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Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area. This paper has been supported by the H2020 Artificial Intelligence for Health Imaging (AI4HI) initiative and more specifically by the Data Storage/Curation/Management working group. The AI4HI working group is formulated by five EU projects on Artificial Intelligence for Medical Imaging, the EuCanImage, INCISIVE, ProCAncer-I, CHAIMELEON, and PRIMAGE that have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements No. 952103, 952179, 952159, 952172, and 826494, respectively.

Developing complex biomolecular workflows is not always straightforward. It requires tedious developments to enable the interoperability between the different biomolecular simulation and analysis tools. Moreover, the need to execute the pipelines on distributed systems increases the complexity of these developments. To address these issues, we propose a methodology to simplify the implementation of these workflows on HPC infrastructures. It combines a library, the BioExcel Building Blocks (BioBBs), that allows scientists to implement biomolecular pipelines as Python scripts, and the PyCOMPSs programming framework which allows to easily convert Python scripts into task-based parallel workflows executed in distributed computing systems such as HPC clusters, clouds, containerized platforms, etc. Using this methodology, we have implemented a set of computational molecular workflows and we have performed several experiments to validate its portability, scalability, reliability and malleability., Accepted in IEEE eScience conference 2022

In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments. We thank The Barcelona Supercomputing Center for providing all the HPC resources to launch the test executions presented in this manuscript. Te project was supported by the following grants: BioExcel Center of Excellence (Horizon 2020 Framework Programme. Grants 675728, and 823830). Elixir-Excelerate (Horizon 2020 Framework Programme. Grant 676559). Spanish National Institute for Bioinformatics (Institute of Health Carlos III. Grants PT13/0001/0019, PT13/0001/0028, PT17/0009/0001). Spanish Government, Severo Ochoa Grant SEV2015-0493, TIN2015-65316-P, Generalitat de Catalunya. 2014-SGR-1051, 2017-SGR-1110.

The usage of workflows has led to progress in many fields of science, where the need to process large amounts of data is coupled with difficulty in accessing and efficiently using High Performance Computing platforms. On the one hand, scientists are focused on their problem and concerned with how to process their data. On top of that, the applications typically have different parts and use different tools for each part, thus complicating the distribution and the reproducibility of the simulations. On the other hand, computer scientists concentrate on how to develop frameworks for the deployment of workflows on HPC or HTC resources; often providing separate solutions for the computational aspects and the data analytic ones.In this paper we present an approach to support biomolecular researchers in the development of complex workflows that i) allow them to compose pipelines of individual simulations built from different tools and interconnected by data dependencies, ii) run them seamlessly on different computational platforms, and iii) scale them up to the large number of cores provided by modern supercomputing infrastructures. Our approach is based on the orchestration of computational building blocks for Molecular Dynamics simulations through an efficient workflow management system that has already been adopted in many scientific fields to run applications on multitudes of computing backends.Results demonstrate the validity of the proposed solution through the execution of massively parallel runs in a supercomputer facility.

To understand the role of the extensive senescence-associated 3D genome reorganization, we generated genome-wide chromatin interaction maps, epigenome, replication-timing, whole-genome bisulfite sequencing, and gene expression profiles from cells entering replicative senescence (RS) or upon oncogene-induced senescence (OIS). We identify senescence-associated heterochromatin domains (SAHDs). Differential intra- versus inter-SAHD interactions lead to the formation of senescence-associated heterochromatin foci (SAHFs) in OIS but not in RS. This OIS-specific configuration brings active genes located in genomic regions adjacent to SAHDs in close spatial proximity and favors their expression. We also identify DNMT1 as a factor that induces SAHFs by promoting HMGA2 expression. Upon DNMT1 depletion, OIS cells transition to a 3D genome conformation akin to that of cells in replicative senescence. These data show how multi-omics and imaging can identify critical features of RS and OIS and discover determinants of acute senescence and SAHF formation. Work at the M.A.M.-R. lab was supported by the European Research Council under the 7th Framework Program FP7/2007-2013 (ERC grant agreement 609989), the European Union’s Horizon 2020 research and innovation programme (grant agreement 676556), the Ministry of Economy and Competitiveness (BFU2017-85926-P), and the Agència de Gestió d’Ajuts Universitaris i de Recerca, AGAUR (SGR468). Work at CRG, BIST, and UPF was in part funded by the Spanish Ministry of Economy and Competitiveness, ‘‘Centro de Excelencia Severo Ochoa 2013-2017’’ (SEV-2012-0208), and ‘‘Centro de Excelencia María de Maeztu 2016-2019.’’ This article/publication is based upon work from COST Action CA18127, supported by COST (European Cooperation in Science and Technology)

Multiscale Genomics (MuG) Virtual Research Environment (MuGVRE) is a cloud-based computational infrastructure created to support the deployment of software tools addressing the various levels of analysis in 3D/4D genomics. Integrated tools tackle needs ranging from high computationally demanding applications (e.g. molecular dynamics simulations) to high-throughput data analysis applications (like the processing of next generation sequencing). The MuG Infrastructure is based on openNebula cloud systems implemented at the Institute for research in Biomedicine, and the Barcelona Supercomputing Center, and has specific interfaces for users and developers. Interoperability of the tools included in MuGVRE is maintained through a rich set of metadata allowing the system to associate tools and data in a transparent manner. Execution scheduling is based in a traditional queueing system to handle demand peaks in applications of fixed needs, and an elastic and multi-scale programming model (pyCOMPSs, controlled by the PMES scheduler), for complex workflows requiring distributed or multi-scale executions schemes. MuGVRE is available athttps://vre.multiscalegenomics.euand documentation and general information athttps://www.multiscalegenomics.eu. The infrastructure is open and freely accessible.

Molecular dynamics (MD) simulation is nowadays the eligible theoretical technique to account for macromolecular flexibility. Molecular simulations, from ab initio to coarse-grained representation levels, allow having a direct visualization of macromolecular dynamic behavior and its influence in molecular recognition. In the postgenomic era, where most bioinformatics studies should be performed genome-wide, molecular simulations should not be an exception. However, moving MD simulations to the high-throughput regime is not a trivial issue. High-performance computing systems (highly parallel supercomputers or graphical processing units-based systems) allow performing large and complex simulations at a significantly reduced time. Besides, data storage strategies have also been largely improved. However, system preparation and trajectory analysis are still performed almost manually and become highly limited by the need of human intervention. A number of projects, like Dynameomics, BioSimGrid, or MoDEL have addressed the issue of high-throughput MD. Such initiatives have released tools that have meant a significant progress in the field. The extension of structural studies, mainly molecular simulations, to the high-throughput regime, will allow matching genomic-wide studies now performed.

Molecular dynamics simulation (MD) is, just behind genomics, the bioinformatics tool that generates the largest amounts of data, and that is using the largest amount of CPU time in supercomputing centres. MD trajectories are obtained after months of calculations, analysed in situ, and in practice forgotten. Several projects to generate stable trajectory databases have been developed for proteins, but no equivalence exists in the nucleic acids world. We present here a novel database system to store MD trajectories and analyses of nucleic acids. The initial data set available consists mainly of the benchmark of the new molecular dynamics force-field, parmBSC1. It contains 156 simulations, with over 120 of total simulation time. A deposition protocol is available to accept the submission of new trajectory data. The database is based on the combination of two NoSQL engines, Cassandra for storing trajectories and MongoDB to store analysis results and simulation metadata. The analyses available include backbone geometries, helical analysis, NMR observables and a variety of mechanical analyses. Individual trajectories and combined meta-trajectories can be downloaded from the portal. The system is accessible through http: //mmb.irbbarcelona.org/BIGNASim/. Supplementary Material is also available on-line at http://mmb. irbbarcelona.org/BIGNASim/SuppMaterial/. Spanish Ministry of Science [BIO2012-32868, SEV-2011-00067, TIN2012-34557]; Catalan Government [2014-SGR-134, 2014-SGR-1051]; Institut Català de Recerca I Estudis Avanc¸ats, ICREA Academia [to M.O.], Instituto de Salud Carlos III-Instituto Nacional de Bioinformática [PT13/0001/0019, PT13/0001/0028]; European Research Council [ERC SimDNA]; European Union, H2020 programme [Elixir-Excellerate: 676559; BioExcel: 674728, MuG: 676566]; PEDECIBA and SNI (ANII, Uruguay) [to P.D.D.]. Funding for open access charge: European Union [MuG: 676566].

Despite of the variety of available Web services registries specially aimed at Life Sciences, their scope is usually restricted to a limited set of well-defined types of services. While dedicated registries are generally tied to a particular format, general-purpose ones are more adherent to standards and usually rely on Web Service Definition Language (WSDL). Although WSDL is quite flexible to support common Web services types, its lack of semantic expressiveness led to various initiatives to describe Web services via ontology languages. Nevertheless, WSDL 2.0 descriptions gained a standard representation based on Web Ontology Language (OWL). BioSWR is a novel Web services registry that provides standard Resource Description Framework (RDF) based Web services descriptions along with the traditional WSDL based ones. The registry provides Web-based interface for Web services registration, querying and annotation, and is also accessible programmatically via Representational State Transfer (REST) API or using a SPARQL Protocol and RDF Query Language. BioSWR server is located at http://inb.bsc.es/BioSWR/and its code is available at https://sourceforge.net/projects/bioswr/under the LGPL license.

Protein engineering is a promising tool to obtain stable proteins. Comparison between homologous thermophilic and mesophilic enzymes from a given structural family can reveal structural features responsible for the enhanced stability of thermophilic proteins. Structures from pig heart cytosolic and Thermus flavus malate dehydrogenases (cMDH, Tf MDH), two proteins showing a 55% sequence homology, were compared with the aim of increasing cMDH stability using features from the Thermus flavus enzyme. Three potential salt bridges from Tf MDH were selected on the basis of their location in the protein (surface R176-D200, inter-subunit E57-K168 and intrasubunit R149-E275) and implemented on cMDH using site-directed mutagenesis. Mutants containing E275 were not produced in any detectable amount, which shows that the energy penalty of introducing a charge imbalance in a region that was not exposed to solvent was too unfavourable to allow proper folding of the protein. The salt bridge R149-E275, if formed, would not enhance stability enough to overcome this effect. The remaining mutants were expressed and active and no differences from wild-type other than stability were found. Of the mutants assayed, Q57E/L168K led to a stability increase of 0.4 kcal/mol, as determined by either guanidinium chloride denaturalization or thermal inactivation experiments. This results in a 15 degrees C shift in the optimal temperature, thus confirming that the inter-subunit salt bridge initially present in the T.flavus enzyme was formed in the cMDH structure and that the extra energy obtained is transformed into an increase in protein stability. These results indicate that the use of structural features of thermophilic enzymes, revealed by a detailed comparison of three-dimensional structures, is a valid strategy to improve the stability of mesophilic malate dehydrogenases.

Mitochondrial malate dehydrogenase (mMDH) shows a complex dependence upon ionic environment that includes kinetic and structural effects. We measured mMDH activity in several buffers (phosphate, MOPS, and MES) at pH 6.5 and 7.5, and in the presence of a number of anions, at highly diluted enzyme concentrations where mMDH showed significant loss of activity. Under these conditions, mMDH activity shows a non-linear dependence on enzyme concentration, in agreement with the existence of a dimer–monomer equilibrium, where only the dimeric form is active. According to this hypothesis, the dissociation constant of mMDH dimer has been determined to be 5.4 nM in the MES buffer at pH 6.5. Either the presence of a small anion like phosphate, or an increase of the pH from 6.5 to 7.5 shifts the equilibrium in favor of the dimeric form with the two effects appearing to be additive. To extend the study, we analysed the effect of a number of anions on the mMDH activity in 50 mM MOPS buffer at pH 7.5. All the anions had a dual effect: at low concentrations, they increased the activity of mMDH, while at high concentrations, they inhibited it. A more accurate analysis of the data revealed that the activation capacity of all the anions tested was similar, although they differed in their inhibitory influence. To show these differences more clearly, the experiment was repeated in 50 mM phosphate buffer at pH 7.5, under conditions where almost all activations were due to the buffer. The analysis of the results obtained under these conditions revealed the following sequence of inhibition potency: phosphate

Apical reabsorption of dibasic amino acids and cystine in kidney is mediated by the heteromeric amino acid antiporter rBAT/b(0,+)AT (system b(0,+)). Mutations in rBAT cause cystinuria type A, whereas mutations in b(0,+)AT cause cystinuria type B. b(0,+)AT is the catalytic subunit, whereas it is believed that rBAT helps the routing of the rBAT/b(0,+)AT heterodimeric complex to the plasma membrane. In the present study, we have functionally characterized the cystinuria-specific R365W (Arg(365)-->Trp) mutation of human rBAT, which in addition to a trafficking defect, alters functional properties of the b(0,+) transporter. In oocytes, where human rBAT interacts with the endogenous b(0,+)AT subunit to form an active transporter, the rBAT(R365W) mutation caused a defect of arginine efflux without altering arginine influx or apparent affinities for intracellular or extracellular arginine. Transport of lysine or leucine remained unaffected. In HeLa cells, functional expression of rBAT(R365W)/b(0,+)AT was observed only at the permissive temperature of 33 degrees C. Under these conditions, the mutated transporter showed 50% reduction of arginine influx and a similar decreased accumulation of dibasic amino acids. Efflux of arginine through the rBAT(R365W)/b(0,+)AT holotransporter was completely abolished. This supports a two-translocation-pathway model for antiporter b(0,+), in which the efflux pathway in the rBAT(R365W)/b(0,+)AT holotransporter is defective for arginine translocation or dissociation. This is the first direct evidence that mutations in rBAT may modify transport properties of system b(0,+).

A macroscopic approach has been used to calculate electrostatic energies in complexes of L-lactate dehydrogenase with substrates. The different steps of the catalytic mechanism that have been analysed separately are binding of coenzymes and substrates, protonation of the enzyme, the substrate-induced conformational rearrangement of the protein and the chemical transformation of substrate to product. The contributions of the different amino acid residues to each step have been determined from their electrostatic interaction energies. The overall topology of lactate dehydrogenase protein subunit creates, through helix dipoles, two regions of positive potential. One, in the coenzyme-binding domain, stabilises the negatively charged coenzyme pyrophosphate group. The other stabilises both the anionic substrate binding and the developing negative charge excess on the substrate carbonyl in the transition state. The calculated electrostatic fields are those expected from the roles of amino acid side chains which had been previously suggested from site-directed mutagenesis experiments: that is Asp-53 is important in NADH binding, Arg-171 for pyruvate binding and Arg-109 for transition-state stabilisation. His-195, the proton donor/acceptor in catalysis, is stabilised in the protonated state by both Asp-168 and pyruvate. The closure of the active-site loop sequesters the substrate and the nicotinamide ring of the coenzyme in an internal active-site vacuole. The closure process is calculated to be electrostatically unfavourable due to the buried ionic species. Calculation suggests that favourable hydrophobic effects balance unfavourable electrostatic effects and give the precise balance of energies to drive loop (residues 99–112) movement which generates the catalytically competent species.

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations. This work was partially supported by ELIXIR-EXCELERATE and CORBEL. ELIXIR-EXCELERATE and CORBEL are funded by the European Commission within the Research Infrastructures programme of Horizon 2020, grant agreement numbers 676559 and 654248. The European workshops were supported by ELIXIR and organised in collaboration with the Software Sustainability Institute and Netherlands eScience Center. The workshop in Australia was supported by EMBL-ABR via its main funders: The University of Melbourne and Bioplatforms Australia.