Methionine Oxidation Footprinting in Intact Proteins (MOFIP) using Top-Down Proteomics

Methionine Oxidation Footprinting in Intact Proteins (MOFIP) using Top-Down Proteomics | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL PROTEOMICS This is a preprint and has not been peer reviewed. Data may be preliminary. 14 August 2025 V1 Latest version Share on Methionine Oxidation Footprinting in Intact Proteins (MOFIP) using Top-Down Proteomics Authors : Anju Sunny , Kellye Cupp-Sutton , Zhitao Zhao , Trishika Chowdhury , Yanting Guo , and Si Wu 0000-0002-6346-7359 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175516569.91192211/v1 Published PROTEOMICS Version of record Peer review timeline 315 views 232 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Mass spectrometry (MS)-based proteomics methods can give unique insight into protein structure and interactions. Here, we have developed a novel top-down footprinting method, Methionine Oxidation Footprinting in Intact Proteins (MOFIP), to probe solvent accessibility in intact proteoforms. For MOFIP, natively folded protein lysates are incubated with and without hydrogen peroxide (H2O2) to evaluate solvent accessibility of methionine residues. Top-down proteomics analysis allows the characterization of the solvent accessibility of each methionine residue within intact proteins to obtain structural information. Here, intact E. coli lysate was used to evaluate the feasibility of complex biological sample analysis using the MOFIP platform. Overall, we profiled 137 methionine residues from 69 proteoforms that contained at least one methionine residue suitable for methionine footprinting. These methionine residues from intact proteoforms fell into three general categories: solvent-accessible (50%), solvent-inaccessible (16%), and partially solvent-accessible (34%). Overall, we have demonstrated that our novel MOFIP platform is capable of characterizing the solvent accessibility of individual methionine residues within intact proteoforms in complex biological samples, which is exceedingly challenging using bottom-up proteomics methods. Methionine Oxidation Footprinting in Intact Proteins (MOFIP) using Top-Down Proteomics Anju Teresa Sunny 1,# , Kellye A. Cupp-Sutton 1,# , Zhitao Zhao 2 , Trishika Chowdhury 1 ,Yanting Guo 2 , Si Wu 1,2* Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, AL 35487, USA Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA # These authors contributed equally to this work *Correspondence: Si Wu, Ph.D. Department of Chemistry and Biochemistry University of Alabama 250 Hackberry Lane, Tuscaloosa, AL 35487, USA E-mail: [email protected] Phone: 205-348-8443 ABSTRACT Mass spectrometry (MS)-based proteomics methods can give unique insight into protein structure and interactions. Here, we have developed a novel top-down footprinting method, Methionine Oxidation Footprinting in Intact Proteins (MOFIP), to probe solvent accessibility in intact proteoforms. For MOFIP, natively folded protein lysates are incubated with and without hydrogen peroxide (H 2 O 2 ) to evaluate solvent accessibility of methionine residues. Top-down proteomics analysis allows the characterization of the solvent accessibility of each methionine residue within intact proteins to obtain structural information. Here, intact E. coli lysate was used to evaluate the feasibility of complex biological sample analysis using the MOFIP platform. Overall, we profiled 137 methionine residues from 69 proteoforms that contained at least one methionine residue suitable for methionine footprinting. These methionine residues from intact proteoforms fell into three general categories: solvent-accessible (50%), solvent-inaccessible (16%), and partially solvent-accessible (34%). Overall, we have demonstrated that our novel MOFIP platform is capable of characterizing the solvent accessibility of individual methionine residues within intact proteoforms in complex biological samples, which is exceedingly challenging using bottom-up proteomics methods. STATEMENT OF SIGNIFICANCE Methionine is a rare amino acid often strategically located in protein structures, contributing to protein stability, and solvent accessibility of methionine residues has often been evaluated to provide valuable insight into protein structure. High-throughput oxidative methionine footprinting techniques have facilitated the examination of protein interactions, stability, and dynamics in complex biological samples such as cell lysates. However, these footprinting methods have primarily been performed using bottom-up proteomics methods that require proteolytic digestion of proteins into peptides prior to mass spectrometry (MS) analysis. Here, we introduce Methionine Oxidation Footprinting for Intact Proteins (MOFIP), a high-throughput top-down proteomics technique that allows intact proteoform-level examination of methionine solvent accessibility. Overall, we profiled 137 methionine residues from 69 proteoforms that contained at least one methionine residue suitable for methionine footprinting. These methionine residues from intact proteoforms fell into three general categories: solvent-accessible (50%), solvent-inaccessible (16%), and partially solvent-accessible (34%). MOFIP enabled the evaluation of methionine accessibility for 69 intact proteoforms in E. coli cell lysate, providing structural insights into proteoforms that are difficult to obtain using bottom-up proteomics. INTRODUCTION Protein structure and its interactions with other biomolecules are indicative of protein functionality. 1-3 As such, many methods that probe changes in protein expression, structure, and interactions have been developed to investigate protein functionality and its effect on biomolecular pathways. Primary structural changes caused by post-translational modifications (PTMs), amino acid substitutions, or alternative splicing events can also affect protein function. 4 However, characterizing protein structure and function has been challenging, particularly in native or native-like environments that maintain dynamic interactions and macromolecular crowding that can be critical to the function of proteins. 5 Thus, it is important to develop tools to accurately evaluate and characterize protein structure and function in complex biological samples. Classical approaches to examine protein structure, such as X-ray crystallography 6 , cryo-electron microscopy (cryo-EM) 7 , and Nuclear Magnetic Resonance (NMR) 8 are widely used to determine the high-resolution structure of proteins. Cryo-EM and X-ray crystallography typically examine static protein structures 3 , and NMR can examine dynamic protein structure in solution. 9 However, these approaches often require large amounts of purified protein that must be recombinantly expressed in model organisms. 10 These expressed and purified proteins may not reflect the protein structure in native organisms, 3 and proteins often cannot be expressed with modifications that can affect protein function and interactions. 11 Apart from experimental methods for protein structural determination, artificial intelligence (AI)-based computational modeling approaches such as AlphaFold have been used to predict protein structures. 12 However, some regions in the predicted structures have low confidence and may not accurately reflect the native protein structure. Additionally, the effect of variation in the primary structure on higher-order protein structure is currently not considered in the predictions. 13 Lastly, these methods typically cannot characterize the structure of proteins in native or native-like environments that maintain dynamic interactions and macromolecular crowding, which can be critical to protein function. 5 Thus, methods that characterize proteoform structure and function in complex biological samples can offer a unique understanding of proteoform structure. Recently, mass spectrometry (MS)-based proteomics methods have been developed to aid in understanding protein structure and interactions, and how these relate to function. 5 These methods, known as functional proteomics, include phenotypic expression and gene knockout methods 14 , protein footprinting 15 , chemical crosslinking (XL-MS) 16, 17 , and stability proteomics methods. 5, 11, 18 The protein footprinting methods are unique from the other functional proteomics methods in that they can give information about the three-dimensional structure of a protein that can be informative to protein function. 15 The most commonly implemented footprinting methods are hydrogen-deuterium exchange mass spectrometry (HDX-MS) 19 and hydroxyl radical footprinting mass spectrometry (HRF-MS). 20 HDX-MS probes protein structure by exchanging accessible backbone amide hydrogens with deuterium by incubation with deuterium oxide (D 2 O). This is a powerful technique used to characterize protein structure, interactions, and dynamics. 21 HRF methods utilize hydroxyl radicals to react with solvent-exposed, oxidizable amino acid side chains. 20 Importantly, the oxidation is irreversible, so the protein labeling is much more robust than HDX-MS. Similarly, fast photochemical oxidation of proteins (FPOP) 2 is an HRF method that labels proteins with hydroxyl radicals generated via photo-dissociation of hydrogen peroxide (H 2 O 2 ). 22 Footprinting methods are primarily applied to pure proteins or simple protein mixtures; however, both HDX-MS 21, 23 and FPOP 2, 24 methods have been recently demonstrated for the analysis of complex biological samples. These footprinting methods can also provide some insights into the function of proteoforms that cannot be probed by the structural biology methods discussed above. 15 These HRF methods offer detailed information regarding solvent-exposed regions in proteins because many amino acid side chains can be oxidized by the hydroxyl radicals. 25 However, methods that specifically target methionine residues for oxidation may allow the examination of the role of methionine residues specifically in protein structure, stability, and function. 26-28 Although methionine oxidation is often disregarded in proteomics experiments as an exogenous modification from air exposure during sample preparation, it can also originate from endogenous sources. 29, 30 These include oxidative damage due to reactive oxygen species (ROS), regulated oxidation that alters protein function, or reversible modifications that enable methionine to act as an antioxidant scavenger. 26, 31-33 Different methionine residues within the same protein can also demonstrate different susceptibility to oxidation, and factors such as solvent accessibility and neighboring sequence effects can influence rates of methionine oxidation. 34, 35 Stability of Proteins from Rates of Oxidation (SPROX) has been developed to examine the change in rate of methionine oxidation in response to a chemical denaturation gradient to probe protein structural alterations and changes in protein stability. 36 SPROX has also been applied to examine the solvent accessibility of methionine residues without chemical denaturation. 34 HRF methods, as well as SPROX methods, are typically performed using bottom-up proteomics methods that require digestion of labeled proteins into peptides for MS analysis. Bottom-up proteomics methods are sensitive and robust; however, information regarding proteoform structure can be lost as a result of protein digestion. In contrast to bottom-up proteomics methods, top-down proteomics methods analyze intact proteoforms directly, thereby allowing the characterization and localization of protein modifications. 37-41 Top-down proteomics has been widely applied to characterize the effect of proteoforms on function. 37, 41-43 Additionally, top-down proteomics has been applied to SPROX to examine relative folding stability of intact proteoforms. 44 However, to our knowledge, there are currently no top-down proteomics methods to systematically evaluate solvent accessibility of methionine residues on intact proteoforms in complex biological samples. We have developed Methionine Oxidation Footprinting in Intact Proteins (MOFIP), a novel method for top-down methionine footprinting in complex biological samples. MOFIP allows for methionine footprinting of natively folded intact proteoforms by examining solvent accessibility of methionine residues at the intact proteoform level. For MOFIP, cell lysate was incubated with H 2 O 2 for a short time so that only methionine residues exposed to solvent in the natively folded protein became oxidized. Intact protein samples with and without incubation with H 2 O 2 were analyzed directly using our quantitative top-down RPLC-MS/MS platform. 45 This novel MOFIP platform was implemented to examine the solvent accessibility of intact proteoforms in E. coli lysate as a model system for proof-of-concept experiments. Overall, we profiled 137 methionine residues from 69 proteoforms that contained at least one methionine residue suitable for methionine footprinting. These methionine residues from intact proteoforms fell into three general categories: solvent-accessible (50%), solvent-inaccessible (16%), and partially solvent-accessible (34%). Overall, we have demonstrated that our novel MOFIP platform successfully characterized the solvent accessibility of individual methionine residues within intact proteoforms in complex biological samples, which is exceedingly challenging using bottom-up proteomics methods. MATERIALS AND METHODS Chemicals and Materials . LC-MS grade water, acetonitrile (ACN), 2-propanol, trifluoroacetic acid (TFA), guanidine hydrochloride (GdmCl), L-methionine, sodium phosphate monobasic (NaH 2 PO 4 ), sodium phosphate dibasic (Na 2 HPO 4 ), ammonium bicarbonate (NH 4 HCO 3 ), and phenylmethylsulfonyl fluoride (PMSF) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Hydrogen peroxide (H 2 O 2 ), Pierce TM BCA Protein Assay kit, and molecular weight cutoff spin filters (10 kDa and 100 kDa MWCO) were obtained from Thermo Fisher Scientific (Waltham, MA, USA). E. coli culture and cell lysate preparation. E. coli K12 cells were cultured, and cell lysate was prepared as described previously. 46 100 kDa molecular weight cutoff (MWCO) filters were washed with 25 mM ammonium bicarbonate buffer, and 10 kDa MWCO filters were washed with 20 mM phosphate buffer (pH 7.4). Low molecular weight E. coli cell lysate proteins (e.g., 10-100 kDa) were enriched by 100 kDa MWCO filtration by centrifuging at 4,200 rpm and 4 °C for 15 min. The filtrate was then concentrated, and the buffer was exchanged for 20 mM pH 7.4 phosphate buffer via 10 kDa MWCO filtration at 4,200 rpm and 4 °C for 15 min. The protein concentration was measured using a Pierce TM BCA Protein Assay kit. Samples were stored at -80 °C prior to use. MOFIP Sample preparation . The MOFIP workflow is shown in Figure 1A . E. coli lysate (2.6 µg/µL) was distributed to 3 aliquots of 3 µL each (e.g., 7.8 µg intact E. coli lysate per aliquot). The first aliquot was used for examination of solvent-accessible methionine residues in natively folded proteoforms, referred to as the +H 2 O 2 sample . This aliquot was first diluted to 12 µL with 20 mM pH 7.4 phosphate buffer for a final E. coli concentration of 0.65 µg/µL. After dilution, this sample was oxidized by incubating with 3.5 µL of 9.8 M H 2 O 2 ([H 2 O 2 ] = 2.21 M, after mixing) at room temperature for 5 minutes. This was previously demonstrated to be a sufficient labeling time to maximize labeling of exposed methionine residues while limiting baseline oxidation. 36 Excess H 2 O 2 was then quenched by the addition of 572 µL of 300 mM L -methionine. This results in a final L -methionine concentration of 292 mM, five times higher than the theoretical H 2 O 2 concentration (58 mM) after mixing, enabling rapid and efficient quenching. 36 The second aliquot, referred to as the -H 2 O 2 control, was used to evaluate baseline endogenous and artifactual oxidation. It was diluted to 12 µL with 20 mM phosphate buffer (pH 7.4), without H 2 O 2 treatment, but underwent quenching as described previously. The final aliquot was used to assess methionine accessibility in denatured proteoforms, providing complementary characterization of inaccessible or partially accessible methionine residues. This aliquot was diluted to 12 µL with 8 M guanidinium chloride (GdmCl) in 20 mM pH 7.4 phosphate buffer ([GdmCl] = 6 M, after mixing), and H 2 O 2 oxidation and quenching were performed as described. After quenching, all samples were subjected to buffer exchange to remove MS-incompatible salts and excess methionine. Buffer exchange was performed using 10 kDa MWCO filters and 25 mM ammonium bicarbonate (ABC) buffer. The filters were first washed with 25 mM ABC, after which samples were added and centrifuged at 4,200 rpm at 4 °C for 15 minutes. This step was repeated twice with fresh ABC buffer, and desalted samples were stored at -80 °C prior to LC-MS analysis. All samples were prepared in duplicate, and mass spectrometry replicates were conducted for each. Top-down RPLC-MS/MS Analysis. Each sample was analyzed using reversed-phase liquid chromatography (RPLC) separations performed on a customized Thermo Scientific (Waltham, MA, USA) Accela nanoLC System. 46-49 For each sample, 2 µg of protein was loaded onto a trapping column (150 µm I.D., 5 cm length, 5 µm diameter Jupiter particles with 300 Å pore size) and separated using a C4 column from CoAnn Technologies (100 µm I.D., 45 cm length, 3.4 µm diameter, and 300 Å pore size). Mobile phase A was made up of 0.01% TFA, 0.585% acetic acid, 2.5% 2-propanol, and 5% acetonitrile in water. Mobile phase B was made up of 0.01% TFA, 0.585% acetic acid, 45% 2-propanol, and 45 % acetonitrile in water. A 60-minute gradient was applied from 10% to 70% of MPB with a flow rate of 200 nL/min. The LC eluent was analyzed using an Orbitrap Ascend Tribrid mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) with a customized nano-ESI interface. 47 MS parameters were set as follows: inlet capillary temperature was 275 °C, ESI voltage was +3.0 kV, and resolution for MS1 and MS2 was 120,000 and 60,000, respectively. AGC was 4 x 10 5 for MS1 and 5 x 10 5 for MS2 scans. The maximum injection time was 251 ms for MS1 and 500 ms for MS2. The isolation window was 3 m / z . The dynamic exclusion was 90 s, and the top six most abundant precursor ion peaks (charge 5-40) from each MS1 scan were selected for MS2 fragmentation with data-dependent acquisition (DDA). The normalized higher-energy collisional dissociation (HCD) energy was 35%. Data Analysis. Proteoform detection and label-free quantitation were performed using Biopharma Finder (Thermo Fisher Scientific). For proteoform identification, RAW files were converted to the mzML file format using MSConvert. 50 Proteoforms were identified using TopPIC Suite (version 1.7.4) 51 and searched against the annotated E . coli protein database (UniProt 2019-10-13, 4519 species). The parameters for TopPIC were as follows: the MS1 signal-to-noise ratio was 3, the precursor window size was 5.0 m/z, and the maximum mass was 50,000 Daltons. Decoy database searching was used with an FDR cutoff of 0.01 for both spectrum and proteoform levels. The maximum number of mass shifts was set to 2, and the mass shift range was ± 500 Da. All the other parameters were default. An in-house Python script was used for label-free quantitation, following a method similar to the accurate mass and time (AMT) approach, 52, 53 as previously described. 48 A comprehensive AMT tag database was created by combining all identified proteoforms across all datasets. Detected proteoform intensity from each run was combined using a ± 10 ppm mass error tolerance and ± 10 min retention time shift. Detected proteoforms with mass shifts of ± 1 Da were also merged. These detected, quantified proteoforms were matched to the AMT tag database using a ± 30 ppm mass error tolerance and a ± 10 min retention time shift. All reported data were manually confirmed. ProSight Lite was used for manual interpretation and spectrum presentation. 54 Chimera was used for three-dimensional protein structure presentation. 55 GraphPad Prism (version 8.0.1) was used for data presentation. RESULTS AND DISCUSSION Here, we demonstrate the feasibility of methionine footprinting of intact proteoforms (MOFIP) in complex biological samples using intact E. coli lysate as a model system (workflow schematic shown in Figure 1A ). Intact E. coli lysate was divided into two samples: one was incubated with H 2 O 2 to oxidize solvent-accessible methionine residues (+H 2 O 2 ), and the other was not incubated with H 2 O 2 (-H 2 O 2 ). This experiment was performed in duplicate, and top-down RPLC-MS/MS analysis was performed in technical duplicate for a total of 8 runs (e.g., 4 -H 2 O 2 and 4 +H 2 O 2 ). In total, 2200 proteoforms were successfully identified in the MOFIP analysis (1% FDR), from the - H 2 O 2 sample ( Supplementary Table 1 ). A total of 1332 proteoforms (approximately 61%) contained at least one methionine residue ( Figure S1 ). For the proof-of-principle MOFIP analysis, we applied a filtering criterion requiring proteoform intensities greater than 5E5 and presence in all 4 runs after deconvolution and manual confirmation, resulting in MOFIP analysis of 69 “high-quality” proteoforms with at least one methionine residue ( Supplementary Table 2 ). Profiling Solvent Accessibility of Methionine Residues in Intact Proteoforms Quantifiable methionine residues were classified based on their solvent accessibility as determined by their oxidation state following H 2 O 2 treatment ( Figure 1B ). Residues that were fully oxidized upon incubation with H 2 O 2 were categorized as solvent-accessible (e.g., the unoxidized methionine residue is not detected in the +H 2 O 2 sample). Methionine residues that remained unoxidized in the +H 2 O 2 sample were considered solvent-inaccessible (e.g., the oxidized methionine residues are not detected in the +H 2 O 2 sample). Methionine residues that exhibited both oxidized and unoxidized forms in the +H 2 O 2 sample were classified as partially solvent-accessible . After classifying each methionine residue as solvent-accessible, inaccessible, or partially accessible, we analyzed their positions within the protein structure and related secondary structures ( Supplementary Table 2 ). Structural classification was performed using data from UniProt (www.uniprot.org), including experimentally derived models from NMR spectroscopy, cryo-electron microscopy, and X-ray crystallography, or AlphaFold computational predictions in the absence of empirical structures. According to these structures, methionine residues were categorized as being in helices, sheets, or coils. Turns, bends, and N -termini were also regarded as coils in this context. 56 Solvent-accessible methionine residues. Overall, 50% (69 methionine residues in 45 proteoforms from 34 proteins) of the profiled methionine residues were solvent accessible. For example, a proteoform of Thioredoxin 1 protein (P0AA25) was identified with N -terminal methionine removal ( Figure 2A and Figure S2) . This proteoform contains one methionine residue at Met-38 located in a helix ( Figure S2A ). Met-38 was categorized as solvent-accessible as this residue was fully oxidized ( Figure S2B ) upon incubation with H 2 O 2 as demonstrated by representative mass spectra, which depict a mass shift in the +8-charge state of this proteoform from 1460.14 m/z (0 Ox) to 1462.14 m/z (1 Ox; mass shift = 16 Da) between the -H 2 O 2 and +H 2 O 2 samples. This trend is further demonstrated in the bar chart depicting the average relative abundance of the 0 Ox and 1 Ox proteoforms across replicate samples in both -H 2 O 2 and +H 2 O 2 samples. Examination of the X-ray diffraction-determined structure of the protein indicates that the methionine side chain protrudes outwards, suggesting its solvent accessibility. 57 An intact proteoform of the Autonomous glycyl radical cofactor protein (P68066) was identified ( Figure 2B and Figure S3) . This proteoform contains one methionine residue at the N -terminal (Met-1) ( Figure S3A ). Here, the N -termini are considered to be coils as previously demonstrated in the literature. 56 Met-1 was categorized as accessible as it is fully oxidized ( Figure S3B ) upon incubation with H 2 O 2 as demonstrated by representative mass spectra, which depict a mass shift in the +13-charge state of this proteoform from 1099.74 m/z (0 Ox) to 1100.97 m/z (1 Ox; mass shift = 16 Da) between the -H 2 O 2 and +H 2 O 2 samples. This is also demonstrated in the bar chart depicting the average relative abundance of the 0 Ox and 1 Ox proteoforms across replicate samples in both -H 2 O 2 and +H 2 O 2 samples. Examination of the AlphaFold-determined structure of the protein indicates that the Met-1 side chain protrudes outwards, suggesting its solvent accessibility. 58 Solvent-Inaccessible Methionine Residues. Overall, 16% (22 methionine residues in 16 proteoforms from 16 proteins) of the profiled methionine residues were solvent-inaccessible. For example, a completely intact phosphocarrier protein (P0AA04) proteoform was identified ( Figure 3A and Figure S4) . This proteoform contains two methionine residues at the N -terminal (Met-1) and Met-81, which is located in a helix ( Figure S4A ). One methionine residue was solvent accessible and completely oxidized upon incubation with H 2 O 2, as demonstrated by representative mass spectra, which depict a mass shift in the +9-charge state of this proteoform from 1014.20 m/z (0 Ox) to 1015.98 m/z (1 Ox; mass shift = 16 Da) between the -H 2 O 2 and +H 2 O 2 samples. Met-1 was demonstrated to be accessible as the 1 Ox species was determined to be oxidized at Met-1 by MS/MS fragmentation ( Figure S4B ). Generally, a solvent-inaccessible residue is characterized by the absence of the oxidized species in the +H 2 O 2 sample. In this case, Met-81 was detected at lower abundance in the -H 2 O 2 (1.15% ± 1.15) and +H 2 O 2 samples (4.3% ± 4.47), indicating there is a small amount of endogenous or environmental oxidation. Met-80 has been categorized as solvent-inaccessible because the abundance of the 2 Ox species does not change between the -H 2 O 2 and +H 2 O 2 samples. This is also demonstrated in the bar chart depicting the average relative abundance of the 0, 1, and 2 Ox species across replicate samples in both -H 2 O 2 and +H 2 O 2 samples. Examination of the X-ray diffraction-determined structure of the protein reveals that Met-1, located in a coil region, has a side chain that protrudes outward, suggesting its solvent accessibility. 59 However, Met-81 is oriented inward toward the protein’s core, suggesting that it is likely to be solvent-inaccessible. A fully intact proteoform of DNA-binding protein HU-alpha (P0ACF0) was identified ( Figure 3B and Figure S5) . This proteoform contains a single methionine at the N -terminal (Met-1) ( Figure S5A ). Met-1 is not oxidized, as demonstrated by representative mass spectra, which depict no mass shift between the -H 2 O 2 and +H 2 O 2 samples. This is also demonstrated in the bar chart depicting the average relative abundance of the 0 and 1 Ox species across replicate samples in both -H 2 O 2 and +H 2 O 2 samples. The 1 Ox species was not observed in the +H 2 O 2 sample, indicating that Met-1 was not solvent accessible. Examination of the X-ray diffraction-determined structure of the protein indicates that the Met-1 side chain is oriented inward toward the protein’s core, suggesting that it is likely to be solvent-inaccessible. 60 Partially Solvent-Accessible Methionine Residues. Partial solvent accessibility of a methionine residue can result from factors such as protein interactions (e.g., protein-protein, protein-ligand, or protein-RNA binding) that sterically block oxidation, or from conformational isomers where the accessibility of a methionine residue varies. 61-63 We found that 34% of the methionine residues profiled (46 methionine residues in 30 proteoforms from 22 proteins) were partially solvent-accessible. For example, a proteoform of the cold shock protein (CspA, P0A9X9) was characterized with N -terminal methionine truncation ( Figure 4A and Figure S6 ). This proteoform contains only one methionine residue, Met-5, located on a sheet ( Figure S6A ). On incubation with H 2 O 2 , both oxidized ( Figure S6B ) and unoxidized proteoforms were detected as shown in the mass spectra (+9-charge state; 0 Ox at 808.98 m/z and 1 Ox at 810.74; mass shift = 16 Da). The bar chart further demonstrates that the 1 Ox species was not observed in the -H 2 O 2 samples, and upon incubation with H 2 O 2 (+H 2 O 2 ), the 1 Ox species appears alongside the 0 Ox species, suggesting that Met-5 is partially accessible. The ratio between unoxidized species and oxidized species (e.g., 0 Ox species abundance/1 Ox species abundance) is 2.98 ± 0.49. Examination of the X-ray diffraction-determined structure of the protein indicates that the Met-5 side chain is oriented outward, suggesting that it may have solvent accessibility. 64 However, CspA is known to be part of a complex with RNA, and this complex formation of the RNA has been previously reported to increase the stability of CspA. 65 Our results suggest that both free CspA and CspA–RNA complexes may co-exist in the E. coli lysate, contributing to the observed partial accessibility of the Met-5 residue. Additionally, we performed an experiment in which the E. coli lysate was denatured using GdmCl (6 M) prior to incubation with H 2 O 2 . Met-5 exhibited partial solvent accessibility under denaturing conditions, indicative of significant protein stability ( Figure 4A ). This behavior is consistent with prior SPROX analyses, which have shown that proteins with high stability may remain folded or resist complete unfolding under aggressive denaturing conditions. 36, 44 An outer-membrane lipoprotein carrier protein (LolA, P61316) proteoform was identified with signal peptide removal after Ala-21 ( Figure 4B and Figure S7) . This proteoform contains two methionine residues: Met-72, located in a sheet, and Met-112, located in a helix ( Figure S7A ). Upon incubation with H 2 O 2 , only the 1 Ox and 2 Ox species were detected, suggesting that one methionine residue was solvent accessible upon incubation with H 2 O 2 , and the second was partially solvent-accessible. The solvent-accessible methionine residue was localized to Met-72, located in a sheet, as confirmed by MS/MS fragmentation for the 1 Ox proteoform ( Figure S7B ). Examination of the X-ray diffraction-determined structure of the protein indicates that the Met-72 side chain protrudes outwards, suggesting its solvent accessibility. 66 Additionally, Met-112 was determined to be partially solvent-accessible due to the presence of the 1 Ox and 2 Ox species ( Figure S7C ) in the +H 2 O 2 samples. The ratio between 1 Ox proteoform and 2 Ox proteoform (e.g., 1 Ox species abundance/2 Ox species abundance) is 0.52 ± 0.19. Additionally, we performed an experiment in which the E. coli lysate was denatured using GdmCl (6 M) prior to incubation with H 2 O 2 . Interestingly, Met-112 becomes solvent-accessible under denaturing conditions ( Figure 4B ). LolA is a periplasmic chaperone that transports lipoproteins, and our results may suggest that at least two conformations of LolA co-exist in the E. coli lysate that facilitate this process. 67 Correlation of methionine solvent accessibility with secondary structure. It has been assumed that, due to the hydrophobic nature of methionine, its primary role apart from initiating protein synthesis is to stabilize the hydrophobic core of proteins. 68 Thus, it may be expected that methionine residues would typically favor solvent inaccessibility. 35 However, methionine has been found to be remarkably diverse as it serves a variety of cellular functions, including acting as a regulatory switch 69 , cellular signaling 33 , endogenous antioxidant 26 , as well as stabilizing protein structure. Here, we classified each characterized methionine residue by its secondary structure based on solvent accessibility ( Figure 5 ). Methionine residues do not have a strong bias for secondary structure but are slightly more enriched in helices compared with coils and sheets, likely due to the role of methionine in maintaining the structural stability of helices. 70 We found that 45% of methionine residues profiled here were located in helices, 35% in coils, and 20% in sheet structures. Bottom-up methionine footprinting data collected using SPROX methodology for the human proteome indicate that methionine residues located in more rigid portions of the structure favor solvent inaccessibility compared with disordered regions (e.g., coils). 34 Our results do not show a strong correlation between secondary structure and solvent accessibility, but this may be due to limited proteome coverage. Disordered regions, such as coils, have also been previously shown to have increased solvent accessibility compared with more ordered regions, such as helices and sheets. 34 Interestingly, we found seven solvent-inaccessible methionine residues from 5 proteoforms localized to coils ( Supplementary Table 2) . Three of these were N -terminal methionine residues found in the toxin-antitoxin biofilm protein (P0AF96) 58 , Cold shock-like protein CspC (P0A9Y6) 71 , and the DNA-binding protein HU-alpha (P0ACF0, Figure 3B ). 60 The structure of these proteins shows that the N -terminal methionine residues are pointed inward, suggesting inaccessibility. Of the remaining four inaccessible methionine residues that were localized to coils, two were Met-85 and Met-88 on the Putative ABC transporter arginine-binding protein (P30859) 58 , and two were Met-90 and Met-219 on the Histidine-binding periplasmic protein (P0AEU0). 58 In both cases, these methionine residues were localized to loops located between helices. Structural analysis of these proteins showed that the loops were situated in internal regions, which likely protects them from oxidation. Overall, our results suggest that MOFIP top-down proteomics provides complementary information on intact proteoform structure and conformation dynamics in cell lysate. Many of the proteoforms analyzed lack X-ray, cryo-EM, or NMR structures, and AlphaFold often predicts low-confidence, unstructured regions. Our MOFIP method offers key advantages in assessing methionine solvent accessibility and detecting structural changes at the proteoform level that are difficult to capture experimentally or computationally. Notably, probing partially accessible methionine residues in intact proteoforms offers new insights into protein dynamics, which can be further evaluated to study native proteoform solvent accessibility and complex formation directly in cell lysates. Proteoforms with Mixed Methionine Accessibility Top-down proteomics offers a key advantage over bottom-up methods by enabling the direct analysis of distinct intact proteoforms from the same protein. Using MOFIP, we have identified two different proteoforms of UPF0337 protein YjbJ (P68206): proteoform #1 is the fully intact and unmodified proteoform, and proteoform #2 is with N -terminal methionine removal ( Figure 6 and Figure S8). Proteoform #1 contains two methionine residues, the N -terminal methionine residue (Met-1) and Met-29 located on a helix, Figure 6A . On incubation with H 2 O 2 , the 0 Ox proteoform is no longer observed, and the 1 Ox and 2 Ox proteoforms are observed as shown in the representative mass spectra and relative abundance plots. This indicates that one methionine residue is solvent-accessible and the second is partially solvent-accessible. MS/MS fragmentation revealed that the 1 Ox proteoform is oxidized at Met-1 in the +H 2 O 2 samples, indicating that this residue is solvent accessible. The 2 Ox proteoform is oxidized at Met-1 and Met-29 in the +H 2 O 2 samples ( Figures S8 ), indicating that Met-29 is partially accessible, as demonstrated by the appearance of both the 1 Ox and 2 Ox species. The ratio between 1 Ox proteoform and 2 Ox proteoform (e.g., 1 Ox species/2 Ox species) is 46 ± 9. Interestingly, when Met-1 is removed (e.g., proteoform #2), Met-29 becomes fully solvent-inaccessible as demonstrated by the representative mass spectra and relative abundance graphs in Figure 6B, which show only the 0 Ox species in the -H 2 O 2 and +H 2 O 2 samples. Examination of the NMR-determined structure of the full-length proteoform (e.g., proteoform #1) indicates that the Met-1 side chain is oriented outward, suggesting that it may be solvent-accessible, while Met-29 is oriented inward and appears shielded. 72 The structure of the N -terminally truncated proteoform is currently unavailable. Although YjbJ’s function is unclear, it is known to interact with the osmosensing transporter ProP. 73 Our results suggest that, with the N -terminal methionine, proteoform #1 of YjbJ may have more than one conformation, potentially due to interactions with other proteins. However, N -terminal methionine removal may introduce structural changes that may prevent Met-29 oxidation. This finding is particularly interesting, as such tertiary structural changes resulting from primary sequence modifications, like truncation, are difficult to detect using bottom-up proteomics. CONCLUSION Here, we have developed MOFIP, a top-down proteomics platform capable of methionine footprinting of intact proteins in complex biological samples. We applied it to profile intact proteoforms in E. coli lysate and demonstrated that the MOFIP platform is capable of determining the solvent accessibility of individual methionine residues from intact proteoforms in complex biological samples. Additionally, we can observe changes in the structure of unique proteoforms. These types of changes can be challenging to observe using bottom-up proteomics methods that may obscure proteoform differences or have limited sequence coverage. Therefore, the MOFIP platform demonstrated here is a powerful method for methionine oxidation footprinting to evaluate the structure of intact proteoforms in complex lysates. However, the E. coli model system used here to develop this platform is limited, as E. coli does not express highly modified proteoforms, so the effect of PTMs on solvent accessibility cannot be studied. Further development and application of this method to more complex systems could pave the way for examining changes in proteoform stability with regard to protein modifications such as PTMs for functional proteomics. We will further apply the MOFIP platform for the analysis of more complex model systems, such as human cell lysate. Primary challenges for the MOFIP platform include (1) limited proteome coverage and (2) limited sequence coverage of identified proteoforms, particularly concerning low-abundance proteoforms. Proteome coverage is frequently constrained by proteoform coelution, which produces convoluted spectra that complicate label-free quantitation and diminish the likelihood of selecting low-abundance proteoforms for MS/MS fragmentation and identification. Enrichment methods for modified proteoforms, which tend to be lower in abundance, such as phosphoprotein enrichment strategies, may be implemented to improve characterization and quantification of low-abundance proteoforms. 74, 75 Additionally, multidimensional separation can improve proteome coverage in top-down proteomics 76 ; however, the label-free proteomics methods implemented here are not compatible with multidimensional separation. Recently, we have developed an intact protein tandem mass tag (TMT) labeling technique that allows multiplexed quantitation of intact proteins in complex biological samples. 46, 77, 78 This multiplexed technique eliminated issues with run-to-run reproducibility and further allows application of multidimensional separation techniques to quantitative top-down proteomics to improve proteome coverage. 76 Limited sequence coverage leads to the inability to localize oxidized methionine residues and protein modifications, such that oxidation footprinting may be challenging. Sequence coverage may be improved by implementing targeted quantitative top-down proteomics methods (e.g., proteoform reaction monitoring) 79 , optimization of fragmentation conditions, or implementation of alternative fragmentation methods (e.g., CID, ETD, ECD, and UVPD) for tandem mass spectrometry to improve proteoform characterization and localization of modified sites. ACKNOWLEDGEMENTS This work was partly supported by grants from OCAST HR23-169, NIH NIAID R01AI141625, and NIH/NIAID2U19AI062629. We also thank the OU Protein Production and Characterization Core (PPC) facility and Dr. Philip Bourne for supporting E. coli protein extraction. The PPC is supported by Institutional Development Awards (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health (Grants P20GM103640 and P30GM145423), the OU Vice President for Research and Partnerships, and the OU College of Arts and Sciences. We would like to thank for the support received from the University of Alabama’s startup fund. CONFLICTS OF INTEREST The authors declare no known competing interests that are relevant to the content of this article. REFERENCES 1. Schopper, S.; Kahraman, A.; Leuenberger, P.; Feng, Y.; Piazza, I.; Müller, O.; Boersema, P. J.; Picotti, P., Measuring protein structural changes on a proteome-wide scale using limited proteolysis-coupled mass spectrometry. Nat Protoc 2017, 12 (11), 2391-2410.2. Espino, J. A.; Mali, V. S.; Jones, L. M., In Cell Footprinting Coupled with Mass Spectrometry for the Structural Analysis of Proteins in Live Cells. Analytical Chemistry 2015, 87 (15), 7971-7978.3. Britt, H. M.; Cragnolini, T.; Thalassinos, K., Integration of Mass Spectrometry Data for Structural Biology. Chemical Reviews 2022, 122 (8), 7952-7986.4. Feng, Y.; De Franceschi, G.; Kahraman, A.; Soste, M.; Melnik, A.; Boersema, P. J.; de Laureto, P. P.; Nikolaev, Y.; Oliveira, A. P.; Picotti, P., Global analysis of protein structural changes in complex proteomes. Nat Biotechnol 2014, 32 (10), 1036-1044.5. Kaur, U.; Meng, H.; Lui, F.; Ma, R.; Ogburn, R. N.; Johnson, J. H. R.; Fitzgerald, M. C.; Jones, L. M., Proteome-Wide Structural Biology: An Emerging Field for the Structural Analysis of Proteins on the Proteomic Scale. J. Proteome Res. 2018, 17 (11), 3614-3627.6. Smyth, M. S., x Ray crystallography. Molecular Pathology 2000, 53 (1), 8-14.7. Nickell, S.; Kofler, C.; Leis, A. P.; Baumeister, W., A visual approach to proteomics. Nat Rev Mol Cell Biol 2006, 7 (3), 225-230.8. Pineda‐Lucena, A.; Liao, J.; Wu, B.; Yee, A.; Cort, J. R.; Kennedy, M. A.; Edwards, A. M.; Arrowsmith, C. H., NMR structure of the hypothetical protein encoded by the YjbJ gene from Escherichia coli. Proteins: Structure, Function, and Bioinformatics 2002, 47 (4), 572-574.9. Purslow, J. A.; Khatiwada, B.; Bayro, M. J.; Venditti, V., NMR Methods for Structural Characterization of Protein-Protein Complexes. Frontiers in Molecular Biosciences 2020, 7 .10. Fitzgerald, M. C.; West, G. M., Painting proteins with covalent labels: What’s in the picture? Journal of the American Society for Mass Spectrometry 2009, 20 (6), 1193-1206.11. Kang, J.; Seshadri, M.; Cupp-Sutton, K. A.; Wu, S., Toward the analysis of functional proteoforms using mass spectrometry-based stability proteomics. Frontiers in Analytical Science 2023, 3 .12. Varadi, M.; Velankar, S., The impact of AlphaFold Protein Structure Database on the fields of life sciences. PROTEOMICS n/a (n/a), 2200128.13. Hekkelman, M. L.; de Vries, I.; Joosten, R. P.; Perrakis, A., AlphaFill: enriching AlphaFold models with ligands and cofactors. Nat Methods 2023, 20 (2), 205-213.14. Öztürk, M.; Freiwald, A.; Cartano, J.; Schmitt, R.; Dejung, M.; Luck, K.; Al-Sady, B.; Braun, S.; Levin, M.; Butter, F., Proteome effects of genome-wide single gene perturbations. Nature Communications 2022, 13 .15. Liu, X. R.; Zhang, M. M.; Gross, M. L., Mass Spectrometry-Based Protein Footprinting for High Order Structure Analysis: Fundamentals and Applications. Chemical reviews 2020, 120 (10), 4355-4454.16. Chavez, J. D.; Bruce, J. E., Chemical cross-linking with mass spectrometry: a tool for systems structural biology. Current Opinion in Chemical Biology 2019, 48 , 8-18.17. Yu, C.; Huang, L., Cross-Linking Mass Spectrometry (XL-MS): an Emerging Technology for Interactomics and Structural Biology. Analytical chemistry 2018, 90 (1), 144-165.18. Le Sueur, C.; Hammarén, H. M.; Sridharan, S.; Savitski, M. M., Thermal proteome profiling: Insights into protein modifications, associations, and functions. Current Opinion in Chemical Biology 2022, 71 , 102225.19. Konermann, L.; Pan, J.; Liu, Y.-H., Hydrogen exchange mass spectrometry for studying protein structure and dynamics. Chemical Society Reviews 2011, 40 (3), 1224-1234.20. McKenzie-Coe, A.; Montes, N. S.; Jones, L. M., Hydroxyl Radical Protein Footprinting: A Mass Spectrometry-Based Structural Method for Studying the Higher Order Structure of Proteins. Chemical Reviews 2022, 122 (8), 7532-7561.21. Fang, M.; Wang, Z.; Cupp-Sutton, K. A.; Welborn, T.; Smith, K.; Wu, S., High-throughput hydrogen deuterium exchange mass spectrometry (HDX-MS) coupled with subzero-temperature ultrahigh pressure liquid chromatography (UPLC) separation for complex sample analysis. Analytica Chimica Acta 2021, 1143 , 65-72.22. Johnson, D. T.; Di Stefano, L. H.; Jones, L. M., Fast photochemical oxidation of proteins (FPOP): A powerful mass spectrometry–based structural proteomics tool. Journal of Biological Chemistry 2019, 294 (32), 11969-11979.23. Fang, M.; Wu, O.; Cupp-Sutton, K. A.; Smith, K.; Wu, S., Elucidating Protein–Ligand Interactions in Cell Lysates Using High-Throughput Hydrogen–Deuterium Exchange Mass Spectrometry with Integrated Protein Thermal Depletion. Analytical Chemistry 2023, 95 (3), 1805-1810.24. Espino, J. A.; Jones, L. M., Illuminating Biological Interactions with in Vivo Protein Footprinting. Analytical Chemistry 2019, 91 (10), 6577-6584.25. Johnson, D. T.; Jones, L. M., Hydroxyl radical protein footprinting for analysis of higher order structure. Trends in Biochemical Sciences 2022, 47 (11), 989-991.26. Levine, R. L.; Mosoni, L.; Berlett, B. S.; Stadtman, E. R., Methionine residues as endogenous antioxidants in proteins. Proceedings of the National Academy of Sciences 1996, 93 (26), 15036-15040.27. Levine, R. L.; Moskovitz, J.; Stadtman, E. R., Oxidation of Methionine in Proteins: Roles in Antioxidant Defense and Cellular Regulation. IUBMB Life 2000, 50 (4-5), 301-307.28. Stadtman, E. R.; Van Remmen, H.; Richardson, A.; Wehr, N. B.; Levine, R. L., Methionine oxidation and aging. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 2005, 1703 (2), 135-140.29. Brot, N.; Weissbach, H., Biochemistry and physiological role of methionine sulfoxide residues in proteins. Archives of Biochemistry and Biophysics 1983, 223 (1), 271-281.30. Mulinacci, F.; Capelle, M. A. H.; Gurny, R.; Drake, A. F.; Arvinte, T., Stability of Human Growth Hormone: Influence of Methionine Oxidation on Thermal Folding. Journal of Pharmaceutical Sciences 2011, 100 (2), 451-463.31. Manta, B.; Gladyshev, V. N., Regulated methionine oxidation by monooxygenases. Free Radical Biology and Medicine 2017, 109 , 141-155.32. Erickson, J. R.; Joiner, M.-L. A.; Guan, X.; Kutschke, W.; Yang, J.; Oddis, C. V.; Bartlett, R. K.; Lowe, J. S.; O’Donnell, S. E.; Aykin-Burns, N.; Zimmerman, M. C.; Zimmerman, K.; Ham, A.-J. L.; Weiss, R. M.; Spitz, D. R.; Shea, M. A.; Colbran, R. J.; Mohler, P. J.; Anderson, M. E., A Dynamic Pathway for Calcium-Independent Activation of CaMKII by Methionine Oxidation. Cell 2008, 133 (3), 462-474.33. Hoshi, T.; Heinemann, S. H., Regulation of cell function by methionine oxidation and reduction. The Journal of Physiology 2001, 531 (1), 1-11.34. Walker, E. J.; Bettinger, J. Q.; Welle, K. A.; Hryhorenko, J. R.; Ghaemmaghami, S., Global analysis of methionine oxidation provides a census of folding stabilities for the human proteome. Proc Natl Acad Sci U S A 2019, 116 (13), 6081-6090.35. Walker, E. J.; Bettinger, J. Q.; Welle, K. A.; Hryhorenko, J. R.; Molina Vargas, A. M.; O’Connell, M. R.; Ghaemmaghami, S., Protein folding stabilities are a major determinant of oxidation rates for buried methionine residues. Journal of Biological Chemistry 2022, 298 (5), 101872.36. West, G. M.; Tang, L.; Fitzgerald, M. C., Thermodynamic Analysis of Protein Stability and Ligand Binding Using a Chemical Modification- and Mass Spectrometry-Based Strategy. Analytical Chemistry 2008, 80 (11), 4175-4185.37. Melby, J. A.; Roberts, D. S.; Larson, E. J.; Brown, K. A.; Bayne, E. F.; Jin, S.; Ge, Y., Novel Strategies to Address the Challenges in Top-Down Proteomics. Journal of the American Society for Mass Spectrometry 2021, 32 (6), 1278-1294.38. Brown, K. A.; Melby, J. A.; Roberts, D. S.; Ge, Y., Top-down proteomics: challenges, innovations, and applications in basic and clinical research. Expert Review of Proteomics 2020, 17 (10), 719-733.39. Smith, L. M.; Agar, J. N.; Chamot-Rooke, J.; Danis, P. O.; Ge, Y.; Loo, J. A.; Paša-Tolić, L.; Tsybin, Y. O.; Kelleher, N. L., The Human Proteoform Project: Defining the human proteome. Science Advances 2021, 7 (46).40. Catherman, A. D.; Skinner, O. S.; Kelleher, N. L., Top Down proteomics: Facts and perspectives. Biochemical and Biophysical Research Communications 2014, 445 (4), 683-693.41. Ansong, C.; Wu, S.; Meng, D.; Liu, X.; Brewer, H. M.; Deatherage Kaiser, B. L.; Nakayasu, E. S.; Cort, J. R.; Pevzner, P.; Smith, R. D.; Heffron, F.; Adkins, J. N.; Paša-Tolić, L., Top-down proteomics reveals a unique protein S-thiolation switch in Salmonella Typhimurium in response to infection-like conditions. Proceedings of the National Academy of Sciences 2013, 110 (25), 10153-10158.42. Peng, Y.; Gregorich, Z. R.; Valeja, S. G.; Zhang, H.; Cai, W.; Chen, Y.-C.; Guner, H.; Chen, A. J.; Schwahn, D. J.; Hacker, T. A.; Liu, X.; Ge, Y., Top-down Proteomics Reveals Concerted Reductions in Myofilament and Z-disc Protein Phosphorylation after Acute Myocardial Infarction. Molecular & Cellular Proteomics 2014, 13 (10), 2752-2764.43. Gregorich, Z. R.; Peng, Y.; Cai, W.; Jin, Y.; Wei, L.; Chen, A. J.; McKiernan, S. H.; Aiken, J. M.; Moss, R. L.; Diffee, G. M.; Ge, Y., Top-Down Targeted Proteomics Reveals Decrease in Myosin Regulatory Light-Chain Phosphorylation That Contributes to Sarcopenic Muscle Dysfunction. J. Proteome Res. 2016, 15 (8), 2706-2716.44. Zou, Y.; Huang, C.-F.; Sturrock, G. R.; Kelleher, N. L.; Fitzgerald, M. C., Top-Down Stability of Proteins from Rates of Oxidation (TD-SPROX) Approach for Measuring Proteoform-Specific Folding Stability. Analytical Chemistry 2024, 96 (49), 19597-19604.45. Wang, Z.; Liu, X.; Muther, J.; James, J. A.; Smith, K.; Wu, S., Top-down Mass Spectrometry Analysis of Human Serum Autoantibody Antigen-Binding Fragments. Scientific Reports 2019, 9 (1).46. Guo, Y.; Yu, D.; Cupp-Sutton, K. A.; Liu, X.; Wu, S., Optimization of protein-level tandem mass tag (TMT) labeling conditions in complex samples with top-down proteomics. Analytica Chimica Acta 2022, 1221 , 340037.47. Yu, D.; Wang, Z.; Cupp-Sutton, K. A.; Guo, Y.; Kou, Q.; Smith, K.; Liu, X.; Wu, S., Quantitative Top-Down Proteomics in Complex Samples Using Protein-Level Tandem Mass Tag Labeling. Journal of the American Society for Mass Spectrometry 2021, 32 (6), 1336-1344.48. Chowdhury, T.; Cupp-Sutton, K. A.; Guo, Y.; Gao, K.; Zhao, Z.; Burgett, A.; Wu, S., Quantitative Top-down Proteomics Revealed Kinase Inhibitor-Induced Proteoform-Level Changes in Cancer Cells. J. Proteome Res. 2024 .49. Ramirez-Sagredo, A.; Sunny, A. T.; Cupp-Sutton, K. A.; Chowdhury, T.; Zhao, Z.; Wu, S.; Chiao, Y. A., Characterizing age-related changes in intact mitochondrial proteoforms in murine hearts using quantitative top-down proteomics. Clinical Proteomics 2024, 21 (1).50. Adusumilli, R.; Mallick, P., Data Conversion with ProteoWizard msConvert. Springer New York: 2017; pp 339-368.51. Kou, Q.; Xun, L.; Liu, X., TopPIC: a software tool for top-down mass spectrometry-based proteoform identification and characterization. Bioinformatics 2016 , btw398.52. Wu, S.; Brown, J. N.; Tolić, N.; Meng, D.; Liu, X.; Zhang, H.; Zhao, R.; Moore, R. J.; Pevzner, P.; Smith, R. D.; Paša‐Tolić, L., Quantitative analysis of human salivary gland‐derived intact proteome using top‐down mass spectrometry. PROTEOMICS 2014, 14 (10), 1211-1222.53. Paša-Tolić, L.; Masselon, C.; Barry, R. C.; Shen, Y.; Smith, R. D., Proteomic Analyses using an Accurate Mass and time Tag Strategy. BioTechniques 2004, 37 (4), 621-639.54. Fellers, R. T.; Greer, J. B.; Early, B. P.; Yu, X.; LeDuc, R. D.; Kelleher, N. L.; Thomas, P. M., ProSight Lite: Graphical software to analyze top-down mass spectrometry data. PROTEOMICS 2015, 15 (7), 1235-1238.55. Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng, E. C.; Ferrin, T. E., UCSF Chimera—A visualization system for exploratory research and analysis. Journal of Computational Chemistry 2004, 25 (13), 1605-1612.56. Long, S.; Tian, P., Protein secondary structure prediction with context convolutional neural network. RSC Advances 2019, 9 (66), 38391-38396.57. Das, M.; Kobayashi, M.; Yamada, Y.; Sreeramulu, S.; Ramakrishnan, C.; Wakatsuki, S.; Kato, R.; Varadarajan, R., Design of Disulfide-linked Thioredoxin Dimers and Multimers Through Analysis of Crystal Contacts. Journal of Molecular Biology 2007, 372 (5), 1278-1292.58. Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S. A. A.; Ballard, A. J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A. W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D., Highly accurate protein structure prediction with AlphaFold. Nature 2021, 596 (7873), 583-589.59. Napper, S.; Delbaere, L. T. J.; Waygood, E. B., The Aspartyl Replacement of the Active Site Histidine in Histidine-containing Protein, HPr, of the Escherichia coliPhosphoenolpyruvate:Sugar Phosphotransferase System Can Accept and Donate a Phosphoryl Group. Journal of Biological Chemistry 1999, 274 (31), 21776-21782.60. Guo, F.; Adhya, S., Spiral structure of Escherichia coli HUαβ provides foundation for DNA supercoiling. Proceedings of the National Academy of Sciences 2007, 104 (11), 4309-4314.61. Parak, F., Proteins in action: the physics of structural fluctuations and conformational changes. Current Opinion in Structural Biology 2003, 13 (5), 552-557.62. Goh, C.-S.; Milburn, D.; Gerstein, M., Conformational changes associated with protein–protein interactions. Current Opinion in Structural Biology 2004, 14 (1), 104-109.63. Wrabl, J. O.; Gu, J.; Liu, T.; Schrank, T. P.; Whitten, S. T.; Hilser, V. J., The role of protein conformational fluctuations in allostery, function, and evolution. Biophysical Chemistry 2011, 159 (1), 129-141.64. Schindelin, H.; Jiang, W.; Inouye, M.; Heinemann, U., Crystal structure of CspA, the major cold shock protein of Escherichia coli. Proceedings of the National Academy of Sciences 1994, 91 (11), 5119-5123.65. Rennella, E.; Sára, T.; Juen, M.; Wunderlich, C.; Imbert, L.; Solyom, Z.; Favier, A.; Ayala, I.; Weinhäupl, K.; Schanda, P.; Konrat, R.; Kreutz, C.; Brutscher, B., RNA binding and chaperone activity of theE. colicold-shock protein CspA. Nucleic Acids Research 2017 , gkx044.66. Takeda, K., Crystal structures of bacterial lipoprotein localization factors, LolA and LolB. EMBO J 2003, 22 (13), 3199-3209.67. Nakada, S.; Sakakura, M.; Takahashi, H.; Okuda, S.; Tokuda, H.; Shimada, I., Structural Investigation of the Interaction between LolA and LolB Using NMR. Journal of Biological Chemistry 2009, 284 (36), 24634-24643.68. Aledo, J. C., Methionine in proteins: The Cinderella of the proteinogenic amino acids. Protein Science 2019, 28 (10), 1785-1796.69. Gallmetzer, A.; Silvestrini, L.; Schinko, T.; Gesslbauer, B.; Hortschansky, P.; Dattenböck, C.; Muro-Pastor, M. I.; Kungl, A.; Brakhage, A. A.; Scazzocchio, C.; Strauss, J., Reversible Oxidation of a Conserved Methionine in the Nuclear Export Sequence Determines Subcellular Distribution and Activity of the Fungal Nitrate Regulator NirA. PLOS Genetics 2015, 11 (7), e1005297.70. Nick Pace, C.; Martin Scholtz, J., A Helix Propensity Scale Based on Experimental Studies of Peptides and Proteins. Biophysical Journal 1998, 75 (1), 422-427.71. Krishnappa, G.; Mandal, M.; Ganesan, S.; Babu, S.; Padavattan, S.; Haradara Bahubali, V. K.; Padmanabhan, B., Structural and biochemical insights into the bacteriophage PlyGRCS endolysin targeting methicillin‐resistant Staphylococcus aureus (MRSA) and serendipitous discovery of its interaction with a cold shock protein C (CspC</s. Protein Science 2023, 32 (9).72. Yee, A.; Chang, X.; Pineda-Lucena, A.; Wu, B.; Semesi, A.; Le, B.; Ramelot, T.; Lee, G. M.; Bhattacharyya, S.; Gutierrez, P.; Denisov, A.; Lee, C.-H.; Cort, J. R.; Kozlov, G.; Liao, J.; Finak, G.; Chen, L.; Wishart, D.; Lee, W.; McIntosh, L. P.; Gehring, K.; Kennedy, M. A.; Edwards, A. M.; Arrowsmith, C. H., An NMR approach to structural proteomics. Proceedings of the National Academy of Sciences 2002, 99 (4), 1825-1830.73. Romantsov, T.; Gonzalez, K.; Sahtout, N.; Culham, D. E.; Coumoundouros, C.; Garner, J.; Kerr, C. H.; Chang, L.; Turner, R. J.; Wood, J. M., Cardiolipin synthase A colocalizes with cardiolipin and osmosensing transporter ProP at the poles of Escherichia coli cells. Molecular Microbiology 2018, 107 (5), 623-638.74. Nelson, C. A.; Szczech, J. R.; Xu, Q.; Lawrence, M. J.; Jin, S.; Ge, Y., Mesoporous zirconium oxide nanomaterials effectively enrich phosphopeptides for mass spectrometry-based phosphoproteomics. Chemical Communications 2009, (43), 6607.75. Wu, S.; Yang, F.; Zhao, R.; Tolić, N.; Robinson, E. W.; Camp, D. G.; Smith, R. D.; Paša-Tolić, L., Integrated Workflow for Characterizing Intact Phosphoproteins from Complex Mixtures. Analytical Chemistry 2009, 81 (11), 4210-4219.76. Guo, Y.; Cupp‐Sutton, K. A.; Zhao, Z.; Anjum, S.; Wu, S., Multidimensional separations in top–down proteomics. Analytical Science Advances 2023, 4 (5-6), 181-203.77. Guo, Y.; Chowdhury, T.; Seshadri, M.; Cupp-Sutton, K. A.; Wang, Q.; Yu, D.; Wu, S., Optimization of Higher-Energy Collisional Dissociation Fragmentation Energy for Intact Protein-Level Tandem Mass Tag Labeling. J. Proteome Res. 2023, 22 (5), 1406-1418.78. Yu, D.; Wang, Z.; Cupp-Sutton, K. A.; Liu, X.; Wu, S., Deep Intact Proteoform Characterization in Human Cell Lysate Using High-pH and Low-pH Reversed-Phase Liquid Chromatography. Journal of the American Society for Mass Spectrometry 2019, 30 (12), 2502-2513.79. Huang, C.-F.; Kline, J. T.; Negrão, F.; Robey, M. T.; Toby, T. K.; Durbin, K. R.; Fellers, R. T.; Friedewald, J. J.; Levitsky, J.; Abecassis, M. M. I.; Melani, R. D.; Kelleher, N. L.; Fornelli, L., Targeted Quantification of Proteoforms in Complex Samples by Proteoform Reaction Monitoring. Analytical Chemistry 2024, 96 (8), 3578-3586. Figure 1. Schematic representation of the MOFIP experimental workflow for the examination of solvent-exposed methionine residues. Figure 2 . Examination of solvent-accessible methionine residues. (A) Thioredoxin 1 protein (P0AA25) with solvent-accessible Met-38 located in a helix, and (B) Autonomous glycyl radical cofactor protein (P68066) with solvent-accessible Met 1located in a coil. Figure 3. Examination of solvent-inaccessible methionine residues. (A) Phosphocarrier protein (P0AA04) with solvent-inaccessible Met-81 located in a helix, and (B) DNA-binding protein HU-alpha (P0ACF0) with solvent-inaccessible Met-1 located in a coil. Figure 4. Examination of partially-accessible methionine residues. (A) Cold shock protein CspA (P0A9X9) with partially solvent-accessible Met 5 located in a sheet, and (B) Outer-membrane lipoprotein carrier protein (P61316) with partially solvent-accessible Met 72 located in a sheet. Figure 5. Classification of the identified proteoforms. The bar graph shows the number of methionine residues that are solvent accessible, inaccessible, and partially accessible, and their secondary structure. Methionine residues in Table S2 that could not be localized using MS/MS were not included in the histogram. Figure 6. Examination of the solvent accessibility of multiple proteoforms of a protein. Two different proteoforms of the UPF0337 protein YjbJ (P68206) are shown here. (A) proteoform #1 has two methionine residues (Met-1 and Met-29). Met-1 is solvent accessible, Met-29 is partially accessible. (B) Sroteoform# 2 has only Met-29, which is solvent inaccessible. Information & Authors Information Version history V1 Version 1 14 August 2025 Peer review timeline Published PROTEOMICS Version of Record 20 Oct 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection PROTEOMICS Authors Affiliations Anju Sunny The University of Alabama System View all articles by this author Kellye Cupp-Sutton The University of Alabama System View all articles by this author Zhitao Zhao The University of Alabama System View all articles by this author Trishika Chowdhury The University of Alabama System View all articles by this author Yanting Guo The University of Alabama System View all articles by this author Si Wu 0000-0002-6346-7359 [email protected] The University of Alabama System View all articles by this author Metrics & Citations Metrics Article Usage 315 views 232 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Anju Sunny, Kellye Cupp-Sutton, Zhitao Zhao, et al. Methionine Oxidation Footprinting in Intact Proteins (MOFIP) using Top-Down Proteomics. Authorea . 14 August 2025. DOI: https://doi.org/10.22541/au.175516569.91192211/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Kellye A. Cupp‐Sutton, Yanting Guo, Thomas Welborn, Si Wu, Top‐Down Thermal Proteome Profiling (TD‐TPP) for Functional Characterization of the Intact Proteoforms in Complex Samples, Journal of Mass Spectrometry, 60 , 11, (2025). https://doi.org/10.1002/jms.5187 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175516569.91192211/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fea5b92ac82f047',t:'MTc3OTI2OTc5Mw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();