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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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. 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.

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

Identification of Fatty Acids in Bacillus cereus

The Bacillus species contain branched chain and unsaturated fatty acids (FAs) with diverse positions of the methyl branch (iso or anteiso) and of the double bond. Changes in FA composition play a crucial role in the adaptation of bacteria to their environment. These modifications entail a change in the ratio of iso versus anteiso branched FAs, and in the proportion of unsaturated FAs relative to saturated FAs, with double bonds created at specific positions. Precise identification of the FA profile is necessary to understand the adaptation mechanisms of Bacillus species.
Many of the FAs from Bacillus are not commercially available. The strategy proposed herein identifies FAs by combining information on the retention time (by calculation of the equivalent chain length (ECL)) with the mass spectra of three types of FA derivatives: fatty acid methyl esters (FAMEs), 4,4-dimethyl oxazoline derivatives (DMOX), and 3-pyridylcarbinyl ester (picolinyl). This method can identify the FAs without the need to purify the unknown FAs.
Comparing chromatographic profiles of FAME prepared from Bacillus cereus with a commercial mixture of standards allows for the identification of straight-chain saturated FAs, the calculation of the ECL, and hypotheses on the identity of the other FAs. FAMEs of branched saturated FAs, iso or anteiso, display a constant negative shift in the ECL, compared to linear saturated FAs with the same number of carbons. FAMEs of unsaturated FAs can be detected by the mass of their molecular ions, and result in a positive shift in the ECL compared to the corresponding saturated FAs.
The branching position of FAs and the double bond position of unsaturated FAs can be identified by the electron ionization mass spectra of picolinyl and DMOX derivatives, respectively. This approach identifies all the unknown saturated branched FAs, unsaturated straight-chain FAs and unsaturated branched FAs from the B. cereus extract.

Identification of Fatty Acids in Bacillus cereus

We propose a protocol to identify fatty acids without the need to purify them. It combines information on the retention times with the mass spectra of three types of fatty acid derivatives: fatty acid methyl esters (FAMEs), 4,4-dimethyl oxazoline derivatives (DMOX), and 3-pyridylcarbinyl esters (picolinyl).

The Bacillus species contain branched chain and unsaturated fatty acids (FAs) with diverse positions of the methyl branch (iso or anteiso) and of the ...

Nuclear Magnetic Resonance Spectroscopy for the Identification of Multiple Phosphorylations of Intrinsically Disordered Proteins

Aggregates of the neuronal Tau protein are found inside neurons of Alzheimer's disease patients. Development of the disease is accompanied by increased, abnormal phosphorylation of Tau. In the course of the molecular investigation of Tau functions and dysfunctions in the disease, nuclear magnetic resonance (NMR) spectroscopy is used to identify the multiple phosphorylations of Tau. We present here detailed protocols of recombinant production of Tau in bacteria, with isotopic enrichment for NMR studies. Purification steps that take advantage of Tau's heat stability and high isoelectric point are described. The protocol for in vitro phosphorylation of Tau by recombinant activated ERK2 allows for generating multiple phosphorylations. The protein sample is ready for data acquisition at the issue of these steps. The parameter setup to start recording on the spectrometer is considered next. Finally, the strategy to identify phosphorylation sites of modified Tau, based on NMR data, is explained. The benefit of this methodology compared to other techniques used to identify phosphorylation sites, such as immuno-detection or mass spectrometry (MS), is discussed.

We describe here a method to identify multiple phosphorylations of an intrinsically disordered protein by Nuclear Magnetic Resonance Spectroscopy (NMR), using Tau protein as a case study. Recombinant Tau is isotopically enriched and modified in vitro by a kinase prior to data acquisition and analysis.

Aggregates of the neuronal Tau protein are found inside neurons of Alzheimer's disease patients. Development of the disease is accompanied by increased, ...

A Protein Preparation Method for the High-throughput Identification of Proteins Interacting with a Nuclear Cofactor Using LC-MS/MS Analysis

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in regulating transcription. These include the direct interaction with transcription factors, the covalent modification of histones and other proteins, and the occasional chromatin conformation alteration. Accordingly, establishing relatively quick methods for identifying proteins that interact within this network is crucial to enhancing our understanding of the underlying regulatory mechanisms. LC-MS/MS-mediated protein binding partner identification is a validated technique used to analyze protein-protein interactions. By immunoprecipitating a previously-identified member of a protein complex with an antibody (occasionally with an antibody for a tagged protein), it is possible to identify its unknown protein interactions via mass spectrometry analysis. Here, we present a method of protein preparation for the LC-MS/MS-mediated high-throughput identification of protein interactions involving nuclear cofactors and their binding partners. This method allows for a better understanding of the transcriptional regulatory mechanisms of the targeted nuclear factors.

We have established a method for the purification of coregulatory interaction proteins using the LC-MS/MS system.

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in ...

The Identification of Sea Lamprey Pheromones Using Bioassay-Guided Fractionation

Bioassay-guided fractionation is an iterative approach that uses the results of physiological and behavioral bioassays to guide the isolation and identification of an active pheromone compound. This method has resulted in the successful characterization of the chemical signals that function as pheromones in a wide range of animal species. Sea lampreys rely on olfaction to detect pheromones that mediate behavioral or physiological responses. We use this knowledge of fish biology to posit functions of putative pheromones and to guide the isolation and identification of active pheromone components. Chromatography is used to extract, concentrate, and separate compounds from the conditioned water. Electro-olfactogram (EOG) recordings are conducted to determine which fractions elicit olfactory responses. Two-choice maze behavioral assays are then used to determine if any of the odorous fractions are also behaviorally active and induce a preference. Spectrometric and spectroscopic methods provide the molecular weight and structural information to assist with the structure elucidation. The bioactivity of the pure compounds is confirmed with EOG and behavioral assays. The behavioral responses observed in the maze should ultimately be validated in a field setting to confirm their function in a natural stream setting. These bioassays play a dual role to 1) guide the fractionation process and 2) confirm and further define the bioactivity of isolated components. Here, we report the representative results of a sea lamprey pheromone identification that exemplify the utility of the bioassay-guided fractionation approach. The identification of sea lamprey pheromones is particularly important because a modulation of its pheromone communication system is among the options considered to control the invasive sea lamprey in the Laurentian Great Lakes. This method can be readily adapted to characterize the chemical communication in a broad array of taxa and shed light on waterborne chemical ecology.

Here, we present a protocol to isolate and characterize the structure, olfactory potency, and behavioral response of putative pheromone compounds of sea lampreys.

Bioassay-guided fractionation is an iterative approach that uses the results of physiological and behavioral bioassays to guide the isolation and ...

Measurement of Ion Concentration in the Unstirred Boundary Layer with Open Patch-Clamp Pipette: Implications in Control of Ion Channels by Fluid Flow

Fluid flow is an important environmental stimulus that controls many physiological and pathological processes, such as fluid flow-induced vasodilation. Although the molecular mechanisms for the biological responses to fluid flow/shear force are not fully understood, fluid flow-mediated regulation of ion channel gating may contribute critically. Therefore, fluid flow/shear force sensitivity of ion channels has been studied using the patch-clamp technique. However, depending on the experimental protocol, the outcomes and interpretation of data can be erroneous. Here, we present experimental and theoretical evidence for fluid flow-related errors and provide methods for estimating, preventing, and correcting these errors. Changes in junction potential between the Ag/AgCl reference electrode and bathing fluid were measured with an open pipette filled with 3 M KCl. Fluid flow could then shift the liquid/metal junction potential to approximately 7 mV. Conversely, by measuring the voltage shift induced by fluid flow, we estimated the ion concentration in the unstirred boundary layer. In the static condition, the real ion concentrations adjacent to the Ag/AgCl reference electrode or ion channel inlet at the cell-membrane surface can reach as low as approximately 30% of that in the flow condition. Placing an agarose 3 M KCl bridge between the bathing fluid and reference electrode may have prevented this problem of junction potential shifting. However, the unstirred layer effect adjacent to the cell membrane surface could not be fixed in this way. Here, we provide a method for measuring real ion concentrations in the unstirred boundary layer with an open patch-clamp pipette, emphasizing the importance of using an agarose salt-bridge while studying fluid flow-induced regulation of ion currents. Therefore, this novel approach, which takes into consideration the real concentrations of ions in the unstirred boundary layer, may provide useful insight on the experimental design and data interpretation related to fluid shear stress regulation of ion channels.

Mechanosensitive ion channels are often studied in terms of fluid flow/shear force sensitivity with patch-clamp recording. However, depending on the experimental protocol, the outcome on fluid flow-regulations of ion channels can be erroneous. Here, we provide methods for preventing and correcting such errors with a theoretical basis.

Fluid flow is an important environmental stimulus that controls many physiological and pathological processes, such as fluid flow-induced vasodilation. ...

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, ...

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Extensive whole genome sequencing has identified many Open Reading Frames (ORFs) providing many potential proteins. These proteins may have important roles for the cell and may unravel new cellular processes. Among proteins, kinases are major actors as they belong to cell signaling pathways and have the ability to switch on or off many processes crucial to the fate of the cell, such as cell growth, division, differentiation, motility, and death.
In this study, we focused on a new potential kinase protein, LIMK2-1. We demonstrated its existence by Western Blot using a specific antibody. We evaluated its interaction with an upstream regulating protein using coimmunoprecipitation experiments. Coimmunoprecipitation is a very powerful technique able to detect the interaction between two target proteins. It may also be used to detect new partners of a bait protein. The bait protein may be purified either via a tag engineered to its sequence or via an antibody specifically targeting it. These protein complexes may then be separated by SDS-PAGE (Sodium Dodecyl Sulfate PolyAcrylamide Gel) and identified using mass spectrometry. Immunoprecipitated LIMK2-1 was also used to test its kinase activity in vitro by &#947;[32P] ATP labeling. This well-established assay may use many different substrates, and mutated versions of the bait may be used to assess the role of specific residues. The effects of pharmacological agents may also be evaluated since this technique is both highly sensitive and quantitative. Nonetheless, radioactivity handling requires particular caution. Kinase activity may also be assessed with specific antibodies targeting the phospho group of the modified amino acid. These kinds of antibodies are not commercially available for all the phospho modified residues.

We characterized a new kinase protein using robust biochemical approaches: Western Blot analysis with a dedicated specific antibody on different cell lines and tissues, interactions by coimmunoprecipitation experiments, kinase activity detected by Western Blot using a phospho-specific antibody and by &#947;[32P] ATP labeling.

Extensive whole genome sequencing has identified many Open Reading Frames (ORFs) providing many potential proteins. These proteins may have important ...

SUMO-Binding Entities (SUBEs) as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer

Post-translational modification is a key mechanism regulating protein homeostasis and function in eukaryotic cells. Among all ubiquitin-like proteins in liver cancer, the modification by SUMO (Small Ubiquitin MOdifier) has been given the most attention. Isolation of endogenous SUMOylated proteins in vivo is challenging due to the presence of active SUMO-specific proteases. Initial studies of SUMOylation in vivo were based on the molecular detection of specific SUMOylated proteins (e.g., by western blot). However, in many cases, antibodies, generally made with non-modified recombinant protein, did not immunoprecipitate SUMOylated forms of the protein of interest.&#160;Nickel chromatography has been the other approach to study SUMOylation by capturing histidine-tagged versions of SUMO molecules. This approach is mainly used in cells stably expressing or transiently transfected with His-SUMO molecules. To overcome these limitations, SUMO-binding entities (SUBEs) were developed to isolate endogenous SUMOylated proteins. Herein, we describe all the steps required for the enrichment, isolation, and identification of SUMOylated substrates from human hepatoma cells and hepatic tissues from a liver cancer mouse model by using SUBEs. Firstly, we describe methods involved in the preparation and lysis of the human hepatoma cells and liver tumor tissue samples. Then, a thorough explanation of the preparation of SUBEs and controls is detailed along with the protocol for the protein pull-down assays. Finally, some examples are provided regarding the options available for the identification and characterization of the SUMOylated proteome, namely the use of western-blot analysis for the detection of a specific SUMOylated substrate from liver tumors or the use of proteomics by mass spectrometry for high-throughput characterization of the SUMOylated proteome and interactome in hepatoma cells.

SUMO-Binding Entities SUBEs as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer

Here, we present a protocol to enrich, isolate, identify, and characterize proteins modified by SUMO in vivo both from human hepatoma cells and liver tumors obtained from mouse models of hepatocellular carcinoma by using SUMO-binding entities (SUBEs).

Post-translational modification is a key mechanism regulating protein homeostasis and function in eukaryotic cells. Among all ubiquitin-like proteins in ...

Making Precise and Accurate Single-Molecule FRET Measurements using the Open-Source smfBox

The smfBox is a recently developed cost-effective, open-source instrument for single-molecule F&#246;rster Resonance Energy Transfer (smFRET), which makes measurements on freely diffusing biomolecules more accessible. This overview includes a step-by-step protocol for using this instrument to make measurements of precise FRET efficiencies in duplex DNA samples, including details of the sample preparation, instrument setup and alignment, data acquisition, and complete analysis routines. The presented approach, which includes how to determine all the correction factors required for accurate FRET-derived distance measurements, builds on a large body of recent collaborative work across the FRET Community, which aims to establish standard protocols and analysis approaches. This protocol, which is easily adaptable to a range of biomolecular systems, adds to the growing efforts in democratising smFRET for the wider scientific community.

This article provides step-by-step instructions for making fully-corrected accurate FRET measurements on individual, freely diffusing biomolecules using the open-source, inexpensive smfBox, from switch on, through alignment and focusing, to data collection and analysis.

The smfBox is a recently developed cost-effective, open-source instrument for single-molecule F&#246;rster Resonance Energy Transfer (smFRET), which makes ...

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One &#945;-Synuclein Monomer at a Time

Using spectroscopic rulers to track multiple conformations of single biomolecules and their dynamics have revolutionized the understanding of structural dynamics and its contributions to biology. While the FRET-based ruler reports on inter-dye distances in the 3-10 nm range, other spectroscopic techniques, such as protein-induced fluorescence enhancement (PIFE), report on the proximity between a dye and a protein surface in the shorter 0-3 nm range. Regardless of the method of choice, its use in measuring freely-diffusing biomolecules one at a time retrieves histograms of the experimental parameter yielding separate centrally-distributed sub-populations of biomolecules, where each sub-population represents either a single conformation that stayed unchanged within milliseconds, or multiple conformations that interconvert much faster than milliseconds, and hence an averaged-out sub-population. In single-molecule FRET, where the reported parameter in histograms is the inter-dye FRET efficiency, an intrinsically disordered protein, such as the &#945;-Synuclein monomer in buffer, was previously reported as exhibiting a single averaged-out sub-population of multiple conformations interconverting rapidly. While these past findings depend on the 3-10 nm range of the FRET-based ruler, we sought to put this protein to the test using single-molecule PIFE, where we track the fluorescence lifetime of site-specific sCy3-labeled &#945;-Synuclein proteins one at a time. Interestingly, using this shorter range spectroscopic proximity sensor, sCy3-labeled &#945;-Synuclein exhibits several lifetime sub-populations with distinctly different mean lifetimes that interconvert in 10-100 ms. These results show that while &#945;-Synuclein might be disordered globally, it nonetheless attains stable local structures. In summary, in this work we highlight the advantage of using different spectroscopic proximity sensors that track local or global structural changes one biomolecule at a time.

Time-resolved single-molecule protein-induced fluorescence enhancement is a useful fluorescence spectroscopic proximity sensor sensitive to local structural changes in proteins. Here we show it can be used to uncover stable local conformations in &#945;-Synuclein, which is otherwise known as globularly unstructured and unstable when measured using the longer range FRET ruler.

Using spectroscopic rulers to track multiple conformations of single biomolecules and their dynamics have revolutionized the understanding of structural ...