Acceleration and Verification of Virtual High-throughput Multiconformer Docking

The work in this dissertation explores the use of massive computational power available through modern supercomputers as a virtual laboratory to aid drug discovery. As of November 2013, Tianhe-2, the fastest supercomputer in the world, has a theoretical performance peak of 54,902 TFlop/s or nearly 55 thousand trillion calculations per second. The Titan supercomputer located at Oak Ridge National Laboratory has 560,640 computing cores that can work in parallel to solve scientific problems. In order to harness this computational power to assist in drug discovery, tools are developed to aid in the preparation and analysis of high-throughput virtual docking screens, a tool to predict how and how well small molecules bind to disease associated proteins and potentially serve as a novel drug candidate. Methods and software for performing large screens are developed that run on high-performance computer systems. The future potential and benefits of using these tools to study polypharmacology and revolutionizing the pharmaceutical industry are also discussed

Distributed Computing in a Pandemic

The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks

Distributed Computing in a Pandemic

The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks

GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis

Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns

Development of High Performance Molecular Dynamics with Application to Multimillion-Atom Biomass Simulations

An understanding of the recalcitrance of plant biomass is important for efficient economic production of biofuel. Lignins are hydrophobic, branched polymers and form a residual barrier to effective hydrolysis of lignocellulosic biomass. Understanding lignin\u27s structure, dynamics and its interaction and binding to cellulose will help with finding more efficient ways to reduce its contribution to the recalcitrance. Molecular dynamics (MD) using the GROMACS software is employed to study these properties in atomic detail. Studying complex, realistic models of pretreated plant cell walls, requires simulations significantly larger than was possible before. The most challenging part of such large simulations is the computation of the electrostatic interaction. As a solution, the reaction-field (RF) method has been shown to give accurate results for lignocellulose systems, as well as good computational efficiency on leadership class supercomputers. The particle-mesh Ewald method has been improved by implementing 2D decomposition and thread level parallelization for molecules not accurately modeled by RF. Other scaling limiting computational components, such as the load balancing and memory requirements, were identified and addressed to allow such large scale simulations for the first time. This work was done with the help of modern software engineering principles, including code-review, continuous integration, and integrated development environments. These methods were adapted to the special requirements for scientific codes. Multiple simulations of lignocellulose were performed. The simulation presented primarily, explains the temperature-dependent structure and dynamics of individual softwood lignin polymers in aqueous solution. With decreasing temperature, the lignins are found to transition from mobile, extended to glassy, compact states. The low-temperature collapse is thermodynamically driven by the increase of the translational entropy and density fluctuations of water molecules removed from the hydration shell

Conceptual Framework and Methodology for Analysing Previous Molecular Docking Results

Modern drug discovery relies on in-silico computational simulations such as molecular docking. Molecular docking models biochemical interactions to predict where and how two molecules would bind. The results of large-scale molecular docking simulations can provide valuable insight into the relationship between two molecules. This is useful to a biomedical scientist before conducting in-vitro or in-vivo wet-lab experiments. Although this ˝eld has seen great advancements, feedback from biomedical scientists shows that there is a need for storage and further analysis of molecular docking results. To meet this need, biomedical scientists need to have access to computing, data, and network resources, and require speci˝c knowledge or skills they might lack. Therefore, a conceptual framework speci˝cally tailored to enable biomedical scientists to reuse molecular docking results, and a methodology which uses regular input from scientists, has been proposed. The framework is composed of 5 types of elements and 13 interfaces. The methodology is light and relies on frequent communication between biomedical sciences and computer science experts, speci˝ed by particular roles. It shows how developers can bene˝t from using the framework which allows them to determine whether a scenario ˝ts the framework, whether an already implemented element can be reused, or whether a newly proposed tool can be used as an element. Three scenarios that show the versatility of this new framework and the methodology based on it, have been identi˝ed and implemented. A methodical planning and design approach was used and it was shown that the implementations are at least as usable as existing solutions. To eliminate the need for access to expensive computing infrastructure, state-of-the-art cloud computing techniques are used. The implementations enable faster identi˝cation of new molecules for use in docking, direct querying of existing databases, and simpler learning of good molecular docking practice without the need to manually run multiple tools. Thus, the framework and methodol-ogy enable more user-friendly implementations, and less error-prone use of computational methods in drug discovery. Their use could lead to more e˙ective discovery of new drugs

Inhibition of bacterial and human zinc-metalloproteases

Antibiotic resistance is one of the major challenges in the present era and it is drastically increasing with the increase in time because of the overuse and misuse of antibiotics. Therefore, there is a demand of new antibiotics with new modes of action or other innovative strategies to overcome bacterial infections as soon as possible. The zinc metalloproteases Themolysin (TLN), Pseudolysin (PLN) and Aureolysin (ALN) are important bacterial virulence factors and the inhibition of these bacterial virulence factors is believed to be a new treatment option of bacterial infections. However, in order to have a therapeutic value, inhibitors of these enzymes should not interfere strongly with the activity of human zinc metalloproteases. In the present thesis, 26 compounds were tested for the inhibition of TLN, PLN, ALN and the human matrix metalloproteases-14(MMP-14). The compounds were selected from a previous virtual screening project at the research group. The inhibition of the compounds was tested by measuring the enzyme activity of PLN, TLN ALN and MMP-14 after exposure of the test compounds. The time resolved fluorescence by the use of fluorogenic substrates was used to measure the enzyme activity. The results showed that some of the compounds inhibited the enzyme activity by 30%-40% and they were not considered as slow binders as there was no significant change in activity with respect to the time. Compounds with highest rate of inhibition in enzyme assays were selected and proceed for molecular modeling studies by docking and MMGBSA calculations. The best compounds were compared with a known strong inhibitor of the zinc- metalloproteases in the molecular modeling part