MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions

IntroductionCollaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community.Areas coveredIn this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds.Expert opinionStructure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files

Sharing data from molecular simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations

Rethinking Electrostatic Solvers in Particle Simulations for the Exascale Era

In preparation to the exascale era, an alternative approach to calculate the electrostatic forces in Particle Mesh (PM) methods is proposed. While the traditional techniques are based on the calculation of the electrostatic potential by solving the Poisson equation, in the new approach the electric field is calculated by solving Amp\`ere's law. When the Ampere's law is discretized explicitly in time, the electric field values on the mesh are simply updated from the previous values. In this way, the electrostatic solver becomes an embarrassingly parallel problem, making the algorithm extremely scalable and suitable for exascale computing platforms. An implementation PM code with the new electrostatic solver is presented to show that the proposed method produces correct results. It is a very promising algorithm for exascale PM simulations.status: publishe

BioExcel Deliverable 1.3 - Roadmap of future hardware and long-term development plan for each pilot application

Future hardware development will have a significant impact on all areas of scientific computing including the Life Sciences. Upcoming extreme-scale compute platforms will offer great opportunities for tackling important, large-scale scientific questions. In this document we analyze from the viewpoint of biomolecular applications the requirements for different Exascale aspects such as HPC architectures, software management, programming environments, I/O and storage etc. We discuss the near-term hardware developments in processors, network, and memory and I/O in the light of these requirements and we explain the impact that they will have on the core applications. Our findings are already well publicized among the European HPC stakeholders via several working groups which are involved in the development of the ETP4HPC Strategic Research Agenda and the PRACE Scientific Case, as well as the EXDCI (http://exdci.eu) project in which BioExcel is leading the Life Science working group. Bio-molecular simulation scientists require effective and usable simulation software that runs well on the hardware resources they can access now. The development of these codes must also target the likely directions of future hardware that we have learned from leading vendors and development consortia. This will ensure that the investment in Exascale-era technologies will deliver the expected benefits of improved bio-molecular simulations. These include supporting the design of new drugs, obtaining better understanding of biochemical pathways, and opening new doors for further innovation. This deliverable gives an overview of what we currently see as potential directions and then implementation plans for each of the pilot codes that will suit those directions

Association Dynamics and Linear and Nonlinear Optical Properties of an <i>N</i>‑Acetylaladanamide Probe in a POPC Membrane

Along with the growing evidence that relates membrane abnormalities to various diseases, biological membranes have been acknowledged as targets for therapy. Any such abnormality in the membrane structure alters the membrane potential which in principle can be captured by measuring properties of specific optical probes. There exists by now many molecular probes with absorption and fluorescence properties that are sensitive to local membrane structure and to the membrane potential. To suggest new high-performance optical probes for membrane-potential imaging it is important to understand in detail the membrane-induced structural changes in the probe, the membrane association dynamics of the probe, and its membrane-specific optical properties. To contribute to this effort, we here study an optical probe, <i>N</i>-acetylaladanamide (NAAA), in the presence of a POPC lipid bilayer using a multiscale integrated approach to assess the probe structure, dynamics, and optical properties in its membrane-bound status and in water solvent. We find that the probe eventually assimilates into the membrane with a specific orientation where the hydrophobic part of the probe is buried inside the lipid bilayer, while the hydrophilic part is exposed to the water solvent. The computed absorption maximum is red-shifted when compared to the gas phase. The computations of the two-photon absorption and second harmonic generation cross sections of the NAAA probe in its membrane-bound state which is of its first kind in the literature suggest that this probe can be used for imaging the membrane potential using nonlinear optical microscopy

GROMACS 4.5 : a high-throughput and highly parallel open source molecular simulation toolkit

Motivation: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources. Results: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations.QC 20130429</p

Sharing Data from Molecular Simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations have become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, and each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 ( https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way towards more open, interoperable and reproducible outputs coming from research studies using MD simulations