in 27 loci that await further confirmation. The genome, hence, is a 4.87 Mb circular molecule with a GC content of 52.2%, and contains four presumably functional prophages: Gifsy-1, Gifsy-2, Gifsy-3, and a phage nearly identical to ST64B. Strain 14028s contains a 94 kb plasmid (deposited as CP001362 at GenBank) with over 99% homology to the Typhimurium LT2 virulence plasmid pSLT.
JCVI employs an automated annotation pipeline that identifies genome features in the raw DNA sequence, gathers evidence for function of the features, and assigns functional annotation based on the weight of the evidence.
DNA Feature Identification: Glimmer3  is used to predict protein coding sequences (CDS), tRNAs are identified with the tRNAscan tool , rRNA genes and other structural RNAs are identified directly from BLAST  matches to Rfam , a database of non-coding RNA families.
CHAR database: JCVI's CHAR is a curated database of experimentally verified proteins, source publications, and functional annotations. Each protein entry has detailed annotation including function, gene symbol, and GO terms and evidence codes
Supporting Evidence for the annotation pipeline includes:
BLAST searches against PANDA: PANDA is JCVI's internal repository of non-redundant and non-identical protein and nucleotide data pulled from public databases that include the latest assembly and protein sequences (e.g., GenBank, RefSeq, UniProt, Protein Data Bank).
Computationally derived assertions: Computations integral to the pipeline include derived physical and chemical metrics including lipoprotein signals (LP) and transmembrane helices (TmHMM, ).
AutoAnnotate: AutoAnnotate weighs the evidence from a precedence-ordered list of evidence types-the CHAR database, trusted protein families, best protein BLAST matches from PANDA, and computationally derived assertions-to annotate each protein by assigning, where possible, a function, gene symbol, EC numbers, JCVI functional role category, and GO terms. AutoAnnotate and the databases on which AutoAnnotate runs are freely available for download and installation via the open source repository SourceForge https://sourceforge.net/projects/prokfunautoanno/.
LC-MS/MS and data analysis
Salmonella Typhimurium strain 14028 was grown under four in vitro conditions: Luria-Bertani (LB) logarithmic and LB stationary phases, and two acidic minimal media conditions (AMM1 and AMM2) as previously described [21, 22]. Given its high osmolarity and nutrient-rich condition, LB broth partially reproduces the small intestine lumen environment, while AMM, providing a low pH, low magnesium, and nutrient-deficient condition, partially mimics the intracellular milieu within the Salmonella-containing vacuole (SCV).
The materials and methods used to prepare the protein samples for LC-MS/MS have already been described in full for similar samples [21, 23]. Briefly, each sample was lysed, extracted into global, soluble, and insoluble fractions then trypsinized; and further fractionated by ion exchange chromatography. Trypsinized protein samples i.e. peptides were analyzed by ultra high pressure reversed-phase HPLC coupled online to a Thermo Finnigan LTQ ion trap or hybrid LTQ-Orbitrap mass spectrometer in a data-dependent MS/MS mode.
To experimentally validate gene annotations low-resolution MS/MS spectra were analyzed using SEQUEST  to search against all possible stop-codon to stop-codon open reading frames (ORFs) ≥ 50 amino acids in length in the STM14028 genome. All identified tryptic and partially tryptic peptides, greater than six amino acids in length, were first filtered by charge state-dependent cross correlation cut-off (Xcorr) scores as follows: a minimum cross-correlation cut-off (Xcorr) of either 1.9, 2.2, or 3.3 for 1+, 2+, or 3+ charge states, respectively; and further filtered using a relatively high confidence PeptideProphet  cut-off score of 0.9. Partially tryptic peptides were additionally filtered by charge state-dependent cross correlation cut-off (Xcorr) scores as follows: a minimum cross-correlation cut-off (Xcorr) of either 3.1, 3.8, or 4.5 for 1+, 2+, or 3+ charge states, respectively. FDR estimated via the decoy database method was < 1% for peptides.
To accurately annotate in vivo proteolytic cleavage events high resolution LC-MS/MS spectra were analyzed using X!Tandem  to search against the computationally predicted genes in the STM 14028 genome sequence. All identified peptides greater than six amino acids in length, were required to have a Log10 E-value < = -1.3, which corresponds to a 5% probability that the peptide sequence identified as the best hit from the X!Tandem process arose from a random match to a sequence. FDR estimated via the decoy database method was < 1% at the unique peptide level.
To accurately annotate post-translational chemical modifications (PTCMs) high precision MS/MS spectra were analyzed as described below.
Raw data and collated peptide identification information are available to the community as supplemental data at http://omics.pnl.gov/view/publication_1039.html
USTags and Unrestricted Post-translational Chemical Modifications (PTCM) Search
Data analysis and search for PTCMs were performed using a high resolution LC-Orbitrap FT MS/MS dataset. Data were deisotoped using Decon2LS , which implements the THRASH algorithm to determine neutral monoisotopic masses of observed molecular species . Note, Decon2LS is publically available at omics.pnl.gov . The UStags process used for inferring peptide sequences and its PTM mass shifts was described and discussed elsewhere [18, 19]. Briefly, de-novo sequences were generated utilizing in-house developed recursive function using neutral monoisotopic fragment masses and sequencing precision of 0.005 a.m.u. and allowing no gaps in sequence tag. Sequence tags are matched in forward and reverse direction against computationally predicted proteins in the STM 14028 genome sequence. If sequence was found to be unique in predicted proteome it was declared a UStag and selected for more precise description from the precursor -fragment spectra pair. A UStag was first aligned within the protein sequence using observed precursor and first fragment mass, preferring tryptic cleavages where possible. If predicted theoretical masses matched observed masses within tolerance of 10 ppm peptide was considered to be non-modified and corresponding spectra explained. The cases where potentially more than one peptide was fragmented were not considered. The remaining spectra containing UStags were subjected for further processing to explain mass shifts with known PTMs. PTM searches are based on the USTag method which provides "near zero" FDR for the tryptic peptide identifications based on decoy database searches. We cannot claim zero FDR since palindromic sequences and sequencing gaps (like Gly-Gly-> Asn or Gly-Ala-> Gln) could occasionally produce false positive hits which are detectable from database inspection. Thus, using the decoy search method of determining FDR this "near zero" FDR would be propagated to modified peptides since the reverse sequence would not produce USTags except in rare cases described above. Thus the FDR of PTM analysis was estimated to be < 1%.
A list of 450 possible PTMs including SNP substitutions as used in this study was obtained from UNIMOD http://www.unimod.org with differential labels and unlikely chemical artifacts manually filtered out. All single and combinations of any two modifications from the list were explored together with the reported residual and terminus specificity of each PTM to produce all plausible explanations of mass shift from non-aligned prefix and suffix sequences with the mass tolerance of 10 ppm. It is important to emphasize that explanation for prefix and suffix mass shifts are completely independent from each other; therefore some modifications could be sufficiently explained even if the whole peptide is not assigned. Expected peptide monoisotopic mass was matched with observed precursor mass from the parent spectra within the 10 ppm. Making the clear numeric cut between two different modification options in an automated fashion is challenging by itself and a decision tree was implemented. Various heuristics and parsimony rules have been historically used to reduce mass ambiguities in order to characterize and estimate counts of modification sites. We argue that in the realm of high resolution mass spectrometry no plausible hypothesis should be rejected except by the strength of experimental evidence. Therefore we developed robust peptide identification scoring function in attempt to extract identification hypotheses which explain UStag containing spectra using the most evidence from observed fragments. A detailed description of the scoring function and default settings used in this study are provided in Additional file 2.
Informatics and Visualization
Visualization of alignment of detected peptides with the six-frame translation of the genome and the called ORFs was performed using Artemis  and/or CLC Genomics Workbench (CLC bio Inc.). BlastP  searches of detected peptides corresponding to novel ORFs against the nr database was performed to assign a putative annotation to novel ORFs discovered.
We thank Dr. Alexandra Rutledge for insightful input and fruitful discussions. This work was supported in part by the National Institute of Allergy and Infectious Diseases NIH/DHHS through interagency agreement Y1-AI-8401-01 (project website http://www.SysBEP.org with links to raw proteomics data). This work used instrumentation and capabilities developed under support from the NIH National Center for Research Resources (Grant RR 018522) and the U. S. Department of Energy Office of Biological and Environmental Research (DOE/BER). Major portions of the work were performed in the Environmental Molecular Sciences Laboratory, a U. S. Department of Energy (DOE)/BER national scientific user facility on the Pacific Northwest National Laboratory (PNNL) campus. PNNL is operated for the DOE by Battelle. The funding entities had no roles in design; collection, analysis, and interpretation of data; writing of the manuscript; and in the decision to submit the manuscript for publication.
- Meyer F, Goesmann A, McHardy AC, Bartels D, Bekel T, Clausen J, Kalinowski J, Linke B, Rupp O, Giegerich R, et al: GenDB--an open source genome annotation system for prokaryote genomes. Nucleic Acids Res. 2003, 31 (8): 2187-2195. 10.1093/nar/gkg312.PubMedPubMed CentralView ArticleGoogle Scholar
- Peterson JD, Umayam LA, Dickinson T, Hickey EK, White O: The Comprehensive Microbial Resource. Nucleic Acids Res. 2001, 29 (1): 123-125. 10.1093/nar/29.1.123.PubMedPubMed CentralView ArticleGoogle Scholar
- Van Domselaar GH, Stothard P, Shrivastava S, Cruz JA, Guo A, Dong X, Lu P, Szafron D, Greiner R, Wishart DS: BASys: a web server for automated bacterial genome annotation. Nucleic Acids Res. 2005, W455-459. 33 Web ServerGoogle Scholar
- Ansong C, Purvine SO, Adkins JN, Lipton MS, Smith RD: Proteogenomics: needs and roles to be filled by proteomics in genome annotation. Brief Funct Genomic Proteomic. 2008, 7 (1): 50-62. 10.1093/bfgp/eln010.PubMedView ArticleGoogle Scholar
- de Groot A, Dulermo R, Ortet P, Blanchard L, Guerin P, Fernandez B, Vacherie B, Dossat C, Jolivet E, Siguier P, et al: Alliance of proteomics and genomics to unravel the specificities of Sahara bacterium Deinococcus deserti. PLoS Genet. 2009, 5 (3): e1000434-10.1371/journal.pgen.1000434.PubMedPubMed CentralView ArticleGoogle Scholar
- Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, Eng J, Chen S, Eddes J, Loevenich SN, Aebersold R: The PeptideAtlas project. Nucleic Acids Res. 2006, D655-658. 34 DatabaseGoogle Scholar
- Jaffe JD, Berg HC, Church GM: Proteogenomic mapping as a complementary method to perform genome annotation. Proteomics. 2004, 4 (1): 59-77. 10.1002/pmic.200300511.PubMedView ArticleGoogle Scholar
- Wright JC, Sugden D, Francis-McIntyre S, Riba-Garcia I, Gaskell SJ, Grigoriev IV, Baker SE, Beynon RJ, Hubbard SJ: Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger. BMC Genomics. 2009, 10: 61-10.1186/1471-2164-10-61.PubMedPubMed CentralView ArticleGoogle Scholar
- Yates JR, Eng JK, McCormack AL: Mining genomes: correlating tandem mass spectra of modified and unmodified peptides to sequences in nucleotide databases. Anal Chem. 1995, 67 (18): 3202-3210. 10.1021/ac00114a016.PubMedView ArticleGoogle Scholar
- Aebersold R, Mann M: Mass spectrometry-based proteomics. Nature. 2003, 422 (6928): 198-207. 10.1038/nature01511.PubMedView ArticleGoogle Scholar
- Washburn MP, Wolters D, Yates JR: Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol. 2001, 19 (3): 242-247. 10.1038/85686.PubMedView ArticleGoogle Scholar
- Craig R, Beavis RC: TANDEM: matching proteins with tandem mass spectra. Bioinformatics. 2004, 20 (9): 1466-1467. 10.1093/bioinformatics/bth092.PubMedView ArticleGoogle Scholar
- Eng JK, McCormack AL, Yates JR: An Approach to Correlate Tandem Mass Spectral Data of Peptides with Amino Acid Sequences in a Protein Database. J Am Soc Mass Spectrom. 1994, 5: 976-989. 10.1016/1044-0305(94)80016-2.PubMedView ArticleGoogle Scholar
- Perkins DN, Pappin DJ, Creasy DM, Cottrell JS: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. 1999, 20 (18): 3551-3567. 10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2.PubMedView ArticleGoogle Scholar
- Frank A, Pevzner P: PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal Chem. 2005, 77 (4): 964-973. 10.1021/ac048788h.PubMedView ArticleGoogle Scholar
- Ma B, Zhang K, Hendrie C, Liang C, Li M, Doherty-Kirby A, Lajoie G: PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun Mass Spectrom. 2003, 17 (20): 2337-2342. 10.1002/rcm.1196.PubMedView ArticleGoogle Scholar
- Gupta N, Tanner S, Jaitly N, Adkins JN, Lipton M, Edwards R, Romine M, Osterman A, Bafna V, Smith RD, et al: Whole proteome analysis of post-translational modifications: applications of mass-spectrometry for proteogenomic annotation. Genome Res. 2007, 17 (9): 1362-1377. 10.1101/gr.6427907.PubMedPubMed CentralView ArticleGoogle Scholar
- Shen Y, Tolic N, Hixson KK, Purvine SO, Anderson GA, Smith RD: De novo sequencing of unique sequence tags for discovery of post-translational modifications of proteins. Anal Chem. 2008, 80 (20): 7742-7754. 10.1021/ac801123p.PubMedPubMed CentralView ArticleGoogle Scholar
- Shen Y, Tolic N, Hixson KK, Purvine SO, Pasa-Tolic L, Qian WJ, Adkins JN, Moore RJ, Smith RD: Proteome-wide identification of proteins and their modifications with decreased ambiguities and improved false discovery rates using unique sequence tags. Anal Chem. 2008, 80 (6): 1871-1882. 10.1021/ac702328x.PubMedPubMed CentralView ArticleGoogle Scholar
- Armengaud J: A perfect genome annotation is within reach with the proteomics and genomics alliance. Curr Opin Microbiol. 2009, 12 (3): 292-300. 10.1016/j.mib.2009.03.005.PubMedView ArticleGoogle Scholar
- Ansong C, Yoon H, Porwollik S, Mottaz-Brewer H, Petritis BO, Jaitly N, Adkins JN, McClelland M, Heffron F, Smith RD: Global systems-level analysis of Hfq and SmpB deletion mutants in Salmonella: implications for virulence and global protein translation. PLoS One. 2009, 4 (3): e4809-10.1371/journal.pone.0004809.PubMedPubMed CentralView ArticleGoogle Scholar
- Yoon H, McDermott JE, Porwollik S, McClelland M, Heffron F: Coordinated regulation of virulence during systemic infection of Salmonella enterica serovar Typhimurium. PLoS Pathog. 2009, 5 (2): e1000306-10.1371/journal.ppat.1000306.PubMedPubMed CentralView ArticleGoogle Scholar
- Adkins JN, Mottaz HM, Norbeck AD, Gustin JK, Rue J, Clauss TR, Purvine SO, Rodland KD, Heffron F, Smith RD: Analysis of the Salmonella typhimurium proteome through environmental response toward infectious conditions. Mol Cell Proteomics. 2006, 5 (8): 1450-1461. 10.1074/mcp.M600139-MCP200.PubMedView ArticleGoogle Scholar
- Shi L, Ansong C, Smallwood H, Rommereim L, McDermott JE, Brewer HM, Norbeck AD, Taylor RC, Gustin JK, Heffron F, et al: Proteome of Salmonella Enterica Serotype Typhimurium Grown in a Low Mg/pH Medium. J Proteomics Bioinform. 2009, 2: 388-397. 10.4172/jpb.1000099.PubMedPubMed CentralView ArticleGoogle Scholar
- Eshghi A, Cullen PA, Cowen L, Zuerner RL, Cameron CE: Global proteome analysis of Leptospira interrogans. J Proteome Res. 2009, 8 (10): 4564-4578. 10.1021/pr9004597.PubMedPubMed CentralView ArticleGoogle Scholar
- Francis AW, Ruggiero CE, Koppisch AT, Dong J, Song J, Brettin T, Iyer S: Proteomic analysis of Bacillus anthracis Sterne vegetative cells. Biochim Biophys Acta. 2005, 1748 (2): 191-200.PubMedView ArticleGoogle Scholar
- Liebeke M, Dorries K, Zuhlke D, Bernhardt J, Fuchs S, Pane-Farre J, Engelmann S, Volker U, Bode R, Dandekar T, et al: A metabolomics and proteomics study of the adaptation of Staphylococcus aureus to glucose starvation. Mol Biosyst. 2011, 7 (4): 1241-1253. 10.1039/c0mb00315h.PubMedView ArticleGoogle Scholar
- Castellana NE, Payne SH, Shen Z, Stanke M, Bafna V, Briggs SP: Discovery and revision of Arabidopsis genes by proteogenomics. Proc Natl Acad Sci USA. 2008, 105 (52): 21034-21038. 10.1073/pnas.0811066106.PubMedPubMed CentralView ArticleGoogle Scholar
- Payne SH, Huang ST, Pieper R: A proteogenomic update to Yersinia: enhancing genome annotation. BMC Genomics. 2010, 11: 460-PubMedPubMed CentralGoogle Scholar
- Sittka A, Lucchini S, Papenfort K, Sharma CM, Rolle K, Binnewies TT, Hinton JC, Vogel J: Deep sequencing analysis of small noncoding RNA and mRNA targets of the global post-transcriptional regulator, Hfq. PLoS Genet. 2008, 4 (8): e1000163-10.1371/journal.pgen.1000163.PubMedPubMed CentralView ArticleGoogle Scholar
- Shi L, Adkins JN, Coleman JR, Schepmoes AA, Dohnkova A, Mottaz HM, Norbeck AD, Purvine SO, Manes NP, Smallwood HS, et al: Proteomic analysis of Salmonella enterica serovar typhimurium isolated from RAW 264.7 macrophages: identification of a novel protein that contributes to the replication of serovar typhimurium inside macrophages. J Biol Chem. 2006, 281 (39): 29131-29140. 10.1074/jbc.M604640200.PubMedView ArticleGoogle Scholar
- Nielsen P, Krogh A: Large-scale prokaryotic gene prediction and comparison to genome annotation. Bioinformatics. 2005, 21 (24): 4322-4329. 10.1093/bioinformatics/bti701.PubMedView ArticleGoogle Scholar
- Gupta N, Benhamida J, Bhargava V, Goodman D, Kain E, Kerman I, Nguyen N, Ollikainen N, Rodriguez J, Wang J, et al: Comparative proteogenomics: combining mass spectrometry and comparative genomics to analyze multiple genomes. Genome Res. 2008, 18 (7): 1133-1142. 10.1101/gr.074344.107.PubMedPubMed CentralView ArticleGoogle Scholar
- Siegele DA: Universal stress proteins in Escherichia coli. J Bacteriol. 2005, 187 (18): 6253-6254. 10.1128/JB.187.18.6253-6254.2005.PubMedPubMed CentralView ArticleGoogle Scholar
- Lu H, Tonge PJ: Inhibitors of FabI, an enzyme drug target in the bacterial fatty acid biosynthesis pathway. Acc Chem Res. 2008, 41 (1): 11-20. 10.1021/ar700156e.PubMedView ArticleGoogle Scholar
- Stein L: Genome annotation: from sequence to biology. Nat Rev Genet. 2001, 2 (7): 493-503.PubMedView ArticleGoogle Scholar
- Kim W, Silby MW, Purvine SO, Nicoll JS, Hixson KK, Monroe M, Nicora CD, Lipton MS, Levy SB: Proteomic detection of non-annotated protein-coding genes in Pseudomonas fluorescens Pf0-1. PLoS One. 2009, 4 (12): e8455-10.1371/journal.pone.0008455.PubMedPubMed CentralView ArticleGoogle Scholar
- Alpuche Aranda CM, Swanson JA, Loomis WP, Miller SI: Salmonella typhimurium activates virulence gene transcription within acidified macrophage phagosomes. Proc Natl Acad Sci USA. 1992, 89 (21): 10079-10083. 10.1073/pnas.89.21.10079.PubMedPubMed CentralView ArticleGoogle Scholar
- Deiwick J, Nikolaus T, Erdogan S, Hensel M: Environmental regulation of Salmonella pathogenicity island 2 gene expression. Mol Microbiol. 1999, 31 (6): 1759-1773. 10.1046/j.1365-2958.1999.01312.x.PubMedView ArticleGoogle Scholar
- Figueroa-Bossi N, Bossi L: Inducible prophages contribute to Salmonella virulence in mice. Mol Microbiol. 1999, 33 (1): 167-176. 10.1046/j.1365-2958.1999.01461.x.PubMedView ArticleGoogle Scholar
- Jarvik T, Smillie C, Groisman EA, Ochman H: Short-term signatures of evolutionary change in the Salmonella enterica serovar typhimurium 14028 genome. J Bacteriol. 2010, 192 (2): 560-567. 10.1128/JB.01233-09.PubMedView ArticleGoogle Scholar
- Findlay GD, MacCoss MJ, Swanson WJ: Proteomic discovery of previously unannotated, rapidly evolving seminal fluid genes in Drosophila. Genome Res. 2009, 19 (5): 886-896. 10.1101/gr.089391.108.PubMedPubMed CentralView ArticleGoogle Scholar
- Jaffe JD, Stange-Thomann N, Smith C, DeCaprio D, Fisher S, Butler J, Calvo S, Elkins T, FitzGerald MG, Hafez N, et al: The complete genome and proteome of Mycoplasma mobile. Genome Res. 2004, 14 (8): 1447-1461. 10.1101/gr.2674004.PubMedPubMed CentralView ArticleGoogle Scholar
- Merrihew GE, Davis C, Ewing B, Williams G, Kall L, Frewen BE, Noble WS, Green P, Thomas JH, MacCoss MJ: Use of shotgun proteomics for the identification, confirmation, and correction of C.elegans gene annotations. Genome Res. 2008, 18 (10): 1660-1669. 10.1101/gr.077644.108.PubMedPubMed CentralView ArticleGoogle Scholar
- Lauber MA, Running WE, Reilly JP: B. subtilis ribosomal proteins: structural homology and post-translational modifications. J Proteome Res. 2009, 8 (9): 4193-4206. 10.1021/pr801114k.PubMedView ArticleGoogle Scholar
- Running WE, Ravipaty S, Karty JA, Reilly JP: A top-down/bottom-up study of the ribosomal proteins of Caulobacter crescentus. J Proteome Res. 2007, 6 (1): 337-347. 10.1021/pr060306q.PubMedPubMed CentralView ArticleGoogle Scholar
- Chao CC, Ma YS, Stadtman ER: Modification of protein surface hydrophobicity and methionine oxidation by oxidative systems. Proc Natl Acad Sci USA. 1997, 94 (7): 2969-2974. 10.1073/pnas.94.7.2969.PubMedPubMed CentralView ArticleGoogle Scholar
- Levine RL, Mosoni L, Berlett BS, Stadtman ER: Methionine residues as endogenous antioxidants in proteins. Proc Natl Acad Sci USA. 1996, 93 (26): 15036-15040. 10.1073/pnas.93.26.15036.PubMedPubMed CentralView ArticleGoogle Scholar
- Chelius D, Rehder DS, Bondarenko PV: Identification and characterization of deamidation sites in the conserved regions of human immunoglobulin gamma antibodies. Anal Chem. 2005, 77 (18): 6004-6011. 10.1021/ac050672d.PubMedView ArticleGoogle Scholar
- Karty JA, Reilly JP: Deamidation as a consequence of beta-elimination of phosphopeptides. Anal Chem. 2005, 77 (14): 4673-4676. 10.1021/ac050294c.PubMedView ArticleGoogle Scholar
- Robinson AB, Robinson LR: Distribution of glutamine and asparagine residues and their near neighbors in peptides and proteins. Proc Natl Acad Sci USA. 1991, 88 (20): 8880-8884. 10.1073/pnas.88.20.8880.PubMedPubMed CentralView ArticleGoogle Scholar
- Gary JD, Clarke S: RNA and protein interactions modulated by protein arginine methylation. Prog Nucleic Acid Res Mol Biol. 1998, 61: 65-131.PubMedView ArticleGoogle Scholar
- Polevoda B, Sherman F: Methylation of proteins involved in translation. Mol Microbiol. 2007, 65 (3): 590-606. 10.1111/j.1365-2958.2007.05831.x.PubMedView ArticleGoogle Scholar
- Chang FN: Methylation of ribosomal proteins during ribosome assembly in Escherichia coli. Mol Gen Genet. 1981, 183 (3): 418-421. 10.1007/BF00268759.PubMedView ArticleGoogle Scholar
- Arnold RJ, Reilly JP: Observation of Escherichia coli ribosomal proteins and their posttranslational modifications by mass spectrometry. Anal Biochem. 1999, 269 (1): 105-112. 10.1006/abio.1998.3077.PubMedView ArticleGoogle Scholar
- Heurgue-Hamard V, Champ S, Engstrom A, Ehrenberg M, Buckingham RH: The hemK gene in Escherichia coli encodes the N(5)-glutamine methyltransferase that modifies peptide release factors. EMBO J. 2002, 21 (4): 769-778. 10.1093/emboj/21.4.769.PubMedPubMed CentralView ArticleGoogle Scholar
- L'Italien JJ, Laursen RA: Location of the site of methylation in elongation factor Tu. FEBS Lett. 1979, 107 (2): 359-362. 10.1016/0014-5793(79)80407-X.PubMedView ArticleGoogle Scholar
- Anderson PM, Carlson JD: Reversible Reaction of Cyanate with a Reactive Sulfhydryl Group at the Glutamine Binding Site of Carbamyl Phosphate Synthetase. Biochemistry. 1975, 14 (16): 3688-3694. 10.1021/bi00687a027.PubMedView ArticleGoogle Scholar
- Anderson PM, Carlson JD, Rosenthal GA, Meister A: Effect of potassium cyanate on the catalytic activities of carbamyl phosphate synthetase. Biochem Biophys Res Commun. 1973, 55: 246-252. 10.1016/S0006-291X(73)80086-5.PubMedView ArticleGoogle Scholar
- Olsen JV, Ong SE, Mann M: Trypsin cleaves exclusively C-terminal to arginine and lysine residues. Mol Cell Proteomics. 2004, 3 (6): 608-614. 10.1074/mcp.T400003-MCP200.PubMedView ArticleGoogle Scholar
- Giglione C, Boularot A, Meinnel T: Protein N-terminal methionine excision. Cell Mol Life Sci. 2004, 61 (12): 1455-1474.PubMedView ArticleGoogle Scholar
- Tobias JW, Shrader TE, Rocap G, Varshavsky A: The N-end rule in bacteria. Science. 1991, 254 (5036): 1374-1377. 10.1126/science.1962196.PubMedView ArticleGoogle Scholar
- Ben-Bassat A, Bauer K, Chang SY, Myambo K, Boosman A, Chang S: Processing of the initiation methionine from proteins: properties of the Escherichia coli methionine aminopeptidase and its gene structure. J Bacteriol. 1987, 169 (2): 751-757.PubMedPubMed CentralGoogle Scholar
- Flinta C, Persson B, Jornvall H, von Heijne G: Sequence determinants of cytosolic N-terminal protein processing. Eur J Biochem. 1986, 154 (1): 193-196. 10.1111/j.1432-1033.1986.tb09378.x.PubMedView ArticleGoogle Scholar
- Frottin F, Martinez A, Peynot P, Mitra S, Holz RC, Giglione C, Meinnel T: The proteomics of N-terminal methionine cleavage. Mol Cell Proteomics. 2006, 5 (12): 2336-2349. 10.1074/mcp.M600225-MCP200.PubMedView ArticleGoogle Scholar
- Hirel PH, Schmitter MJ, Dessen P, Fayat G, Blanquet S: Extent of N-terminal methionine excision from Escherichia coli proteins is governed by the side-chain length of the penultimate amino acid. Proc Natl Acad Sci USA. 1989, 86 (21): 8247-8251. 10.1073/pnas.86.21.8247.PubMedPubMed CentralView ArticleGoogle Scholar
- Miller CG, Strauch KL, Kukral AM, Miller JL, Wingfield PT, Mazzei GJ, Werlen RC, Graber P, Movva NR: N-terminal methionine-specific peptidase in Salmonella typhimurium. Proc Natl Acad Sci USA. 1987, 84 (9): 2718-2722. 10.1073/pnas.84.9.2718.PubMedPubMed CentralView ArticleGoogle Scholar
- Nielsen H, Engelbrecht J, Brunak S, von Heijne G: Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. 1997, 10 (1): 1-6. 10.1093/protein/10.1.1.PubMedView ArticleGoogle Scholar
- Paetzel M, Karla A, Strynadka NC, Dalbey RE: Signal peptidases. Chem Rev. 2002, 102 (12): 4549-4580. 10.1021/cr010166y.PubMedView ArticleGoogle Scholar
- Crooks GE, Hon G, Chandonia JM, Brenner SE: WebLogo: a sequence logo generator. Genome Res. 2004, 14 (6): 1188-1190. 10.1101/gr.849004.PubMedPubMed CentralView ArticleGoogle Scholar
- Bendtsen JD, Nielsen H, von Heijne G, Brunak S: Improved prediction of signal peptides: SignalP 3.0. J Mol Biol. 2004, 340 (4): 783-795. 10.1016/j.jmb.2004.05.028.PubMedView ArticleGoogle Scholar
- Choo KH, Tan TW, Ranganathan S: A comprehensive assessment of N-terminal signal peptides prediction methods. BMC Bioinformatics. 2009, 10 (Suppl 15): S2-10.1186/1471-2105-10-S15-S2.PubMedPubMed CentralView ArticleGoogle Scholar
- Mann M, Pandey A: Use of mass spectrometry-derived data to annotate nucleotide and protein sequence databases. Trends Biochem Sci. 2001, 26 (1): 54-61. 10.1016/S0968-0004(00)01726-6.PubMedView ArticleGoogle Scholar
- Erickson BK, Mueller RS, VerBerkmoes NC, Shah M, Singer SW, Thelen MP, Banfield JF, Hettich RL: Computational prediction and experimental validation of signal peptide cleavages in the extracellular proteome of a natural microbial community. J Proteome Res. 2010, 9 (5): 2148-2159. 10.1021/pr900877a.PubMedView ArticleGoogle Scholar
- Delcher AL, Harmon D, Kasif S, White O, Salzberg SL: Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 1999, 27 (23): 4636-4641. 10.1093/nar/27.23.4636.PubMedPubMed CentralView ArticleGoogle Scholar
- Lowe TM, Eddy SR: tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997, 25 (5): 955-964. 10.1093/nar/25.5.955.PubMedPubMed CentralView ArticleGoogle Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410.PubMedView ArticleGoogle Scholar
- Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR: Rfam: an RNA family database. Nucleic Acids Res. 2003, 31 (1): 439-441. 10.1093/nar/gkg006.PubMedPubMed CentralView ArticleGoogle Scholar
- Haft DH, Selengut JD, White O: The TIGRFAMs database of protein families. Nucleic Acids Res. 2003, 31 (1): 371-373. 10.1093/nar/gkg128.PubMedPubMed CentralView ArticleGoogle Scholar
- Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR, Ceric G, Forslund K, Eddy SR, Sonnhammer EL: The Pfam protein families database. Nucleic Acids Res. 2008, D281-288. 36 DatabaseGoogle Scholar
- Klimke W, Agarwala R, Badretdin A, Chetvernin S, Ciufo S, Fedorov B, Kiryutin B, O'Neill K, Resch W, Resenchuk S: The National Center for Biotechnology Information's Protein Clusters Database. Nucleic Acids Res. 2009, D216-223. 37 DatabaseGoogle Scholar
- Sonnhammer EL, von Heijne G, Krogh A: A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol. 1998, 6: 175-182.PubMedGoogle Scholar
- Keller A, Nesvizhskii AI, Kolker E, Aebersold R: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem. 2002, 74 (20): 5383-5392. 10.1021/ac025747h.PubMedView ArticleGoogle Scholar
- Jaitly N, Mayampurath A, Littlefield K, Adkins JN, Anderson GA, Smith RD: Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data. BMC Bioinformatics. 2009, 10: 87-10.1186/1471-2105-10-87.PubMedPubMed CentralView ArticleGoogle Scholar
- Horn DM, Zubarev RA, McLafferty FW: Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules. J Am Soc Mass Spectrom. 2000, 11 (4): 320-332. 10.1016/S1044-0305(99)00157-9.PubMedView ArticleGoogle Scholar
- Auberry KJ, Kiebel GR, Monroe ME, Adkins JN, Anderson GA, Smith RD: Omics.pnl.gov: A Portal for the Distribution and Sharing of Multi-Disciplinary Pan-Omics Information. J Proteomics Bioinform. 2010, 3 (1): 1-4. 10.4172/jpb.1000114.PubMedPubMed CentralView ArticleGoogle Scholar
- Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Rajandream MA, Barrell B: Artemis: sequence visualization and annotation. Bioinformatics. 2000, 16 (10): 944-945. 10.1093/bioinformatics/16.10.944.PubMedView ArticleGoogle Scholar
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