Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests. A program suite for macromolecular modellingMX macromolecular X-ray crystallographyNMR: nuclear magnetic resonance, a spectroscopic methodOpenEBench: infra-structure designed to establish a continuous automated benchmarking system for bioinformaticsPDB: Protein data bankPDBe-KB: Protein data bank in Europe - knowledge basePDB-Redo: procedure to optimise crystallographic structure modelsPhenix: software suite for the automated determination of molecular structuresPHYRE: automatic fold recognition server for predicting the structure and/or function of the protein sequenceProteopedia: wiki and 3D encyclopedia of proteins and other biomoleculesPubchem: database of chemical molecules and their activitiesRefmac: program for refinement of experimental structures of biomoleculesRNA Puzzles: collective experiment for blind RNA structure predictionSAXS: small angle X-ray scatteringSBDD: structure-based drug designSwiss-model: structural bioinformatics web-server dedicated to homology modelling of 3D protein structuresTeSS portal: ELIXIR's training portalUniProt: Universal protein esource, a comprehensive resource for protein sequence and annotation dataWeb-Beagle: web server for the alignment of RNA secondary structures1 which provides open access to macromolecular structure models.Structural bioinformatics is a well-established scientific activity, which started in the 1970s following the establishment of the Protein Data Bank3 and the world-wide adoption of these tools by the structural biology community. They have also initiated collaborations and joint activities between structural bioinformaticians and structural biologists, expanding recently to meet the need to develop tools and validation protocols for structures determined using new techniques such as EM  and Integrative/hybrid methods.Structure models are experimentally determined by macromolecular X-ray crystallography (MX) and small angle X-ray scattering (SAXS), nuclear magnetic resonance spectroscopy (NMR), or cryo-electron microscopy (EM). The technological developments in MX in the previous decade, largely catalysed by the structural genomics initiatives and the on-going revolution in the field of cryo-EM, are expanding the volume of structural data both quantitatively and qualitatively see. Europea4 and thereby promote both fundamental and translational research. For example, characterisation of protein binding pockets provided vital information for rational drug design. Europe also pioneered the establishment of comprehensive structure-based protein classifications6 giving structural insights into protein evolution and European structure-based tools have facilitated enzyme reaction mechanism studies by chemists and biochemists.European bioinformatics groups have also been at the forefront of efforts to compare protein structures and characterise their features in order to understand the underlying principles of protein structure and function8, together with the development of methods for assessing model quality. European bioinformatic groups have also provided key solutions for the hardest task ofde novo prediction of protein spatial structures9. Furthermore, European groups have made seminal contributions to the development of methods for modelling the 3D structure of protein complexes10, a very difficult problem, which is centre stage for today’s molecular biology. These activities have been further expanded in the field of multi-scale modelling where a wide variety of experimental and bioinformatics data are integrated into the modelling process. Importantly European groups have made major contributions to initiatives assessing the performance of structure prediction and protein docking methods  simulation techniques and applying them to investigate dynamic properties of protein systems, functionally important conformational transitions in proteins, as well as folding and unfolding reactions24, providing crucial insight into dynamics aspects that are notoriously difficult to capture by experimental approaches.Protein function is strongly related to molecular recognition of small molecules such as substrates, inhibitors, or signalling compounds and many European groups have been active in this area over the last 50 years25 spurred the development of the first methods for accurate structure prediction in antibodies26. Other biocomputational methods have been important for enzyme engineering. Such contributions by European bioinformaticians have transformed the face of protein engineering and were the basis for establishing major biotechnological companies for developing new research and clinical tools.Protein structural data and functional residue annotations also inform protein engineering, another important activity with significant European representation. For instance, the discovery of canonical conformations in antibody variable domains27, CAMEO28 and CAPRI30 for assessment of the prediction of protein structures and complexes have driven the field by independently validating methods and highlighting innovations that increase performance. However, many challenges still exist. It remains computationally expensive to build 3D models on a proteome-wide scale. Furthermore, prediction methods are still error prone. It is therefore important to increase coverage and confidence measures by consolidating results from multiple methods. ELIXIR is already supporting some Europe-wide collaborative initiatives. For example,a recent implementation study links several major structure prediction and annotation resources  with ELIXIR Core Resources, PDBe37 and InterPro38 to increase the coverage and reliability of predicted protein structure data  are integrating data from multiple European groups allowing easy access, development of meta-predictors and common benchmarking to improve accuracy. Since some disease-associated genetic variations result in modifications of protein residues in or near functional sites, these initiatives provide a natural link with the39 of the data, interoperability of the data and software tools are serious issues that require close collaboration between structural biologists and bioinformaticians.Recent and future technological challenges of structural biology such as EM, serial crystallography, fragment screening, bio-SAXS, time-resolved structural methods, and techniques of integrated biology in general, are important areas that can be addressed by structural (3D) bioinformatics, albeit always in close collaboration with structural biology research groups. Optimal data formats, FAIRnessWith regard to prediction of protein-ligand interactions, protein/drug design, and modelling of dynamic properties of proteins and their interactions, much work remains to be done in benchmarking of methods and better integration of methods and data. 3D-Bioinfo will endeavour to facilitate collaborations and new initiatives in these areas.The major goals of 3D-Bioinfo will be to increase interoperability between resources by developing and promoting data standards, integrating data where appropriate and developing robust benchmarking strategies for prediction algorithms . We will also develop better visualization frameworks for protein and nucleic acid structures and work closely with the structural biology community and initiatives such as Instruct-ERIC to develop improved validation metrics for nucleic acid structures, an important area, which is currently underdeveloped.The 3D-Bioinfo major goals can be summarized as follows:• Promote and develop data standards to drive data integration• Plan the long-term sustainability for key computational tools and data resources• Drive the integration of resources and tools for analysis of structural data• Develop robust standard methods for benchmarking and validating prediction tools• Facilitate the access of tools requiring high compute power to the appropriate facilities• Improve integration of structural data with other quantitative biological data• Pool and expand the available training and outreach material in structural bioinformatics.31, Phyre32) and Core Data Resources  in 3D-Bioinfo.The structural bioinformatics community forms an indispensable interface between producers of structural data and their users. There are already several ELIXIR endorsed node resources/servers  and COST Actions , WP3 Entangled and Self-entangled Proteins). Other international collaborations include the Protein Structure Initiative (PSI) for structural genomics. A significant fraction of the tools/resources provided by ELIXIR nodes relates to structural bioinformatics and the combined resources receive hundreds of thousands of web-accesses/month from the wider global research community.European groups participating in 3D-Bioinfo). They are responsible for the development and sustainable operation of complex tools ranging from databases to infrastructures promoting interoperability between multiple areas of research that rely on the reusability of structural data provided by the structural biology community (e.g. as represented by Instruct-ERIC).The 3D-Bioinfo Community includes leading figures in the field of structural bioinformatics across Europe. It was initiated in March 2018 with the formation of a Steering Committee and an Executive Committee comprising representatives from 16 ELIXIR nodes. These members actively sought further participants from within their nodes, covering a wide spectrum of skills and needs  gave presentations indicating possible areas of future collaboration. There were also oral contributions from representatives of other European ESFRI initiatives like Instruct-ERIC and BioExcel, again highlighting synergies and possible collaborations. Posters from many research groups, keen to participate in 3D-Bioinfo, were presented at the meeting highlighting their possible contributions and an interactive session was held allowing participants to discuss the proposed Activities and suggest future Activities. Participants also discussed ideas for interaction with the ELIXIR platforms.Following their formation in March 2018, the 3D-Bioinfo Executive and Steering Committees held regular conference calls to determine the highest priority areas, which would become the main Activities to be first undertaken by the community. These 3D-BioInfo Activities, outlined below, were further discussed and refined at the launch meeting, and involve multiple participating groups across the nodes.Structural Bioinformatics is well established, as the field began more than 40 years ago following the establishment of the protein structure databank in the 1970s, so most of these Activities represent mature areas of research, some of which had already received sustained node funding to develop their tools and resources. Each Activity will have the major goals, listed above, as the core of their mission. 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.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.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 proposed Activity will address these omissions by further developing the PDBe Knowledge Base  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).Identifying additional predicted or manually curated annotations that could further enrich the integrated data of PDBe-KB: The lack of data standards and fragmented nature of specialist data resources is a barrier to the FAIR principle. We will bring together the community experts to define data standards for different types of annotations and integration of these annotations using a community-driven data exchange format will facilitate finding, accessing and reusing sparse annotations in an interoperable manner. Activity I will ensure that PDBe-KB will be an effective platform for the collaborating partners to share and compare their value-added, structure-derived annotations. Furthermore, we will continue to identify gaps in coverage of the various types of annotations in PDBe-KB and bring together community experts to fill in those gaps. The workshops will also facilitate the development and testing of new methods by providing valuable, standardized benchmarking data sets.Integration of annotations related to the impacts of disease-associated variants on structure and function: By bringing together developers of specialist data resources and scientific software tools that can provide information on the effects of amino-acid variation on structure and function, we will establish data standards to represent these data. The data standards will support integration of these annotations in PDBe-KB with particular emphasis on the effect of disease-related mutations on conformational stability. Compiling and integrating these annotations will be highly beneficial when investigating naturally occurring variations, and when used together with all the other types of integrated annotations it may facilitate development of better tools ultimately benefiting the wider biomedical research community.Integrating predicted protein structure models (3D-Beacons): Taking advantage of the highly interconnected data of PDBe-KB will allow the transfer of valuable annotations to structural models based on sequence- or structural similarity when experimentally determined structural data is unavailable. Development of 3D-Beacons infrastructure by incorporating structural models from existing European archives  and other international resources (e.g. MODBASE41 and Gremlin42) will facilitate an increase in the coverage of structure data in the sequence space. The PDBe-KB data alongside the predicted structure models will potentially allow the scientific community to gain valuable insights regarding the biological context.The focused activities will take advantage of the existing PDBe-KB infrastructure, and will include developing community-driven data standards and a uniform data access mechanism in addition to novel, portable and distributed web-based visualisation components. Activity I will build on existing collaborations involving five ELIXIR nodes .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. This focus will subsequently be broadened to include other areas where 3D modelling of proteins and their interactions is most impactful. The following specific aims will be pursued.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. More specifically tools for template-based modeling of protein assemblies, and for Integration of template-based modeling of individual subunits, with protein-protein and protein-peptide docking servers developed by members of the CAPRI community. For a more in-depth understanding of the resources please refer to theCAPRI andCAMEO websites. 3D-Bioinfo will build on these efforts and take them to the next level.Develop a set of standard freely available tools for evaluating the quality of 3D models of proteins and protein complexes. Evaluating the quality and accuracy of 3D models of proteins and protein complexes plays a crucial role in evaluating the performance of molecular modeling methods, and helping methods developers to optimize their procedures. Furthermore, to effectively compare the performance across methods, agreed upon standard quality measures and evaluation protocols are necessary, as implemented for single protein models by CAMEO modeling (3D), the CAMEO Quality Estimation (QE) category and for protein – protein complexes by the CAPRI community. Major goals will include:1) Full automation of the CAPRI quality assessment procedures for models of protein-protein and protein-peptide complexes, and larger assemblies and making them widely accessible online and for download,2) Integration of the CAPRI and CAMEO model quality assessment tools,GitHub established by the CAPRI community).3) Making the integrated tools available as open software through a community repository  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.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-Set49 derived from predicted models submitted to CAPRI, 3) a dynamic benchmark for protein complexes beyond binary interactions, considering different difficulty levels . In addition a mechanism will be developed for users to contribute datasets51, following well-defined community-approved standards such as the mmCIF based modeling extension developed in collaboration with the RCSB within the macromolecularModelArchive.org project.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)Develop the infrastructure to manage the CAPRI challenge, in coordination with CAMEO. For CAPRI: Develop automated registration and submission procedures , as well as tools for accessing and navigating target information, predicted models and prediction results on the CAPRI website. Work on these tasks is currently underway at CAPRI-EBI, but further support is needed to complete it. For CAMEO: CAMEO is currently adding a new category for heteromeric complex modeling. We propose a close collaboration with CAPRI concerning the prediction and scoring applied during fully automated evaluations.Develop a knowledge portal to user-friendly bioinformatics and computational tools for modeling conformational flexibility of proteins. Adequate modeling of conformational changes is currently a major bottleneck in protein assembly modeling. To foster progress an important first step would be to develop a knowledge portal offering the modeling community at large, workflows and guidelines to various available computational and bioinformatics tools for modeling conformational flexibility. We will use the bio.tools registry established by the ELIXIR Tools Platform as infrastructure for this. bio.tools has tags, one of which is structure prediction, but it should be possible to add more customized tags. With such tools playing an important role in modeling intrinsically unstructured proteins, collaborations with the ELIXIR Community on protein intrinsic disorder on topics of common interest will be undertaken.Coordinator: Vincent Zoete. The biological activity of biomacromolecules is often linked to the three-dimensional recognition and binding of small molecules such as substrates, activators or inhibitors. Indeed, a large fraction of drugs are ligands targeting macromolecules like enzymes, receptors, transporters or ion channels. Consequently, important efforts have been dedicated to develop computer-aided drug design (CADD) and notably structure-based drug design (SBDD) approaches over the last decades, contributing significantly to the design of small molecules of therapeutic interest.in silico the poly-pharmacology of the compounds. New target classes are also emerging, beyond the usual well-defined binding pockets, including among others the interaction of proteins with other proteins, nucleic acids, lipids or sugars. These additions in the target classes are mirrored by the use of new classes of ligands, including peptides and macrocyclic compounds. These new types of target-ligand interactions will foster the development of novelin silico approaches, which will require new algorithms and thorough evaluations of their descriptive and predictive capacities.The field of drug discovery and development will face several challenges in the future. The on-going needs in medicinal chemistry prompts a dramatic demand for new molecular entities and the exploitation of chemical spaces that are yet unexplored. Despite important progress over the last few decades, toxicity issues remain a problem for small compounds. This has to be addressed through increased specificity, but also via a better characterization of the possible targets and the anticipation of multiple off-target effects. Toxicogenomics, pharmacogenomics, and phenotypic screening data should be collected, organized and disseminated to get a clearer overview of biomacromolecule-ligand interactions, and to ultimately predictin silico technologies, structure based drug design (SBDD) methods remain in great need of comprehensive evaluations of their performance and domain of applicability. In particular, there is a need for improved docking and scoring methods. Although protein-ligand complexes can be predicted for small ligands and almost rigid proteins, large, flexible molecules like peptides or macrocycles and proteins with flexible binding regions are still very difficult to handle. The reliable prediction of the binding free energy of a protein-ligand complex also remains a major challenge. Better methods making use of precise chemical models, modern optimization techniques and innovative scoring approaches are still much needed but must be accompanied by comprehensive and rigorous evaluations of their performance and domain of applicability. This should go hand in hand with the creation of processing pipelines for the proper use of structural data, and of standardizing tools for IO.Among otherThe efficiency of SBDD tools often depends on molecular/physicochemical properties such as the charge, polarity or size of the protein binding site and ligand, as well as on the target class of the biomacromolecule or the chemical class of the ligand. To address this, benchmark sets should clearly list and quantify these different properties for each complex with suitable descriptors, transparent for the user community. For Activity III, we plan the following goals:Creation of benchmark datasets to assess structure-based drug design tools. Benchmarking studies performed so far, including those carried out in the CSAR52 or D3R Grand Challenge53 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  for each entry in the benchmark, to enable evaluation of SBDD tools as a function of these properties.54 and Pubchem55.3) 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 ChEMBL4) Adding information on the non-bioactive conformations of ligands to standardize the comparison of docking calculations starting from such geometries.54) to be used as negative examples for testing virtual screening procedures.5) Adding information on experimentally determined non-active compounds  structures. The availability and sophistication of tools dealing with various hierarchies of the nucleic acid structure lag behind the tools used to explore protein structures and this situation must be remedied. In particular, standards are needed for initial model building and refinement of nucleic acid molecular structures. This task has become urgent as new techniques, including, but not limited to cryo-EM, are generating experimental data on 3D structures containing RNA and DNA molecules, such as ribosomes, spliceosomes, polymerase assemblies, and histone complexes, faster than ever before. The modelling of large nucleic acid molecules into low-resolution electron densities is particularly challenging. The RNA structural bioinformatics community has provided prototype tools, with which to model RNA and RNA-protein complexes based on experimental data, but these tools are currently not compatible with community-wide standards describing RNA and DNA conformational space and geometry at the local (nucleotide or dinucleotide) levels.Therefore, efforts need to be directed towards formulating community-accepted benchmarks that integrate the different levels of nucleic acid structure descriptions, and more generally to improving software tools that describe, analyse, annotate, and model nucleic acid structures. To enable these developments, Activity IV will focus on the following specific goals:1) Cataloguing software tools for building nucleic acid models based on their sequences alone as well as for modelling their 3D structures using experimental data, and facilitate integration of these tools.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. Dealing with software to solve, model, and refine NA structures based on the experimental data will require a close collaboration with experimentalists. Hence joining forces with Instruct-ERIC will be essential, for reaching all the stated goals, including the development of benchmarking tools and standards.We list a few examples of steps, which could assist useful integration of the existing tools:18 has developed a set of tools for linking 2D and 3D structures, software for RNA 3D structure model evaluation also exists, e.g. RASP56 or MacroMoleculeBuilder, MMB57.1) Adding links from existing servers to other tools, especially those linking 2D and 3D structure prediction. The RNA Tools and Software Consortium have already recapitulated methods for RNA secondary structure prediction and to some extent RNA 3D tools. The RNA Puzzles community of RNA structural bioinformaticians59 and trinucleotide fragments60. 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.2) Unifying the libraries of RNA/DNA dinucleotide fragments based on the analysis of experimental structures61, RNAComposer62 MMB57, FARNA/FARFAR63, Web-Beagle64.3) Integrating and benchmarking methods dealing with RNA structures, e.g. SimRNA65, COOT66, Phenix67, PDB-REDO68) to encourage consistent handling of NAs.4) Strengthening the collaboration with developers of the main experimental nucleic acid structure determination software tools . 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. Workflows for modeling protein conformational flexibility and for modelling protein assemblies in the context of Cryo-EM structure determination will be designed in collaboration with respectively, BioExcel and Instruct-ERIC. The development of improved tools for validation of nucleic acid models in Activity IV feeds directly into this platform and all new methods will be registered with BioTools.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 theTeSS 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  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.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 theattended 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 theMeet-U initiative. This is an innovative pedagogical initiative created in 2016 that teaches MSc students by involving them in real world research projects, with results evaluated by experts in the field during a final scientific symposium. Meet-U is currently implemented as a collaborative course between three universities of Paris/France area: Sorbonne Université (SU/UPMC), Universités Paris-Sud/Paris-Saclay, and Université Paris-Diderot. It is proposed to implement an international version of Meet-U, involving universities linked to ELIXIR nodes across Europe.Building on the experience of the CAPRI community in organizing the well-There has also been some support from the ELIXIR UK node to develop training workflows in protein structure prediction and variant impact analysis. This has enabled pilot work and the establishment of small-scale training workflows in the ELIXIR TESS registry. For all activities, we will organise training workshops for trainers and users, and add workflows to TeSS where appropriate.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 , 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. The action plan of this community includes the development of new strains with designed metabolic pathways and expression systems. The synergy of the expertise in the 3D-Bioinfo and Synthetic Biology communities will therefore be very important to implement the new biotech applications that can be expected to be generated through the acquired knowledge and expertise generated by the 3D-Bioinfo ELIXIR community.The tools and services offered by the proposed 3D-Bioinfo Community already have many millions of users per year and the new Activities, described above, will undoubtedly broaden this scope.As regards potential overlap with Instruct-ERIC, the 3D-Bioinfo community develops tools that go beyond the scope of structural biology and are not covered by the Instruct-ERIC initiatives and efforts. We will work closely with Instruct-ERIC on common areas of interest  and seek support for joint implementations studies.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 structure-based 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 . 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. We also expect to build up links to the pharmaceutical Industry via the collaborations with BioExcel, and Instruct-ERIC. Future 3D-Bioinfo Activities around protein and enzyme engineering are expected to provide strong links with the industry and it is anticipated that these links will very much strengthen the competitiveness of the developing European biotechnology SMEs.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. This will publicise our activities to the wider bioinformatics community, enable us to recruit additional European participants and promote links with other international initiatives.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 , agriculture, and sustainable production methods. Finally, 3D-Bioinfo will facilitate collaborations worldwide and promote European research in structural bioinformatics, on a global scale.No data are associated with this article. The manuscript of Orengo et al presents the goals of the Elixir initiative in the context of historical achievements in the field of 3D Bioinformatics.  It is a conceptual piece to provide guidance to an entire field and therefore I would reflect on that. ‘Computational approaches in biology’ always suffered from an identity problem and I believe this manuscript does not help to resolve it and as such it might be a missed opportunity. One major issue is if one approaches computational structural biology as a discipline or a technology. I believe Elixir efforts all about boosting the potential of this discipline but the scope of this effort is still presented rather as a technology that can support other disciplines.   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.e to “organize, analyze an manipulate data”.  And do not make the argument that “modeling”, i.e. gaining new, testable hypothesis and generating new knowledge is also part of this field . NIH made an attempt to distinguish between “bioinformatics” and “computational biology”. If I try paraphrase it in a 3D context, 3D-Bioinfirmatics is: research, development, or application of computational tools and approaches for expanding the use of 3D biological data, including those to acquire, store, organize, archive, analyze, or visualize such data. In contrast to 3D-Computational Biology of biomolecular structures, which is the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of macromolecular structures.    This review so far was concerned with the first sentence of the Abstract only. The rest of the paper does touch on a number of innovative, activities that generate new knowledge, i.e. a discipline like activity, but at least to this reviewer this is not formulated in the framework enough.Is the topic of the opinion article discussed accurately in the context of the current literature?YesAre arguments sufficiently supported by evidence from the published literature?YesAre all factual statements correct and adequately supported by citations?YesAre the conclusions drawn balanced and justified on the basis of the presented arguments?YesReviewer Expertise:computational structural biologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. 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  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. Perhaps this section could be shortened, or it could briefly mention parallel developments in other parts of the world. For instance, Helen Berman’s efforts in transforming and remediating the PDB into a modern bioinformatics infrastructure resource at Rutgers University starting in 1998 and the establishment of the wwPDB in 2003 with PDBe and PDBj deserve some comment. The second section briefly describes “Major challenges that 3D-Bioinfo will help to address” including proteome-wide protein structure prediction, establishing benchmarks and confidence assessments for structure prediction accuracy, evaluating the impact of sequence variants on protein function and disease, improvements in structure determination by cryo-EM, SAXS, integrative modeling, and other methods that depend on the appropriate use of high-resolution structural data from crystallography, and the prediction of protein-ligand interactions. 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 . One challenge that was left out is the validation of experimental structures at the atom/residue level , 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. These are all very worthy, if rather broad and general goals. The next section on “Links between 3D-Bioinfo and the wider European research environment” reads more like a grant progress report and is probably of little interest to the general reader, especially the section on the launch meeting, which could be deleted. This section mentions collaboration with the Protein Structure Initiative (PSI) by some of the current 3D-Bioinfo participants, but the PSI was disbanded in 2015 and this reference should be removed. This section mentions Instruct-ERIC, and it would be helpful to describe what are the similarities and differences in the goals of 3D-Bioinfo and Instruct-ERIC, which may not be that familiar to non-European structural biologists. Instruct-ERIC is more focused on experimental technologies. Finally, we get to the meat of the matter – the four “Activity” areas: I. Infrastructure for FAIR structural and functional annotations; II. Open resources for sharing, integrating and benchmarking software tools for modelling the proteome in 3D; III. Protein-ligand interactions; IV: Tools to describe, analyze, annotate, and predict nucleic acid structures. (Full disclosure: I am a participant in Activity II). The first activity (I) is to improve annotations of protein structures, in particular to bring 3rd party annotation resources into the PDBe-KB (knowledge-base) via a standard data exchange format . These will include functional site annotations, predicted structures, and impact of disease-associated variants on structure. This is appealing to me, since my group generates these kinds of annotations, for example on antibodies, kinases, and protein-protein interactions. Access to these annotations via the PDB would greatly increase the impact of such resources and the depth of annotation of PDB structures. One example that is already in practice is the import of QSBIO and 3Dcomplex annotations for protein assemblies of X-ray structures in the PDB. Activity II is focused on integrating and benchmarking software for modeling proteins in 3D, including in particular modeling or annotating protein assemblies and conformational flexibility. This aspect of 3D-Bioinfo will include integration of CAMEO and CAPRI, both of which assess structure predictions of proteins or protein complexes, into OpenEBench, which is also part of ELIXIR. It will also involve developing benchmarks for the prediction of the structures of protein assemblies  and providing a portal for community-derived benchmarks. While there are many such benchmarks, they are not fully integrated into one resource which will likely be productive in the way that CAMEO, CAPRI, and CASP have been. This section could use a more succinct statement of what is missing in existing benchmarks and what 3D-Bioinfo would do to fill the gaps. Activity III is focused on developing and disseminating benchmarks  for modeling protein-ligand complexes, including peptides and macrocyclic compounds, and their affinities. The inclusion of experimentally determined non-active compounds (for the construction of negative data sets) is an important suggestion in the paper. Another goal is to “open the world of structure-based modeling to the broader science community.” Worthy, but easier said than done, and it is not clear what this would look like exactly. Activity IV is focused on refinement of experimental structures, structural bioinformatics, structure prediction, and benchmarking studies of nucleic acid structures, which, as the authors point out, lag behind comparable efforts on proteins. As with the other Activities, the efforts will  be on establishing benchmarks, collecting software tools, and enhancing interoperability. The authors explicitly propose bringing additional research groups outside of 3D-Bioinfo into Activity IV, presumably including those outside of Europe, which would greatly strengthen all of the activities of 3D-Bioinfo. The paper concludes with a discussion of further interactions of 3D-Bioinfo with other research communities including: (1) ELIXIR “platforms” on Interoperability, Data, Tools, Compute, and Training; (2) European bioinformatics infrastructure initiatives such as those on cheminformatics, systems biology, and intrinsically disordered proteins; (3) connections with industry; and (4) global initiatives. The last of these is given rather short shrift, considering that the efforts of a European 3D-Bioinfo project will not succeed if they don’t include input and adoption by research groups worldwide. In particular, it is surprising not to see any discussion of interactions with the RCSB or PDBj, which are after all components of the World-Wide PDB (wwPDB), along with PDBe.Figure 2: typo, “Uniport” --> “Uniprot”p. 14: “EMBO courses on Integrative of Biomolecular Complexes” --> “EMBO courses on Integrative Modeling of Biomolecular Complexes” Very minor:Is the topic of the opinion article discussed accurately in the context of the current literature?YesAre arguments sufficiently supported by evidence from the published literature?YesAre all factual statements correct and adequately supported by citations?YesAre the conclusions drawn balanced and justified on the basis of the presented arguments?YesReviewer Expertise:Structural bioinformatics and protein structure predictionI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. The manuscript by Orengo et al. describes the proposed activities of the ELIXIR 3D-Bioinfo user community. This is a useful and necessary contribution to describe the scope and plans of a new ELIXIR user community. The introduction provides a recapitulation of the uses and relevance of structural bioinformatics in different contexts. As the authors note, this is a mature field where several key data resources and initiatives alredy exist. The manuscript reads well and tries to make convincing arguments for the proposed activities.Specific comments:From the scientific point of view:Limits of structural data. Structural data is very important for many purposes, and the authors make this point clearly. However, it has also limits which should be spelled out clearly to avoid the impression of being a panacea. One limit is the availability of experimental data, with some molecules defying structural characterization. A more specific limit is also related to intrinsically disordered proteins (IDPs). This is a different (sub-)field which is not covered by 3D-Bioinfo and has spawned a separate ELIXIR user community with different priorities dictated by the lack of structural data.Conformational variability. This important aspect is mentioned several times and forms a continuum between fully rigid proteins on one end and IDPs at the other. It is important to clarify this concept in the manuscript. By doing so it also offers areas for further collaboration in ELIXIR across user communities.Proteomics Standards Initiative (PSI). Part of the Human Proteomics Organization (HUPO), it should be referred to as HUPO-PSI. This is the main standardization body in the field and an obvious choice for promoting the standards 3D-Bioinfo aims to establish. As such, it should be introduced in the manuscript and its implications discussed. Of note, at present there is no HUPO-PSI working group in the areas covered by 3D-Bioinfo.Activity 1. The PDBe-KB part is well described and convincing. There is clear potential for synergies on representing IDP data.Activity 2. The dscription appears somewhat repetitive and its focus may be improved. OpenEBench is a good idea for CAMEO and CARPI, but how can integration be optimally achieved? The comformational modeling portal part is also a bit unclear, especially in light of the overlap with IDPs. A suggestion would be to highlight this as a way to foster collaboration with the IDP user community.Activity 3. The relevance of protein-ligand interactions is out of question. Since this field is of such pharmaceutical relevance, it is also quite mature. It is however not immediately clear what novel benefit 3D-Bioinfo can provide in terms of instrastructure beyond coordination.Activity 4. This is scientifically perhaps the most "novel" part of the proposed activities, as nucleic acid structures have traditionally been a minority in the field. The description seems more typical of a COST Action setup though and the link to ELIXIR should be improved. E.g. how can the ELIXIR services help to achieve the goals of this activity?Future 3D-Bioinfo activities. This section appears somewhat redundant. In the Green Chemistry example it is unclear how this relates to ELIXIR.ISCB. Given the prominent role of at least one main author in the ISCB leadership, this reviewer was expecting a stronger commitment regarding synergies with the society and its COSIs (most notably 3D-SIG). The goals clearly overlap and can benefit from each other.From the ELIXIR point of view:Platforms. The description of interactions with ELIXIR platforms appears to denote a somewhat cursory knowledge of their activities. The interaction with OpenEBench is well described and convincing. The Data platform description is good for PBDe-KB but after this does not appear related to platform activities. The Compute platform description is also unclear. In all cases, the question is: how will 3D-Bioinfo make convincing use of platform services?Other user communities. A more thorough description of synergies with existing ELIXIR user communites would be welcome. This should also be separated from non-ELIXIR initiatives . Of note, the IDP user community is already established and not proposed. Likewise, "Synthetic Biology" should probably be Microbial Biotechnology. The Proteomics community is an easy connection but missing. Other communities, e.g. Plants, may also be relevant. In general, it would be good to have specific items for collaboration listed.Minor points:The manuscript is somewhat redundant and a bit lengthy at 16 pages without references. Streamlining it would improve focus and can contribute to mitigate some of the concerns above.The text refers extensively to "Europe" and "European" resources, ca. 60 times throughout. While this is a truism for ELIXIR, it sometimes feels just a bit too much emphasis in its present form.Figure 1 contains data for the PDB up to the year 2016 (a), 2017 (b) and 2018 (c). It should be updated and unified with data until 2019.When listing COST Actions , a unified format should be used. I.e. either list all titles or none.Is the topic of the opinion article discussed accurately in the context of the current literature?YesAre arguments sufficiently supported by evidence from the published literature?YesAre all factual statements correct and adequately supported by citations?YesAre the conclusions drawn balanced and justified on the basis of the presented arguments?PartlyReviewer Expertise:bioinformatics, computational biology, structural bioinformatics, intrinsically disordered proteins, databases, tools, infrastructureI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Summary The authors describe the main goals and provide a road-map of the 3D-Bioinfo community, a new Elixir community. A first part states the major contributions and the leading role of many European teams and initiatives in the structural bioinformatics field. Then, the current limitations and challenges faced by this field are discussed, as well as current European initiatives to address them. The core of the paper is the detailed presentation of the goals of the 3D-Bioinfo community, subdivided in activities (or topics) and few main tasks per activity. Details on the implementation of those tasks and on their interconnections are also provided, as well as their complementarity with other European initiatives .Major comment In the paragraph “Develop a knowledge portal to user-friendly bioinformatics and computational tools for modeling conformational flexibility of proteins”, it is surprising to see no mention of any task to define common standards and formats in the description of proteins flexibility.Minor comments “However, many challenges still exist. It remains computationally expensive to build 3D models on a proteome-wide scale” => Yet this aim is being addressed by the SWISS-MODEL initiative. This assertion should be justified. “Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design.” => As I understand it, those educational and training efforts are targeting non-informatician structural biologists, and the idea would be to provide them enough insights in 3D-bioinfo for them to foresee possible applications on their system, and apply tools or seek collaboration in 3D-bioinfo. If the training efforts target young 3D-bioinformaticians, then the link with facilitating applications is unclear. The audience could be mentioned here, for clarity. “Structural bioinformatics tools link sequence and structure data to predict protein functional sites” => This sounds like a (restrictive) definition of Structural bioinformatics tools. “[Some] structural bioinformatics tools “ would be more correct. “As for protein structure prediction, integration of data on sites predicted by different methods will increase both coverage and accuracy.” => This assumption could be softened , as the success of this integration is expected but not certain. “This enables the design of new experiments to study the function of macromolecules as well as rational design of proteins and drugs, to modify their function and properties.” “European structure-based tools have facilitated enzyme reaction mechanism studies by chemists and biochemists.“ => some references would be welcome here “The first workshop of the Activity IV is going to take place in May 2020” => This should be updated “The above mentioned themes will not only form the initial focus for 3D-Bioinfo but the steering committee will actively monitor the emergence of new technologies and/or new research fields relevant for bioinformatics approaches, which then can be fostered further as new activities. “ => why “will not only form the initial focus”, if the other focuses are supposed to emerge in the future?Cosmetic comments  = to add ; { } = to removeThese are small typo or missing comas that are pretty harmless individually, but some can be misleading. And summed up, they impair a fluid reading of this already quite dense paper. “The technological developments in MX in the previous decade, largely catalysed by the structural genomics initiatives and the on-going revolution in the field of cryo-EM” “some of the most critical contributions to building protein 3D models from structural templates of homologous proteins{,} happened in Europe in the 1990s” “Importantly European groups have made major contributions to initiatives “ “In particular, the RNA-Puzzles experiment for evaluation of RNA structure prediction methods, and a series of associated workshops have been introduced in Europe,” “ ] for assessment of the prediction “ “Below we detail{,} the specific thematic aims of each Activity” “We will bring together the community experts to define data standards for different types of annotations  and integration of these annotations using a community-driven data exchange format will facilitate finding,” “Extend the content of OpenEBench by adding {a} knowledge base integrating software tools for modeling the 3D struc-ture of proteins, protein complexes and assemblies.” “methods for generating scoring, and ranking models of protein complexes” “Benchmarking studies performed so far [-] including those carried out in the CSAR52 or D3R Grand Challenge53 (...) - rely on datasets of limited size and diversity (…).” “Developing benchmarks dataset[s] / Developing [a] benchmarks dataset” “In order to foster new developments focused courses/workshops will be organised on such topics” “ exploit ELIXIR{s} platforms “Is the topic of the opinion article discussed accurately in the context of the current literature?YesAre arguments sufficiently supported by evidence from the published literature?YesAre all factual statements correct and adequately supported by citations?YesAre the conclusions drawn balanced and justified on the basis of the presented arguments?YesReviewer Expertise:Computational molecular modeling, structural bioinformaticsWe confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


This minireview provides an overview on the current knowledge of protein–protein interactions, common characterisation methods to characterise them, and their role in protein complex formation with some examples. A deep understanding of protein–protein interactions and their molecular interactions is important for a number of applications, including drug design. Protein–protein interactions and their discovery are thus an interesting avenue for understanding how protein complexes, which make up the majority of proteins, work. From signalling over transport to catalysis, the broad functionality of proteins is essential in the cellular machinery. To this effect, proteins can be seen as the workforce of the cell. Proteins relay some of their functionality via interactions between protein nodes called protein–protein interactions (PPIs). Hedin characterised the first PPI with trypsin and antitrypsin in 1906 , proteas, any particular protein is present amongst a plethora of proteins and other biological molecules, all of which have their own biological role and chemical interactions. Characterisation methods that can be employed to study PPIs can be divided into four categories: atomic resolution methods, mass spectrometry methods, biophysical methods, and computational methods. These methods are summarised in Particular PPIs can be relatively difficult to study since in vivoBacillus subtilis 50S subunit-nascent chain-tRNA complex, EMD-4799, no available PDB [Atomic resolution methods focus on the structure determination of protein complexes and elucidating the PPIs at a near-atomic resolution. Such methods include X-ray crystallography , nuclearable PDB ) were soable PDB ) structuable PDB ) were obable PDB .As another category, mass spectrometry methods are used to determine the amino acid sequences at the PPIs site. Examples include protein painting  and chemKd, can be calculated [Kb, the reaction stoichiometry (n), the observed molar calorimetric enthalpy ΔHobs, the entropy ΔS, the heat capacity of the binding ΔCp,obs, and the change in the Gibbs free energy ΔG. As a result, ITC is used to provide a complete thermodynamic characterisation of the binding reaction.Biophysical methods are used to characterise the protein complex and to confirm the presence of certain interactions. Biophysical approaches are important to determine binding affinities, enthalpy changes, entropy changes, and the on- and off-rates of binding, amongst others. Examples include surface plasmon resonance (SPR) spectroscopy –28, gel lculated . AnotherComputational methods are used to predict PPIs and interfaces. The advantage of performing in silico experiments includes narrowing down the number of the binding partners to be tested in vitro or in vivo. Computational methods include supervised machine learning, where models are predicted from positive and negative training datasets , and staIt is important to note that a full characteristic profile of PPIs can only be achieved when the aforementioned methodologies are used in conjunction with each other. For example, computational methodologies can be the initial starting point to predict and study PPIs, thereby reducing the project laboratory workforce and costs. The validation of PPIs can then be determined by using HDXMS together with cryoEM. The full thermodynamic profile can subsequently be determined using biophysical methods, yielding the full picture of a particular PPI. For instance, Cash et al. used cryo-EM complemented with HDXMS and enzymatic assays to fully resolve the structure of the P-Rex1 IP4P domain . AnotherThere are different forces and mechanisms that affect PPIs and their formation, including hydrogen bonding –43, van PPIs have to be specific enough for a particular protein to be able to recognize and interact with another partner protein among hundreds or thousands of other biomolecules . ConsequKa shift of acidic or basic amino acids present at the complex interface. Due to this shift, the interface amino acids experience either a significant desolvation energy, where there is a disruption in the residue charge–water interaction, resulting in water exclusion, and thus a hydrophobic effect [After binding occurs, ionisation changes can be induced in the protein molecules due to proton uptake and release. Therefore, these events are strongly affected by the pH value and the ionic strength. Variation of the pH value or the ionic strength can result in substantial binding free energy changes  or changc effect , or an ic effect . In a sac effect  and is cc effect .Kd against the buried surface area from the complex formation. The smallest complex studied was the transthyretin complex (2ROY), burying 381 Å2, whereas the largest complex, the SidM/DrrA-Rab1 complex (2WWX) has 3393 Å2 buried. They indicated that there is a trend between the buried interfacial surface area and the binding energy, where increasing the surface area increases the binding energy and thus Kd. The authors also observed that the buried surface area has a high level of hydrophobicity, an average of 60% across all complexes studied. They also observed that there is no direct relationship between hydrophobic, aliphatic, polar charged, and polar uncharged, i.e., the nature of the residues on the surface area buried. On analysing the free energy per unit surface area buried, they show that the surface energy density is greater for smaller complexes (less than 2000 Å2 buried) than for larger ones (above 2000 Å2 buried). This is particularly important as, for smaller complexes, the interface has a high energetic contribution. Subsequently, any changes (i.e. mutations) in the interface residues have a higher energy contribution in smaller complexes than in larger complexes [Chen et al. analysed the structural and thermodynamic data of 113 heterodimeric complexes and discussed the correlation between binding affinity and amount of surface area buried at the interface . The automplexes .Complexes can be classified as either obligate, where the monomers are either unstable or/and non-functional when isolated or in solution, or non-obligate, where the monomers are stable. In obligate complexes, the interface is characterised by hydrophobic and aromatic residues while in non-obligate, the residues are more polar and charged, with the interface area being smaller  and contThe majority of proteins are found in complexes, with most of the complexes being homooligomeric complexes . In factJayashree et al.  analysedn, where the ‘a’ and ‘d’ positions are hydrophobic residues (with leucine being in the d position most of the times). These residues interact to form the coiled coil structure. Different residues at the ‘a’ and ‘d’ positions give rise to different oligomeric states, with the yeast transcription factor GCN4 being a perfect example of this. GCN4 has isoleucine as ‘a’ and leucine as ‘d’ and forms a dimer (Examples of homooligomeric complex formation include domain swapping and the formation of leucine zippers. In domain swapping, a region from the monomer interacts with the adjacent protein partner, forming the protein–protein interface. Most of the times, the interacting region is present on either the N- or C-terminus, although any part of the structure can produce this interaction . An exam a dimer . When isPPIs are responsible for the assembly of large protein complexes, such as capsid proteins in viruses and protein containers. This section will give a brief overview on how such interactions can give rise to these complex molecules.KNA, is dependent on the cooperativity coefficient ω, which is based on the protein–protein association constant. A ω value of 1 represents no cooperativity, whereas as a value greater than 1000 represents a high cooperativity, with the assembly occurring in two steps. CCMV shows a low cooperativity, and thus assembles gradually [The infectivity in viruses is dependent on the correct assembly of viral capsids and surface proteins. A virus is made up of a genome enclosed by a number of copies of proteins to form a shell known as a capsid . Some viradually . In contradually . These rradually  or can aradually . CCMV waradually . The CCMradually . In thesradually . Here, tradually .The advent of cryo-EM technology has enabled the characterisation of more virus structures at a higher resolution. An example includes the tobacco mosaic virus, for which the structure has been characterised by both X-ray crystallography and cryo-EM. In fact, herein, the resolution obtained by cryo-EM (1.9 Å, PDB: 6SAE, EMD-10129 Some non-viral proteins spontaneously self-assemble to form capsid structures . ExampleThe discovery of PPIs and their mechanisms is an important avenue for understanding how protein–protein complexes function within the cell. In this minireview, we outlined some of the common characterisation methods employed to detect PPIs and gave a brief account on the shortcomings. Understanding the limitations provides a clearer approach on how to use different methodologies in unison to study and gain a better comprehension of PPIs. This review also examined the current knowledge of the molecular interactions and mechanisms that govern PPIs and their role in protein–protein complexes. We provided an outline on the different complexes known and how they are assembled. A clear perspective and a deeper understanding of these mechanisms and the roles of the complexes is fundamental in proteomics, and the identification of PPIs has the potential to confer new drug targets for diseases, such as cancer. As a result, research involving PPIs is imperative for our biological knowledge and future aspects in medicine.


The maintenance of protein structural stability requires the cooperativity among spatially neighboring residues. Previous studies have shown that conserved residues tend to occur clustered together within enzyme active sites and protein-protein/DNA interfaces. It is possible that conserved residues form one or more local clusters in protein tertiary structures as it can facilitate the formation of functional motifs. In this work, we systematically investigate the spatial distributions of conserved residues as well as hot spot ones within protein-RNA interfaces.The analysis of 191 polypeptide chains from 160 complexes shows the polypeptides interacting with tRNAs evolve relatively rapidly. A statistical analysis of residues in different regions shows that the interface residues are often more conserved, while the most conserved ones are those occurring at protein interiors which maintain the stability of folded polypeptide chains. Additionally, we found that 77.8% of the interfaces have the conserved residues clustered within the entire interface regions. Appling the clustering characteristics to the identification of the real interface, there are 31.1% of cases where the real interfaces are ranked in top 10% of 1000 randomly generated surface patches. In the conserved clusters, the preferred residues are the hydrophobic , aromatic  and interestingly only one positively charged Arg residues. For the hot spot residues, 51.5% of them are situated in the conserved residue clusters, and they are largely consistent with the preferred residue types in the conserved clusters.The protein-RNA interface residues are often more conserved than non-interface surface ones. The conserved interface residues occur more spatially clustered relative to the entire interface residues. The high consistence of hot spot residue types and the preferred residue types in the conserved clusters has important implications for the experimental alanine scanning mutagenesis study. This work deepens the understanding of the residual organization at protein-RNA interface and is of potential applications in the identification of binding site and hot spot residues. Protein-RNA interactions play important roles in a wide variety of cellular processes, such as regulation of gene expression, RNA splicing, protein synthesis and virus assembly , 2. ProtHow are these conserved residues organized at protein-RNA interfaces? Are they scattered across the interface, or clustered together in three dimensions? How are the preferences of residue types and hot spot residues (contributing significantly to the binding free energy) for different interface regions? Whether can these characteristics be used to identify the real interface? These questions are not quite clear currently.The previous study has shown that for protein-protein interactions, 96.7 and 86.7% of the interfaces of homodimers and heterocomplexes respectively have the conserved residues clustered within the overall interface regions [The higher packing density of the conserved residues within the interface may suggest the cooperativity between them in the cluster. It is likely that the most stabilizing residues or putative hot spot residues are those that occur as clusters of conserved residues, contributing more to the stability and function of interactions than others, which has been confirmed in protein-DNA interfaces by Ahmad et al. . LandgraIn this work, we investigate the spatial distribution characteristics and amino acid composition of the evolutionary conserved residues within protein-RNA interfaces, and also explore the relationship between interface hot spots and the conserved residue clusters. The results show that the conserved residues are not randomly distributed within the interface, but are obviously clustered together, which can be used to identify the real protein-RNA interfaces. Furthermore, the identification of these clusters will be a useful guide for mutagenesis studies to determine the appropriate hot spot regions.A total of 1031 protein-RNA complexes were extracted from the Protein Data Bank (PDB), which were solved by X-ray diffraction with resolution better than 3.0 Å   were carried out by ClustalW , 13 agaiFor the aligned sequences, we removed the sequences with sequence identity less than 45% or missing residues more than five. A protein that has more than five homologous sequences retained was put into the dataset. Eventually, we constructed a dataset of 160 protein-RNA complexes (183 interfaces) involving 191 polypeptide chains. According to the molecular functions, the dataset is divided into five different classes: protein-mRNA (8), protein-tRNA (53), protein-rRNA (23), protein-viral RNA (7) and protein-other RNA (69) complexes (X-Ala), where X is the concerned residue. The interface residues of proteins are those that lose more than 0.1 Å2 of SASA upon complexation with RNA. And those residues that are not the interface and the surface ones are protein interior residues [The non-interface surface residues are defined as those having relative solvent accessible surface area  > 5% inresidues .s) in a set of homologous protein sequences [pi(k) is the probability that a residue of type k occurs at the ith position in the sequence alignment. The lower value in sequence entropy of a position hints that it has suffered a higher evolutionary pressure.The protein sequence conservations at each interior, interface and non-interface surface residue position are calculated as the Shannon entropy , an average value of sequence entropy is computed:For each interface (owning s > int) of the interface where it occurs, (2) lower than half of the average value (<s > int/2), and (3) equal to 0.0, namely, the fully conserved residues, respectively.Three criteria are utilized to define the conserved interface residues, which have different stringent levels. Here the purpose of using different criteria is to see that with the decrease of the number of interface conserved residues, what changes occur to the clustering property of conserved residues within the interface? The conserved interface residue is defined as that with sequence entropy value (1) lower than the average value  the evolutionary conserved residues are clustered within the interface. The conserved interface residues are clustered, then ρ > 1.0. Here, we remove the interfaces that own an isolated conserved residue when measuring the size of the conserved residue cluster.For each interface, we define a ratio We found the conserved residues are spatially clustered together, rather than scattered in the structure. And within the entire interface, the conserved residues may constitute be consist of one or more sub-clusters. The average linkage method  is used We utilize three methods to generate surface patches. From method 1 to 3, the generated surface patch in a protein is more and more close to its own interface in size. Method 1: for all the proteins with their partner RNAs removed, NACCESS is conducted on their atomic coordinates and surface residues are identified based on the same definition mentioned above. In generating a surface patch process, we take a random surface residue (represented by its Ca atom) and then choose all the surface residues that are less than a fixed radius away from the taken residue as belonging to the surface patch with the taken residue as the center. Between any two atoms of all the interfaces, the mean maximum distance is 40 Å, and thus we use 20 Å- half of 40 Å to produce surface patches. Method 2: for each protein we use its own cutoff, rather than a uniform one, according to its interface size. Method 3: surface neighbors meeting two criteria the distance cutoff, and a vector constraint are selected around the randomly selected central residue . The vecMs values  to 1.36  in the entire dataset, which suggests that some RNA binding proteins (RBP) evolve rapidly compared to others. From the average values of <s > in different types of complexes, the protein chains binding with tRNAs have a largest average value (0.90) compared with other types of protein chains, indicating that the polypeptides interacting with tRNAs evolve relatively rapid, which is consistent with previous work [s > = 1.00), while chain B, participating in the main interaction with tRNA, experiences a higher evolutionary pressure (<s > = 0.68).To explore the difference in evolutionary conservation of polypeptide chains with difference functions, we calculated the average sequence entropy of each chain  and its distribution for the five function classes of protein-RNA complexes, as shown in Fig. ous work . Additios > of the residues presented in protein interiors and solvent exposed surfaces are equal to 0.34 (the lowest) and 1.05 (the highest) respectively, and the value in between - 0.64 corresponds to the residues at protein-RNA interfaces. The distribution data in Fig. We concern whether different parts of proteins experience different sequence evolutionary pressures. In order to detect this point, we calculated the average sequence entropies of the interior, interface and non-interface surface residues for different types of protein-RNA complexes. The results are shown in Table Additionally, it should be pointed out that the residues in the three regions of the protein chains interacting with tRNAs possess significantly higher entropies than those in corresponding regions of the other four types of complexes Table , which is>: 1.34 and 1.41, relatively) are more frequently mutated compared with others. As for interiors, the hydrophobic amino acids Trp, Val, Leu and Ile  and charged Asp, Lys and Arg  are most conserved. Hydrophobic amino acids are prone to occurring inside proteins [Next we want to know the relative conservations of 20 types of amino acid residues at protein interiors, interfaces and solvent exposed surfaces. We calculated the average sequence entropies of 20 types of amino acid residues in the three regions for all complexes, and the results are shown in Fig. proteins  while chMs  and all the interface residues , and their ratio ρ  and s = 0.0), respectively, which indicates that for most of the interfaces Ms,cons is greater than Ms,int with p-value 0.0311, 0.0044 and 0.0427. For all the protein-RNA complexes, there are 77.8% (140/180), 78.5% (135/172) and 76.0% (114/150) of interfaces where a ρ value of greater than 1.0 is obtained  except for two which possess three or more sub-clusters have the areas > 3000 Å2. Thus, most of the protein-RNA interfaces own one single cluster of conserved residues, and roughly, the larger interfaces often form multiple detached sub-clusters. Three representative cases whose interfaces own respectively one, two and three sub-clusters of conserved residues are shown in The maintenance of the stability of biological systems requires synergy among different functional units. Then we concern whether multiple conserved residue sub-clusters form at an interface. We calculated the number of sub-clusters composed of the conserved interface residues for each interface with the average linkage method. The distribution of the numbers of conserved residue sub-clusters in interfaces as a function of the interface area is displayed in The conserved residues can occur alone, or organize into multiple sub-clusters containing different numbers of conserved residues. We analyzed the distribution of the sub-cluster sizes  for the 204 different sub-clusters in all interfaces, and the result is shown in Ex which defines a probability of type X of the 20 amino acid residue types occurring in the conserved interface subsets compared to the whole interfaces.Certain types of amino acid residues may have propensities to occur in the conserved residue clusters. Here, the propensity is evaluated using the relative enrichment The result of preferences of amino acids in conserved residue clusters is given in Fig. ρ value of the interface region with those of the randomly generated surface patches for each protein. For each protein, 1000 random surface patches were produced using the method described in Materials and Methods. We ranked the real interface and the 1000 random surface patches in descending order of ρ for all proteins. A ranking of the real interface relative to the 1000 random surface patches was then calculated , and the results are shown in Fig. To what extent can the clustering property of conserved residues be used to distinguish a real interface from the random surface patches? We compared ρ can distinguish the real interface region from all random surface patches. The similar ranking results are obtained by the three approaches of producing random surface patches. Out of 180 interfaces, there are 56 (31.1%), 46 (25.6%) and 33 (18.3%) interfaces that are ranked in the top 10%  among all generated random surface patches by method 1 to 3, respectively. Therefore, to some extent, we can apply the clustering characteristics of interface conserved residues to distinguish the real interface from random surface patches.Fig. ρ value to identify the real interface, because this method is closest to the actual condition when predicting the real interface . Fig. Taking the SXL-UNR translation regulatory complex (PDB code: 4qqb) for example, we used the random surface patches generated by method 1 to measure the ability of The analysis on the clustering of the conserved residues at the interface is instructive in identifying functionally important regions, because it is likely that hot spot residues are located in such clusters. For the 139 experimental alanine scanning mutagenesis data (involved in 41 protein-RNA complexes), we categorized them according to the seven amino acid classes, and the result shows they are distributed in all amino acid classes (Table From ρ) to distinguish the real interface from random surface regions, (4) exploring the extent of hot spot residues occurring in conserved residue clusters.This work mainly investigates the clustering extent of the conserved residues within protein-RNA interfaces. Four questions here are discussed: (1) probing evolutionary conservations of polypeptide sequences, (2) evaluating the clustering degree of the conserved residues within the interface, (3) analyzing the feasibility of using clustering degree  for the n random surface patches in a protein and ρint is the ρ value for the real interface. For all the complexes, about 42.8% (77/180) of interfaces have conserved residues significantly more clustered compared with those present within surface patches . For protein-protein complexes, previous study shows that this value is 40% (49/121) and 38% (148/389) for the homodimers and heterocomplexes, respectively [The clustering property of interface conserved residues can be utilized to distinguish the real interface from the random surface patches. In our result, 31% of the real interface regions are ranked in the top 10% of all random surface regions. Here we use the Z test to investigate whether it is of statistical significance that the clustering degree of the conserved residues within the real interface is relatively higher than that of the conserved ones in random surface region.For the hot spot residues , 51.5% of them are localized in the conserved residue clusters, and they are largely consistent with the preferred residue types in the conserved clusters, which indicates there exists the overlap to some extent between the conserved cluster and hot spot region, and the preferred residues can be used as targets for drug design and reference sites for experimental scanning mutagenesis studies. Now, the alanine scanning mutagenesis data are relatively limited, and with its increase, the further analysis can be performed and important findings may be achieved.Additional file 1: Table S1. Dataset of 160 protein-RNA complexes (classified into five different function classes based on the type of RNA associated with the protein). Table S2. Location of experimental hot spots within the conserved residue clusters in protein interfaces. Table S3. Average entropy <s> of 20 types of amino acid residues in interior, interface and non-interface surface regions of protein-RNA complexes. Table S4. Values of the parameters indicating the clustering of conserved residues in individual interfaces. Table S5. Parameters describing the clustering of conserved interface residues in five classes of protein-RNA complexes. Figure S1. Conservation of the amino acid residues in five different protein-RNA complexes. Residue conservation is mapped at the protein surface with the color code provided at the bottom. Red stands for the maximum conservation (lowest <s>), and blue stands for the minimum conservation (highest <s>). The RNA backbone is shown in Stick and colored green. (A) The SXL-UNR translation regulatory complex (PDB code: 4qqb). (B) The prolyl-tRNA synthetase from thermus thermophilus complexed with tRNA (PDB code: 1h4q). (C) The ribosomal protein s8-rRNA complex (PDB code: 1i6u). (D) The bacterial protein-RNA toxin-antitoxin system (PDB code: 4rmo). (E) The human adenosine bound to dsRNA (PDB code: 5ed2). Figure S2. Distribution of the number of conserved interface residue sub-clusters as a function of the interface area in protein-RNA complexes. The x-axis labels mark the origin of the range in each column. Bins are of size 400 Å². Figure S3. Multiple clusters of evolutionary conserved residues in protein interfaces. (A) In the complex of prolyl-tRNA synthetase from thermus thermophilus complexed with tRNA , the interface contains one well-clustered region of conserved residues. (B) In the complex of tRNA synthetase complexed with tRNA , the interface contains two regions of conserved residues. (C) Three conserved clusters in the interface of E. coli leucyl-tRNA synthetase with tRNA . Figures show the protein domains as CPK (green and blue for conserved and other residues), the RNA domains as Stick (yellow). Figure S4. Distribution of sub-cluster size (the number of interface residues in the conserved cluster). Figure S5 Plot of ΔΔG values vs. sequence entropies for the 139 interface residues involved in 41 protein-RNA complexes for which experimental alanine scanning mutagenesis data are available. Figure S6. Percentage distribution of the ρ values for all protein-RNA interfaces.


SMART version 9 contains manually curated models for more than 1300 protein domains, with a topical set of 68 new models added since our last update article (SMART (Simple Modular Architecture Research Tool) is a web resource ( article , Ensembl article , greatly Protein domain databases remain important annotation and research tools. The SMART database  integratSMART was never intended to be exhaustive, and was initially focused on mobile domains, which occur in various contexts while retaining similar function. Nevertheless, it continues to gradually expand its domain coverage with each new release. The current version introduces a topical expansion geared towards a complete coverage of known prokaryotic recombinases, which can be searched together for a report on mobile genetic elements in bacteria and archaebacteria. For this purpose, we bring together a comprehensive set of 68 domains that cover five major recombinase families namely transposase—DDE , casposaThe main underlying protein database in SMART combines the complete Uniprot  with allEukaryota, 4445 Bacteria and 168 Archaea), which is a 2.5-fold increase in both the number of proteins and genomes.In addition to the regular protein database described above, SMART offers a ‘genomic’ analysis mode which contains only proteins from completely sequenced genomes. Synchronized with the current STRING version 11 , it currSince the previous SMART release (version 8), all SMART protein schematics (‘bubblograms’) are generated in vector graphics formats Figure . ProteinVersion 9 brings a new version of the display applet used in the single protein annotation mode. It uses interactive SVG for the visualization and user interaction, offering the widest browser support. Protein schematics can be zoomed in to any level without quality loss, and saved as vector graphics for easy inclusion in user documents or publications.Like the previous versions, the new SMART protein viewer interactively ties various parts of the annotation page. Selecting a predicted domain or other feature in any of the data tables will automatically highlight its position in the protein. Since many predicted features are not directly displayed in the protein schematic (mostly due to overlaps), this function simplifies the visual identification of relations among different protein features.If a more fine-grained evaluation of a protein region is required, the viewer allows interactive selection of various parts of the protein sequence independent of the annotated features, and their submission to BLAST analysis.Detailed information about any detected protein feature can be displayed in streamlined floating popup dialogs, enhancing the user experience and lowering the need to navigate across different web pages. A condensed version of domain annotation pages is included in the dialogs, with optional links to the complete annotation. In addition, several convenience functions are included, allowing users to copy the underlying amino acid sequence to their clipboard, or to submit the feature subsequence for further BLAST analysis.With the update of the underlying protein databases, we have also synchronized our protein interaction data with the version 11 of the STRING database . UpdatedDomain architecture analysis functions in SMART allow users to simply access proteins containing combinations of particular domains. These can be also generated using combinations of GO terms associated to protein domains, and restricted to various taxonomic classes. In addition to the standard SMART protein schematic visualization, these data can also be exported into FASTA files or phylogenetic trees. The phylogenetic tree export has been updated and made compatible with the version 5 of the Interactive Tree of Life (iTOL) , with whThe backend of SMART is a relational database management system (RDBMS), powered by the PostgreSQL engine, which stores the annotation of all SMART domains, protein annotation and sequences, taxonomy information and the pre-calculated protein analyses for the entire Uniprot , Ensemblwww.embl-em.de).SMART data is freely available to academic users through EMBLem (