dataesr/scientific-paragraphs-categorization huggingface dataset:
In all cases, the percentage of the C-terminal lysine variant was very low and comparable, with an average amount of 2. 1%, and values ranging from 1. 5 to 2. 5%. Interestingly, no statistical difference was observed between the three instruments. However, it appears that one of the software solutions (UNIFI) systematically underestimates the amount of C-terminal lysine variant by about 0. 7%. To better understand this observation, the quality of the data was manually checked. At the chromatographic level, the two peaks corresponding to the SLSLSLGK and SLSLSLG peptides are easily separated (retention times of 26. 79 and 29. 56 min) and the S/N ratio was very good in both cases, so the integration of these two peaks was easily obtained and could not be the reason for the observed discrepancies.
We next checked if the same charge states were considered for the two species. In both cases, the +1 and +2 species were considered, so this cannot explain the slight difference observed with the UNIFI software. The difference was probably due to differences between the algorithms used in UNIFI and waters_connect: in UNIFI, each injection is processed separately, without any retention time alignment between injections. In waters_connect (with Peptide MAM application), the injections' data are first aligned based on retention times and then combined to produce a map of all the ion species in all the injections. The corresponding map is then used for peak picking, and the alignment information allows us to quantify each peak on all the injections, rather than just those injections for which there was a strong signal.
This could explain why a higher average response is observed in the wa-ters_connect for low-level species. Despite this minor difference observed with one software solution, it appears that the presence of the C-terminal lysine variant can be reliably evaluated with the peptide MAM method developed in this work. To better understand this observation, the quality of the data was manually checked. At the chromatographic level, the two peaks corresponding to the SLSLSLGK and SLSLSLG peptides are easily separated (retention times of 26. 79 and 29. 56 min) and the S/N ratio was very good in both cases, so the integration of these two peaks was easily obtained and could not be the reason for the observed discrepancies. We next checked if the same charge states were considered for the two species.
In both cases, the +1 and +2 species were considered, so this cannot explain the slight difference observed with the UNIFI software. The difference was probably due to differences between the algorithms used in UNIFI and waters_connect: in UNIFI, each injection is processed separately, without any retention time alignment between injections. In waters_connect (with Peptide MAM application), the injections' data are first aligned based on retention times and then combined to produce a map of all the ion species in all the injections. The corresponding map is then used for peak picking, and the alignment information allows us to quantify each peak on all the injections, rather than just those injections for which there was a strong signal. This could explain why a higher average response is observed in the waters_connect for low-level species.
For these last two peptides, the issue was due to the presence of a contaminant peak eluted quite close to the oxidized peptide and not differentiated by MS. Interestingly, Skyline offers much more flexibility for peak integration, as the start and end of the peak integration can be directly changed on the XIC by the analyst. On the other hand, UNIFI and waters_connect only allow performing an automatic peak integration based on parameters set by the user (21 CFR Part 11-compliant software). Finally, another source of variation could be related to an inappropriate automatic selection of charge states in the MS spectra. Indeed, in some cases, different charge states were considered depending on the software. The selected charge states can be easily modified with Skyline or waters_connect, but are automatically selected in UNIFI, with no possible modification.
This can lead to issues; as an example, for the peptide GQPREPQVYTLPP-SQEEMTK in laboratory #1, the charge states +2 and +3 were selected in UNIFI for the first replicate, while only the charge state +2 was selected for the two other replicates. In laboratory #2, charge state +3 was selected for the first and third replicates, while +2 was selected for the second replicate. In laboratory #3, the +3-charge state was systematically selected for the three replicates. In the end, the variability in terms of the percentage of oxidation between the different laboratories and software was found to be acceptable for the DTLMISR, ASGITFSNS-GMHWVR and NTLFLQMNSL peptides with the peptide MAM approach. However, the variability was clearly too high for GQPREPQVYTLPPSQEEMTK, mostly due to the difficulty with peak integration, which should be corrected manually to draw reliable conclusions.
It should be noted that this peptide contains three proline residues, which can induce an uncommon behavior in mass spectrometry, notably in terms of fragmentation. By selecting this kind of peptide in a MAM study, one should accept higher variability. Finally, another source of variation could be related to an inappropriate automatic selection of charge states in the MS spectra. Indeed, in some cases, different charge states were considered depending on the software. The selected charge states can be easily modified with Skyline or waters_connect, but are automatically selected in UNIFI, with no possible modification. This can lead to issues; as an example, for the peptide GQPREPQVYTLPPSQEEMTK in laboratory #1, the charge states +2 and +3 were selected in UNIFI for the first replicate, while only the charge state +2 was selected for the two other replicates.
The full scan acquisition was performed with Intelligent Data Capture (IDC) enabled and a mass range of 50-2000 m/z with a scan rate of 5 Hz. The data acquisition was performed with UNIFI Software (Waters, Milford, MA, USA): UNIFI v1. 9. 4 in lab 1, v1. 9. 9 in lab 2 and v1. 9. 13. 9 in lab3.
Data Analysis The extent of the digestion and data quality was evaluated by first analyzing a dataset with UNIFI 1. 9. 4 (Waters) with the peptide mapping process. The peptide search was based on the nivolumab light and heavy chain sequences. The peptide identification was performed with a mass tolerance of 10 ppm on the precursor and 15 ppm for the fragments. Carbamidomethylation was set as a fixed modification as well as the pyroglutamic acid E Nterm and Q N-term and loss of Lysine C-term. The oxidation of methionine, glycosylation (G0F, G1F, G2F, G0) and the deamidation of asparagine were selected as the variable modifications. This process allowed for the selection of peptides for the multisite analysis. The seven selected peptides and their modified counterparts were directly sent to the UNIFI library (peptide sequence, m/z and retention time).
The percentages of modification in these different peptides were compared on three different software platforms. One process was performed in UNIFI using the Accurate Mass screening process (UNIFI 1. 9. 4), with the peptide list in the created library. The quantification was based on the extracted ion chromatograms, as the sum of all the adducts and isotopes. The data were expressed as a percentage modification based on the response of the extracted ion chromatograms. Another process was performed in the peptide MAM application (waters_connect v1. 1. 0. 5, Waters) in which the attributes were imported from the same UNIFI library. The process was performed automatically using different charge states and the isotopes of each attribute and data were reported as a percentage modification. A third process was performed in the freely available and open-source Skyline application  [34]  (MacCoss Lab, Department of Genome Sciences, University of Washington, v.
Conclusions We provide here a small-sized interlaboratory study to evaluate a MAM workflow performed on benchtop LC-MS equipment. Overall, our study highlights the consistency of the peptide mapping results obtained within this study using identical benchtop LC-MS platforms and nivolumab samples prepared by one group and distributed to the other laboratories to minimize sample preparation artifacts. The post-processing and relevance of the bioinformatics data treatment were investigated by centralizing the data interpretation at one site with three different commercial or open-source pipelines, avoiding the study of the impact of parametrization of the different software packages. We highlighted the importance of the downstream process of the analytical pipeline, i. e. , the bioinformatics processing and statistical analysis, which represent critical steps to generate the final comparative results, by comparing two 21 CFR Part 11-compliant software solutions provided by the vendor and an open-source tool classically used for quantification in targeted proteomics (used as the versatile workflow reference).
Our study first demonstrates that, independently of the post-processing workflow, peptide-mapping MAM provides consistent results for the glycoform analysis of the major drug product, with excellent repeatability, suggesting that glycoform analysis can be monitored by peptidemapping MAM. For more minor proteoforms (oxidation, deamidation, N-terminal Kclipping), the performance of the software might be significantly different, mainly due to the automatic peak integration, selection of charges states or quantification algorithms, putting the selection of the bioinformatics pipeline at the forefront. More flexible tools such as Skyline offer improved possibilities for back corrections of mis-peak integration or mis-charge selection than 21 CFR part 11-compliant software, with waters_connect being more flexible than UNIFI, and thus better adapted for MAM studies.
Phylogeny of VPS13 Proteins in Archaeplastida Sequences were retrieved by BLASTP searches  (Altschul, 1997)  in different databases using A. thaliana or HsVPS13B sequences as queries (see Material and Methods). Only sequences with at least one VPS13 canonical domain annotation were retrieved to populate a first dataset of 537 sequences (Table  S1 ). However, numerous sequences were fragmented (size below 2000 amino acids), likely because of poorly assembled genomes and to errors performed by automatic genome annotation that failed to properly define the correct borders of such long proteins that contain many introns/exons (63 exons for AtVPS13M1, for example). An extreme case is illustrated in Figure  S1  where a VPS13 gene in Coffea arabica on scaffold 440 is divided in at least seven successive gene models (evm.
model. Scaffold_440. 43 to evm. model. Scaffold_440. 49) with predicted Vacuolar sorting associated protein 13 domains (IPR026847/ PTHR16166) and translated to proteins ranging from 96 to 2207 aa. Some particularly long sequences were retrieved corresponding to different protein model predictions of the same gene. This is the case as example in Chlamydomonas reinhardtii in which two sequences, A0A2K3DAJ8 (8089 aa) and A8IIB7 (7192 aa), corresponding to the same VPS13 gene with different exon/intron border prediction, were retrieved (Table  S1 ). In order to perform analyses with a significant number of likely full-length sequences from Archaeplastida, ≥2400 aa long sequences were selected to create a dataset containing 383 sequences (Dataset 1). Within this dataset, major variations in gene copy number and sequence length are observed among clades (Figure  1B  and C ).
Nevertheless, these results have to be taken with caution as the dataset was not manually curated and hence some paralogs might be missing, might correspond to different models of the same gene, or might be incomplete. The highest number of VPS13 sequences is observed in the Charophyte Spirogloea muscicola (13 sequences) and the Embryophyte Tinopyrum intermedium (12 sequences) (Figure  1B ). Limited variations of sequence length are observed for Embryophytes and the Glaucophytes in contrast to other clades (Figure  1C ) and sequences more than 7000 aa long are recorded in some Chlorophytes (Figure  1C ) as previously reported  (Levine, 2022). Noteworthy, the 13 Glaucophyte sequences are very short (below 3000 aa), although according to domain analysis (see below), most of them are full length (i.
e. , presence of a N-term Chorein and a C-term PH domains). A maximum likelihood (ML) phylogenetic analysis was then performed adding HsVPS13A through D and ScVPS13 to the dataset in order to infer their relationship with those from Archaeplastida. Because of the length of the alignment, 5,000 boostrap pseudoreplicates were performed and nodes supported by a bootstrap value above 4,000 (80%) were considered as significant. The unrooted tree presented in Figure  2  shows the complex evolutionary history of VPS13 proteins in each phylum. VPS13 proteins show high primary sequence diversity and no protein family present in all of the three Archaeplastida groups can be unambiguously defined. Embryophyte VPS13 proteins clearly split into four clades likely corresponding to the AtVPS13M1, M2, S, and X paralogs described in A.
Therefore, VPS13B was likely present in the LECA. In order to more confidently trace the evolution of the VPS13 paralogs in Archaeplastida, a Bayesian and a ML analyses were performed on a reduced dataset of 104 sequences composed of two Glaucophytes, three Rhodophytes, two Prasinodermatophytes, one organism in each of the five Charophyte groups pictured in Figure  1 , four Chlorophytes and eight Embryophytes (including lower and higher plants, one gymnosperm and angiosperms, monocotyledons, and dicotyledons) as well as the human and yeast VPS13 sequences (Dataset 2). All the sequences in Dataset 2 were manually curated to eliminate redundant sequences. As VPS13 primary sequences can be highly divergent but share a conserved overall 3D structure and domain arrangement, we mathematically formalized the domain occurrence for each sequence and included such information in the Bayesian analysis (see Material and Methods).
Remote homology analyses using HHPred  (Zimmermann et al. , 2018)  were performed for all the sequences included in the dataset in order to identify putative domains. Most of the canonical VPS13 domains such as Chorein, VAB, or ATG2_C were found with additional domains for some sequences that will be further discussed below (Table  S2 ). VPS13B clade was used to root the trees (Figure  S2  and S3 ). These analyses confirmed that no group including sequences from the three Archaeplastida phyla (i. e. , Viridiplantae, Glaucophyte, Rhodophyte) was found, showing that VPS13 paralogs have highly diverged. Eight clades, namely M, S, X, B, R, Glaucophyte, and opisthokont were highlighted by both the Bayesian (Figure  S2 ) and the ML (Figure  S3 ) analysis. However, the analyses do not allow to establish any relationship among the different VPS13 groups.
Bayesian Analysis of Viridiplantae VPS13 Paralogs From the results presented above, Viridiplantae VPS13 family showed an interesting evolutionary history, hence a reduced dataset (Dataset 3, Table  S3 , 79 sequences) including Embryophytes, Streptophytes, Chlorophytes, and Prasinodermatophytes was produced and analyzed as described above. Human and yeast sequences were excluded to avoid destabilization of the analysis. The tree presented in Figure  3  shows that VPS13 protein sequences clustered into five robustly supported (PP = 1. 00) clades, namely M, in turn divided into three sub-clades (M, M1, and M2), S, X, B, and Y. VPS13Y has never been reported before. We named it "Y" because no clear and unambiguous homology with other known VPS13 protein groups was revealed by this analysis. Within each VPS13 clade, the organization of the branches is in agreement with the expected taxonomy: Prasinodermatophytes and Chlorophytes are always basal to Streptophytes within which Charophytes are basal to Embryophytes, with non-vascular plants being well separated from vascular plants, showing a linear evolution of VPS13 proteins in each phylum.
Most of the ESTs aligned with the 3' end of the transcripts, but hits were also found all along the sequences (Figure  S5 ). This analysis also revealed errors in the predicted VPS13 gene models as several ESTs support a different intron/exon organization. In addition, an AGO1 domain was initially predicted at the C-term of CsVPS13B protein but EST analyses clearly show that the transcript ends after the PH domain (Figure  S5 ). Thus, ESTs were used to curate CsVPS13 paralog sequences and obtain more accurate mRNA and protein sequences for further analyses (see below) (Supplemental File S2). We also investigated the VPS13 paralogs expression in two Charophyte species used in this study (C. braunii and K. nitens) as well as in S. fallax. Assembled transcriptomes were found on NCBI for Chara braunii (Bioproject PRJNA492241), Klebsormidium flaccidum (Bioproject PRJN242253) and two Sphagmun species: S.
recurvum (Biosample UHLI) and S. palustre (Biosample RCBT). TBLASTN searches were performed using respective VPS13 protein sequences as queries. Interestingly, transcripts were identified with a high confidence for all the paralogs (e-value = 0, Table  S4 ). In addition, transcripts covering almost the entire sequences were found for the six VPS13 paralogs in K. flaccidum. KfVPS13M2 and Y transcripts were likely full length whereas the others are split into different transcripts likely due to errors in the assembly. Transcripts corresponding to the five S. fallax VPS13 paralogs were detected in both S. recurvum and S. palustre, confirming the presence and the expression of VPS13B in the Sphagnum clade. In addition, the two SfVPS13M1 isoforms are also present and expressed in both Sphagnum species, showing that they remained functional after VPS13M1 gene duplication.
In C. braunii, only three paralogs (VPS13B, M2, and X) were previously identified by BLASTP searches and transcripts were found for all with a high confidence (Table  S4 ). We also found other putative hits of shorter length (around 500 aa) and lower confidence that might correspond to other VPS13 paralogs initially undetected by BLASTP searches. Thus, TBLASTN searches on C. braunii transcriptome were performed using all VPS13 paralogs from K. nitens and Closterium as queries. This analysis allowed the detection of putative CbVPS13X paralogs identified in both TBALSTN search and covering around 2000 aa of KnVPS13X. A putative VPS13S was detected only with KnVPS13S but with a low coverage (559 aa). However, no putative M1 sequences were retrieved, maybe due to lower expression level, poor transcript assembly, or the absence of this paralog in C.
subellipsoidea (3698 aa) also contain 12 RBG domains, even if they are longer than AtVPS13S (3466 aa) therefore an increase in protein size is likely not correlated to an increase of RBG repeats or tunnel length (Figure  4 ). Overall, the domain organization of VPS13S proteins seems to be conversed across the Viridiplantae phylum without addition of any new domain. It is noteworthy that a similar basic organization is found in the single VPS13 paralog in yeast  (Levine, 2022). VPS13M. A. thaliana VPS13M1 and M2 proteins are composed of 12 RBG domains and are characterized by the occurrence of up to seven additional domains (Figures  3  and 4 )  (Levine, 2022). Indeed, a (1) PH domain is inserted between RBG4 and 5 in addition to the canonical one located downstream of the C-term ATG2_C domain, (2) two β-tripod domains in tandem are inserted between RBG9 and 10 and a third one is present at the C-term end of most of the VPS13M paralogs, (3) a C2 domain is inserted right upstream of the VAB domain, (4) a β-helix domain, and (5) a Dysferlin/Pex24 (DysF, IPR010482) domain is predicted within the VAB of several VPS13M orthologues.
RBG domain prediction shows that RBG1, 2, 10, 11, and 13 were easily detected using HsVPS13B RBG sequences as queries. Surprisingly, we failed to detect any RBG domains in the N-term portion of the proteins between RBG2 and 10 in contrast to HsVPS13B (Figure  4 ). We hypothesized that this might be linked to a higher divergence of RBG repeats in this region in Viridiplantae and performed a HHPred search for RBG domains using RBGs from AtVPS13 paralogs without any success. Neither specific domains nor motifs were found. Structural predictions also failed to reveal clear RBG domain folding (see below). As AlphaFold2 predictions can be challenging for these proteins  (Neuman et al. , 2022) , we performed secondary structure predictions for C. subellipsoidea, K. nitens, and S. fallax (from N-term to the VAB domain) using Quick2D, which gathers predictions from several softwares  (Zimmermann et al.
Interestingly, using TMHMM2. 0 software  (Krogh et al. , 2001) , a transmembrane (TM) helix was predicted at the N-term for most of the VPS13Y sequences. No TM domains were predicted in other VPS13 paralogs apart in Beta vulgaris VPS13X, implying that membrane anchoring might be a VPS13Y group specific feature. Thus, the ancestral VPS13Y protein might present the following domain organization: TM-Chorein-VAB-C2-C2-ATG2_C-PH. An additional Kinase domain was found in an extra-region located between the VAB and the first C2 KnVPS13Y. In K. nitens, we were able to predict the presence of 12 RBG domains (Figure  4 ), some of them being more divergent and predicted with the help of structural modeling. Unexpectedly, we failed to predict RBG domains in the N-term part of CsVPS13Y, even using KnVPS13Y RBG domains as a query (Figure  4 ).
Noteworthy is the presence of long, mostly unfolded, sequences in modules 3 and 4 of CsVPS13 (Figure  S8 ) which explain the prediction of a fragmented VAB by HHPred (Figure  4 ). S. fallax and C. subellipsoidea VPS13B VAB also fold into a six-module unit in contrast to K. nitens VPS13B for which no clear structure was predicted (Figure  6  and Figure  S8 ). Thus, while AlphaFold2 and RoseTTAFold failed to predict a canonical tunnel structure for the N-term part (Figures  S6  and S7 ), the presence of a canonical VAB domain in CsVPS13Y, CsVPS13B, and SfVPS13B suggests that they are indeed VPS13 proteins.
Evolutionary History of VPS13 Proteins Viridiplantae In this work, we performed phylogenetic analyses of VPS13 proteins in Archaeplastida. Our results revealed the complex evolutionary history of this protein family with several losses, acquisitions, and duplications. As VPS13 proteins are mainly characterized by a specific domain organization and structural similarities rather than conservation of the amino acid sequences, in addition to ML analyses, we performed Bayesian analyses in which domains were inferred as characters in a reduced dataset of Rhodophytes, Glaucophytes, and Viridiplantae and then, only in Viridiplantae where VPS13 paralogs seemed more conserved. Our results showed that one VPS13 group, VPS13B, mainly represented in Metazoa  (Velayos-Baeza et al. , 2004; Levine, 2022) , is found also in different phyla of Viridiplantae and was likely present in the LECA corroborating Levine's hypothesis  (Levine, 2022).
In Viridiplantae, four groups, VPS13M, S, X, and Y were clearly identified and interestingly, none of the Rhodophytes' and Glaucophytes' sequences clustered with these groups suggesting a high divergence of VPS13 sequences after the primary endosymbiosis. Rhodophytes' sequences produced one robust clade that we named VPS13R grouping proteins with a similar domain organization. The other red algal sequences, though, are spread in different clades of the tree. Glaucophytes' sequences clustered together in the different analyses performed but with a weak support and no clear groups or domain organization was detected, likely because of the reduced dataset available for this clade. The evolutionary history of VPS13 proteins in Viridiplantae is more linear and summarized in Figure  7. The Viridiplantae MRCA had likely four VPS13 paralogs: VPS13S, M, Y, and B with the domain organization presented in Figure  7.