N.E.S is supported by an Australian Research Council Future Fellowship (FT200100270) and an ARC Discovery Project Grant (DP210100362). We thank the Melbourne Mass Spectrometry and Proteomics Facility of The Bio21 Molecular Science and Biotechnology Institute for access to MS instrumentation.

NOTE: The preparation and analysis of bacterial glycopeptide samples can be divided into four sections (Figure 1).&#160;For this study, the glycosylation of three sequenced A. baumannii&#160;strains was assessed (Table 1). Proteome FASTA databases of each of these strains are accessible via Uniprot. Refer to Table 2 for the composition of buffers used in this protocol.
1. Preparation of protein samples for proteomic analysis
<ol>
	<li>Isolation of proteome samples of interest
	<ol>
		<li>If using whole cells, ensure that the cells have been washed with a phosphate-buffered saline solution (PBS) to remove potential protein contaminants present in the media. Snap-freeze whole cells after washing and store them at -80 &#176;C until required.</li>
		<li>If fractionated samples are used (such as membrane preparations), ensure that the reagents used will not interfere with downstream liquid chromatography MS (LC-MS) analysis50.</li>
		<li>If detergents, such as sodium dodecyl-sulfate (SDS), Triton X-100, NP-40, or lauroylsarcosine, have been used, remove these detergents using acetone precipitation, SP3 sample preparation methods51, or commercial proteomic clean-up columns such as S-traps52. Alternatively, substitute incompatible detergents with an MS-compatible or removable detergent such as sodium deoxycholate (SDC) or octyl glucopyranoside.</li>
		<li>Ensure all plasticware and glassware to be used for sample preparation has not been autoclaved. Autoclaved glassware and plastics are typically heavily contaminated with small molecular weight compounds, such as polymers, which are readily detected within the MS.</li>
	</ol>
	</li>
	<li>Solubilization of whole-cell samples
	<ol>
		<li>Resuspend ~10 mg of washed, snap-frozen cells in 200 &#181;L of freshly prepared sodium deoxycholate lysis buffer (SDC lysis buffer: 4% SDC in 100 mM Tris, pH 8.5). 
		NOTE: Protease inhibitors can be added to the SDC lysis buffer to limit protein degradation.</li>
		<li>Boil the samples for 10 min at 95 &#176;C with shaking (2000 rpm on a thermomixer), and then leave on ice for 10 min. Repeat this process twice to ensure efficient lysis and solubilization of the samples. 
		NOTE: Samples can be stored long-term at this point at -80 &#176;C. If stored, resolubilize by heating at 95 &#176;C before further processing.</li>
	</ol>
	</li>
	<li>Quantify sample protein concentrations using a bicinchoninic acid (BCA) protein assay53. Store samples on ice while undertaking quantification to limit protein degradation. 
	NOTE: For total proteome analysis, 20-100 &#181;g of protein is more than sufficient for nano LC-MS, which typically requires less than 2 &#181;g of protein digest per analysis. The preparation of excess peptide allows for replicate analysis or further fractionation if deep proteomic coverage is required. For glycopeptide enrichment-based analysis using hydrophilic interaction chromatography (HILIC), 100-500 &#181;g of protein is required.</li>
	<li>Reduce and alkylate samples.
	<ol>
		<li>Add 1/10th the volume of 10x reduction/alkylation buffer (100 mM Tris 2-carboxyethyl phosphine hydrochloride; 400 mM 2-chloroacetamide in 1 M Tris, pH 8.5) to samples for a final concentration of 1x and incubate samples in the dark for 30 min at 45 &#176;C with shaking at 1,500 rpm. 
		NOTE: Check the pH of the 10x reduction/alkylation buffer to ensure a pH of approximately 7.0-8.0 before adding to the samples, as a lower pH will cause the SDC to precipitate.</li>
	</ol>
	</li>
	<li>Briefly spin down the samples and add the proteases Trypsin/Lys-C (~10 &#181;L, resuspended in 100 mM Tris, pH 8.5) for a final protease:protein ratio of 1:100. Incubate the digests overnight at 37 &#176;C with shaking at 1,500 rpm (up to 18 h). To ensure complete protein digestion, use a Trypsin/Lys-C protease:protein ratio of 1:50 to 1:200.</li>
	<li>Quench digests by adding 1.25 volumes of 100% isopropanol to the samples. Vortex the samples for 1 min to mix and briefly spin them down. 
	NOTE: Samples can be stored at -20 &#176;C to be further processed later.</li>
	<li>Acidify the samples by adding 0.115 volumes of 10% trifluoroacetic acid (TFA; final concentration of ~1% TFA), vortex the samples, and briefly spin them down.</li>
</ol>
2. Processing of proteome samples
<ol>
	<li>Peptide clean-up of proteome samples
	<ol>
		<li>Prepare one styrenedivinylbenzene-reverse-phase sulfonate (SDB-RPS) Stop-and-go-extraction (Stage) Tip for each sample as previously described54.
		<ol>
			<li>Empirically, for binding 50 &#956;g of peptide, excise three SDB-RPS discs from a 47 mm2 SDB-RPS membrane using a blunt needle (14 G). For larger peptide amounts, increase the number of discs accordingly.</li>
		</ol>
		</li>
		<li>Prior to using SDB-RPS Stage Tips, prepare the tips by sequentially adding at least ten bed volumes of the following buffers and either spinning the buffer through the column by centrifugation (25 &#176;C, 3 min, 500 &#215; g) or by pushing the buffer through the column by gently applying pressure using a syringe.
		<ol>
			<li>Wet the tips with 150 &#181;L of 100% acetonitrile.</li>
			<li>Wash the tips with 150 &#181;L of 30% methanol, 1% TFA in 18.2 M&#937; H2O.</li>
			<li>Equilibrate the tips with 150 &#181;L of 90% isopropanol, 1% TFA balanced with 18.2 M&#937; H2O.</li>
		</ol>
		</li>
		<li>Load the samples (containing 50% isopropanol, 1% TFA) onto the SDB-RPS Stage Tips by centrifugation (25 &#176;C, 3 min, 500 &#215; g) or by gently applying pressure using a syringe.</li>
		<li>Wash the SDB-RPS Stage Tips with the following buffers by centrifugation (25 &#176;C, 3 min, 500 &#215; g) or by gently applying pressure using a syringe. 
		NOTE: Additional washes or alternative buffers can be used to remove non-peptide contaminants, such as the use of ethyl acetate instead of isopropanol55.
		<ol>
			<li>Wash the tips with 150 &#181;L of 90% isopropanol, 1% TFA.</li>
			<li>Wash the tips with 150 &#181;L of 1% TFA in 18.2 M&#937; H2O.</li>
		</ol>
		</li>
		<li>Elute the peptides from the SDB-RPS Stage Tips with 150 &#181;L of 5% ammonium hydroxide in 80% acetonitrile by centrifugation or by gently applying pressure using a syringe. Collect the samples in individual tubes. 
		NOTE: Prepare 5% ammonium hydroxide in 80% acetonitrile in a plastic container immediately prior to use within a fume hood.</li>
		<li>Dry the eluted peptides by vacuum centrifugation at 25 &#176;C. 
		NOTE: If undertaking HILIC enrichment, 1-10% of the peptide eluates can be removed at this point, dried, and used as total proteome input controls.</li>
	</ol>
	</li>
	<li>Enrichment of glycopeptide samples
	<ol>
		<li>Prepare Zwitterionic Hydrophilic Interaction Liquid Chromatography (ZIC-HILIC) Stage Tips as previously described54,56.
		<ol>
			<li>Briefly, excise one C8 disc from a 47 mm2 C8 membrane using a blunt needle (14 G) and pack the disc into a P200 tip to create a frit. Add approximately 5 mm of ZIC-HILIC material, resuspended in 50% acetonitrile, 50% 18.2 M&#937; H2O, onto the frit by gently applying pressure using a syringe.</li>
		</ol>
		</li>
		<li>Prior to using ZIC-HILIC Stage Tips, condition the resin by sequentially adding the following buffers and gently applying pressure using a syringe. 
		&#8203;NOTE: To ensure the integrity of the pseudo-water layer on the surface of the ZIC-HILIC resin (required to enrich glycopeptides), the resin must always remain wet. When washing the resin, always leave ~10 &#181;L of solvent above the resin and ensure the washes/samples are pipetted directly into this residual solvent.
		<ol>
			<li>Equilibrate the resin with 20 bed volumes (200 &#181;L) of ZIC-HILIC elution buffer (0.1% TFA in 18.2 M&#937; H2O).</li>
			<li>Wash the resin with 20 bed volumes (200 &#181;L) of ZIC-HILIC preparation buffer (95% acetonitrile in 18.2 M&#937; H2O).</li>
			<li>Wash the resin with 20 bed volumes (200 &#181;L) of ZIC-HILIC loading/wash buffer (80% acetonitrile, 1% TFA balanced with 18.2 M&#937; H2O).</li>
		</ol>
		</li>
		<li>Resuspend the dried digested samples (from step 2.1.6) in ZIC-HILIC loading/wash buffer to a final concentration of 4 &#181;g/&#181;L (e.g., for 200 &#181;g of peptide, resuspend in 50 &#181;L of ZIC-HILIC loading/wash buffer). Vortex briefly for 1 min to ensure the samples are resuspended, and spin down for 1 min at 2,000 &#215; g at 25 &#176;C.</li>
		<li>Load the resuspended peptide sample onto a conditioned ZIC-HILIC column.
		<ol>
			<li>Wash three times with 20 bed volumes (200 &#181;L) of ZIC-HILIC loading/wash buffer (for 60 bed volume washes total) by gently applying pressure using a syringe.</li>
			<li>Elute glycopeptides with 20 bed volumes (200 &#181;L) of ZIC-HILIC elution buffer into a 1.5 mL tube by gently applying pressure using a syringe and then dry the eluate by vacuum centrifugation at 25 &#176;C.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. LC-MS of proteome/glycopeptide-enriched samples
<ol>
	<li>Resuspend the samples in Buffer A* (2% acetonitrile, 0.1% TFA) to a final concentration of 1 &#181;g/&#181;L (for example, for 50 &#181;g of peptide, resuspend in 50 &#181;L of Buffer A*).</li>
	<li>Load the samples onto an HPLC/UPLC coupled to an MS to enable the separation and identification of glycopeptides. 
	NOTE: The column parameters, including inner diameter, length, flow rates, type of chromatography resin, and required peptide injection amounts, should be optimized for the analytical setup and gradient length to be used; for an example of how to undertake optimization of analytical setups, see57.</li>
	<li>Monitor the collection of the resulting MS data ensuring the data is being collected with the desired parameters. 
	NOTE: For compositional analysis, CID fragmentation is sufficient. Due to the addition of glycans to glycopeptides, glycopeptide ions are typically observed with a higher m/z and lower charge density than unglycosylated peptides. To ensure these ions are observable, allow a MS1 mass range from 400 to 2,000 m/z.</li>
	<li>Fragment selected ions using CID, ensuring the collection of low m/z fragment ions that contain oxonium ions important for the characterization of glycans. 
	NOTE: Fragmentation of glycopeptides using CID is influenced by both the peptide and glycan sequences, as well as the energy applied during fragmentation25,58,59. While a range of different collision energies can be used, an optimal strategy for fragmenting glycopeptides is the use of stepped collision energies combining the use of multiple collision energies25,59,60.</li>
	<li>Use alternative fragmentation methods, if available, such as ETD for site localization, or IT-CID to aid in the determination of glycan compositions. 
	&#8203;NOTE: Neither of these fragmentation approaches are essential for compositional analysis, yet can be collected to enable further interrogation of glycopeptides of interest.</li>
</ol>
4. Analysis of proteome/glycopeptide-enriched samples
<ol>
	<li>Prefiltering data files to enable searching in FragPipe
	<ol>
		<li>If ETD or IT-CID scans have been acquired within datasets, filter these scan events from the datafiles using MSConvert61 prior to searching with FragPipe. 
		NOTE: For the open searching parameters outlined below, only beam-type CID data are required.</li>
	</ol>
	</li>
	<li>Performing open searches in FragPipe
	<ol>
		<li>Open FragPipe and click the Workflow tab. In the workflow pulldown menu, select the Open search option, and click Add files to import the data files to be searched into FragPipe (Figure 2A).</li>
		<li>Click the&#160;Database&#160;tab and launch the download manager by clicking&#160;Download. This allows proteome databases to be downloaded from Uniprot using a Uniprot Proteome ID.&#160;Click the Add decoys and contaminants option within the download manager to incorporate decoy and contaminant proteins into databases.</li>
		<li>For more stringent FDR thresholds, click the Validation tab and modify the Filter and report value from 0.01 to the required FDR. 
		NOTE: The default FragPipe settings will ensure a 1% FDR at the protein level.</li>
		<li>Click the MSfragger tab. Within the Peak matching box, increase the Precursor mass tolerance from the default 500 Da to 2,000 Da to allow the identification of large modifications (Figure 2A).</li>
		<li>Click the Run tab and define the location of the outputs of FragPipe. Click the Run button to begin the search.</li>
	</ol>
	</li>
	<li>Using the PSMs identified across datasets (contained within the psm.tsv outputs from FragPipe), identify potential glycans by plotting the frequency of observed delta masses within datasets (Figure 3). Create delta mass plots from MSfragger outputs using the R scripts accessible via PRIDE accession PXD027820.&#160; 
	NOTE: Minimal postprocessing of the open searching results is undertaken within these scripts, as the main purpose of these scripts is to aid in the visualization of delta mass profiles. Importantly, the observation of abundant delta masses alone is not proof that a modification is a potential glycan, as assigning delta masses as glycans requires further analysis of the corresponding MS2 events.</li>
	<li>To enable the characterization of glycopeptides within samples, focus on high confidence delta mass identifications, corresponding to assignments with high hyperscores.
	<ol>
		<li>To aid in assessing glycopeptide spectra, use peptide annotation tools, such as the Interactive Peptide Spectral Annotator63, which enables the assignment of peptide-associated ions within spectra, allowing the manual identification of the glycan-associated ions (Figure 4). 
		NOTE: Within the datasets presented here, hyperscores of &#62;30 are considered high scoring, as these correspond to scores within the top 50% of all identified glycopeptides (Figure 5).</li>
	</ol>
	</li>
	<li>With high-confidence glycopeptides assigned, identify commonly observed glycan-associated ions (Figure 4) to improve the identification of glycopeptides. 
	NOTE: By incorporating glycan-associated ions within searches, known as glycan-focused searches, the quality of glycopeptide assignments can be improved.
	<ol>
		<li>Click the MSfragger tab of FragPipe, incorporate the determined delta masses of the observed glycans into the Variable modifications and Mass Offsets sections. Add these masses by typing values into the Variable modifications and Mass Offsets sections with individual masses separated with a /. Add the glycan-associated fragment masses of these glycans into the Glyco/Labile mods section of MSFragger. 
		NOTE: Figure 2B outlines the key information required for a glycan-focused search of the A. baumannii strain AB307-0294.</li>
	</ol>
	</li>
	<li>Upload all MS data associated with proteomic studies to centralized Proteomic repositories such as the PRIDE or MASSIVE repositories. 
	NOTE: All data associated with this study have been deposited into the PRIDE proteomic repository and can be accessed via the PRIDE accession: PXD027820.</li>
</ol>

The authors have no conflicts of interest.

Open searching is an effective and systematic method for the identification of unknown modifications. While the identification of unknown glycans within bacterial proteome samples has traditionally been a time-consuming and technically specialized undertaking, the recent developments of tools such as MSfragger21,41 and Byonic31,38 now enable the quick and effective identification of delta masses for furth...

Protein glycosylation, the process of attaching carbohydrates to protein molecules, is one of the most common post-translational modifications (PTMs) in nature1,2. Across all domains of life, a range of complex machinery has evolved dedicated to the generation of glycoproteins that impact a myriad of cellular functions1,3,4,5. While protein glycosylation occurs on a range of amino acids6,7, N-linked and O-linked glycosylation events are two dominant forms observed in nature. N-linked glycosylation involves the attachment of glycans to a nitrogen atom of asparagine (Asn) residues, while in O-linked glycosylation, glycans are attached to an oxygen atom of serine (Ser), threonine (Thr), or tyrosine (Tyr) residues7. Despite the similarities in residues targeted by glycosylation systems, the differences within the glycans attached to proteins result in glycosylation being the most chemically diverse class of PTMs found in nature.
While eukaryotic glycosylation systems possess glycan diversity, these systems are typically restricted in the number of unique carbohydrates utilized. The resulting diversity stems from how these carbohydrates are arranged into glycans8,9,10,11,12. In contrast, bacterial and archaeal species possess virtually unlimited glycan diversity due to the sheer array of unique sugars generated within these systems2,10,13,14,15,16,17. These differences in the glycan diversity observed across domains of life represent a significant analytical challenge for the characterization and identification of glycosylation events. For eukaryotic glycosylation, the ability to anticipate glycan compositions has facilitated the growing interest in glycobiology; yet, the same is not true for bacterial glycosylation, which is still largely restricted to study by specialized laboratories. As the accessibility of mass spectrometry (MS) instrumentation has increased in the biosciences, MS-based approaches are now the primary method for glycoproteomic analysis.
MS has emerged as the quintessential tool for the characterization of glycosylation, with both top-down and bottom-up approaches now commonly used to characterize glycoproteins6. While top-down proteomics is used to assess global glycosylation patterns of specific proteins18,19, bottom-up approaches are used to enable the glycan-specific characterization of glycopeptides, even from complex mixtures6,20,21,22,23. For the analysis of glycopeptides, the generation of informative fragmentation information is essential for the characterization of glycosylation events24,25. A range of fragmentation approaches is now routinely accessible on instruments, including resonance ion trap-based collision-induced dissociation (IT-CID), beam-type collision-induced dissociation (CID), and electron transfer dissociation (ETD). Each approach possesses different strengths and weaknesses for glycopeptide analysis25,26, with significant progress over the last decade in applying these fragmentation approaches to analyze glycosylation6,20. However, for bacterial glycosylation analysis, the critical limitation has not been the ability to fragment glycopeptides but rather the inability to predict the potential glycan compositions within samples. Within these systems, the unknown nature of diverse bacterial glycans limits the identification of glycopeptides, even with glycosylation-focused searching tools now commonplace for the analysis of eukaryotic glycopeptides, such as O-Pair27, GlycopeptideGraphMS28, and GlycReSoft29. To overcome this issue, an alternative searching method is required, with the use of open searching tools emerging as a powerful approach for the study of bacterial glycosylation30.
Open searching, also known as blind or wildcard searching, allows the identification of peptides with unknown or unexpected PTMs21,30,31,32. Open searches utilize a variety of computational techniques, including curated modification searches, multistep database searches, or wide-mass tolerant searching33,34,35,36,37. Although open searching has great potential, its use has typically been hindered by the significant increase in analysis times and loss in sensitivity of the detection of unmodified peptides compared to restricted searches31,32. The decrease in the detection of unmodified peptide-spectral matches (PSMs) is a result of the increased false-positive PSM rates associated with these techniques, which requires increased stringent filtering to maintain the desired false discovery rates (FDRs)33,34,35,36,37. Recently, several tools have become available that significantly improve the accessibility of open searching, including Byonic31,38, Open-pFind39, ANN-SoLo40, and MSFragger21,41. These tools enable the robust identification of glycosylation events by significantly reducing analysis times and implementing approaches to handle heterogeneous glycan compositions.
This article presents a streamlined method for the identification of bacterial glycopeptides by open searching, using the Gram-negative nosocomial pathogen, Acinetobacter baumannii, as a model. A. baumannii possesses a conserved O-linked glycosylation system responsible for the modification of multiple protein substrates, known as the PglL protein glycosylation system42,43,44. While similar proteins are targeted for glycosylation between strains, the PglL glycosylation system is highly variable due to the biosynthesis of the glycan used for protein glycosylation being derived from the capsule locus (known as the K-locus)44,45,46. This results in diverse glycans (also known as a K-unit), derived from single or limited polymerized K-units, being added to protein substrates30,44,46. Within this work, the use of the open searching tool, MSfragger, within the software FragPipe, is used to identify glycans across A. baumannii strains. By combining open searching and manual curation, &#34;glycan-focused searches&#34; can be undertaken to further improve the identification of bacterial glycopeptides. Together, this multistep identification approach enables the identification of glycopeptides without extensive experience in the characterization of novel glycosylation events.

<table><tbody><tr><td>14 G Kel-F Hub point style 3</td><td>Hamilton company</td><td>hanc90514</td><td></td></tr><tr><td>2-Chloroacetamide</td><td>Sigma Aldrich Pty Ltd</td><td>C0267-100G</td><td></td></tr><tr><td>Acetonitrile</td><td>Sigma Aldrich Pty Ltd</td><td>34851-4L</td><td></td></tr><tr><td>Ammonium hydroxide (28%)</td><td>Sigma Aldrich Pty Ltd</td><td>338818-100ML</td><td></td></tr><tr><td>BCA Protein Assay Reagent A</td><td>Pierce</td><td>23228</td><td></td></tr><tr><td>BCA Protein Assay Reagent B</td><td>Pierce</td><td>23224</td><td></td></tr><tr><td>C8 Empore SPE</td><td>Sigma Aldrich Pty Ltd</td><td>66882-U</td><td>An alterative vendor for C8 material is Affinisep (https://www.affinisep.com/about-us/)</td></tr><tr><td>Formic acid</td><td>Sigma Aldrich Pty Ltd</td><td>5.33002</td><td></td></tr><tr><td>Isopropanol</td><td>Sigma Aldrich Pty Ltd</td><td>650447-2.5L</td><td></td></tr><tr><td>Methanol</td><td>Fisher Chemical</td><td>M/4058/17</td><td></td></tr><tr><td>SDB-RPS Empore SPE (Reversed-Phase Sulfonate)</td><td>Sigma Aldrich Pty Ltd</td><td>66886-U</td><td>An alterative vendor for SDB-RPS is Affinisep (https://www.affinisep.com/about-us/)</td></tr><tr><td>Sodium Deoxycholate</td><td>Sigma Aldrich Pty Ltd</td><td>D6750-100G</td><td></td></tr><tr><td>ThermoMixer C</td><td>Eppendorf</td><td>2232000083</td><td></td></tr><tr><td>trifluoroacetic acid</td><td>Sigma Aldrich Pty Ltd</td><td>302031-10X1ML</td><td></td></tr><tr><td>Tris 2-carboxyethyl phosphine hydrochloride</td><td>Sigma Aldrich Pty Ltd</td><td>C4706-2G</td><td></td></tr><tr><td>Tris(hydroxymethyl)aminomethane</td><td>Sigma Aldrich Pty Ltd</td><td>252859-500G</td><td></td></tr><tr><td>Trypsin/Lys-C protease mixture</td><td>Promega</td><td>V5073</td><td></td></tr><tr><td>Vacuum concentrator</td><td>Labconco</td><td>7810040</td><td></td></tr><tr><td>ZIC-HILIC material</td><td>Merck</td><td>1504580001</td><td>Resin for use in single use SPE columns can be obtain by emptying a larger form column and using the free resin</td></tr></tbody></table>

the application of open searching-based approaches for the identification of acinetobacter baumannii o-linked glycopeptides

Protein glycosylation is increasingly recognized as a common modification within bacterial organisms, contributing to prokaryotic physiology and optimal infectivity of pathogenic species. Due to this, there is increasing interest in characterizing bacterial glycosylation and a need for high-throughput analytical tools to identify these events. Although bottom-up proteomics readily enables the generation of rich glycopeptide data, the breadth and diversity of glycans observed in prokaryotic species make the identification of bacterial glycosylation events extremely challenging.
Traditionally, the manual determination of glycan compositions within bacterial proteomic datasets made this a largely bespoke analysis restricted to field-specific experts. Recently, open searching-based approaches have emerged as a powerful alternative for the identification of unknown modifications. By analyzing the frequency of unique modifications observed on peptide sequences, open searching techniques allow the identification of common glycans attached to peptides within complex samples. This article presents a streamlined workflow for the interpretation and analysis of glycoproteomic data, demonstrating how open searching techniques can be used to identify bacterial glycopeptides without prior knowledge of the glycan compositions.
Using this approach, glycopeptides within samples can rapidly be identified to understand glycosylation differences. Using Acinetobacter baumannii as a model, these approaches enable the comparison of glycan compositions between strains and the identification of novel glycoproteins. Taken together, this work demonstrates the versatility of open database-searching techniques for the identification of bacterial glycosylation, making the characterization of these highly diverse glycoproteomes easier than ever before.

To illustrate the utility of open searching for bacterial glycopeptide analysis, the chemical diversity of O-linked glycans within three strains of A. baumannii-AB307-0294, ACICU, and D1279779-was assessed. The O-linked glycoproteomes are highly variable between A. baumannii strains as the glycans used for glycosylation are derived from the highly variable capsule loci44,45,46. This chemical d...

Watch this Scientific Journal Video about The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides at JoVE.com

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

Open searching enables the identification of glycopeptides decorated with previously unknown glycan compositions. Within this article, a streamlined approach for undertaking open searching and subsequent glycan-focused glycopeptide searches are presented for bacterial samples using Acinetobacter baumannii as a model.

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

Protein glycosylation is increasingly recognized as a common modification within bacterial organisms, contributing to prokaryotic physiology and optimal ...

the-application-open-searching-based-approaches-for-identification

Research

JoVE Journal

Biochemistry

2.1K Views.  The University of Melbourne. Dit protocol stroomlijnt de identificatie van bacteriële glycop%eptides, waardoor onderzoekers nieuwe glycosylatiegebeurtenissen kunnen identificeren, evenals de verschillen in de glycanen die worden gebruikt voor glycosylering tussen stammen. Het belangrijkste voordeel van deze techniek is dat het niet-glycosylatie-experts in staat stelt om potentiële glycosylatiegebeurtenissen in bacteriële proteoommonsters te identificeren. Open zoeken is een algemeen toepasbare benadering die kan worde...

Video: The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

Improved In-gel Reductive &#946;-Elimination for Comprehensive O-linked and Sulfo-glycomics by Mass Spectrometry

Separation of proteins by SDS-PAGE followed by in-gel proteolytic digestion of resolved protein bands has produced high-resolution proteomic analysis of biological samples. Similar approaches, that would allow in-depth analysis of the glycans carried by glycoproteins resolved by SDS-PAGE, require special considerations in order to maximize recovery and sensitivity when using mass spectrometry (MS) as the detection method. A major hurdle to be overcome in achieving high-quality data is the removal of gel-derived contaminants that interfere with MS analysis. The sample workflow presented here is robust, efficient, and eliminates the need for in-line HPLC clean-up prior to MS. Gel pieces containing target proteins are washed in acetonitrile, water, and ethyl acetate to remove contaminants, including polymeric acrylamide fragments. O-linked glycans are released from target proteins by in-gel reductive &#946;-elimination and recovered through robust, simple clean-up procedures. An advantage of this workflow is that it improves sensitivity for detecting and characterizing sulfated glycans. These procedures produce an efficient separation of sulfated permethylated glycans from non-sulfated (sialylated and neutral) permethylated glycans by a rapid phase-partition prior to MS analysis, and thereby enhance glycomic and sulfoglycomic analyses of glycoproteins resolved by SDS-PAGE.

In order to comprehensively explore the diversity of O-linked glycans, a new procedure for in-gel reductive &#946;-elimination, combined with permethylation and a rapid phase-partition method, is applied to the analysis of O-linked glycans directly released from glycoproteins resolved by SDS-PAGE and amenable to subsequent glycomic analysis by mass spectrometry.

Separation of proteins by SDS-PAGE followed by in-gel proteolytic digestion of resolved protein bands has produced high-resolution proteomic analysis of ...

Chemistry

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Biopanning bacterial display libraries is a proven technique for peptide affinity reagent discovery for recognition of both biotic and abiotic targets. Peptide affinity reagents can be used for similar applications to antibodies, including sensing and therapeutics, but are more robust and able to perform in more extreme environments. Specific enrichment of peptide capture agents to a protein target of interest is enhanced using semi-automated sorting methods which improve binding and wash steps and therefore decrease the occurrence of false positive binders. A semi-automated sorting method is described herein for use with a commercial automated magnetic-activated cell sorting device with an unconstrained bacterial display sorting library expressing random 15-mer peptides. With slight modifications, these methods are extendable to other automated devices, other sorting libraries, and other organisms. A primary goal of this work is to provide a comprehensive methodology and expound the thought process applied in analyzing and minimizing the resulting pool of candidates. These techniques include analysis of on-cell binding using fluorescence-activated cell sorting (FACS), to assess affinity and specificity during sorting and in comparing individual candidates, and the analysis of peptide sequences to identify trends and consensus sequences for understanding and potentially improving the affinity to and specificity for the target of interest.

Biopanning bacterial display libraries is a proven technique for discovery of peptide affinity reagents, a robust alternative to antibodies. The semi-automated sorting method herein has streamlined biopanning to decrease the occurrence of false positives. Here we illustrate the thought process and techniques applied in evaluating candidates and minimizing downstream analysis.

Biopanning bacterial display libraries is a proven technique for peptide affinity reagent discovery for recognition of both biotic and abiotic targets. ...

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, we developed IDBac&#8212;a low-cost and high-throughput matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) bioinformatics pipeline. IDBac software is designed for non-experts, is freely available, and capable of analyzing a few to thousands of bacterial colonies. Here, we present procedures for the preparation of bacterial colonies for MALDI-TOF MS analysis, MS instrument operation, and data processing and visualization in IDBac. In particular, we instruct users how to cluster bacteria into dendrograms based on protein MS fingerprints and interactively create Metabolite Association Networks (MANs) from specialized metabolite data.

IDBac is an open-source mass spectrometry-based bioinformatics pipeline that integrates data from both intact protein and specialized metabolite spectra, collected on cell material scraped from bacterial colonies. The pipeline allows researchers to rapidly organize hundreds to thousands of bacterial colonies into putative taxonomic groups, and further differentiate them based on specialized metabolite production.

In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, ...

Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics

Peptidoglycan is an important component of bacterial cell walls and a common cellular target for antimicrobials. Although aspects of peptidoglycan structure are fairly conserved across all bacteria, there is also considerable variation between Gram-positives/negatives and between species. In addition, there are numerous known variations, modifications, or adaptations to the peptidoglycan that can occur within a bacterial species in response to growth phase and/or environmental stimuli. These variations produce a highly dynamic structure that is known to participate in many cellular functions, including growth/division, antibiotic resistance, and host defense avoidance. To understand the variation within peptidoglycan, the overall structure must be broken down into its constitutive parts (known as muropeptides) and assessed for overall cellular composition. Peptidoglycomics uses advanced mass spectrometry combined with high-powered bioinformatic data analysis to examine peptidoglycan composition in fine detail. The following protocol describes the purification of peptidoglycan from bacterial cultures, the acquisition of muropeptide intensity data through a liquid chromatograph&#8212;mass spectrometer, and the differential analysis of peptidoglycan composition using bioinformatics.

This protocol covers a detailed analysis of peptidoglycan composition using liquid chromatography mass spectrometry coupled with advanced feature extraction and bioinformatic analysis software.

Peptidoglycan is an important component of bacterial cell walls and a common cellular target for antimicrobials. Although aspects of peptidoglycan ...

Recombinant Production of Bifidobacterial Endoglycosidases for N-glycan Release

Protein glycosylation is a diverse and common post-translational modification that has been associated with many important roles such as protein function, including protein folding, stability, enzymatic protection, and biological recognition. N-glycans attached to glycoproteins (such as lactoferrin, lactadherin, and immunoglobulins) cannot be digested by the host and reach the large intestine, where they are consumed by certain beneficial microbes. Therefore, they are considered next-generation prebiotic compounds that can selectively stimulate the gut microbiome's beneficial microorganisms. However, the isolation of these new classes of prebiotics requires novel enzymes. Here, we describe the recombinant production of novel glycosidases from different Bifidobacteria strains (isolated from infants, rabbits, chicken, and bumblebee) for improved N-glycan isolation from glycoproteins. The method presented in this study includes the following steps: molecular cloning of Bifidobacterial genes by an in vivo recombinational cloning strategy, control of transformation success, protein induction, and protein purification.

Recombinant Production of Bifidobacterial Endoglycosidases for N-glycan Release

Bifidobacteria possess a unique genomic capability for N-glycan cleavage. Recombinantly producing these enzymes would be a promising novel tool to release bioactive N-glycans from glycoprotein-rich substrates such as colostrum.

Protein glycosylation is a diverse and common post-translational modification that has been associated with many important roles such as protein function, ...

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

This protocol identifies the immunity proteins of the bactericidal enzymes: colicin E3 and bacteriocin, produced by a pathogenic Escherichia coli strain using antibiotic induction, and identified by MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. The immunity protein of colicin E3 (Im3) and the immunity protein of bacteriocin (Im-Bac) were identified from prominent b- and/or y-type fragment ions generated by the polypeptide backbone cleavage (PBC) on the C-terminal side of aspartic acid, glutamic acid, and asparagine residues by the aspartic acid effect fragmentation mechanism. The software rapidly scans in silico protein sequences derived from the whole genome sequencing of the bacterial strain. The software also iteratively removes amino acid residues of a protein sequence in the event that the mature protein sequence is truncated. A single protein sequence possessed mass and fragment ions consistent with those detected for each immunity protein. The candidate sequence was then manually inspected to confirm that all detected fragment ions could be assigned. The N-terminal methionine of Im3 was post-translationally removed, whereas Im-Bac had the complete sequence. In addition, we found that only two or three non-complementary fragment ions formed by PBC are necessary to identify the correct protein sequence. Finally, a promoter (SOS box) was identified upstream of the antibacterial and immunity genes in a plasmid genome of the bacterial strain.

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

Here we present a protocol for the rapid identification of proteins produced by genomically sequenced pathogenic bacteria using MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. Metastable protein ions fragment because of the aspartic acid effect and this specificity is exploited for protein identification.

This protocol identifies the immunity proteins of the bactericidal enzymes: colicin E3 and bacteriocin, produced by a pathogenic Escherichia coli strain ...

A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues

Mass spectrometry can provide deep coverage of post-translational modifications (PTMs), although enrichment of these modifications from complex biological matrices is often necessary due to their low stoichiometry in comparison to non-modified analytes. Most enrichment workflows of PTMs on peptides in bottom-up proteomics workflows, where proteins are enzymatically digested before the resulting peptides are analyzed, only enrich one type of modification. It is the entire complement of PTMs, however, that leads to biological functions, and enrichment of a single type of PTM may miss such crosstalk of PTMs. PTM crosstalk has been observed between protein glycosylation and phosphorylation, the two most common PTMs in human proteins and also the two most studied PTMs using mass spectrometry workflows. Using the simultaneous enrichment strategy described herein, both PTMs are enriched from post-mortem human pancreatic tissue, a complex biological matrix. Dual-functional Ti(IV)-immobilized metal affinity chromatography is used to separate various forms of glycosylation and phosphorylation simultaneously in multiple fractions in a convenient spin tip-based method, allowing downstream analyses of potential PTM crosstalk interactions. This enrichment workflow for glyco- and phosphopeptides can be applied to various sample types to achieve deep profiling of multiple PTMs and identify potential target molecules for future studies.

Post-translational modifications (PTMs) change protein structures and functions. Methods for the simultaneous enrichment of multiple PTM types can maximize coverage in analyses. We present a protocol using dual-functional Ti(IV)-immobilized metal affinity chromatography followed by mass spectrometry for the simultaneous enrichment and analysis of protein N-glycosylation and phosphorylation in pancreatic tissues.

Mass spectrometry can provide deep coverage of post-translational modifications (PTMs), although enrichment of these modifications from complex biological ...

Nonspecific Proteolysis-Based Glycomics Strategy for Bacterial Glycoproteins

Protein glycosylation is one of the most common and complex post-translational modifications. Many techniques have been developed to characterize the specific roles of glycans, the relationship between their structures and their impact on the functions of proteins. A common method for glycan analysis is to employ exoglycosidase cleavage to release N-linked glycans from glycoproteins or glycopeptides using Peptide-N-Glycosidase F (PNGase F). However, the glycan-protein linkages in bacteria are different and there is no enzyme available to release glycans from bacterial glycoproteins. In addition, free glycans have also been described in mammalian cells, bacteria, yeast, plants, and fish. In this article, we present a method that can characterize the N-linked glycosylation system in Campylobacter jejuni by detecting asparagine (Asn)-linked and free glycans that are not attached to their target proteins. In this method, total proteins from C. jejuni were digested by Pronase E with a higher enzyme to protein ratio (2:1&#8722;3:1) and a longer incubation time (48&#8722;72 h). The resulted Asn-glycans and free glycans were then purified using porous graphitic carbon cartridges, permethylated, and analyzed by mass spectrometry.

This study presents a method for glycomics analysis of glycoproteins by a combination of pronase E digestion, permethylation, and mass spectrometry analysis. This method is capable of analyzing all types of N-linked glycans, including bacterial N-glycans.

Protein glycosylation is one of the most common and complex post-translational modifications. Many techniques have been developed to characterize the ...

Profiling of Colorectal Cancer Tissues Using Glycomics-Guided Glycoproteomics

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Glycosylation is a common and structurally diverse protein modification that impacts a wide range of tumorigenic processes. Mass spectrometry-driven glycomics and glycoproteomics have emerged as powerful approaches to analyze liberated glycans and intact glycopeptides, respectively, offering a deeper understanding of the heterogeneous glycoproteome in the tumor microenvironment (TME). This protocol details the glycomics-guided glycoproteomics method, an integrated omics technology, which firstly employs porous graphitized carbon-LC-MS/MS-based glycomics to elucidate the glycan structures and their quantitative distribution in the glycome of tumor tissues, cell populations, or bodily fluids being investigated. This allows for a comparative glycomics analysis to identify altered glycosylation across patient groups, disease stages, or conditions, and, importantly, serves to enhance the downstream glycoproteomics analysis of the same sample(s) by creating a library of known glycan structures, thus reducing the data search time and the glycoprotein misidentification rate. Focusing on N-glycoproteome profiling, the sample preparation steps for the glycomics-guided glycoproteomics method are detailed in this protocol, and key aspects of the data collection and analysis are discussed. The glycomics-guided glycoproteomics method provides quantitative information on the glycoproteins present in the TME and their glycosylation sites, site occupancy, and site-specific glycan structures. Representative results are presented from formalin-fixed paraffin-embedded tumor tissues from colorectal cancer patients, demonstrating that the method is sensitive and compatible with tissue sections commonly found in biobanks. Glycomics-guided glycoproteomics, therefore, offers a comprehensive view into the heterogeneity and dynamics of the glycoproteome in complex TMEs, generating robust biochemical data required to better understand the glycobiology of cancers.

This protocol details the glycomics-guided glycoproteomics method, an integrated omics technology that offers comprehensive insights into the heterogeneous glycoproteome in complex tumor microenvironments required to better understand the glycobiology of cancers.

Glycosylation is a common and structurally diverse protein modification that impacts a wide range of tumorigenic processes. Mass spectrometry-driven ...

Cancer Research

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins

Considerable progress has been made during the last decade towards the identification and characterization of enzymes involved in the synthesis (diguanylate cyclases) and degradation (phosphodiesterases) of the second messenger c-di-GMP. In contrast, little information is available regarding the molecular mechanisms and cellular components through which this signaling molecule regulates a diverse range of cellular processes. Most of the known effector proteins belong to the PilZ family or are degenerated diguanylate cyclases or phosphodiesterases that have given up on catalysis and have adopted effector function. Thus, to better define the cellular c-di-GMP network in a wide range of bacteria experimental methods are required to identify and validate novel effectors for which reliable in silico predictions fail.
We have recently developed a novel Capture Compound Mass Spectrometry (CCMS) based technology as a powerful tool to biochemically identify and characterize c-di-GMP binding proteins. This technique has previously been reported to be applicable to a wide range of organisms1. Here we give a detailed description of the protocol that we utilize to probe such signaling components. As an example, we use Pseudomonas aeruginosa, an opportunistic pathogen in which c-di-GMP plays a critical role in virulence and biofilm control. CCMS identified 74% (38/51) of the known or predicted components of the c-di-GMP network. This study explains the CCMS procedure in detail, and establishes it as a powerful and versatile tool to identify novel components involved in small molecule signaling.

The ubiquitous second messenger c-di-GMP controls growth and behavior of many bacteria. We have developed a novel Capture Compound Mass Spectrometry based technology to biochemically identify and characterize c-di-GMP binding proteins in virtually any bacterial species.

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

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