ELIXIR-EXCELERATE: establishing Europe's data infrastructure for the life science research of the future.

A new inter-governmental research infrastructure, ELIXIR, aims to unify bioinformatics resources and life science data across Europe, thereby facilitating their mining and (re-)use.

Leveraging European infrastructures to access 1 million human genomes by 2022.

Human genomics is undergoing a step change from being a predominantly research-driven activity to one driven through health care as many countries in Europe now have nascent precision medicine programmes. To maximize the value of the genomic data generated, these data will need to be shared between institutions and across countries. In recognition of this challenge, 21 European countries recently signed a declaration to transnationally share data on at least 1 million human genomes by 2022. In this Roadmap, we identify the challenges of data sharing across borders and demonstrate that European research infrastructures are well-positioned to support the rapid implementation of widespread genomic data access.

Author Correction: Leveraging European infrastructures to access 1 million human genomes by 2022.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The COVID-19 Data Portal: accelerating SARS-CoV-2 and COVID-19 research through rapid open access data sharing.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic will be remembered as one of the defining events of the 21st century. The rapid global outbreak has had significant impacts on human society and is already responsible for millions of deaths. Understanding and tackling the impact of the virus has required a worldwide mobilisation and coordination of scientific research. The COVID-19 Data Portal (https://www.covid19dataportal.org/) was first released as part of the European COVID-19 Data Platform, on April 20th 2020 to facilitate rapid and open data sharing and analysis, to accelerate global SARS-CoV-2 and COVID-19 research. The COVID-19 Data Portal has fortnightly feature releases to continue to add new data types, search options, visualisations and improvements based on user feedback and research. The open datasets and intuitive suite of search, identification and download services, represent a truly FAIR (Findable, Accessible, Interoperable and Reusable) resource that enables researchers to easily identify and quickly obtain the key datasets needed for their COVID-19 research.

The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

A quick guide for building a successful bioinformatics community.

"Scientific community" refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding activities; (iv) interactions and engagement with professionals from other fields; and (v) other activities beneficial to individual participants, communities, and the scientific field as a whole. It is thus beneficial at many different levels to understand the general features of successful, high-impact bioinformatics communities; how individual participants can contribute to the success of these communities; and the role of education and training within these communities. We present here a quick guide to building and maintaining a successful, high-impact bioinformatics community, along with an overview of the general benefits of participating in such communities. This article grew out of contributions made by organizers, presenters, panelists, and other participants of the ISMB/ECCB 2013 workshop "The 'How To Guide' for Establishing a Successful Bioinformatics Network" at the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 12th European Conference on Computational Biology (ECCB).

FAIR+E pathogen data for surveillance and research: lessons from COVID-19.

The COVID-19 pandemic has exemplified the importance of interoperable and equitable data sharing for global surveillance and to support research. While many challenges could be overcome, at least in some countries, many hurdles within the organizational, scientific, technical and cultural realms still remain to be tackled to be prepared for future threats. We propose to (i) continue supporting global efforts that have proven to be efficient and trustworthy toward addressing challenges in pathogen molecular data sharing; (ii) establish a distributed network of Pathogen Data Platforms to (a) ensure high quality data, metadata standardization and data analysis, (b) perform data brokering on behalf of data providers both for research and surveillance, (c) foster capacity building and continuous improvements, also for pandemic preparedness; (iii) establish an International One Health Pathogens Portal, connecting pathogen data isolated from various sources (human, animal, food, environment), in a truly One Health approach and following FAIR principles. To address these challenging endeavors, we have started an ELIXIR Focus Group where we invite all interested experts to join in a concerted, expert-driven effort toward sustaining and ensuring high-quality data for global surveillance and research.

Theoretical studies of chemical reactivity of metabolically activated forms of aromatic amines toward DNA.

The metabolism of aromatic and heteroaromatic amines (ArNH2) results in nitrenium ions (ArNH⁺) that modify nucleobases of DNA, primarily deoxyguanosine (dG), by forming dG-C8 adducts. The activated amine nitrogen in ArNH⁺ reacts with the C8 of dG, which gives rise to mutations in DNA. For the most mutagenic ArNH2, including the majority of known genotoxic carcinogens, the stability of ArNH⁺ is of intermediate magnitude. To understand the origin of this observation as well as the specificity of reactions of ArNH⁺ with guanines in DNA, we investigated the chemical reactivity of the metabolically activated forms of ArNH2, that is, ArNHOH and ArNHOAc, toward 9-methylguanine by DFT calculations. The chemical reactivity of these forms is determined by the rate constants of two consecutive reactions leading to cationic guanine intermediates. The formation of ArNH⁺ accelerates with resonance stabilization of ArNH⁺, whereas the formed ArNH⁺ reacts with guanine derivatives with the constant diffusion-limited rate until the reaction slows down when ArNH⁺ is about 20 kcal/mol more stable than PhNH⁺. At this point, ArNHOH and ArNHOAc show maximum reactivity. The lowest activation energy of the reaction of ArNH⁺ with 9-methylguanine corresponds to the charge-transfer π-stacked transition state (π-TS) that leads to the direct formation of the C8 intermediate. The predicted activation barriers of this reaction match the observed absolute rate constants for a number of ArNH⁺. We demonstrate that the mutagenic potency of ArNH2 correlates with the rate of formation and the chemical reactivity of the metabolically activated forms toward the C8 atom of dG. On the basis of geometric consideration of the π-TS complex made of genotoxic compounds with long aromatic systems, we propose that precovalent intercalation in DNA is not an essential step in the genotoxicity pathway of ArNH2. The mechanism-based reasoning suggests rational design strategies to avoid genotoxicity of ArNH2 primarily by preventing N-hydroxylation of ArNH2.

eTRANSAFE: Building a sustainable framework to share reproducible drug safety knowledge with the public domain.

Integrative drug safety research in translational health informatics has rapidly evolved and included data that are drawn in from many resources, combining diverse data that are either reused from (curated) repositories, or newly generated at source. Each resource is mandated by different sets of metadata rules that are imposed on the incoming data. Combination of the data cannot be readily achieved without interference of data stewardship and the top-down policy guidelines that supervise and inform the process for data combination to aid meaningful interpretation and analysis of such data. The eTRANSAFE Consortium's effort to drive integrative drug safety research at a large scale hereby present the lessons learnt and the proposal of solution at the guidelines in practice at this Innovative Medicines Initiative (IMI) project. Recommendations in these guidelines were compiled from feedback received from key stakeholders in regulatory agencies, EFPIA companies, and academic partners. The research reproducibility guidelines presented in this study lay the foundation for a comprehensive data sharing and knowledge management plans accounting for research data management in the drug safety space - FAIR data sharing guidelines, and the model verification guidelines as generic deliverables that best practices that can be reused by other scientific community members at large. FAIR data sharing is a dynamic landscape that rapidly evolves with fast-paced technology advancements. The research reproducibility in drug safety guidelines introduced in this study provides a reusable framework that can be adopted by other research communities that aim to integrate public and private data in biomedical research space.

Strategies to improve in vivo toxicology outcomes for basic candidate drug molecules.

A valid PLS-DA model to predict attrition in pre-clinical toxicology for basic oral candidate drugs was built. A combination of aromatic/aliphatic balance, flatness, charge distribution and size descriptors helped predict the successful progression of compounds through a wide range of toxicity testing. Eighty percent of an independent test set of marketed post-2000 basic drugs could be successfully classified using the model, indicating useful forward predictivity. The themes within this work provide additional guidance for medicinal design chemists and complement other literature property guidelines.

The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives.

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

Comparative binding energy analysis for binding affinity and target selectivity prediction.

A major challenge in drug design is to obtain compounds that bind selectively to their target receptors and do not cause side-effects by binding to other similar receptors. Here, we investigate strategies for applying COMBINE (COMparative BINding Energy) analysis, in conjunction with PIPSA (Protein Interaction Property Similarity Analysis) and ligand docking methods, to address this problem. We evaluate these approaches by application to diverse sets of inhibitors of three structurally related serine proteases of medical relevance: thrombin, trypsin, and urokinase-type plasminogen activator (uPA). We generated target-specific scoring functions (COMBINE models) for the three targets using training sets of ligands with known inhibition constants and structures of their receptor-ligand complexes. These COMBINE models were compared with the PIPSA results and experimental data on receptor selectivity. These scoring functions highlight the ligand-receptor interactions that are particularly important for binding specificity for the different targets. To predict target selectivity in virtual screening, compounds were docked into the three protein binding sites using the program GOLD and the docking solutions were re-ranked with the target-specific scoring functions and computed electrostatic binding free energies. Limits in the accuracy of some of the docking solutions and difficulties in scoring them adversely affected the predictive ability of the target specific scoring functions. Nevertheless, the target-specific scoring functions enabled the selectivity of ligands to thrombin versus trypsin and uPA to be predicted.

Design and synthesis of macrocyclic indoles targeting blood coagulation cascade Factor XIa.

The synthesis of a series of novel macrocyclic compounds designed to target blood coagulation Factor XIa is described. The compounds were evaluated for their inhibition of a small set of serine proteases. Several compounds displayed modest activity and good selectivity for Factor XIa. Within the series, a promising lead structure for developing novel macrocyclic inhibitors of thrombin was identified.

Connecting data, tools and people across Europe: ELIXIR's response to the COVID-19 pandemic.

ELIXIR, the European research infrastructure for life science data, provides open access to data, tools and workflows in the response to the COVID-19 pandemic. ELIXIR's 23 nodes have reacted swiftly to support researchers in their combined efforts against the pandemic setting out three joint priorities: 1. Connecting national COVID-19 data platforms to create federated European COVID-19 Data Spaces; 2. Fostering good data management to make COVID-19 data open, FAIR and reusable over the long term; 3. Providing open tools, workflows and computational resources to drive reproducible and collaborative science. ELIXIR's strategy is based on the support given by our national nodes - collectively spanning over 200 institutes - to research projects and on partnering with community initiatives to drive development and adoption of good data practice and community driven standards. ELIXIR Nodes provide support activities locally and internationally, from provisioning compute capabilities to helping collect viral sequence data from hospitals. Some Nodes have prioritised access to their national cloud and compute facilities for all COVID-19 research projects, while others have developed tools to search, access and share all data related to the pandemic in a national healthcare setting.

Physicochemical property profiles of marketed drugs, clinical candidates and bioactive compounds.

We performed a comparison of several simple physicochemical properties between marketed drugs, clinical candidates and bioactive compounds using commercially available databases (GVKBIO, Hyderabad, India). In contrast to previous studies this comparison was performed at the individual target level. Confirming earlier studies this shows that marketed drugs have, on average and taken as a single set, lower physicochemical property values than the corresponding clinical candidates and bioactive compounds but that there is considerable variation between drug targets. This work complements earlier studies by using a much larger annotated dataset and confirms that there is a shift in physicochemical properties for targets with launched drugs and clinical candidates compared to bioactive compounds.

Design of compound libraries for fragment screening.

Approaches to the design of libraries for fragment screening are illustrated with reference to a 20 k generic fragment screening library and a 1.2 k generic NMR screening library. Tools and methods for library design that have been developed within AstraZeneca are described, including Foyfi fingerprints and the Flush program for neighborhood characterization. It will be shown how Flush and the BigPicker, which selects maximally diverse sets of compounds, are used to apply the Core and Layer method for library design. Approaches to partitioning libraries into cocktails are also described.