dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION In this study, we simulated three plant samples containing PepMV with different variants at several frequencies. Then, a performance study with 14 laboratories was carried out to highlight the key points leading to the failure or success of the SNPs detection in plant viruses. We showed that steps often considered very important  (Koboldt, 2020) , like pre-processing, were not that impactful when the base quality was already decent (Q30 > 88% for sample 1 and 2). The effect of the complexity of the datasets was not as conclusive because the dark matter or the viruses mix did not add the expected level of complexity. In addition, the SNPs frequency analysis only showed us that higher frequencies SNPs are easier to predict than low frequencies. The two most important factors corresponded to the strategy to identify the closest reference for mapping and the manual validation of the predicted SNPs.
Data Availability The following information was supplied regarding data availability: The script is available on GitHub and Zenodo: https://github. com/johrollin/jupyter_ variant_calling. Rollin. (  2023 ). Detection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: large-scale performance testing of sequence analysis strategies (Version 1). Zenodo. https://doi. org/10. 5281/zenodo. 7948362. The data is available at Zenodo: Johan Rollin. (2022). Plant virus SNP prediction artificial dataset Performance Study [Data set]. Zenodo. https://doi. org/10. 5281/zenodo. 7431632.
Macromolecular complexes are essential functional units in nearly all cellular processes, and their atomic-level understanding is critical for elucidating and modulating molecular mechanisms. The Protein Data Bank (PDB) serves as the global repository for experimentally determined structures of macromolecules. Structural data in the PDB offer valuable insights into the dynamics, conformation, and functional states of biological assemblies. However, the current annotation practices lack standardised naming conventions for assemblies in the PDB, complicating the identification of instances representing the same assembly. In this study, we introduce a method leveraging resources external to PDB, such as the Complex Portal, UniProt and Gene Ontology, to describe assemblies and contextualise them within their biological settings accurately.
The Protein Data Bank (PDB)  (30)  is the unified, global repository for experimentally determined macromolecular structures. The worldwide PDB (wwPDB) organisation manages the archive, ensuring free and public access to structural data for the global community  (31). The Electron Microscopy Data Bank (EMDB; 32) is a public repository for cryo-microscopy electric potential maps and tomograms of macromolecular complexes and subcellular structures. The Small Angle Biological Data Bank (SASBDB; 33) is a curated repository for bio-macromolecular small-angle scattering of X-rays and neutrons (SAXS and SANS) data and models. The Biological Magnetic Resonance Bank (BMRB; 34) archives spectral and quantitative data from NMR spectroscopic investigations of biological macromolecules. Lastly, PDB-DEV (  35 ) is a prototype archiving system for structural models obtained using integrative or hybrid modelling approaches.
The PDB archive may house multiple structures representing the same assembly, offering unique opportunities to study structure ensembles and gain a mechanistic understanding of large macromolecular assemblies. Molecular structures can be determined under various conditions to explore the conformational space or obtain functional insights. Researchers may introduce engineered mutations, modify pH and ion concentrations, or add molecules ranging from ligands to antibodies. Additionally, structures may be solved at different resolutions or in distinct space groups. Multiple assemblies may be derived from the same crystal form describing various potential quaternary structures. For instance, PDB entry 1e94 (HslV-HslU protease complex) features one assembly representing a homo-hexamer and another a homo-dodecamer.
To address this ambiguity, the Protein Data Bank in Europe (PDBe)  (36)  defines the preferred assembly as the smallest assembly containing all polymeric entities. This approach enables users to identify the most probable assembly observed in the experiment represented in the PDB entry for a given macromolecule, which is also displayed in PDBe's search results. In the HslV-HslU complex example, the homo-hexamer form is displayed as the preferred assembly. Identifying and analysing protein assemblies involves distinguishing biologically relevant interfaces from protein-protein contacts caused by crystal packing  (37) (38) (39). Several methods have been developed to address this issue, such as PISA (Protein Interfaces, Surfaces, and Assemblies)  (37) , which employs thermodynamic estimation of interface stability, and EPPIC (Evolutionary Protein-Protein Interface Classifier)  (40) , which uses evolutionary information from protein sequences to differentiate biological interfaces from lattice contacts.
Another method, QSalign  (41) , identifies biologically relevant assemblies by structurally aligning quaternary structures and inferring ones with conserved interfaces as biologically relevant. These three methods were integrated into a single predictor called QSbio, which provides a confidence score for biological relevance of given assemblies  (41). Finding instances representing the same assembly in the PDB is challenging due to current annotation practices, which do not include consistent naming and analysis of observed complexes across the PDB archive. For instance, the assembly in PDB entry 6kat is not described as haemoglobin but as a complex of two haemoglobin subunit alpha chains and two haemoglobin subunit beta chains. Additionally, since a given set of coordinates and space group symmetry described in a PDB entry can result in multiple assemblies with different stoichiometry or components, it is not easy to identify the correct biologically relevant assembly and the entry title does not necessarily reflect these directly.
Therefore, the lack of consistent naming makes it difficult to find whole or partial complexes through PDB searches. To address this issue, we have compiled all unique assemblies in the PDB by identifying individual components using their mappings to external resources, such as UniProt for proteins through the SIFTS resource  (42)  and Rfam  (43)  for RNA molecules. We then determined the stoichiometry of individual components within a complex. Based on this process, we generated a list of PDB entries corresponding to each assembly with unique composition. Establishing this mechanism and assigning stable identifiers for each unique assembly across the PDB archive will promote the study and understanding of conformational changes and molecular mechanisms. In addition to stable identifiers, we have assigned human-readable, and in some cases, manually curated names and mapped the PDB assemblies to Complex Portal (44) entries where possible.
We identified five main reasons for observing multiple stoichiometries for an assembly. These differences can be caused by experimental conditions (panel A), difficulties in automated assembly assignments (panel B), challenges in the curation and annotation process of assemblies (panel C), genuine errors in curation (panel D), and differences in the sample, for example in the sequence length (panel E). An example of a protein with multiple stoichiometries in the PDB, explainable by experimental conditions, is bacterioferritin (UniProt accession P0ABD3), which typically forms a 24-meric sphere (e. g. , PDB 1bcf) but in some PDB entries, an engineered version forms dimers (e. g. , PDB 3e2c). However, other cases exemplify the challenges of automatically selecting the representative assembly for a given PDB, such as PDB entry 1ryz, which includes four curated assemblies.
The first two represent different hexameric forms, while the latter represents alternative dimeric forms. The PDBe process for selecting preferred assemblies favours one of the dimeric forms, but the QSbio assembly predictor assigns the highest confidence to the hexamer. The curation and annotation of PDB entries present significant challenges, as illustrated by several examples. For instance, the protein Histone-arginine methyltransferase CARM1 (UniProt accession Q9WVG6) is represented as a homo-tetramer in 30 PDB entries (e. g. PDB entry 5ih3) and as a homodimer in 2 PDB entries (e. g. PDB 2v74). The homodimers in PDB entry 2v74 have a 98% confidence level, according to the QSbio predictor. Similarly, human Pannexin1 (UniProt accession Q96RD7) is a homo-heptamer in 18 PDB entries (e. g.
PDB entry 6ltn ) and a homo-tetradecamer in one example (PDB entry 6wbn). The publication associated with the homo-tetradecamer structure suggests that this assembly arrangement does not result from incorrect curation  (45). Differences in sample sequence when solving the assembly structure can also result in multiple stoichiometries for an assembly. For example, the mannose-binding protein forms a trimer in its active form (e. g. PDB 1buu). However, PDB entry 2msb represents a deviation from the usual stoichiometry as it forms a dimer due to its shorter sample sequence that does not contain the necessary helix that forms the trimer interface. Our analysis revealed a few incorrect assembly annotations, such as human Kynurenine/alpha-aminoadipate aminotransferase (UniProt accession Q8N5Z0).
Since almost two-thirds of protein assemblies in the PDB are either monomeric or homomeric, we could use the protein name from UniProt to name these assemblies. Using this approach, we could name nearly 70% of the assemblies in the PDB. The remaining assemblies contain additional components, such as nucleic acids, antibodies, or peptides. In these cases, we can assign names by combining the name of the protein with a generic component label. For example, the assembly in PDB entry 5hi4 can be named "Interleukin-17A, IG-heavy chain, IG-light lambda chain, peptide complex" by combining the Interleukin-17A name from UniProt with the antibody and peptide names. In addition, we used a combination of antibody and molecule names to name another 7,097 protein assemblies that consist of unmapped components.
To name each ribosome complex, we used either the name of the ribosomal subunit, such as 30S ribosomal subunit (e. g. PDB 6v3e), or the full ribosome name if both ribosomal subunits were present, such as 70S ribosome (e. g. PDB 5j7l). If the ribosome contained tRNA, identified through matching Rfam accessions RF00005 or RF01852, we added that information to the name. Any additional RNA molecules that are bound to the ribosome are simply named as RNA including messenger RNAs (mRNAs). For example, the PDB entry 6bok is an E. coli ribosome with both tRNA and mRNA bound and thus, is named as 70S ribosome and tRNA and RNA.
Analysing the symmetry of assemblies To infer the point-group symmetry operators of the members of each unique protein assembly composition based on the preferred assemblies, we used the AnAnaS software  (48). This tool can detect five symmetry groups: cyclic, dihedral, tetrahedral, octahedral, and icosahedral. Among the symmetrical protein assemblies with unique compositions, cyclic (Cn, n≥2) and dihedral (Dn, n≥2) symmetry groups were the most frequent in the PDB, occurring in 77% and 20% of cases, respectively. Most cyclic symmetries were either C2 or C3, while the most common dihedral symmetries were D2, D3, and D4, in descending order of frequency (Figure  4 ). Our analysis revealed that close to 6. 0% (1,651 out of 28,374) of these assemblies appear to have inconsistent symmetry operators among its members.
For example, the PDB entry 4u7n is an inactive histidine kinase dimer (consists of two copies of UniProt A0A0M3KKX3) that has C2 symmetry. But other instances of this assembly (e. g. PDB entries 4u7o and 4zki) appear to lack symmetry. Individual domains of histidine kinases have been shown to adopt both symmetric and asymmetric conformations in different catalytic states which might explain the differences in the symmetry groups of these structures  (49). Symmetry analysis can also help detect unlikely assembly arrangements. For instance, the cytochrome P450 3A4 complex is formed as a homodimer in 35 PDB entries, with 29 PDB entries exhibiting C2 symmetry (e. g. PDB entry 5g5j). However, in PDB entries 7kvh, 7kvn, 7kvo, 7kvq, and 7kvs, there is no symmetry, and the arrangement of the monomers is different compared to other instances of the assembly due to very different crystal packing leading to a lack of symmetry.
Another example is the homo-tetramer of L-asparaginase 2 (UniProt P00805) in PDB entry 6pa3, the only example among 45 PDB entries that does not have D2 symmetry in the provided biological assembly although the symmetry can be found in the crystal indicating a possible mistake in the annotation. In a few examples, symmetry is observed in one PDB entry, but most other entries do not have any symmetry. For example, RNA-binding protein Hfq (UniProt P0A6X3) is asymmetrical in PDB entry 4jrk but not in the 14 other examples of the same protein that exhibit c6 symmetry again indicating a probable issue with the identification of assembly that needs further investigation. Based on our analysis, the AnAnaS software has proven useful in inferring symmetry operators for unique protein assembly compositions, with cyclic and dihedral symmetry groups being the most common in the PDB archive.
Discussion Applying common data standards is crucial for making data Findable, Accessible, Interoperable, and Reusable (FAIR)  (50). In this study, we have developed an automated process to identify over 90,000 unique assemblies in the Protein Data Bank by mapping individual components to external databases such as UniProt and Rfam. With the help of additional resources such as Complex Portal and Gene Ontology, we have assigned human-readable, consistent names to over 90% of these unique assemblies, addressing a significant deficiency in the curation process of structure data. Our approach improves accessibility of structure data for a given complex via PDBe search mechanism. Previously, this type of search relied on the PDB entry title or the individual components in the assembly having a common naming convention, e.
g. both Hemoglobin assembly components have Hemoglobin in their name, which makes it challenging to recover all the relevant information. The standardisation of assembly names and unique identifiers allow users to specify the PDBe complex identifier or the complex name under the advanced search option to find all the relevant information for a complex of interest. As shown in Figure  5 , querying for Hemoglobin yields multiple variants of hemoglobin such as mutant adult human hemoglobin (PDB-CPX-159518), hemoglobin from parasitic flatworm Fasciola hepatica (PDB-CPX-163279) and foetal hemoglobin (PDB-CPX-159679). By clicking on the individual complex name, users can discover the same assembly across different species. Alternatively, clicking on the PDBe complex identifier would enable users to easily find all PDB entries containing a given assembly with identical composition and species.
Identification of unique assemblies To create distinct assembly identifiers, we begin by attempting to map each component in the assembly to an accession/identifier from a reference database (refer to Table  5 ). UniProt accessions are used to map protein components, while mappings from the Rfam database  (43)  are used for RNA molecules. In addition, we also use antibody annotation generated by the tool ANARCI  (51)  to identify and name antibodies in the PDB. These accessions/identifiers are combined with stoichiometry numbers to generate description labels. We append the unmapped component type, PDB entry-id, entity-id, and stoichiometry to describe components that cannot be mapped to an external reference. We continue to work on classifying unmapped components and identifying suitable external references.
To create a unique identifier for each complex, we combine the descriptions of the individual components and assign a distinct ID to each unique complex description. For instance, the description "P12345_1, DNA_1uty_2_1" is mapped to the identifier PDB-CPX-100487. We then compare the composition and stoichiometry of each assembly to those of complexes in the Complex Portal  (44)  and assign the relevant Complex Portal ID to the assembly whenever possible. To ensure the persistence of these IDs, we generate and store an md5 hash value for each unique complex composition, which serves as a reference for creating and maintaining persistent identifiers for all unique complexes in the PDB archive. At present, our approach only identifies assemblies within a given species and does not compare them across different species.
The next step in the process is to define sub-assemblies and super-assemblies based on the composition and stoichiometry data. We define a sub-assembly as an assembly where all its components are a subset of the components in another larger assembly. For instance, haemoglobin (two copies of UniProt P68871 and two copies of UniProt P69905) represented by the PDB entry 7jy3 is a subassembly of the haemoglobin-iron surface determinant B receptor complex (contains additional two copies of UniProt Q8NX66) represented by the PDB entry 7pch. We define a super-assembly as an assembly containing all the components of another assembly and additional members. Thus, in the previous example, the PDB entry 7pch is a super-assembly of haemoglobin (PDB entry 7jy3).
Naming unique assemblies To automate the naming of unique complexes, we developed a decision tree (Figure  6 ). First, we check if the assembly from the PDB has the same composition as a complex in the Complex Portal. If so, we assign the name of the Complex Portal entry to the PDB assembly. For example, the PDB entry 4f3l consists of two chains (UniProt entries Q9WTL8 and O08785) that can be mapped to the Complex Portal entry, CPX-3225. Therefore, we can name this heterodimer "CLOCK-Bmal1 transcription complex" using the name from the Complex Portal. We use the UniProt recommended name for homomeric protein assemblies in the PDB since they comprise repeated units of the same subunit. For homomeric protein assemblies that consist exclusively of unmapped components, we use either the entity names or the antibody names to name these assemblies.
For example, the PDB entry 1ivi is named as dihydrolipoamide dehydrogenase while the PDB entry 12e8 is named as IG-heavy chain and IG-light kappa chain. However, many PDB assemblies have additional components not present in Complex Portal or UniProt entries. In such cases, we add names depending on the molecule type (i. e. DNA, RNA, or antibody). Nucleic acid assemblies that consist of a single component are named DNA, RNA, or DNA/RNA hybrid, depending on the polymer type of the assembly, unless they have components mapped to Rfam. Most RNA-containing assemblies in the PDB are either ribosomes or spliceosomes, while the rest are mostly small assemblies that represent various small non-coding RNAs and tRNAs  (52). Rfam is a valuable resource for identifying and naming RNA-containing assemblies in the PDB since it is a database of non-coding RNA families classified using multiple sequence alignments, consensus secondary structure, and covariance models  (43).
For example, there are 102 unique assemblies in the PDB consisting exclusively of components that can be mapped to Rfam. These include the S-adenosylmethionine (SAM) riboswitch (PDB 2gis), group-I intron (PDB 1grz), group-II intron (PDB 4y1n), and glucosamine-6-phosphate ribozyme (PDB 2gcv). Our process uses a decision tree to automatically generate descriptions for assembly components based on data from external data resources and component categories. After the automated naming process, we check if the assembly is in the PDBe's manually curated list of assembly names (Figure  7 ), which includes selected macromolecular complexes and their mapping to UniProt for each component. We also verify if all components in the assembly have a consistent Gene Ontology (GO) term associated with them  (53, 54).
Code availability The source code for our data pipeline is publicly available on GitHub at https://github. com/PDBe-KB/process-complex-data. This repository contains a Python package that aggregates data for macromolecular complexes from the PDBe graph database, assigns unique identifiers, and generates human-readable names. Additionally, we have created a demo repository that allows developers to test and benchmark the complex identifier-generating process. This repository is available on GitHub at https://github. com/PDBe-KB/pdbe-complex-analysis-demo.
Data availability The data pipeline described in this paper is integrated into the PDBe weekly release process to ensure that all new and updated PDB entries are assigned persistent complex identifiers. We provide public access to the data mapping between PDB and Complex Portal entries on our FTP area at https://ftp. ebi. ac. uk/pub/databases/pdbe-kb/complexes/, updated quarterly. Moreover, the code we used to analyse the data presented in this paper is available in a Jupyter notebook at https://github. com/PDBe-KB/pdbe-assemblies-analysis.
An example is PDB entry 6r8z, which contains Nucleosome variant H3. 1-H2A. 2-H2B. 1, UV DNA damage recognition assembly DBB1-DBB2 with DNA and maps to two Complex Portal accessions CPX-2556 and CPX-308, respectively. Following these conventions allowed us to name 2,473 unique PDB assemblies using names obtained from Complex Portal. We have made the generated mapping files available to the Complex Portal team and the broader scientific community through the public PDBe-KB FTP area, which is available at https://ftp. ebi. ac. uk/pub/databases/pdbe-kb/complexes/. Complex Portal to 2,129 PDB assemblies. Of these, 371 contain an additional protein component, 67 have an additional DNA component, and 13 include an additional RNA component. For example, the PDB contains three assemblies of the Cyclin A2-CK2 complex with Cyclin-dependent kinase inhibitor 1B (PDB 1h27, 1jsu, and 6ath).
Design and printing of composite scaffolds Square scaffolds (10 mm × 10 mm) were designed using computer-assisted design (CAD) software. To create the computer model of a square scaffold for printing, the scaffold was designed using Magics 13 EnvisionTEC software. Afterwards, slicing the designed scaffold was performed using Bioplotter RP software. A needle with an internal diameter (d) of 0. 510 mm was applied and the layer thickness was set at 80% of the strand diameter (i. e. , 0. 408 mm) for gravitational spreading considerations  [63]. A distance between two adjacent strands (L) of 1. 00 mm was used; this distance was measured from the center of strands. PCL/30% (wt. ) nHAp scaffolds were then printed using an extrusionbased 3D Bioplotter Manufacturer Series system (EnvisionTEC GmbH) equipped with a high-temperature printing head and a nozzle extruding the material onto a printing bed.
Statistical analysis Scaffold mechanical properties were assessed as a function of porosity using linear regression for both the lattice and staggered structures (Fig. 2A, B ). Overall tests for coincidence were used to determine if the modulus-porosity and strength-porosity relationships for the lattice and staggered structures were similar  [67]. This approach uses F-test statistics to assess whether fitting separate regression curves to lattice and staggered datasets more effectively predicts mechanical properties than a single regression curve fit to both datasets. GraphPad Prism 9. 3. 0 was used to complete the statistical analyses. The significance level was set at p ≤ 0. 05.