dataesr/scientific-paragraphs-categorization huggingface dataset:
Infection-enhancing antibodies may limit the efficiency of Covid-19 vaccines. We analyzed the evolution of neutralizing and facilitating epitopes in 1,860,489 SARS-CoV-2 genomes stored in the Los Alamos database from June to November 2021. The structural dynamics of these epitopes was determined by molecular modeling of the spike protein on a representative panel of SARS-CoV-2 variants. D614, which belongs to an antibodydependent-enhancement (ADE) epitope common to SARS-CoV-1 and SARS-CoV-2, has mutated to D614G in 2020, which could explain why ADE has not been detected following mass vaccination. A second epitope located in the N-terminal domain (NTD), specific of SARS-CoV-2, is highly conserved among most variants. In contrast, the neutralizing epitope of the NTD showed extensive variations in SARS-CoV-2 variants.
Analysis of amino acid sequence variations in ADE and neutralizing epitopes during the global spreading of the Covid-19 pandemic Then, we analyzed the evolution of the amino acid sequence of ADE epitopes among SARS-CoV-2 variants (Figure  3 ). The 611-617 epitope (lower left panel), which is common to SARS-CoV-1 and SARS-CoV-2, has a unique signature in all variants, i. e. the D614G mutation. As position 614 is central to the epitope, this epitope is probably no longer recognized by ADE antibodies generated by previous coronavirus infections in humans. The second ADE epitope is formed by several distinct areas in the NTD and in the rod-like regions of the spike protein (Figure  3 , upper panel). In the NTD, the epitope is divided in three linear segments that represent ca. 80% of the total energy of interaction of the 1052 antibody-NTD complex (as calculated from PDB: 7LAB): 27-32, 64-69 and 211-219 (accounting respectively for 12, 19 and 51 % of the energy of interaction).
The changes included deletions, insertions and single point mutations that are distributed among two key regions, 144-158 (N3 loop) and 242-249 (N5 loop) that constitute the three-dimensional site recognized by the neutralizing 4A8 antibody  [43]. The localization of the neutralization epitope of the NTD at the virus/host cell interface is consistent with this high variability as it is submitted to a strong pressure of selection for SARS-CoV-2 variants. Conversely, the ADE epitope, which is on the lateral side of the NTD, is not facing the plasma membrane of the host cell and for this reason is not subjected to such a high selective pressure. The frequency of amino acid sequence variations of the ADE and neutralizing epitopes was analyzed by specific queries of the Los Alamos database over the last six-month period (2021-06-01 to 2021-11-27) (Table  2 ).
Instead, we believe that it is critical to design new vaccine formulations able to induce neutralizing antibodies against this strain and, most importantly, lacking ADE epitopes in the NTD. Molecular epidemiology surveillance of SARS-CoV-2 coupled with structural analysis of variant spike proteins will certainly help to reach this goal. Table  2. Frequency of ADE and neutralizing epitope sequences. The frequency of mutations of each epitope sequence is calculated as the percentage of identity with the reference amino acid sequence of the SARS-CoV-2 spike protein (Wuhan strain). The most variable amino acid residues of each epitope are underlined. 1,860,489 sequences were analyzed from 2021-06-01 to 2021-11-27. The raw data were obtained from the Los Alamos website (https://cov. lanl. gov/content/sequence/ANALYZEALIGN/analyze_align.
As new disorder prediction methods keep emerging  (5) (6) (7) , CAID takes on monitoring the field in real time, aiming to establish dependable standards. This ambition has a special caveat, as predicting and identifying regions in IDPs/IDRs that engage in functional interactions remains a significant challenge  (6, 8). The prediction of such functional regions may support and inspire dedicated experimental approaches. The Eukaryotic Linear Motif (ELM) database is a primary repository of such data  (9). Of similar ambition pursued along different lines, the database of fuzzy complexes, FuzDB, compiles experimentally observed fuzzy protein complexes, in which intrinsic disorder is maintained upon partner interaction, directly impacting biological function  (10). An instructive example of this behavior is the extremely tight and functional interaction between the disordered histone H1 and its chaperone prothymosin-␣, which retain their highly dynamic, fully disordered state in that complex  (11).
Structural interpretation of such a behavior may be assisted by data in the Protein Ensemble Database (PED) that encompasses experimentally determined structural ensembles of IDPs/IDRs  (12). There appears to be a consensus that these and other types of data and approaches will act in synergy to drive the field forward  (13) , toward reaching a better structuralfunctional understanding of the 'disorderome'. Combined with data in the MobiDB database, which provides predictions and annotations for all IDPs/IDRs  (14) , this effort is also critical for integrating disorder-related information into other data resources, such as UniProtKB  (15)  or PDBe  (16). Not surprisingly, the field is also getting strong impetus from traditional structural biology: as missing regions from solved structures constitute a good proxy for structural disorder, improvements in structure determination techniques boost the identification of structural disorder.
Recent successes in the field, i. e. targeting c-Myc  (25) , androgen receptor  (26)  or alpha-synuclein  (27) , encourage us to adhere to this ambition. The concept of liquid-liquid phase separation (LLPS) leading to the formation of membraneless organelles (MLOs) raises hope that IDP/IDR function and therapeutic targeting can now be approached from a novel angle  (28, 29). To reflect the steady progress of the protein disorder field, it is important to update and upgrade DisProt as one of the primary resources of manually curated, experimentally confirmed protein disorder. The previous release of the database, DisProt 8 (30) contained about 1500 entries and 3500 disordered protein regions. In the current release, DisProt 9, not only did we increase these numbers, but also improved the reliability of entries by introducing a reviewing process.
In addition, a great effort was allocated to training activities by providing DisProt biocurators with detailed curation guidelines and virtual training sessions and a published protocol describing how to explore manually curated annotations in DisProt  (13) , alongside the dissemination of new database content in a dedicated DisProt blog (https://disprot. org/blog/) and Twitter account (https://twitter. com/disprot db). DisProt 9 presents a new graphical interface and updated features, such as the integration of two ontologies and the connection with API-CURON, a database to credit and acknowledge the work of biocurators  (31). With these improvements, DisProt continues to be a primary resource of protein disorder for the structural-molecular biology community.
Database content DisProt 9 includes 2038 protein entries and 4,477 pieces of evidence of state transitions, interactions and functions, featuring a 30% increase over the last release (DisProt 8), along with 2578 publications annotated, accounting for a 28% increase compared with DisProt 8. In addition, about 14% of the annotations, corresponding to 237 entries, have been reviewed and validated by an expert biocurator, e. g. the High mobility group protein HMG-I/HMG-Y (DP00040) has been thoroughly reannotated by revising spurious annotations and integrating new functions, such as DNA bending (GO:0008301, IDPO:00514), DNA binding (GO:0003677, IDPO:00065), RNA binding (GO:0003723, IDPO:00066) and protein binding (GO:0005515, IDPO:00063). In DisProt 9, annotations of amino acid repeats displaying the typical properties of IDPs, specifically poly-glutamate (polyE), poly-lysine (polyK) and poly-arginine (polyR) regions, have been added based on indirect evidence.
These regions are always disordered but experimental evidence is provided for only a handful of cases or for engineered fragments. For example the polyE repeat in the PEVK region of human titin has a Stokes radius 2-4 times larger than expected based on its molecular mass, and it shows a minimum at 200 nm in its circular dichroism (CD) spectrum  (32). In order to be included in DisProt a repeated region must be predicted by MobiDB-lite  (33)  (and be available in MobiDB) as a negative or positive polyelectrolyte subregion  (14, 32 ) and be at least 10 residues long. In Dis-Prot 9, such cases are highlighted by a specific evidence code, curator inference from database (ECO:0007636). The distribution of regions based on the experimental detection method is shown in Figure  1. In accordance with the recent improvements that recognize cryo-EM as a wellestablished technique in structural biology  (17) , DisProt includes a total of 149 IDRs annotated by this method.
Thematic datasets Starting 2020, DisProt periodically releases 'thematic datasets' describing biological areas where IDPs play a crucial role. The first two thematic DisProt datasets illustrate the role of intrinsic disorder in unicellular toxins and antitoxins (December 2020) and extracellular matrix proteins (June 2021), by collecting carefully curated examples of IDPs and IDRs involved in the toxin-antitoxin system and in the extracellular matrix composition and function, respectively. DisProt thematic datasets are easily accessible from the Dis-Prot home page under the 'Datasets' section. All the entries included in these datasets are tagged with the name of the theme and it is possible to download them as single files.
Implementation DisProt 9 adopts the Document Versioning Pattern to precisely track all changes between different versions of the same record. This improves reproducibility, simplifies tracking of curation activities and allows the user to compare different versions of an annotated entry directly on the web site. Each protein has a history page and each IDR has an assigned version number. Whenever a curator modifies an existing region, the version counter increases by one. DisProt exposes disordered region descriptions with Bioschemas metadata, including disordered region boundaries and type of disorder. The information content from the entry pages is aggregated together with complementary resources (MobiDB and PED) to the IDPcentral registry and knowledgebase  (41). The DisProt web application source code underwent a major upgrade, updating Angular version 12 and transitioning to Bootstrap version 5.
Outreach activities DisProt has an active social media presence on Twitter (https://twitter. com/disprot db). This account promotes communication with users and experts in the field. It provides updated information about the latest release, webinars and training activities. Finally, a dedicated blog website (https://disprot. org/blog) contains descriptive extended posts about thematic datasets, general statistics and novel, interesting annotations. Training material for the users is provided via a published protocol describing how to explore manually curated annotations in DisProt  (13). Training material for the curators is offered via recorded webinars and an updated user manual.
CONCLUSIONS AND FUTURE WORK DisProt is the gold standard for IDP/IDR annotations, and serves the community as a fundamental resource that drives biological hypotheses, experimental design and the training and benchmarking of disorder and function predictors. Compared to the previous version, it has improved data accessibility and quality, and significantly increased annotation volume. The content is updated frequently and is now more focused on function. A team of expert reviewers validate annotations provided by the community and continuously check the literature for novel experimental evidence. DisProt is well connected to other databases and consortia, and is active in the development of new standards and ontologies. The Intrinsically Disordered Proteins Ontology (IDPO) has been refactored and systematically crossreferenced with Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO).
We grew the segregant pools at permissive (26°C) and restrictive (34°C) temperatures and measured their fitness. In the control cross with S288C, no segregants are expected to grow at the restrictive temperature due to temperature sensitivity of the TS allele. However, in cases where the wild yeast strain harbours variants that can suppress the TS phenotype, the haploid segregants that carry them will be able to grow at the restrictive temperature and will take over the population  (Fig 1B). We first measured the growth defect of each TS allele in complex pools of wild yeast strain cross progeny (Dataset EV1, Appendix Fig High-throughput measurement and mapping of suppressor effects from a panel of wild strains. A Strains used in the study. Seven wine / European strains, and three other distant ones (all blue) were crossed to the S288C reference (yellow).
S1 ). We estimated suppression as normalised log 2 -scale growth difference between the wild and reference strain crosses at the restrictive temperature and considered a TS allele phenotype suppressed, if this value was above 0. 75, i. e. the wild strain segregants had a 1. 68-fold improvement in growth, and if this was unlikely due to chance (Bonferroni-corrected P = 0. 04, Methods, Dataset EV2). Our screen included several positive control crosses that were expected to show suppression. Three of the wild strains harbour a chrVIII-chrXVI reciprocal translocation  (P erez-Ort ın et al, 2002; Fig EV1A). Crossing these strains to the reference strain results in 25% of the progeny carrying a duplication of a substantial part of the left arm of chromosome XVI  (Fig EV1B). This creates an extra copy of the essential query genes in this region that complements the TS allele.
We sequenced the populations and compared variant allele frequencies between the two temperatures (Datasets EV4 and EV5). We first considered positive controls expected to involve suppression by an additional, wildtype copy of the query gene described above, either generated by the chrVIII-chrXVI translocation (six samples) or located on an aneuploid chromosome (six samples). In all 12 cases, we could indeed observe selection for either the translocation or the aneuploidy and further confirmed that suppression occurred by the presence of a second, wild-type allele of the query gene (Figs EV1C and D, and EV2D). Genetic suppression of essential gene TS mutants is not frequent for individual allele-strain combinations, but relatively common for a gene. A A positive control region shows suppression signal.
The GPI16 gene suppression was not estimated in the NCYC110 strain due to chromosome II copy-number variation ("X"). Second, we sequenced meiotic progeny of nine crosses that showed weak "suppression" in our screen (suppression score below 0. 7), unrelated to any known translocations or aneuploidies. Five cases showed selection for newly acquired aneuploidies of either the chromosome carrying the query gene or other loci selected in the crossing protocol. These cases often represent ways of cells to escape the strong selection applied in our protocol, rather than true cases of suppression. The remaining four cases harboured regions of selection for the wild strain sequence specific to high temperature (Datasets EV4 and EV5), suggesting that some of the weaker scores in our screen also represent true cases of suppression and corroborating the observations from the confirmation of individual suppression effects.
Third, we analysed 17 crosses that showed strong suppression in our screen (suppression score above 0. 75). The large majority (  14 ) showed regions of specific selection for the wild sequence at high temperature  (Fig 3A, Datasets EV4 and EV5) , whereas two populations diploidised or showed selection for an aneuploidy. The one remaining cross did not show any suppressor loci or aneuploidies. Thus, we could map suppressor loci for 14/17 (82%) of the crosses that showed strong suppression in our screen, and in 4/9 (44%) of the crosses that showed weak suppression. The landscape of suppressors is diverse. We identified 31 suppressor loci in the 19 crosses without aneuploidies (1. 6 on average, Figs  3B  and 4A, Dataset EV5)  and an additional 48 weaker  reproducible signals (2. 5 on average, Figs 3B  and 4A, Dataset EV5 ).
Suppressor gene identification and validation We next sought to identify the causal suppressor genes. As each of the mapped regions harbours tens of plausible candidates, we computationally prioritised them based on their functional connection to the query gene (Dataset EV6, Methods, van Leeuwen et al, 2020). We also included known general modifiers, such as MKT1 and HAP1, that each affects the expression of thousands of genes  (Fay, 2013   EV4 ), validating both our mapping strategy, as well as the computational prioritisation. The 14 suppressor loci without a confirmed suppressor gene resulted from failed experiments, inconclusive results, or cases in which no suppression was observed for the wild allele (Dataset EV7). Causal suppressor genes were more likely to be identified for strong suppressor loci compared to weaker signals, suggesting that in unconfirmed cases the suppression phenotype may have been too weak to detect in our validation assay  (Fig 4B).
We did not observe examples consistent with strong suppression by many small effect variants. Conversely, multiple mutations within a locus could be required for suppression. The ORM2-DBVPG1373 allele that independently suppressed tsc11-5 carries two missense mutations, P26T and G134S, that affect conserved residues, are predicted to be highly deleterious and are both required for a robust suppression phenotype  (Fig EV4). Next, we compared the suppressor genes identified by our mapping results to those previously found in the reference strain background  (Oughtred et al, 2019). In cases where we confirmed a candidate suppressor, we also often found prior evidence of suppression in that gene (4/9 unique suppressor-query pairs; Dataset EV7). In all four cases, the suppressor and query gene pairs encoded members of the same protein complex or pathway, and in three cases the suppressor and query proteins interact physically (Dataset EV7).
The five suppressor-query gene pairs that had not been previously described included four cases of suppression by the general modifier MKT1. We have previously observed a similar prevalence of general suppressor genes that affect the expression of the query mutant among spontaneous suppressor mutations of TS alleles isolated in the reference strain (~50% of all suppressor genes; van  Leeuwen et al, 2016 Leeuwen et al, , 2017)). Out of the nine suppressorquery pairs, 7 (78%) appear to involve a gain-of-function suppressor allele (Methods; Dataset EV7). When we exclude MKT1, three out of 5 (60%) suppressor genes were classified as gain-of-function alleles compared to the reference allele. This relatively large proportion of gain-of-function alleles is consistent with the idea that loss-of-function alleles may be under stronger negative selection in natural populations.
The range of possible ways to escape the various selection steps was such that it is arguable that most of the logically consistent and physically possible scenarios took place. While such chromosome-scale plasticity may not be common in higher eukaryotes where imbalances in gene dosage are often deleterious, it underscores the evolutionary potential of largescale rearrangements compared to point mutations. De novo aneuploidies and diploidization events also explain a large fraction (56%) of the weak "suppression" signals that we sequenced, with a suppression score < 0. 7 in our screen, where a few cells had escaped one of the selection steps, and could partially take over the population. These events could also explain the suppression signals that were not confirmed by individual segregant colony analysis  (Dataset EV3, Fig EV2E) , where the number of examined segregants was in the low hundreds, so that infrequent escapes plausibly present in a complex pool of millions of cells were unlikely to occur.
Consistency of suppression across experiments A subset of the genes we mapped suppressors for had previously been analysed to identify spontaneously evolved modifiers in the reference background. The suppressor genes that had a functional connection to the query gene were often identical in both studies, consistent with the shared selection targets of de novo and preexisting variation observed under drug treatment  (Li et al, 2019). This could indicate that the suppressor allele complements an independent deficiency in the reference strain (consistent suppression of GAB1 in all wild strains by the same GPI8 allele) or that the suppressor has co-evolved with the complex or pathway within (SMC5/6 complex and TORC2 pathway). Further, the vast majority of validated suppressor alleles likely conferred a gain-of-function phenotype compared to the reference allele (Dataset EV7), whereas many of the spontaneous suppressor mutations isolated in the reference background had a loss-of-function effect  (van Leeuwen et al, 2016 (van Leeuwen et al, , 2020)).
This enables identifying suppressors that can completely bypass gene function, but also those that rescue partially functional alleles. For example, the nse3-ts4 allele could be completely rescued by mutations in NSE1, both encoding members of the Nse1-Nse3-Nse4 complex module (Fig  5 ). However, this subcomplex would not assemble in the absence of the NSE3 gene, and the NSE1 mutant allele does not rescue a nse3Δ deletion mutant  (Fig EV5). Comparison of our data with a systematic survey of bypass suppression of essential gene deletion mutants (van Leeuwen et al, 2020) showed little overlap in the identified suppressable essential genes, suggesting that the vast majority of standing variation suppressors will depend on the presence of the TS allele.
Making the wild yeast strains SGA compatible Twenty-six wild yeast strains had previously been deleted for HO and URA3, and haploid MATa spores had been isolated (MATa hoΔ:: hphMX6 ura3Δ::kanMX4;  (Cubillos et al, 2009) ). To make these strains compatible with SGA analysis and facilitate further genetic manipulations, we (partially) deleted the LEU2 and HIS3 genes. First, to delete LEU2, we used plasmid p7410 (Dataset EV8) that contains in the following order: a SwaI restriction site, base pair À403 to eight of LEU2, base pair +62 to +258 downstream of the LEU2 stop codon, the TDH3 promoter from Ashbya gossypii (Ag) driving the nrsR ("natR") gene followed by the AgTDH3 terminator, the GAL1 promoter driving KAR1 followed by the AgCYC1 terminator, the kanMX4 cassette and base pair +62 to +783 downstream of the LEU2 stop codon.
We digested the plasmid using SwaI and transformed the wild yeast strains with the linearised plasmid. Transformants were isolated on YPD + NAT and subsequently replica plated onto YPGal media, to induce overexpression of KAR1, which is lethal and thus selects for recombination between the two LEU2-3' sequences. Recombination was confirmed by testing for growth on YPD + G418 media. Second, to partially delete HIS3, we used plasmid p7411 (Dataset EV8), that contains in the following order: a SwaI restriction site, base pair 137 to 310 of the HIS3 gene, base pair 495 to +112 of the HIS3 gene, the AgTEF1 promoter driving LEU2 followed by its endogenous terminator, the URA3 gene under control of its own promoter and terminator, and base pair 495 to +707 bp of HIS3. We digested the plasmid using SwaI and transformed the wild yeast strains with the linearised plasmid.