dataesr/scientific-paragraphs-categorization huggingface dataset:
RNAseq Col-0 wild type (WT) and puchi-1 mutant seeds were surface-sterilized and sown on ½ MS solid medium containing 0. 7% (w/v) plant agar supplemented with B5 vitamins. Plates were kept at 4°C for 2 days and then placed in continuous light conditions in a vertical position. Gravistimulation were performed on 4-day-old seedlings and root bends were sampled at 12h, 18h, 24h, 30h and 36h after gravistimulation. Three biological replicates (for both Col-0 and puchi-1) were used for the RNAseq experiment. For each replicate, root bends of more than 400 seedlings were dissected under a binocular microscope and frozen in liquid nitrogen immediately on harvesting. For reference, approximately 400 mature root segments located between the bend and the shoot were harvested in 4-day-old seedlings at 12h after gravistimulation to be used as a reference of nongravitropic-stimulated root tissues devoid of developing LRP (termed "No LR" in the data set).
This sequencing produced between 36 and 44 million passed filter clusters per library. For sequencing quality control, image analyses and base calling were performed using the NovaSeq Control Software and Real-Time Analysis component (Illumina, San Diego, CA, USA). Demultiplexing and trimming were performed using Illumina's conversion software (bcl2fastq 2. 20). The quality of the raw data was assessed using FastQC from the Babraham Institute and the Illumina software SAV (Sequencing Analysis Viewer). FastqScreen was used to estimate the potential level of contamination. For alignment and statistical analysis, a splice junction mapper, TopHat 2. 1. 1  (Kim et al. , 2013)  (using Bowtie 2. 3. 5. 1  (Langmead et al. , 2009) ), was used to align the RNA-Seq reads to the A. thaliana genome (NCBI TAIR10.
1) with a set of gene model annotations (gff file downloaded from NCBI on November 13, 2020). Final read alignments with more than 6 mismatches were discarded. Samtools (v1. 9) was used to sort the alignment files. Then, gene counting was performed using Featurecounts 2. 0. 0  (Liao et al. , 2014). As the data is from a strand-specific assay, the reads needed to be mapped to the opposite strand of the gene (-s 2 option). Before statistical analysis, genes with less than 15 reads (cumulating all the analysed samples) were filtered out. Differentially expressed genes (DEGs) were identified using the Bioconductor  (Gentleman et al. , 2004)  package (http://www. bioconductor. org) DESeq2 1. 26. 0  (Love et al. , 2014)  (R version 3. 6. 1). Data were normalised using the DESeq2 normalisation method.
Quantification, purity and contaminations assessment of the RNA samples were tested using a gel electrophoresis and a Nanodrop ND100 spectrophotometer (Nanodrop, Wilimington, USA). cDNA generated from the three biological replicates were synthetised following the manufacturer's recommended protocol (Omniscript RT Kit, Qiagen, Crawley, UK): 1000 ng of RNA in RNAse free water were heated 10min at 70°C, added to the manufacturer mix (up to 20µL) for 1h at 37°C. Generated cDNA diluted 1/10 were added to the Brilliant III Ultra-Fast SYBR® Green QPCR Master Mix with Low ROX reaction mix and used to performed qPCR analysis as recommended by the manufacturer (Qiagen, Crawley, UK). All qPCR reactions were carried out and analysed using the Roche® LC480 Lightcycler system and software (qPCR analysis program, Cq determination, curves calibration).
Database mining for PUCHI target genes Genes showing differential expression (fold change ≥ 1. 5, p-value ≤0. 05 as determined by Welch two sample t-test) in auxin-treated PUCHI-GR roots upon dexamethasone treatment and in presence of cycloheximide were retrieved from  (Trinh et al. , 2019)   (Thomas et al. , 2003; Mi et al. , 2013). Among the list of those 150 genes, transcription factors and genes annotated as related to auxin, cytokinin, VLCFA, LR development, or meristem regulation were selected for closer inspection (see suppl. Table  1  for detailed lists).
S1A , S1B and S1C)  (Goh et al. , 2016; Trinh et al. , 2019). A root segment was sampled directly upward of the root bend in both genotypes at 12 hpg to be used as "No LR" reference material. RNAseq profiling of these samples was performed and differentially expressed genes (DEGs) between successive time points or between the 2 genotypes at a single time point were identified (Log2FC > 1 or Log2FC < -1, p*<0. 05; see Material and methods section). A total of 5791 genes in WT and 9571 genes in the puchi-1 mutant background were shown to be differentially expressed during the LRP developmental time course. Noticeably, comparison of WT and puchi-1 reference "No LR" root segments revealed 361 genes differentially expressed between the two genotypes (Fig. 1B ). By contrast, comparison of puchi-1 to WT LRP transcriptomic datasets at 12, 18, 24, 30, and 36 hpg yielded a minimum of 314 up to a maximum of 1009 DEGs, with the highest divergence between the two genotypes observed at 24 hpg and 36 hpg (Fig.
1C-D, Supp. Fig. S2 , Supp. Fig. S3 )  (Trinh et al. , 2019). In addition, transcriptomic profiling in puchi-1 and WT revealed other biological processes dependent on PUCHI. Interestingly, root development related genes were overrepresented in the puchi-1 RNAseq dataset as compared to WT (p*<0. 05 when comparing puchi-1 and WT at 24h). These genes include PLT1, PLT2, and PLT4/BBM genes, which are well known regulators of root apical meristem establishment and maintenance (Fig. 1D )  (Aida et al. , 2004). Other genes whose function can be related to root meristem organisation or activity (e. g. , predominantly expressed in the QC  (Nawy et al. , 2005) ) displayed altered expression dynamics during LRP development in the puchi-1 background (Supp. Fig. S3 ). Especially our RNAseq analysis shows robust overexpression of PLT1 and PLT4 genes in puchi-1 root segments throughout the four time points of our dataset and especially at 18h and 24h, which corresponds to root segments harbouring young LRP from stage II to stage III, i.
e. , before the transition to the meristematic phase (Fig. 1D , Supp. Fig. S1 ). Conversely other regulators of the PLETHORA family, PLT5 and PLT7, were downregulated in puchi-1 root segments as compared to WT (Fig. 1D ). These latter results are consistent with previously published LR GRN prediction analyses and confirm that both PLT5 and PLT7 genes are positively regulated downstream of PUCHI in the early morphogenetic phase  (Lavenus et al. , 2015). Under expression of PLT5 and PLT7 were confirmed by independent RT-qPCR analyses in puchi-1 roots compared to WT 24h after LR induction by auxin (which correspond to stages II-III LRP in Col-0 and stages I-III LRP in puchi-1)  (Himanen et al. , 2002)  (Supp. Fig. S4A ). We took advantage of published in vitro and in vivo datasets to compare identified DEGs to previously reported PUCHI-dependent gene expression or PUCHI genomic binding sites.
Fig. S3 , Supp. Fig. S6E ). Interestingly, this gene encodes a component of the cytokinin signalling pathway that modulates a wide range of developmental processes  (Raines et al. , 2016)  (Supp. Fig. S5 ). Next, a Gene Ontology analysis revealed that genes associated to responses to stresses and to chemical stimuli are overrepresented among the 150 genes shared by at least two of these three experimental datasets (Supp. Table  1 ). Transcription factor genes and genes associated to auxin and cytokinin homeostasis and signalling pathways, LR development, meristem regulation, and VLCFA biosynthesis are also in this list (Suppl. Fig. S5B  and Supp. Table  1 ). Remarkably, PLT5, whose expression dynamics in branching root segments is dependent on PUCHI (this work), was associated to PUCHI-binding sites identified by DAP-seq analysis.
, 2016). CRF-dependent expression of PIN-family, auxin transporter genes was shown to contribute to the regulation of primary root growth and lateral root formation by cytokinins  (Šimášková et al. , 2015). The expression pattern of the cytokinin reporter construct promTCSn::GFP and the distribution of the PIN1 reporter PIN1:GFP were shown to be altered in puchi-1 mutant LRPs as compared to WT (Fig. 4 ). In the RNAseq profiling of LR development, CRF1 is the only CRF gene displaying such expression dynamics typical of the early expressed subnetwork and positively controlled by PUCHI (Supp. Fig. S3 ). Thus, CRF1 may represent a key target of PUCHI impacting the cytokinin and auxin-dependent pathways that regulate LRP organogenesis and patterning. Interestingly, a number of other transcription factors of the AP2/ERF family were identified as putative PUCHI targets supported by both the RNAseq dataset and the DAP-seq dataset (PLT5, ERF2, ERF12, ERF53, ERF113, DDF1) (Supp.
Supplementary data A supplementary figure  S1  shows the LRP phenotype in Col-0 and puchi-1 LRP during the timecourse transcriptomic analysis after gravistimulation. A supplementary figure  S2  shows the list of all DEGs GO-terms enrichment during the time-course transcriptomic analysis in puchi-1 compared to Col-0 upon gravistimulation A supplementary figure  S3  provides the heat map of selected gene patterns up-and downregulated in puchi-1 compared to Col-0 background during the formation of LRP. A supplementary figure  S4  investigates PLTs gene expression analysis in puchi-1 compared to Col-0 using q-RT PCR and confocal microscopy. A supplementary figure S5 identified the PUCHI direct target genes using available datasets. A supplementary figure  S6  shows predicted PUCHI target gene expression patterns in puchi-1 and Col-0 background during the formation of LRP.
A supplementary figure  S7  shows selected gene expression patterns in puchi-1 and Col-0 background during the formation of LRP. cytokinin-; VLCFA-; LR-; meristematic-related and transcription factors) that were common between two datasets (among a full list of 150 genes, Supp. Table  1 ) were selected and their AGI number, aliases and TAIR summary are described in the table. Transcriptional activator of the NAC gene family, with CUC1 redundantly required for embryonic apical meristem formation, cotyledon separation and expression of STM. Proper timing of CUC2 expression is required to maintain the phyllotactic pattern initiated in the meristem. CUC2 expression in leaf sinus region is required for serration and the extent of serration is modulated by mir164A mediated repression of CUC2. Together wi th CUC3-DA1-UBP15 part of a regulatory module which controls the initiation of axillary meristems, thereby determining plant architecture.
Acknowledgments We thank  Prof. M. J. Bennett  for critical reading of the manuscript. We also thank the  "Montpellier Ressources Imagerie" imaging facility  and  Carine Alcon  for providing valuable advice for confocal imaging. We thank  Julien Lavenus  for seminal discussions about this project. This work was supported by the  Institute of Research for Development , the  University of Montpellier , and the  French Agence Nationale de la Recherche  through the  NewRoot grant  ( ANR-17-CE13-0004-01 ). MGX acknowledges financial support from the  France Génomique National  infrastructure, funded as part of " Investissement d'Avenir" programme at  the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ).
Funding This work was supported by the  Institute of Research for Development , the  University of Montpellier , and the  French Agence Nationale de la Recherche  through the  NewRoot grant  ( ANR-17-CE13-0004-01 ). MGX acknowledges financial support from the  France Génomique National  infrastructure, funded as part of " Investissement d'Avenir" programme at  the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ).
Liquid State Nuclear Magnetic Resonance (NMR) NMR spectra were recorded on a Bruker AVANCE III (400 MHz for 19 F, 125. 77 MHz for 13 C) using d 6 -DMSO. 19 F NMR spectra were carried out using a standard fluorine 90 • pulse program, a relaxation delay of 3 s, and 256 scans acquired between -35 and -85 ppm. The processing of spectra was conducted with MestReNova software, and baseline (Breinstein polynomial order 3) and phase correction were applied. Finally, the spectra were referenced to internal standard at -62. 15 ppm. The 13 C NMR spectra were recorded with a 2 s relaxation delay and 6000 scans between -27. 4 and 266. 3 ppm. A high number of scans was used to ensure good signal/noise ratio in the carbonyl area.
Differential Scanning Calorimetry (DSC) The DSC measurements were performed on a DSC1 from Mettler Toledo equipped with a FRS5 sensor (with 56 thermocouples Au-Au/Pd). The temperature and enthalpy calibrations were performed using indium and zinc standards and the data were analyzed with STARe software. The samples weighted from 5 mg to 10 mg. Volatilization of the polycondensation byproduct occurs during the curing of the different mixtures, which is why the samples were placed into 30 µL high-pressure gold-plated crucibles. According to previous work, the conversion degree (α) was calculated using the following Equation (  2 )  [34, 37, 38] : α = Q Max -Q residual Q Max (2) Here, Q Max is the total heat released during the polymerization. Q residual corresponds to the heat generated when completing the polymerization of oligomers.
Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank.
Around half the residues in AF2 predictions of the 11 model species are of low confidence, many of which may correspond to regions without a well-defined structure in isolation. It has been shown that regions with low pLDDT are often intrinsically disordered proteins or regions (IDPs/IDRs)  13. We benchmarked AF2-derived metrics against IUPred2 (ref. 22 ), a commonly used disorder predictor (Fig. 1c ), using regions annotated for order/disorder (Supplementary Table  2 ). In addition to using pLDDT, we tested the relative solvent accessible surface area (SASA) of each residue and smoothed versions of these metrics (Fig. 1d  and Supplementary Fig. 4 ). pLDDT and window averages of pLDDT or SASA outperformed IUPred2, indicating that AF2's low-confidence predictions are enriched for IDRs. To facilitate the study of human IDRs, we provide these predictions for human proteins in Supplementary Dataset 1 and in ProViz  23  : http://slim.
Characterization of structural elements in AlphaFold2's predicted models across 21 proteomes The AF2 database is likely to contain structural elements that may not have been extensively seen in experimental structures. Owing to the presence of low-confidence regions in the AF2 proteins, we first split each prediction into smaller high-confidence units (see Methods). We then performed a global comparison of structural elements between the 365,198 proteins in the AF2 database and 104,323 proteins from the CASP12 dataset in the PDB. We applied the Geometricus algorithm  24  to obtain a description of protein structures as a collection of discrete and comparable shape-mers, analogous to k-mers in protein sequences. We then obtained a matrix of such shape-mer counts for all proteins, which we clustered using non-negative matrix factorization (NMF) (see Methods).
The clustering identified 250 groups of proteins, dubbed 'topics' (Supplementary Dataset 2), with characteristic combinations of shape-mers. These characteristic shape-mers could include small structural elements, such as repeats, the specific arrangements of ion-binding sites or larger structural elements that could define specific folds. For visualization, we performed a t-distributed stochastic alignments  [2] [3] [4] [5]  , and an understanding of how this information is eventually encoded into a predicted 3D structure  [6] [7] [8]. These developments have been reviewed recently  9  and can be characterized by the increasing use of neural-network models in key aspects of the challenge of predicting protein structures from their primary sequence. Along with advances in computational methods, there have been large expansions of protein sequence and structure databases  10, 11  , which have served as key resources for input and training of sophisticated prediction methods.
These advances have led to the recent leap in performance demonstrated by Deepmind at CASP14 (ref. 12 ). AF2 has been shown to be able to predict the structure of protein domains with an accuracy matching that of experimental methods. Both the method and a database of 365,198 protein models have been released  13  , enabling the scientific community to better understand the accomplishments, abilities and limitations of AF2. The accuracy of AF2 has been independently evaluated in blind assessments. Yet many questions remain regarding the extent to which these approaches extend our coverage of structural biology, and the limitations of the AF2 method or structures derived from AF2 for applications in biology. Regarding coverage, previous attempts to generate 'proteome-wide' structural models include those based on homology models, such as the SWISS-MODEL Repository (SMR)  14  , and more recently, the modeling of known protein domains in the Pfam database 15 using trRosetta  16.
Added structural coverage by AlphaFold2 predictions of model proteomes The AF2 database has released predictions of the canonical protein isoforms for 21 model species, covering nearly every residue in 365,198 proteins. This represents around twice the number of experimental structures and six times the number of unique proteins in the Protein Data Bank (PDB). It is important to assess the extent to which AF2 predictions extend the structural coverage beyond previous proteome-wide structural predictions. We compared the structures of 11 model species that were included in both the SMR and AF2 databases and that had an average additional coverage of 44% of residues by AF2 (Fig. 1a , residues). However, not all of AF2's residue predictions have high confidence. For residues that are not present in the SMR, we observed that an average of 49.
Tandem repeat proteins with repetitive units of 6-10 residues predominantly have beta-solenoid structures  25. Analyzing the AF2 results, we found a novel beta-solenoid structure predicted for a large family of pentapeptide repeats  26  , found in the mycobacterial PPE proteins (Pfam: PF01469) (Fig. 2e  and Supplementary Fig. 6 ). This structure represents a beta-solenoid, with the shortest possible coil of ten residues (two pentapeptide repeats) (Supplementary Fig. 6b ). Although such a beta-solenoid has not yet been resolved, our evaluation of the quality of the atomic structure (stereochemistry and contacts) suggests that the AF2 model is highly probable. Thus, AF2 may have allowed us to answer the question of what is the shortest length of repeat that forms a beta-solenoid. Finally, we also considered protein groups consisting primarily of PDB proteins to study why AF2 proteins are absent from them.
In some cases, this seemed to be due to the limited number of species and proteins covered by the current AF2 database. Topics 209 and consist of immune response proteins, such as immunoglobulins and T-cell receptors, mainly from the PDB. As many of these antibodies are under intense study, there are many more PDB structures (based on multiple individuals and antibody-drug research) than the actual number of such proteins in the respective UniProt proteomes. Topic consists of short fragments of PDB structures, with an average length of 63 residues-there are no AF2 proteins, because AlphaFold models the entire structure instead of returning fragments.
Application of AlphaFold2 models for structure-based variant effect prediction A protein structure facilitates the generation of hypotheses regarding the impact of missense mutations. Conversely, an agreement between the expected and observed impacts of mutations provides confidence   Article https://doi. org/10. 1038/s41594-022-00849-w in the accuracy of a structural model. We obtained two independent compilations of experimentally measured impacts of protein mutations on protein function: (1) a compilation of measured changes in stability upon mutations  27, 28  ; and (2) a compilation of deep mutational scanning (DMS) experiments  29, 30  measuring the outcome of any possible single point mutation on most protein positions. The DMS data were available for 33 proteins with 117,135 mutations; we obtained experimentally derived models for 31 of the proteins and AF2 models for all 33.
31) and for which there are no previous structural data. A sequence search of the 323-residue protein against all existing entries in the PDB shows that the best sequence match is 23. 5%, with PDB entry 1VHB (Bacterial dimeric hemoglobin, 9115439), indicating the lack of any structural models from homology. A scan of 400 auto-generated 3-residue templates from the AF2-predicted structure against representative structures in the PDB (reverse template comparison  38  ) yielded a possible 3-residue template match: PDB entry 4ZYO (EC 1. 14. 19. 1, human stearoyl-CoA desaturase  39  , Fig. 4d ). A close up of the metal-binding center (Fig. 4e ) of DEGS1 and 4YZO (overall sequence homology, 12. 1%) superimposed via the 3-residue templates (Fig. 4d ) clearly indicates the potential dimetal catalytic center for DEGS1.
We next examined the Dockground 4. 3 heterodimeric benchmark set  43. We predicted complex structures using the DeepMind default dataset and the small Big Fantastic Database (BFD) database. This method does not include any 'pairing' of interacting chains, as was used in earlier fold-and-dock approaches. The docking quality was evaluated using DockQ  44, 45. Only one model for each target was made, and a maximum of three recycles were allowed. In Figure  5e , it can be seen that the performance is far superior to traditional docking methods, with 31% of correctly predicted protein complex models, compared with 7% using GRAMM, a standard shape-complementarity docking method  44. Finally, we studied examples of complexes containing IDPs/IDRs that adopt a stable structure upon binding. IDRs often bind through short linear motifs (SLiMs), recognizing folded domains driven by a few residues.
53 ). The AlphaFold database was initially released with >300,000 proteins modeled with a more recent expansion to over 200 million proteins with predicted structures, sampling the universe of protein sequences and structures. As we show here, even on a relatively modest set, we can identify what are likely to correspond to rarely explored combinations of structural elements. As an example, we have identified to our knowledge the shortest length of repeat that forms a beta-solenoid to date. Among other areas, the expansion of high-confidence predictions will allow prioritization of experimental structure determination of novel folds; the large-scale prediction of protein function from structure; the identification of novel enzymes; and the study of the evolution of protein structure and function.