dataesr/scientific-paragraphs-categorization huggingface dataset:
This matrix is defined as the matrix of column-wise vectorized OFs that can be three times the dimensions of the input subtomogram dataset; for instance, generating animations after processing a dataset that comprises 1000 subtomograms of volume size 64 3 will require the inversion of 3*64 3 rows by 1000 columns, which becomes computationally challenging for large datasets, mainly in terms of the required memory. However, a downsampling (e. g. , by 2) of the OFs before reconstructing the matrix significantly reduces the computational requirements, resulting in less detailed animations. Despite the limitations discussed above, the presented TomoFlow method provides a promising new insight into what can be achieved in cryo-ET studies of macromolecular conformational variability. The advancement in OF development might allow even better TomoFlow performance in the future.
Acknowledgments We acknowledge the support of the  French National Research Agency -ANR  ( ANR-20-CE11-0020-01  to A. L. ,  ANR-20-CE11-0020-02  to M. E. ,  ANR-20-CE11-0020-03  and  ANR-19-CE11-0008-01  to S. J. );  German Research Foundation  ( DFG EL 861/1  to M. E. ); the  Sorbonne University  ( 2019  " Interface pour le Vivant" PhD scholarship grant  to M. H. ); and the access to the HPC resources of CINES and IDRIS granted by  GENCI  ( A0100710998 ,  A0070710998 ,  AP010712190 ,  AD011012188  to S. J. ).
Data Availability Statement The original contributions presented in the study are included in the article/supplementary files. TomoFlow software code will be publicly available on Github (https://github. com/scipion-em/scipionem-continuousflex) and will also be part of the open-source ContinuousFlex plugin of Scipion V3. 0. The nucleosome data used in this article are public (available in EMPIAR and EMDB databases under the accession codes EMPIAR-10679 and EMD-12699). The simulated datasets used in this article, with their ground-truth tables, will be made publicly available on Zenodo (DOI: 10. 5281/zenodo. XXXXXXX). All questions regarding the software or data availability can be addressed to the corresponding author.
Short linear motifs (SLiMs) in proteins are short functionally independent sequence stretches with a defined function and required for proteins to interact with their environment. Their functional importance makes it interesting to analyse SLiM features further, such as their structural or evolutionary properties, to understand better how SLiMs evolve to shape protein functions. We developed an automated pipeline to analyse features of SLiMs, called evo-MOTiF. This pipeline takes as input a single protein sequence and its associated SLiM(s) and returns a set of scores associated with SLiM features, including their disorder, as well as their overall, positional and amino acid property conservation. To store and easily mine data from the evo-MOTiF pipeline, we developed the evo-MOTiF database, which currently holds ~9500 motifs, combining data from ELM, PhosphoSitePlus, as well as from cross-linking mass-spectrometry (XL-MS) experiments.
This explains why the Eukaryotic Linear Motif (ELM) database, the most comprehensive and reliable database of functional and experimentally validated SLiMs, contains, nearly 20 years after its inception, still not more than ~4000 SLiMs  [14]. This number is in relation to the number of proteins known to date and the importance of SLiMs for interaction of proteins with other molecules, probably just the tip of the SLiM iceberg  [15, 16]. While the ELM database can be seen as the golden standard of SLiMs, methods based on mass-spectrometry have emerged that allow to characterise interacting or modified SLiMs in higher throughput. With the advent of novel proteomics techniques characterising protein modifications to identify modification sites in proteins, or mass-spec coupled to cross-linking (see e.
g. [17] [18] [19] ) to identify binding sites between two proteins, it has become possible to experimentally find functional SLiMs more easily. While the quality of experimental information on those SLiMs might not meet the high standard of ELM motifs, these identified SLiMs nonetheless contain valuable information on the sequence motif space in proteins. Davey and colleagues performed a comprehensive analysis of SLiM attributes based on SLiMs stored in the ELM database in 2012  [12]. Using a manually curated sub-set of ELM and by analysing co-crystallized protein structures, they investigated SLiM position, physicochemical properties, evolution and convergent evolution, and the structural context, relating to secondary structure, disorder, as well as overlap with globular domains. Among many interesting observations, two conclusions that are relevant for this study should be pointed out.
In order to assess this correlation, though, a systematic analysis of existing SLiMs is necessary. In this study, we set out to develop a straight-forward pipeline and an associated database to store and mine results, called evo-MOTiF, that allows us to extract and correlate features of SLiMs, including their evolution, their secondary structure, their disorder and their surface accessibility. The evo-MOTiF pipeline returns a set of scores describing these attributes and thus enables easy analysis of their correlation to each other. We apply evo-MOTiF to SLiMs from the ELM database  [14] , to modification sites from PhosphoSitePlus  [23] , as well as to SLiM regions of XL-MS studies  [24]  to enlarge the analysed SLiM space. We store results in the publicly available evo-MOTiF database, that contains an exhaustive search mask.
This search mask makes analysed SLiMs, together with their scores on disorder, surface accessibility or conservation, easily searchable. Moreover, the evo-MOTiF database offers to view the multiple sequence alignments of the protein families containing SLiMs, as well as 3D-structures of SLiMs alone, as well as in the context of their native protein, if available. The source code of the evo-MOTiF pipeline is freely available at https://gitlab. com/habermann_lab/slims; the evo-MOTiF database is freely accessible at http://etnadb. ibdm. univ-mrs. fr.
evo-MOTiF input: As input, evo-MOTiF requires a protein sequence in fasta-format, as well as a list of SLiMs. For our purpose, this list contained all SLiMs that are associated with the specific input sequence. evo-MOTiF can however also be used to search for SLiMs in an input protein, e. g. by providing the list of known ELM-motifs, or other user-provided SLiMs as input. Identification of orthologs. In order to identify orthologs of a SLiM input protein sequence, we encoded three different methods in our pipeline. First, orthologs can be extracted from the OrthoDB database  [25]. We chose this database as it is one of the best available orthology databases that has a relatively easy to use API. Second, should the sequence not have any orthologs in OrthoDB, we implemented reciprocal best hit searches, either with the MMSeqs2 algorithm  [26] , or with BLAST  [27].
The best hit, as well as a minimum similarity coverage of 40% was chosen for reciprocal searches. For BLAST, the e-value threshold was set to 0. 5. The database for reciprocal searches was reduced to contain only the proteome of the original input species. While MMSeqs2 is much faster than BLAST, BLAST is more sensitive in our hands. Construction of the Multiple Sequence Alignment. The collected orthologs are aligned using the MAFFT program (version 7) using default parameters (FFT-NS-2 method)  [28]. The multiple sequence alignment (MSA) is required to further evaluate the evolutionary information for the SLiM under study. SLiM identification. For SLiM identification, we used the SLiMProb algorithm from the SLiM-Suite  [4] , as it was perfectly suited for this task. It takes the protein of interest together with the SLiM under study (in the form of a regular expression) and the MSA of homologs/orthologs of the protein of interest as input, calculates overall, positional and amino acid property conservation as well as, through the IUPRED program (v1,  [20] ), a disorder score of the SLiM under study.
As output, it produces scores of conservation together with the disorder prediction of the SLiM. Finally, a normalised surface accessibility score is calculated  [29]. All scores reported by SLiMProb are also returned by evo-MOTiF and stored in the evo-MOTiF database. Extraction of taxonomic identifiers. In order to make identification of species group-or taxonspecific SLiMs easier, we extract the NCBI TaxID from all orthologs. The TaxID can then be used to relate SLiM presence to the species of the protein.
Construction of a phylogenetic tree (optional). We also offer in the pipeline the construction of an evolutionary tree. This function is optional and should help users to analyse the protein family of the SLiM under study. For phylogenetic tree reconstruction, the user can choose between the PhyML version 3 (with JTT as matrix and 100 iterations for bootstrapping)  [30]  and the IQ-TREE version 2 algorithm, which produces more accurate phylogenetic trees  [31]. This function is optional and can lead to complex trees, depending on the number of orthologs found. The evo-MOTiF pipeline is freely available for download and local installation from our gitlab repository (https://gitlab. com/habermann_lab/slims).
Additional necessary processing scripts We created a script, EXP-MOTiF, that allows the user to choose the original data repository, ELM, PhosphoSitePlus or from XL-MS data, and then prepares data extracted from the respective resources for an evo-MOTiF run. EXP-MOTiF extracts the information about SLiMs from the respective repository and converts UniProt to RefSeq identifiers using the python package MyGene  [32]. The RefSeq ID is then used to download the sequence using the Entrez programmatic access  [33]  and parsed for sequence length. SLiMs are parsed for their corresponding sequences and converted to be readable by SLiMProb. Then, the pipeline is run on each sequence with its specific SLiMs. When SLiM data from ELM are processed, only true positive SLiMs are retained and all other instances of SLiMs found that do not correspond to the true positive ones, are removed.
Extraction of SLiM structure To provide structural information for SLiMs, we developed a small pipeline, SLiMs-SCAR (for Short Linear Motifs StruCture extrActoR). It takes evo-MOTiF results in a tsv format as input. Using the submodule PDB from Biopython  [34] , the corresponding PDB files are downloaded and submitted to our parser, which extracts secondary structure information using DSSP classification  [35]. The 2D representation is drawn using Biotite  [36]. For displaying the isolated SLiM structure, a PDB file containing the SLiM structure is generated using pdb-tools  [37]. 3D visualisation of SLiMs in the evo-MOTiF database is done using JSmol  [38].
Statistical analysis SLiM data stored in the evo-MOTiF database were further analysed using R. Correlations between SLiM properties were calculated and plots generated with the package corrplot (https://github. com/taiyun/corrplot). Other statistics, such as score distributions and pie charts, were generated using ggplot2 (https://ggplot2. tidyverse. org) and plotly (https://plotly. com/r/).
The evo-MOTiF database The evo-MOTiF database was coded using PHP version 7 and HTML version 5. Some plugins are implemented using JavaScript version 1. 7. The entries of the database are stored using MySQL version 5. Data from the MySQL database are extracted through PHP and JavaScript. We used the plug-in DataTables (https://www. datatables. net/) to design the main page of the database in which every motif is stored and can be searched. For each motif, a page is automatically generated, extracting information from the protein XML file from UniProt and the MySQL database. JSmol is used to display PDB structures in 3D, allowing users to change the appearance of the visualised protein-and SLiM structures. The structure of the evo-MOTiF database is shown and further discussed in Supplementary Figure  S1.
Surface accessibility: The surface accessibility gives the probable accessibility of the SLiM with respect to its immediate neighbourhood of amino acids. It is calculated by SLiMProb. It ranges from 0 (buried) to more than 9 (accessible). Probability: The probability is extracted from the ELM database and gives the probability, a specific SLiM exists in the protein. This score is only available for ELM motifs. All scores calculated by the evo-MOTiF pipeline are searchable in the evo-MOTiF database.
The evo-MOTiF database We developed the evo-MOTiF database, accessible at http://etnadb. ibdm. univ-mrs. fr, to allow an easy mining of protein SLiM evolution in a structural context. A schematic representation of the evo-MOTiF database is shown in Figure  2. We collected protein SLiMs from the ELM database  [14] , PhosphoSitePlus  [23] , as well as from XL-MS experiments  [24]. Each SLiM, together with the full-length protein sequence, were submitted to the evo-MOTIF pipeline to find orthologs, create the MSA, refind and score the SLiM in the protein sequence and its orthologs, and collect information on the overall evolution (i. e. the presence of the SLiM in the orthologs), the context of the secondary structure (helical, beta-strand, unstructured/disordered), as well as the taxonomic identifier of the protein and all orthologs.
In addition to these core scores, the structure of the protein and its SLiM were subtracted from the protein data bank (PDB) database and is accessible at a pop-up page to view from the database. Additional information was extracted from the different resources and were stored: the interacting domain (ELM) or motif (XL-MS data); ELM motif classes (ELM), as well as Gene Ontology (GO) information (ELM). The main database page is shown in Figure  3. In the database table, each motif is shown, with identifiers, origin, disorder and conservation scores, surface accessibility, as well as the taxonomic identifier of the protein the SLiM originates from. Each column can be individually searched, to filter for SLiMs with a specific feature, using SLiM scores such as disorder, conservation, etc. or the taxID / species.
A general search field at the top of the table also allows to perform free-text searches, applying to all fields of the database. The individual search fields for the SLiM scores makes it exceptionally easy to filter for SLiMs that have certain amino acid property-, positional-or overall-conservation, disorder, or surface accessibility scores. This filtering function helps to quickly find SLiMs that are found in highly disordered or ordered regions of the protein, or those that have high-, or low conservation. The database page for an individual SLiM (Supplementary Figure  S2 ) contains a short information section on the protein, and information on the SLiM, including the regular expression, sequence and a description of the associated function, with the link to the original source (ELM or PhosphoSitePlus).
This section also contains information on the evolution of the protein, with the number of orthologs, as well as a link to view the multiple sequence alignment of the protein family (Figure  4  b). This is followed by the scores calculated by evo-MOTiF (Figure  4 a ) and the interacting domain (in case of ELM) or SLiM (in case of XL-MS motifs). At the next section, the GO annotation of the SLiM is provided. At the bottom of the web-page (Supplementary Figure  S2 ), all SLiMs are shown for the protein, schematically, as well as in a table. The structure of the selected SLiM is given, as is a link to the structure page of the SLiM, which is shown in Figure  5. One of the main features of the evo-MOTiF database is the direct availability of evolutionary as well as structural information on the protein and the SLiM.
Statistical information on the evo-MOTiF database The evo-MOTiF database at the time of writing holds 9523 SLiMs, with 4733 from XL-MS data, 1982 from ELM and 2808 from PhosphoSitePlus (Figure  6 a ). SLiMs from ELM and XL-MS show marked size differences: The distribution of SLiM lengths is different, with ELM SLiMs peaking at length 6 and 7 and a mean motif size of 7 amino acids, while XL-MS motifs are not shorter than 7, and a mean motif size of 13 amino acids (Figure  6 b ). XL-MS SLiMs can also be as long as 35 amino acids. PhosphoSitePlus motifs, unless located in N-or C-terminus of the protein, have a size of 15, due to technical reasons of mass-spec motif discovery (data not shown). Many of SLiMs (41%) are overall only remotely conserved, with an overall conservation score of <0. 25.
Only PhosphoSitePlus motifs have a lower % of high conservation on positional level (43%) and more motifs that have a positional conservation score below 0. 5 (Supplementary Figure  S3 b ). Finally, amino acid properties of SLiMs are well conserved across all three resources (Supplementary Figure  S3 c ). Interestingly, the majority of SLiMs in our database are defined as structured (with an IUPRED score <0. 5  [41] , 62%, Figure  6 f ). However, SLiM disorder peaks around 0. 5 and most SLiMs are found close to this cut-off, being either partially structured, or partially disordered (see Supplementary Figure  S4 ). We found this result surprising, because of the described preference of SLiMs for disordered regions  [12]. We therefore took a closer look at the disorder distributions of the three different resources (Figure  6 g ).
The majority (60%) of ELM motifs are indeed found in disordered regions, while 80% of SLiMs from the XL-MS study analysed are found in structured regions, indicating a bias in the resource, which might be due XL-MS motifs being slightly longer. PhosphoSitePlus SLiMs show an even distribution between structured and disordered regions. It should be noted here that in  [12] , following recommendations of the developers of SLiMSearch  [11]  and SLiMPrints  [10]  for motif finding in disordered regions, an IUPRED cut-off of 0. 4 was chosen to distinguish between a sequence being structured or disordered. Using this cut-off changes the interpretation of our data and flips the balance of SLiM structural properties, with 52% of all evo-MOTiF database motifs being disordered and 48% being structured.
We finally looked at the correlation between structural and evolutionary features of SLiMs (Figure  6 h ). There is high positive correlation between the conservation scores, as is expected. Disorder is negatively correlated with all three conservation scores, whereby negative correlation with overall conservation is very low (0. 04), however statistically significant (Supplementary Table  S1  i). This finding is in agreement with previous hypotheses and observations that structural disorder may be related to SLiM evolution  [12, 21]. Correlation plots for the individual resources are shown and further discussed in Supplementary Figure  S5. All statistical analysis of SLiM features done is also provided for further mining in the evo-MOTiF database (http://etnadb. ibdm. univ-mrs. fr/statistics.
Discussion We present here a new pipeline and resource for short linear motifs in proteins, the evo-MOTiF pipeline and database. The evo-MOTiF pipeline was designed to ease analysis of short linear motifs and allow to easily find and analyse SLiMs with different structural, as well as evolutionary features. We were and are analysing SLiMs from ELM, XL-MS experiments, as well as from PhosphoSitePlus with the evo-MOTiF pipeline. To effortlessly mine those data, we store the results in the evo-MOTiF database, which has an extensive search interface and allows searching and filtering for SLiMs with certain properties, by filtering for scores of their disorder, and their overall, positional, or amino acid property conservation. We wanted to systematically analyse SLiMs for their properties to get a better picture on their evolutionary or structural features.
We designed our pipeline such that it would run automatically, given a (user) input of a sequence and its associated motifs, perform all the steps required and return scores for several SLiM properties relating to their structure and evolution. Among the challenges in this workflow are the choices for reliably finding and correctly aligning orthologs. We chose to offer three different workflows for finding orthologs: reciprocal BLAST-searches, reciprocal MMSeqs2-searches and mining orthologs from the orthoDB database. While BLAST is the most sensitive method, it is also the computationally most expensive one. We therefore chose to run our reciprocal homology searches using MMSeqs2. We aligned identified orthologs using the MAFFT algorithm, which is reliable and fast. Another choice that had to be made was to select the algorithm to search for known SLiMs.
We chose the SLiMProb algorithm, as it already returns some of the scores we were interested in. We could effectively use the three conservation scores, as well as the disorder score, while we chose to not further consider the surface accessibility returned by SLiMProb. According to the resulting surface accessibility scores, more than 90% of SLiMs were buried, indicating that this measure has little use for further distinguishing SLiM properties. This score nonetheless remains to be accessible in the evo-MOTiF database. The evo-MOTiF pipeline is computationally expensive, which is why only a portion of available motifs have so far been processed. While we make the pipeline available to others, it should be noted that a high-performance computer is required to run it. Improvements could be made in making the pipeline parallelized and suitable for a high-performance computer cluster.
We designed the evo-MOTiF database as a complementary resource to other motif databases, such as foremost ELM, as well as the PhosphoSitePlus database. Each motif in the evo-MOTiF database is linked to its original source, so its origin should be clear to evo-MOTiF database users. We wanted to create a database that contains only experimentally identified SLiMs, but not be as exclusive as ELM. All SLiMs found in evo-MOTiF have experimental proof, while only motifs originating from ELM meet the 'golden standard' criterion of a bona fide protein motif. PhosphoSitePlus motifs are less deeply curated and XL-MS motifs are by definition originating from primary experimental data without further manual curation or verification of the motifs. The evo-MOTiF database is in fact the first motif resource that stores XL-MS data in this form.
P-values, providing a certainty to the functionality of the motif, are extracted from, and thus only given for ELM motifs. As we do not further curate SLiMs from the other resources, the high standard of ELM is also only available for ELM motifs stored in our database. Next to a broader range of motifs stored, the evo-MOTiF database distinguishes itself from other motif databases by further functional features: only our database has the possibility to effortlessly search for individual motifs based on their properties, such as structural disorder, or conservation attributes. It is for instance possible to search for overall remotely conserved -or ex nihilo born -motifs by simply filtering for motifs with a low overall (or positional) evolutionary score. The evo-MOTiF database provides identified orthologs of a motif-carrying protein and allows users to download, or view the multiple sequence alignment of all orthologs.
The evo-MOTiF database provides secondary structure predictions, as well as disorder predictions of SLiMs and allows users to filter for structural SLiM attributes. Finally, the evo-MOTiF database provides and displays structural information, if available, of a SLiM alone, as well as in the context of the entire protein structure. It is therefore possible to easily visualise the position of the SLiM in the overall 3D structure, which might aid in further analysis of the SLiM and its function. Our preliminary statistical analysis of SLiMs stored in the evo-MOTiF database revealed that some features are specific to the source of the SLiMs: ELM, PhosphoSitePlus or XL-MS data. For instance, we found a difference in length distribution depending on the source. Motifs found with XL-MS tend to be longer than ELM motifs.