The Astrophysical Multipurpose Software Environment

We present the open source Astrophysical Multi-purpose Software Environment (AMUSE, www.amusecode.org), a component library for performing astrophysical simulations involving different physical domains and scales. It couples existing codes within a Python framework based on a communication layer using MPI. The interfaces are standardized for each domain and their implementation based on MPI guarantees that the whole framework is well-suited for distributed computation. It includes facilities for unit handling and data storage. Currently it includes codes for gravitational dynamics, stellar evolution, hydrodynamics and radiative transfer. Within each domain the interfaces to the codes are as similar as possible. We describe the design and implementation of AMUSE, as well as the main components and community codes currently supported and we discuss the code interactions facilitated by the framework. Additionally, we demonstrate how AMUSE can be used to resolve complex astrophysical problems by presenting example applications.Comment: 23 pages, 25 figures, accepted for A&

BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows.

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

Distributed Bioinformatics Computing System for DNA Sequence Analysis

This paper provides an effective design of computing technique of a distributed bioinformatics computing system for analysis of DNA sequences using OPTSDNA algorithm. This system could be used for disease detection, criminal forensic analysis, gene prediction, genetic system and protein analysis. Different types of distributed algorithms for the search and identification for DNA segments and repeat pattern in a given DNA sequence are developed. The search algorithm was developed to compute the number of DNA sequence which contains the same consecutive types of DNA segments. A distributed subsequence identifications algorithm was designed and implemented to detect the segment containing DNA sequences. Sequential and distributed implementation of these algorithms was executed with different length of search segments patterns and genetic sequences. OPTSDNA algorithm is used for storing various sizes of DNA sequence into database. DNA sequences of different lengths were tested by using this algorithm. These input DNA sequences varied in size from very small to very large. The performance of search technique distributed system is compared with sequential approach

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

The Caltech Tomography Database and Automatic Processing Pipeline

Here we describe the Caltech Tomography Database and automatic image processing pipeline, designed to process, store, display, and distribute electron tomographic data including tilt-series, sample information, data collection parameters, 3D reconstructions, correlated light microscope images, snapshots, segmentations, movies, and other associated files. Tilt-series are typically uploaded automatically during collection to a user’s “Inbox” and processed automatically, but can also be entered and processed in batches via scripts or file-by-file through an internet interface. As with the video website YouTube, each tilt-series is represented on the browsing page with a link to the full record, a thumbnail image and a video icon that delivers a movie of the tomogram in a pop-out window. Annotation tools allow users to add notes and snapshots. The database is fully searchable, and sets of tilt-series can be selected and re-processed, edited, or downloaded to a personal workstation. The results of further processing and snapshots of key results can be recorded in the database, automatically linked to the appropriate tilt-series. While the database is password-protected for local browsing and searching, datasets can be made public and individual files can be shared with collaborators over the Internet. Together these tools facilitate high-throughput tomography work by both individuals and groups

Incompressible flow simulation using S.I.M.P.L.E method on parallel computer

Komputer selari merupakan gabungan beberapa pemproses yang bertujuan meningkatkan keupayaan se~ebt,iah sistem komputer dalam melaksanakafi sesuatu pengatucaraan. Dalam projek ini, sistem komputer seiad yang digu[)akan dikenali selJagaf sistem komputer serari berkelompok. Kelebihan menggunakan sistem ,komputer selari berkelo,mpok ini ialah ia mampu bergerak-sendiri sebagai komputer sesiri jika tidak beropera~i sebagai komputer s~laci. Peri sian komputer selari yang boleh digunakan sebagai sistem operasi kepada sistem komputer selari berkeIompok ini termasuklah uNIX, Window NT atau Linux. Projek ini rnemberikan penumpuan dalam penggumian sistem kotnputer seIari berkelompok rnenggunakan perisian PVM untuk rnenyelesaikan persamaan Navier-Stoke dalam membuat simulasi dua dimensi aliran tidak boleh mampa~ dalam ruang segiempat. Kaedah yang digunakan adalah berasaskan algorfuna SIMPLE dan algoritrna SIMPLE yang {elah diubahsuai dengan men,.ggunakan kaedah pel'tIbahagian domain dan kaedah pembahagian fungsi. Ketepatan kedua-dua kaedah tersebut telah dibandiflgkan dengan keputusan piawai yang berkaitan dengan rnasalah aliran dua dimensi dalam ruang segiempat. Keupayaan kedua-dua kaedah tersebut dari segi rnasa perlaksanaan, kecepatan dan keberkesanan juga telalLJ!iperoIehi dan didapati penggunaan komputer selari telah rnemberikan prestasi yang Iebih baik dalam menyelesaikan masalah persamaan Navier-Stoke tersebut. Dengan kaedah pembahagian domain, didapati masa perla1>.Sanaan daPJIt £il)urangkan sebanyak 700/6-' rnanakala dengan menggunakan kaedah pembah~gf~ri fungsi, masa perlaksanaan dapat dikurangkan sebanyak 25 % berbartding dengan menggunakan komputer sesiri

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

INTRODUCTION COVID-19 became a global pandemic partially as a result of the lack of easily deployable, broad-spectrum oral antivirals, which complicated its containment. Even endemically, and with effective vaccinations, it will continue to cause acute disease, death, and long-term sequelae globally unless there are accessible treatments. COVID-19 is not an isolated event but instead is the latest example of a viral pandemic threat to human health. Therefore, antiviral discovery and development should be a key pillar of pandemic preparedness efforts. RATIONALE One route to accelerate antiviral drug discovery is the establishment of open knowledge bases, the development of effective technology infrastructures, and the discovery of multiple potent antivirals suitable as starting points for the development of therapeutics. In this work, we report the results of the COVID Moonshot—a fully open science, crowdsourced, and structure-enabled drug discovery campaign—against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). This collaboration may serve as a roadmap for the potential development of future antivirals. RESULTS On the basis of the results of a crystallographic fragment screen, we crowdsourced design ideas to progress from fragment to lead compounds. The crowdsourcing strategy yielded several key compounds along the optimization trajectory, including the starting compound of what became the primary lead series. Three additional chemically distinct lead series were also explored, spanning a diversity of chemotypes. The collaborative and highly automated nature of the COVID Moonshot Consortium resulted in >18,000 compound designs, >2400 synthesized compounds, >490 ligand-bound x-ray structures, >22,000 alchemical free-energy calculations, and >10,000 biochemical measurements—all of which were made publicly available in real time. The recently approved antiviral ensitrelvir was identified in part based on crystallographic data from the COVID Moonshot Consortium. This campaign led to the discovery of a potent [median inhibitory concentration (IC50) = 37 ± 2 nM] and differentiated (noncovalent and nonpeptidic) lead compound that also exhibited potent cellular activity, with a median effective concentration (EC50) of 64 nM in A549-ACE2-TMPRSS2 cells and 126 nM in HeLa-ACE2 cells without measurable cytotoxicity. Although the pharmacokinetics of the reported compound is not yet optimal for therapeutic development, it is a promising starting point for further antiviral discovery and development. CONCLUSION The success of the COVID Moonshot project in producing potent antivirals, building open knowledge bases, accelerating external discovery efforts, and functioning as a useful information-exchange hub is an example of the potential effectiveness of open science antiviral discovery programs. The open science, patent-free nature of the project enabled a large number of collaborators to provide in-kind support, including synthesis, assays, and in vitro and in vivo experiments. By making all data immediately available and ensuring that all compounds are purchasable from Enamine without the need for materials transfer agreements, we aim to accelerate research globally along parallel tracks. In the process, we generated a detailed map of the structural plasticity of Mpro, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data to spur further research into antivirals and discovery methodologies. We hope that this can serve as an alternative model for antiviral discovery and future pandemic preparedness. Further, the project also showcases the role of machine learning, computational chemistry, and high-throughput structural biology as force multipliers in drug design. Artificial intelligence and machine learning algorithms help accelerate chemical synthesis while balancing multiple competing molecular properties. The design-make-test-analyze cycle was accelerated by these algorithms combined with planetary-scale biomolecular simulations of protein-ligand interactions and rapid structure determination