dataesr/scientific-paragraphs-categorization huggingface dataset:
They are being coordinated via regular conference calls and are each evolving specific tasks. Below we detail, the specific thematic aims of each Activity and highlight the planned interactions with the ELIXIR platforms. Activity I: Infrastructure for FAIR structural and functional annotations Coordinator: Sameer Velankar. The Protein Data Bank (PDB) and the Electron Microscopy Data Bank (EMDB) both follow the FAIR principles, thus enabling many niche data resources to derive added value annotations from the archived structural data, such as structure domain classifications, information on ligand-and macromolecule binding sites and effects of mutations on structure and function. However, a lack of coordination between these specialist data resources has prevented the creation of data standards and uniform data access mechanisms, consequently reducing the impact of these valuable data.
The proposed Activity will address these omissions by further developing the PDBe Knowledge Base (PDBe-KB) -a community-driven data resource for structural and functional annotations that places structural data in its biological context. PDBe-KB increases the visibility and interoperability of niche data resources by collating minimally required common structure-based annotation data in a standardized data exchange format and by enabling comparisons between specific types of annotations obtained from different software tools. The initial focus of this Activity will include three major goals: i) continue to expand the scope of integrating known and predicted functional site annotations with PDBe-KB; ii) integration of annotations concerning the impacts of disease-associated variants on structure and function in PDBe-KB; and iii) integration of predicted protein structure data via existing model archives by establishing a federated infrastructure for access (3D-Beacons).
Activity I will build on existing collaborations involving five ELIXIR nodes (UK, Italy, Spain, Czech Republic, Belgium). Activity II: Open resources for sharing, integrating and benchmarking software tools for modelling the proteome in 3D Coordinator: Shoshana Wodak. Activity II is about empowering the scientific community to extend the current information on protein 3D structures, protein interactions and assemblies, and extract knowledge from this information by using computational and bioinformatics methods, and integrating biological data from different sources. Its initial focus will be on software tools and benchmark datasets for modeling the 3D structures and conformational flexibility of proteins and protein assemblies, and on community-wide benchmarking activities that advance the field.
Extend the content of OpenEBench by adding a knowledge base integrating software tools for modeling the 3D structure of proteins, protein complexes and assemblies. A variety of software tools are available for modeling protein structures and protein complexes by exploiting available protein sequence data and information on known structures in the Protein Data Bank (PDB). Exploiting these data successfully involves integrating the appropriate set of tools for the problem at hand. The Community will tap into OpenEBench a knowledge portal fostering benchmarking in the life sciences domain and an important component of the ELIXIR Tools Platform, with exemplary datasets of CAMEO and CAPRI already present. The extension will include workflows and guidelines to software tools and servers for modeling protein structures and complexes, based on known structures (templates) in the PDB.
Develop a one-stop-shop of benchmark datasets for testing and evaluating methods for generating scoring, and ranking models of protein complexes. The availability of appropriate benchmark datasets has been crucial for the development of protein modeling procedures at all levels. Protein docking benchmarks, which assemble high resolution 3D structures of selected sets of known protein complexes and their components  43, 44, 45  as well as their experimentally measured affinities  46, 47  , have been widely used for benchmarking methods for protein-protein docking, and for predicting and scoring protein interaction interfaces. The OpenEBench project within ELIXIR-EXCEL-ERATE (OpenEBench. bsc. es  48 ) has established a transparent data model that allows not only the sharing of benchmarking datasets, but also analyzing and comparing the performance of different prediction algorithms on these datasets.
OpenEBench will thus foster a collection of benchmark datasets relevant to the field of modeling the 3D structure of monomeric, homo-oligomeric and heteromeric protein complexes, extending to proteins -peptide and protein -nucleic acid interactions. It will include: 1) protein docking and affinity benchmark datasets developed by members of the CAPRI community, 2) datasets comprising all the predicted models of protein assemblies submitted to CAPRI and CASP blind prediction challenges, following the examples of the Score-Set  49  derived from predicted models submitted to CAPRI, 3) a dynamic benchmark for protein complexes beyond binary interactions, considering different difficulty levels (See DynBench3D;  50  ). In addition a mechanism will be developed for users to contribute datasets  51  , following well-defined community-approved standards such as the mmCIF based modeling extension developed in collaboration with the RCSB within the macromolecular ModelArchive.
Creation of benchmark datasets to assess structure-based drug design tools. Benchmarking studies performed so far, including those carried out in the CSAR  52  or D3R Grand Challenge  53  which focus on compound series consistently measured within one lab/institute -rely on datasets of limited size and diversity, precluding large-scale, FAIR comparisons of their performance, especially as a function of ligand and binding site properties. Activity III will therefore involve: 1) Building benchmark datasets, extracted from the PDB and curated, for assessing SBDD tools on a large-scale, under well-defined FAIR conditions, thereby complementing efforts such as the D3R grand challenges. A particular effort will be devoted to collecting complexes involving peptides and macromolecules. 2) Quantifying different properties (e.
g. charge, polarity, size, flexibility of the binding site and ligand etc. ) for each entry in the benchmark, to enable evaluation of SBDD tools as a function of these properties. 3) Developing links to other databases and standardizing the retrieved data to complement the information provided for each protein-ligand complex in the benchmark sets; notably, collecting information on ligands in collaboration with PDBe-KB, and associating complexes with reliable binding affinity data using databases like ChEMBL  54  and Pubchem 55. 4) Adding information on the non-bioactive conformations of ligands to standardize the comparison of docking calculations starting from such geometries. 5) Adding information on experimentally determined non-active compounds (taken e. g. from ChEMBL  54  ) to be used as negative examples for testing virtual screening procedures.
Dissemination and promotion of the benchmark datasets and results. Benchmark datasets will be made publicly available by following Open Access and FAIR principles via a collaboration with the OpenEBench project within ELIXIR EXCELERATE. The latter will also allow the sharing of benchmarking results, and comparing the performance of different prediction algorithms under FAIR conditions. Preferred standardized benchmark workflows and protocols will be published, to guarantee an objective comparison of different tools. Researchers will be encouraged to evaluate their preferred sets of SBDD approaches using the above-mentioned standardized benchmarking workflows and protocols, and to report their results. We will collect the latter and provide them to the community. This activity will be of major value to Pharma and Biotech researchers, who acknowledge the importance of standard benchmarking protocols.
2) Coordinating the unification of the existing NA geometry standards and formulate specifications for missing standards. 3) Developing benchmarks dataset for evaluating the quality of predicted or experimentally determined NA structures. To limit the redundancy and increase the synergy between the methods, databases, web services, and other tools, we plan to continuously update the catalogue of software tools developed by the RNA tools and software consortium, extend these tools to DNA structures and enable integration of emerging tools. These efforts will build on existing ontologies while preserving consistency with new extensions. This integration effort will require a significant level of interoperability between data exchange protocols and software, which will be implemented following FAIR principles.
g. RASP  56  or MacroMoleculeBuilder, MMB  57. 2) Unifying the libraries of RNA/DNA dinucleotide fragments based on the analysis of experimental structures  58, 59  and trinucleotide fragments  60. Ultimately, the community should reach a consensus on what is the meaning of "preferred", "allowed" and "wrong" conformers in analogy with the use of these terms in the Ramachandran plot. 3) Integrating and benchmarking methods dealing with RNA structures, e. g. SimRNA  61  , RNAComposer 62 MMB  57  , FARNA/FARFAR  63  , Web-Beagle  64. 4) Strengthening the collaboration with developers of the main experimental nucleic acid structure determination software tools (e. g. REFMAC  65  , COOT  66  , Phenix 67 , PDB-REDO  68  ) to encourage consistent handling of NAs. The goals of Activity IV are quite ambitious, and their implementation will require close and friendly collaboration of all research teams willing to participate; including the teams involved in this Activity, and other teams active in the field.
Interaction of 3D-Bioinfo with other research communities Alignment with the ELIXIR platforms All the above 3D-Bioinfo Activities will engage with the ELIXIR platforms as described below. Interoperability platform -The outcomes of our projects must be easy to discover, to access and to integrate into users' pipelines. This necessitates the use of standardised file formats, metadata, vocabularies and identifiers. We plan to include our resources in FAIRshairing and Identifiers. org. For all Activities we will organise community workshops to develop data exchange and retrieval standards to improve compliance with FAIR principles. Participating teams will also adopt BioSchemas  69  , a European led initiative. For example, different community-wide standards for evaluating predicted models of proteins and protein complexes would be integrated to promote community-wide use.
FAIR-ification of benchmark datasets will be undertaken. To implement the 3D-Beacons infrastructure, we will implement a common API specification for macromolecular structure data from both experimentally determined and predicted models. Where possible, the services and tools will also be linked via workflows using common workflow language to help with interoperable workflow software development. Data platform -We will link key structural bioinformatics data resources to drive the use and re-use of data. For example, data from five UK based structure prediction resources have already been integrated via Genome3D, and ELIXIR implementation studies are already supporting further integration of the data in Genome3D with data from SWISS-MODEL, developed by the Swiss node. In addition, Activity I will be responsible for the integration of data on known and predicted functional sites, from a large number of participating European groups, in PDBe-KB.
Information on the structural impacts of genetic variations predicted by multiple groups will also be integrated and novel visualization strategies for presenting this integrated data will be developed. These will need to clearly distinguish between experimentally known and predicted data. Activity II will be responsible for integrating various benchmark datasets on predicted protein complexes and assemblies, and on experimentally determined complexes annotated with data from other sources (in close coordination with Activity I). Activity III will also integrate benchmark datasets. For example, we will establish Open Access benchmarks sets under FAIR principles, for assessing structure-based drug design applications. This must be done in a robust, sustainable and scalable data ecosystem, allowing the use and re-use of the data, in line with the ELIXIR Data Platform goals.
Tools platform -The participating tools and resources will be registered in BioTools to make them discoverable and sustainable. Currently, there are several hundred tools relating to structural bioinformatics registered in BioTools covering a range of themes. Extending the repertoire of structural bioinformatics tools in BioTools will a) ensure reproducibility, b) allow scaling up by working in cloud environments, e. g. EOSC c) make tools widely available and sustainable for non-expert users. Containerization of these tools in BioContainers will support development of complex workflows. We will use the benchmarking infrastructure OpenEBench to assess tools and help methods development. For example, all tools related to Activity I will be registered in BioTools and the developers will have the opportunity to access expertise on containerization; for Activity II, these will incorporate exemplary datasets from CAMEO and CAPRI.
Compute platform -The ELIXIR Authentication & Authorisation Infrastructure (ELIXIR-AAI) which connects to other European AAI initiatives like EGI-CheckIn, INSTRACT ARIA, will be adopted to support depositions. We will link with the compute platform activities to address scalability of the underlying infrastructure for data transfer as well as data access. For example, access to the ELIXIR compute infrastructure and to the European Open Science Cloud resources (EOSC-Hub) will be explored, to enable large-scale assessment of predicted models of protein assemblies in CAPRI prediction rounds, and to disseminate software tools and web-services. Similarly, groups providing large-scale protein structure predictions and variant impacts will seek to benefit from access to the ELIXIR compute infrastructure.
We will adopt ELIXIR Authentication & Authorisation Infrastructure (ELIXIR-AAI) to support deposition of ligand-protein complexes or benchmark results into our future infrastructure. We also need to provide an easy way to store and synchronise our datasets and users' benchmarks results across ELIXIR and other e-Infrastructures. Training platform -PDBe-KB and participating data resources will work to add training workflows to the TeSS portal. As mentioned already, ELIXIR-UK funding has already supported preliminary work on these workflows involving collaborations between multiple partners. Proteopedia, which is being developed in collaboration between the Israeli node and PDBe at the hub, will also be a valuable mechanism for training and outreach (see Figure  4 ). Connections with existing initiatives such as the BioExcel Knowledge Resource Center will be established.
The BioExcel knowledge resource center is a repository for computational biomolecular training resources. The resources are primarily online based, such as tutorials, online courses and videos but also include face-to-face events. With regards to protein ligand interactions, we will train researchers on the best way to retrieve and use our benchmark datasets, and promote the usage of preferred benchmark conditions. The Molecular Modeling Group of the SIB Swiss Institute of Bioinformatics will enhance the Drug Design Workshop. This is a web-based educational tool  70  to introduce structure-based computer-aided drug design to the general public. This online workshop constitutes a helpful tool to introduce the of structure-based drug design to young students (15-19 years old) and to the general public.
It can also be used as an introductory tool for more advanced students. Several routes of enhancements will be followed, including a better visualization and analysis of the ligand-protein complex, or the selection of new protein targets. Building on the experience of the CAPRI community in organizing the well-attended EMBO courses on Integrative of Biomolecular Complexes (3 editions so far), Activity II will coordinate various training activities and workshops on teaching non-experts how to use protein structure and assembly prediction tools and how to benchmark new prediction methods using various datasets assembled in Activity II. Training workflows will be derived and added to the TeSS portal. Activity II will also help coordinate training tasks related to protein structure prediction in the Meet-U initiative.
For all activities, we will organise training workshops for trainers and users, and add workflows to TeSS where appropriate. Connection to other communities and European infrastructure initiatives 3D-Bioinfo community users: The 3D-Bioinfo community bridges several infrastructures and their providers as well as users, namely people and tools of structural biology (Instruct-ERIC, iNEXT), cheminformatics (OpenScreen), system biology (ISBE), molecular simulations (BioExcel) and the proposed community for intrinsically disordered proteins (IDP). In addition, the value of structural data in providing insights into the impacts of genetic variations has led to involvement of some of the 3D-BioInfo participating groups with the ELIXIR Rare Disease Community. Similarly, work in all Activities on structural impacts of residue mutations and other research fields of protein engineering and nucleic acid analogues would clearly enable links with the newly emerging ELIXIR Community on Synthetic Biology.
Connection with industry Industry and SME involvement -As mentioned already, Activity III (linked to structure-based drug design) is of particular relevance for the pharmaceutical and biotechnology industries, particularly those using computer-aided structurebased drug design programs. Companies involved in the development of scientific software as well as novel bioactive compounds acknowledge the importance of developing benchmarking protocols, and participate in the D3R Grand Challenges by providing undisclosed data. Furthermore, as regards Activity I, PDBe-KB has interactions with OpenTargets platform and provides annotations to major data resources (UniProt, InterPro and Pfam). Development of data standards and distributable infrastructure would further improve accessibility of the added value annotation data for industry users where all four Activities will contribute.
Integration at a global level Clearly the ontologies and data exchange formats established and endorsed through 3D-Bioinfo enabled collaborations will be valuable in a global context as well as across Europe. In fact many of the initiatives we will foster such as PDBe-KB, CAPRI and CAMEO already involve other international partners outside Europe. Furthermore, 3D-Bioinfo groups are involved in organising the international community-wide CASP benchmarking of protein structure prediction. We will support and where possible engage in initiatives for global health, for example the Global Alliance for Genomics and Health (GA4GH). We plan to present activities and output from 3D-Bioinfo in special sessions or technology tracks at the European ECCB and international ISMB conferences, the latter of which is held in Europe on alternate years.
Conclusions This proposal capitalises on the extensive European structural bioinformatics expertise. It provides a discussion and action framework for joint development of current and future activities within the European structural bioinformatics community. 3D-Bioinfo will very much foster the interactions with experimental research groups to efficiently reach a better understanding of proteins and their functional properties at a quantitative level. 3D-Bioinfo will also foster focused outreach and training activities. The outlined aims of our planned Activities should facilitate valuable coordination for optimal use of resources and exploit ELIXIRs platforms to good purpose. As demonstrated in our background to this paper and the many tools/resources listed in the BioTools, Europe is very strong in this area of research and the establishment of the 3D-Bioinfo Community will assist in promoting research interactions, integration of data and tools and standardised practices, making it even stronger.
Furthermore, the Community will facilitate the translation of structurally derived insights -in medicine (pharmaceuticals, diagnostics), agriculture, and sustainable production methods. Finally, 3D-Bioinfo will facilitate collaborations worldwide and promote European research in structural bioinformatics, on a global scale. The first sentence of the abstract immediately leaves this question un-answered "Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. ". By leaving out aspects of "modeling" from the possible contribution, which I believe is a frequent and important aspect of computational structural biology, the authors restrict the activities for retrospective analysis of experimental data, to "bioinformatics", i.
Roland L. Dunbrack Fox Chase Cancer Center, Philadelphia, PA, USA This opinion article summarizes the activities, both existing and planned, of the 3D-Bioinfo Community within the ELIXIR biological data infrastructure project in Europe. As such, it represents a useful statement of purpose that can be used to guide the efforts of the many participating groups and in the future as a potential means of evaluating the success of those efforts. The first section of this article ("Major European contributions in structural bioinformatics") describes the historical impact of European research groups in the field of structural bioinformatics. These contributions are extensive and indisputable but I am not sure that most scientists will find it very interesting. I suppose it is necessary for European funding bodies to hear it, and in the United States we often find ourselves doing the same thing while trying to secure funding for bioinformatics infrastructure like the PDB from the National Institutes of Health and the National Science Foundation.
All of these require significant infrastructure and integration and interoperability of tools and data, and 3D-Bioinfo is well placed to operate in this sphere. All of these make sense, even if they are fairly obvious. It may be an unpopular opinion, but I don't think protein structure prediction on the proteome scale is such a worthwhile goal, except for organisms that are of significant experimental interest. Because we can make models of millions of proteins from 100s or 1000s of organisms, doesn't mean we should. Most of that data would be unused. The fact that it "remains computationally expensive to build 3D models on a proteome-wide scale" as the article states is not really an issue. The more important issue is making biologically relevant models of proteins -as homo-and heterooligomeric complexes, interacting with ligands and nucleic acids, and in different functional states (e.
g. , the active and several inactive conformations of kinases). One challenge that was left out is the validation of experimental structures at the atom/residue level (amino acids, nucleic acid bases, and ligands), and more importantly, their re-refinement with new methods as they are developed. I am a fan of two tools developed by some of the authors on this paper, EDIA for evaluating electron density in X-ray crystal structures on an atom-by-atom basis, and the PDB-REDO database of re-refined crystal structures. The next section is a general statement of the goals of 3D-Bioinfo: 1) improved interoperable data standards 2) planning for sustainability and integration of data resources; 3) standards, benchmarking, and validation of prediction tools; 4) access to high compute facilities; 5) integration of structural data with other biological data and databases; and 6) training and outreach in bioinformatics.