The accumulation of abnormal prion protein (PrPSc) converted from the normal cellular isoform of PrP (PrPC) is assumed to induce pathogenesis in prion diseases. Therefore, drug discovery studies for these diseases have focused on the protein conversion process. We used a structure-based drug discovery algorithm (termed Nagasaki University Docking Engine: NUDE) that ran on an intensive supercomputer with a graphic-processing unit to identify several compounds with anti-prion effects. Among the candidates showing a high-binding score, the compounds exhibited direct interaction with recombinant PrP in vitro, and drastically reduced PrPSc and protein-aggresomes in the prion-infected cells. The fragment molecular orbital calculation showed that the van der Waals interaction played a key role in PrPC binding as the intermolecular interaction mode. Furthermore, PrPSc accumulation and microgliosis were significantly reduced in the brains of treated mice, suggesting that the drug candidates provided protection from prion disease, although further in vivo tests are needed to confirm these findings. This NUDE-based structure-based drug discovery for normal protein structures is likely useful for the development of drugs to treat other conformational disorders, such as Alzheimer's disease.

SUMMARYFast algorithms for the computation of N -body problems can be broadly classified into mesh-based interpolation methods, and hierarchical or multiresolution methods. To this latter class belongs the wellknown fast multipole method (FMM), which offers O(N ) complexity. The FMM is a complex algorithm, and the programming difficulty associated with it has arguably diminished its impact, being a barrier for adoption. This paper presents an extensible parallel library for N -body interactions utilizing the FMM algorithm. A prominent feature of this library is that it is designed to be extensible, with a view to unifying efforts involving many algorithms based on the same principles as the FMM and enabling easy development of scientific application codes. The paper also details an exhaustive model for the computation of tree-based N -body algorithms in parallel, including both work estimates and communications estimates. With this model, we are able to implement a method to provide automatic, a priori load balancing of the parallel execution, achieving optimal distribution of the computational work among processors and minimal interprocessor communications. Using a client application that performs the calculation of velocity induced by N vortex particles in two dimensions, ample verification and testing of the library was performed. Strong scaling results are presented with 10 million particles on up to 256 processors, including both speedup and parallel efficiency. The largest problem size that has been run with the PetFMM library at this point was 64 million particles in 64 processors. The library is currently able to achieve over 85% parallel efficiency for 64 processes. The performance study, computational model, and application demonstrations presented in this paper are limited to 2D. However, the software architecture was designed to make an extension of this work to 3D straightforward, as the framework is templated over the dimension. The software library is open source under the PETSc license, even less restrictive than the BSD license; this guarantees the maximum impact to the scientific community and encourages peer-based collaboration for the extensions and applications.

SUMMARYThe fast multipole method (FMM) is a fast summation algorithm capable of accelerating pairwise interaction calculations, known as N -body problems, from an algorithmic complexity of O(N 2 ) to O(N ) for N particles. The algorithm has brought a dramatic increase in the capability of particle simulations in many application areas, such as electrostatics, particle formulations of fluid mechanics, and others. Although the literature on the subject provides theoretical error bounds for the FMM approximation, there are not many reports on the measured errors in a suite of computational experiments that characterize the accuracy of the method in relation with the different parameters available to the user. We have performed such an experimental investigation, and summarized the results of about 1500 calculations using the FMM algorithm, applied to the 2D vortex particle method. In addition to the more standard diagnostic of the maximum error, we supply illustrations of the spatial distribution of the errors, offering visual evidence of all the contributing factors to the overall approximation accuracy: multipole expansion, local expansion, hierarchical spatial decomposition (interaction lists, local domain, far domain). This presentation is a contribution to any researcher wishing to incorporate the FMM acceleration to their application code, as it aids in understanding where accuracy is gained or compromised.

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Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.