dataesr/scientific-paragraphs-categorization huggingface dataset:
The PrSM yield for TopPIC reflects the removal of PrSMs containing unanticipated PTMs (this effect is quantified in Supporting Information Text S2). considered a known false PrSM, and vice versa. These known false matches could be used to estimate the number of false matches hidden among the plausible accessions based on the ratio of sequence counts for the two species. (We tested these two reference proteomes for shared sequences by seeking the ortholog pair in ProteinOrtho 6 that produced the highest BLASTP bitscore; 57 the longest run of identical amino acids for M9UBS4_SULIS and NARG_ECOLI was 14 amino acids in length. ) Our intent with this SULIS-ECOLI database was to limit PrSM-level FDR. LeDuc et al. have noted that this type of decoy strategy is much less effective at controlling isoform-or protein-level false discoveries.
2% for the HCD experiments. A similar pattern appeared in the E. coli experiments, where MSPathFinderT delivered an effective FDR of 1. 10% and pTop yielded 1. 05%, but ProSight PD gave FDRs of 2. 3 and 2. 8% for XML and FASTA sequence inputs, respectively. If the ProSight PD truncation search is limited to 10 ppm precursor tolerance rather than 1. 1 Da precursor tolerance, its FDR falls below 1%. Estimates of FDR using target-decoy search may be compromised if the number of database entries is low or the number of identified tandem mass spectra is low. 59 he ECOLI and SULIS experiments employed thousands of sequences in the FASTA and yielded thousands of confidently identified PrSMs, reducing potential error in FDR estimates. A discussion of why a search may report multiple PrSMs from an individual MS/MS is in Supporting Information Text S3.
I accompanied by some challenges. The Informed Proteomics suite that includes MSPathFinderT is not currently under active development, and its deconvolution and search efficiency lags behind the other tools. A user may wait more than twice as long for the results from ProMex/MSPathFinderT as from other workflows (see Supporting Information Text S4 for time trials of all four workflows). Our testing of pTop 2 software represented a "mix-and-match" pairing the pParseTD deconvolution engine from version 1. 2 with an early build of the pTop search engine from version 2. 0. The December 2022 release version of pTop2 with a fixed pParseTD and modified pTop arrived after the finalization of the search results for this study. Because the E. coli UniProtKB database annotates many PTM-modified residues, we detected differences between the ProSight PD searches when using FASTA and UniProtKB XML.
The use of UniProtKB XML instead of FASTA produced 3. 1% more PrSMs at the "high" filtering criterion. 725 proteoforms were found in common between the searches, 31 were found only with the UniProtKB XML database, and 28 were found only with the FASTA database. The UniProtKB XML search loses some proteoforms that appear in the FASTA search because its search space was larger by including all UniProtKBannotated PTMs and coding SNPs, reducing PrSM yield. The proteoform that represented the most PrSMs gained in the UniProtKB XML search was P0A7N9 (50S ribosomal protein L33), with the N-terminal Met clipped and the Ala at position two methylated (rather than acetylated). By itself, this proteoform accounted for 106 PrSMs, making it the eighth most frequent proteoform in the ProSight PD XML search.
L- Beta-methylthioaspartic acid (+46 Da on Asp) was also a frequently observed modification due to its occurrence in 30S ribosomal protein S12. The use of "subset" databases from bottom-up proteomics can reduce the time required to search proteoforms from large eukaryotic proteomes and greatly accelerate PTM search (as employed in the PTM section below). Note, however, that this approach precludes the identification of very small proteins, or proteins bearing a limited number of Lys/Arg residues that could be missed in bottom-up analyses because of their biochemical properties. D. rerio data analysis faced challenges of both a large sequence database and a need for added variable PTMs. First, its proteome of 47,204 sequences could be reduced to 9432 sequences through the identification of bottom-up spectra accompanying the top-down files.
Second, iodoacetamide induced many proteoforms where Cys side-chains were incompletely labeled, slowing identification considerably (see PTM section). The fivefold reduction in number of sequences through subsetting certainly accelerated identification (see Supporting Information Text S5 for time trials of database subsetting), but it did come at a cost. In all four search engines, the number of PrSMs was slightly lower in the subset database searches than it was in the complete database (the biggest difference was -646 PrSMs in pTop, while the smallest was -58 PrSMs in TopPIC). When compared with the overall numbers of PrSMs identified by these engines, these losses are probably manageable (9281 in the complete FASTA search by pTop and 9504 by TopPIC). These losses may have been mitigated if we had employed non-parsimonious protein inference in producing our subset database.
While we always associate a possibly truncated sequence with its protein database accession, we ignored gene associations. Two of these search engines may agree entirely on truncated sequence and mass of added PTMs for a given spectrum and yet associate those sequences with different accessions, particularly if multiple isoforms for that protein-coding gene exist and contain the same Journal of Proteome Research truncated sequence. Of all four pipelines, only TopPIC reports all protein accessions that contain a given truncated proteoform sequence, and that feature was not yet present in version 1. 4. 13, used here. Since we do not take PTM localization and gene-oforigin into account when comparing proteoforms, the best-case agreement our strategy can achieve is level 3 on the Smith proteoform level classification system.
In D. rerio, accession matching was markedly lower than for the other species, ranging from 83 to 91%, probably reflecting that isoforms in the sequence database increase the opportunity for disagreements in identification (and algorithms may differ in which accession is reported when multiple accessions contain the PrSM sequence). The proteoform agreement tables showed diminished concordance for pTop versus other algorithms in S. islandicus (43-69%) and D. rerio (77-89%) compared to what it produced in E. coli (86-94%). Even when search engines agree that a particular spectrum has been identified, they may disagree considerably in detailing the proteoform a tandem mass spectrum represents.
Deconvolution Contributes to PrSM Inconsistency The term "deconvolution" is frequently used to describe many roles for top-down analysis at both MS and MS/MS levels, including the following:  6  • feature detection: recognizing persistent precursor chromatograms in MS signals, • charge state inference: recognizing isotope spacing or ions at multiple charges, • charge reduction: representing all fragments at neutrality or unit charge, and • deisotoping: combining intensity across an isotopomer envelope. Deconvolution methods may disagree on whether a precursor can be deconvolved at all, which charge the precursor represents, and the monoisotopic mass attributed to the precursor. Identification methods may disagree on whether an MS/MS has been identified successfully, which database accession produced the sequence, and the truncation and PTM decoration borne by the proteoform.
As a result, the number of peak lists it exports is far smaller than what TopFD exports, since TopFD aspires to report each MS/MS individually to its output, whether or not it can detect their precursors in the MS. FLASHDeconv integrated functions from OpenMS SpectralMerger to emulate the Bruker software's behavior. Meanwhile, Mascot Distiller frequently found multiple precursor masses in the MS1, so it reported some MS/MS scans multiple times. In processing the ThermoFisher data, Xtract employed a Sliding Window Algorithm  65  to average multiple MS scans preceding and succeeding a particular MS/MS to increase signal-to-noise ratio for the precursor ion. However, combining replicated MS/MS scans was quite rare among the Xtract peak lists. Supporting Information Figure  S4  illustrates the disparity observed in these MS/MS scan counts.
Deconvolution discrepancies can have a substantial but sometimes subtle effect on identification, and the lower part of Figure  5  seeks to characterize these effects. Again, it is possible that the TopPIC search engine makes no report about a particular scan number because the PrSM does not pass the expectation value filters. In the E. coli data, 66% of the MS/MS scans identified by TopPIC after any type of deconvolution were identified after all three deconvolution engines. Of the spectra identified after all three deconvolution routes, 96% were matched to the same accession in the database (this would likely have been lower if we evaluated the data from D. rerio due to the presence of paralogs and isoform variants). When all three search engines identified an MS/MS to the same accession, however, the TopPIC-reported sequence truncation agreed across the three deconvolution routes only 87% of the time.
PTM Identification Incorporating post-translational modifications in top-down identification can seem inconsistent among search engines. For example, is the acetylation of proteoform N-termini configured alongside other PTMs (pTop, MSPathFinderT), or is this modification handled as a special case (ProSight PD, TopPIC)? Because the cost of variable modifications (such as Met oxidation) can be substantial in top-down search, the way they are handled by the different software tools is important. The example of Cys carbamidomethylation (+57 Da) in the D. rerio experiment gives an interesting effect. In bottom-up experiments, database searches are often configured to assume all Cys are shifted to 160 rather than 103 Da, implying that the alkylation reaction has run to completion. The TopPIC identifications from the original publication of PXD020342 employed a "fixed" shift of +57 Da for all Cys; correspondingly, when iodoacetamide failed to react with a Cys side chain, TopPIC often used its unanticipated modification feature to denote a shift of -57 Da (back to normal side chain mass) at these sites.
Our searches incorporated +57 as a dynamic or variable modification, typically allowing up to four PTMs per PrSM. Of all Cys residues identified by the four algorithms in the complete database, an unexpectedly small fraction was found to bear the expected carbamidomethylation: MSPathFinderT = 30. 8%, ProSight PD = 30. 9%, pTop = 28. 6%, and TopPIC = 20. 9%. This low apparent reaction stoichiometry may be peculiar to this experiment, but it reinforces that one should not assume these mass shift-inducing reactions will mark all potential sites. Note that any incomplete reaction in sample handling would lead to an artificial increase in the number of proteoforms, entirely through artifacts of chemical processing, while also complicating identification.
Proteoform Phospho-Search in Bovine Milk We created in this study a new milk data set (PXD031744-BOVIN), analyzed on three different instruments, that should be useful for improving the identification of phospho-proteoforms. To evaluate phosphorylation detection against a more challenging background of acetylated or unmodified proteins, we also evaluated ovarian cancer samples (PXD005420-HUMAN). We used two different strategies for reducing the database to a manageable size for phosphorylation searching. In the milk data, we used the TMT "bottom-up" proteome to reduce the complete bovine FASTA (37,883 sequences) to those matching peptides after parsimony (5388 sequences) (see Figure  7 ). We then performed an identification search in all four top-down engines to reduce to a 378 accession FASTA that we employed for PTM search.
Our UniProt XML Subsetter script produced both FASTA and UniProtKB XML subsets for our list of accessions. In the human ovarian experiments, however, we did not have bottom-up data available. Instead, we used the two faster search engines (ProSight PD and pTop) to search the Journal of Proteome Research complete human proteome FASTA (101,014 sequences) to generate a 558 protein subset. The PTM search for the ovarian data employed this compact collection of sequences. We hypothesized the PTM search might show a greater disparity between FASTA and UniProtKB XML databases in ProSight PD, so both searches are included in our comparison. The TopMG tool in TopPIC Suite is intended to detect multiply-modified proteoforms, and so it was included alongside TopPIC (as before, any PrSMs containing unknown PTMs from TopPIC were excluded from consideration).
Singly phosphorylated PrSMs comprised the following percentages of PrSMs by search engine: 17. 9% (TopMG), 17. 8% (pTop), 13. 0% (MSPathFinderT), 12. 9% (TopPIC), 1. 4% (ProSight PD with XML database), and 1. 2% (ProSight PD with FASTA database). ProSight PD and TopPIC also identified very small numbers of doubly-phosphorylated PrSMs but no PrSMs with more than two phosphorylations. Of all the search engines, only MSPathFinderT was able to identify many PrSMs for doubly-(3. 8%), triply-(5. 0%), and quadruplyphosphorylated (3. 4%) proteoforms with no other modifications. The milk proteome highlights MSPathFinderT, pTop, and TopMG for phosphorylation searching. Supporting Information Text S6 evaluates the ProSight PD searches from FASTA and from XML for detecting phosphorylations. Anecdotally, when ProSight PD was configured for annotated proteoform search rather than the truncation search, the phosphorylated proteoforms were a larger proportion of a smaller number of PrSMs.
PTMs in Human Ovarian and S. islandicus Proteomes The ovarian cancer samples (PXD005420-HUMAN) lead to very different results. Singly phosphorylated proteoforms comprise a smaller fraction of all spectra in the set; instead, Nterminal acetylation (+42) is the most dominant PTM. For this set, pTop held the advantage in the number of PrSMs identified, apparently through improved sensitivity from the "PB" and "PT" triplicates (see Supporting Information Figure  S9 ). Five of the six searches concurred that between 36. 9 and 44. 2% of identified PrSMs represented acetylated proteoforms, but for TopMG only 17. 2% of PrSMs were acetylated (the PTM distributions for all six identification pathways in the ovarian cancer samples are shown in Supporting Information Figure  S10 ). Recovery of +80 PrSMs was generally consistent among search engines: TopMG (8.
7%), ProSight PD (5. 4 or 5. 3%, depending on database format), MSPathFinderT (3. 2%), and pTop (3. 1%). For TopPIC, however, only 0. 4% of PrSMs were singly phosphory-Figure  7. A complex process prepared a sequence database for bovine milk phosphorylation searches. First we created a subset database based on bottom-up experiments, and second we filtered that subset database to include only the proteins detected in top-down searches that incorporated minimal numbers of PTMs per PrSM. The very compact 378 protein databases could then be drawn from FASTA or XMLformatted UniProtKB databases, using the list of accessions as input. Met oxidation was always added as a variable modification for searches, but Ser, Thr, and Tyr phosphorylations were only added as a variable modification when the sequences were provided in FASTA format.
lated. While the milk data suggested that ProSight PD identified phosphorylated PrSMs with poor sensitivity, the ovarian cancer set ranks it as the second-best at detecting this class of PTMs. Both sets demonstrate the relative strength of TopMG over TopPIC for recognizing multiple-PTM PrSMs. While the ovarian cancer data might have been expected to reveal glycosylated proteoforms, as well, TopPIC analysis of unanticipated mass shifts revealed calcium and sodium adducts were far more prominent in the set (see Supporting Information Text S7). The SULIS data set examined in the identification yield section above also contains interesting challenges for PTM hunting. The S. islandicus proteome is extensively Lysmethylated, and this variable PTM was included in all searches of that set. The 7029 sequences of its combined ECOLI/SULIS database considerably outnumbered the hundreds used for the final PTM searches in bovine milk and human ovarian sets.
■ CONCLUSIONS Having analyzed several different datasets, this study may offer some insight on the question "which instruments can be used for top-down proteomics?" The results above indicate that hundreds of proteoforms can be identified even with a ThermoFisher Q-Exactive mass spectrometer (released in 2011). Features that boost mass range, acquire spectra from precursor ions representing different neutral masses, and accelerate the overall rate of MS/MS acquisition can all play a role in improving the quality of a data set representing a topdown proteome. Many top-down proteomics practitioners need answers to the question "what algorithm should I use to identify proteoforms from my data?" A case can be made for the use of any of these four identification pipelines, depending upon the priorities of the researcher.
TopPIC frequently lagged behind pTop and MSPathFinderT in PrSM counts, largely because our ProForma reformatter suppressed all PrSMs that contain unanticipated modifications. The software comes with substantial benefits, O though, such as PTM localization ambiguity reporting, the ability of all its component tools to run in the Linux operating system, and the greater explanatory power afforded by the unanticipated modifications. 66 Although this study routinely disabled the HTML reporting of TopPIC, users get considerable value from its voluminous output, visualizing PrSMs and proteoforms in a portable report that can be browsed without specialized software. ProSight PD will continue to be a popular option for users familiar with the Proteome Discoverer framework, and its conservative estimation of FDR  58  protects against falsely identified proteoforms.
The search speed of ProSight PD is also impressive, with deconvolution often requiring more time than matching sequences to fragment masses. The cost for a license to this software provides technical support and continuous improvement in the software suite. MSPathFinderT delivered very impressive sensitivity throughout and because it exports identifications in the mzIdentML format, it is the easiest search engine for producing a "complete" entry in ProteomeXchange. On the other hand, because MSPathFin-derT is only being maintained rather than actively developed, it will likely remain the slowest of the four search engines. The pTop algorithm is in a transitional state, with pTop 1 completed and well-characterized while pTop 2 receives final touches. The hybrid of pParseTD from version 1. 2 and pTop from version 2.
The Informed Proteomics software suite, featuring MSPathFinderT, stands out for recording its identifications in HUPO-PSI mzIdentML format. HUPO-PSI mzTab format  67  is a lighter-weight alternative that may represent an easier option for software developers. • At present, search engines all employ different formats for reporting truncated sequence and PTM information for each PrSM. The HUPO-PSI ProForma version 2 format is intended to resolve this problem, but at the time of this writing, none of the search engines format their proteoform descriptions by this standard. In particular, the communication of PTM localization ambiguity needs attention; only TopPIC, of all four pipelines analyzed here, records to its text reports when a PTM location is unambiguous. • As Cesnik et al. observe,  68  relating proteoforms to protein accessions is a first step, but associating proteoforms to genes will be a necessary step for proteogenomics.
As a first step, all top-down search engines should enumerate all FASTA accessions that match a particular proteoform, not just the first in the database. • As noted above, simply "taking the union" of all PrSMs from multiple search engines will escalate FDR in the accepted identifications. Similarly, accepting only the PrSMs on which multiple search algorithms agree ("the intersection") is far too conservative. Models are needed to compare multiple deconvolution assessments on the same data and integrate multiple attempts at identification in a way that emphasizes agreements and flags disagreements. In short, the top-down identification field contains excellent opportunities for bioinformatics advances. The state-of-the art for proteoform identification already produces solid results, and yet more will become possible with new advances in the field.
2c00673. Text S1 (PDF): Detailed sample handling details for PXD031744 bovine milk samples. Text S2 (PDF): Impact of "suppressing" PrSMs for unanticipated PTMs in TopPIC search. Text S3 (PDF): Why would a search identify multiple PrSMs for an individual MS/MS? Text S4 (PDF): How do identification pathways compare in duration. Text S5 (PDF): How much time does using a subset sequence database save. Text S6 (PDF): Testing ProSight PD for detecting phosphorylation in PXD031744 bovine milk. Text S7 (PDF): Are glycosylated proteoforms prominent in PXD005420 ovarian tumor data. Figure S1 (PDF): Configuration in Mascot Distiller 2. 8. Figure S2 (PDF): Configuration in Bruker DataAnalysis 5. 3. Figure S3 (PDF): Overlap of identified proteoforms and scan numbers in PXD003074 SULIS. Figure S4 (PDF): Deconvolution may produce very different numbers of MS/MS mass lists.
Experimental Data Employed in This Data Set a enumerates the LC-MS/MS experiments in each set (CE-MS/MS was employed in PXD020342-DANRE). "Median Z" reports the median of precursor charge among all tandem mass spectra in a set. In PXD031744-BOVIN from the Fusion Lumos, two tandem mass spectra were collected for each precursor ion, one in HCD and one in ETD. b For these experiments, +1 precursors were excluded when computing the median. charge detected in ETD was +50, while HCD rose to +44. The sequence database combined UniProtKB reference proteomes UP000013006 (S. islandicus LAL14/1: 2591 proteins) and UP000000625 (E. coli: 4438 proteins). The E. coli proteins, comprising 63% of all entries, were included as unlabeled decoys, since no E. coli was expected to be present in the samples. PXD019247 ECOLI.
PXD019247 ECOLI. Dupréet al. analyzed E. coli lysates to optimize the detection of pathogen-specific proteoforms on a ThermoFisher Orbitrap Fusion Lumos. study instrument author experiments median Z MS count MS/MS count PXD003074-SULIS-ETD Orbitrap Fusion Vorontsov 6 11 5818 12,664 PXD003074-SULIS-HCD Orbitrap Fusion Vorontsov 6 9 6541 24,015 PXD020342-DANRE Q-Exactive HF Xu 18 5 32,505 31,905 PXD019247-ECOLI Orbitrap Fusion Lumos Dupré6 9 27,878 44,609 PXD010825-PIG Bruker maXis II Brown 3 14 b 7402 15,276 PXD019368-HUMAN Bruker maXis II Brown 8 21 b 15,533 30,508 MSV000082070-BOVIN Bruker maXis I Vincent 18 15 b 4662 5565 PXD031744-BOVIN Orbitrap Eclipse Zhou 4 11 8567 90,685 PXD031744-BOVIN Orbitrap Fusion Lumos Zhou 4 7 10,749 53,632 PXD031744-BOVIN Q-Exactive HF Zhou 4 6 17,582 35,281 PXD005420-HUMAN Q-Exactive Delcourt 12 7 b 38,608 58,423 a "Experiments".
Statistics Statistical analysis was performed in JMP Pro 14. 0. 0 (SAS Institute Inc. , Cary, NC. ) or Matlab (MathWorks, Natick, MA). All tests were performed with probability level P < 0. 05. Differences between tree pieces were calculated using ANOVA, and multi-comparison corrections were done using the Tukey-Kramer method. For calculating differences in intervessel wall thickness among tree types, the dependent variable was log transformed to fulfill model assumptions. D. Dimensional reduction by t-SNE enables visualization of the 28-dimension network analysis  (40). Each point represents one sample, with species indicated in colors and wood types distinguished by shape. Distance between points on the 2D graph reflects distance in the full multidimensional space. Numbers on graph indicate the rank order derived from species-averaged simulated P50 (Sim P50, Arabic numerals in green) and conductance exponential decay rate (CEDR, Roman numerals in red).