High Throughput Virtual Screening with Data Level Parallelism in Multi-core Processors

Improving the throughput of molecular docking, a computationally intensive phase of the virtual screening process, is a highly sought area of research since it has a significant weight in the drug designing process. With such improvements, the world might find cures for incurable diseases like HIV disease and Cancer sooner. Our approach presented in this paper is to utilize a multi-core environment to introduce Data Level Parallelism (DLP) to the Autodock Vina software, which is a widely used for molecular docking software. Autodock Vina already exploits Instruction Level Parallelism (ILP) in multi-core environments and therefore optimized for such environments. However, with the results we have obtained, it can be clearly seen that our approach has enhanced the throughput of the already optimized software by more than six times. This will dramatically reduce the time consumed for the lead identification phase in drug designing along with the shift in the processor technology from multi-core to many-core of the current era. Therefore, we believe that the contribution of this project will effectively make it possible to expand the number of small molecules docked against a drug target and improving the chances to design drugs for incurable diseases.Comment: Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference o

Neural Network Approach To Classification Of Infrasound Signals

Thesis (Ph.D.) University of Alaska Fairbanks, 2010As part of the International Monitoring Systems of the Preparatory Commissions for the Comprehensive Nuclear Test-Ban Treaty Organization, the Infrasound Group at the University of Alaska Fairbanks maintains and operates two infrasound stations to monitor global nuclear activity. In addition, the group specializes in detecting and classifying the man-made and naturally produced signals recorded at both stations by computing various characterization parameters (e.g. mean of the cross correlation maxima, trace velocity, direction of arrival, and planarity values) using the in-house developed weighted least-squares algorithm. Classifying commonly observed low-frequency (0.015--0.1 Hz) signals at out stations, namely mountain associated waves and high trace-velocity signals, using traditional approach (e.g. analysis of power spectral density) presents a problem. Such signals can be separated statistically by setting a window to the trace-velocity estimate for each signal types, and the feasibility of such technique is demonstrated by displaying and comparing various summary plots (e.g. universal, seasonal and azimuthal variations) produced by analyzing infrasound data (2004--2007) from the Fairbanks and Antarctic arrays. Such plots with the availability of magnetic activity information (from the College International Geophysical Observatory located at Fairbanks, Alaska) leads to possible physical sources of the two signal types. Throughout this thesis a newly developed robust algorithm (sum of squares of variance ratios) with improved detection quality (under low signal to noise ratios) over two well-known detection algorithms (mean of the cross correlation maxima and Fisher Statistics) are investigated for its efficacy as a new detector. A neural network is examined for its ability to automatically classify the two signals described above against clutter (spurious signals with common characteristics). Four identical perceptron networks are trained and validated (with >92% classification rates) using eight independent datasets; each dataset consists of three-element (each element being a characterization parameter) feature vectors. The validated networks are tested against an expert, Prof. Charles R. Wilson, who has been studying those signals for decades. From the graphical comparisons, we conclude that such networks are excellent candidate for substituting the expert. Advantages to such networks include robustness and resistance to errors and the bias of a human operator

Accessible High-Throughput Virtual Screening Molecular Docking Software for Students and Educators

We survey low cost high-throughput virtual screening (HTVS) computer programs for instructors who wish to demonstrate molecular docking in their courses. Since HTVS programs are a useful adjunct to the time consuming and expensive wet bench experiments necessary to discover new drug therapies, the topic of molecular docking is core to the instruction of biochemistry and molecular biology. The availability of HTVS programs coupled with decreasing costs and advances in computer hardware have made computational approaches to drug discovery possible at institutional and non-profit budgets. This paper focuses on HTVS programs with graphical user interfaces (GUIs) that use either DOCK or AutoDock for the prediction of DockoMatic, PyRx, DockingServer, and MOLA since their utility has been proven by the research community, they are free or affordable, and the programs operate on a range of computer platforms

Risk assessment of biogas in kitchens

International audienceThe health risk associated with human exposure to pollutants while using biogas for cooking was assessed following the methodology described by the US - National Research Council. Information of hazardous compounds and compositions of several biogas types were extracted from scientific literature. Compositions were dependent on the biogas origin (production process). First, a quantitative approach was conducted to identify substances with a high health risk based on their Human Toxicity Values. Then, a subsequent qualitative analysis was performed to complete the health risk assessment based on other toxicology data, effectiveness of purification processes, variability of the waste materials used for biogas generation and, when possible, a comparison with natural gas. The main conclusion of the study was that the injection in the grid of upgraded biogas originating from household and organic waste landfills, did not present an increase of health risks when compared to the domestic use of natural gas

IDENTIFICATION OF NOVEL INHIBITORS AGAINST POTENTIAL TARGETS OF CAMPYLOBACTER JEJUNI

Objective: The aim of the present study is the structure identification of UDP-N-acetyl muramate dehydrogenase and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase for Campylobacter jejuni and designing their inhibitors using docking and simulation studies.Methods: Uniprot, BLAST P, Discovery Studio, Verify 3D and Maestro Schrödinger suit have been used for structure identification, validation and docking studies.Results: The structures of UDP-N-acetylmuramic dehydrogenase and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase were predicted and validated generating 87.80% and 85.82% score respectively. For 4-hydroxy-3-methylbut-2-enyl diphosphate reductase, HTVS resulted in 5801 compounds while SP and XP resulted in 5781 ligands. For UDP-N-acetylmuramate dehydrogenase, HTVS resulted in 5474 compounds whereas SP and XP resulted in 5359 ligands.Conclusion: The structures of UDP-N-acetylmuramate dehydrogenase and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase were detected and verified. The list of top 10 inhibitors was acquired that can be considered as putative and potential drug targets.Keywords: Campylobacter jejuni, Structure prediction, Active site, Docking, Inhibitor.Â

The Clapham Saints: A Correlational Study between a Christian\u27s Level of Commitment to the Christian Faith and Their Engagement in Human Trafficking Political Matters

The purpose of this correlational study is to determine if a relationship exists between the commitment level of Christians who attend a church in the Northern Shenandoah Valley portion of Virginia that falls under the covering of Abba’s House––Chattanooga, Tennessee as assessed by the Belief into Action (BIAC) scale, and their level of engagement in human trafficking political matters, as assessed by the Human Trafficking Political Engagement (HTPE) scale. Every year, millions of individuals become victims of human trafficking. Human trafficking victims are created in God\u27s image, leaving Christians obligated to protect them. William Wilberforce and the Clapham Saints’ political engagement ended slavery and fought against other social ills. Similarly, Christians, today can leave God\u27s imprint on human trafficking legislation by engaging in politics. To determine if Christians are interested in political engagement, similar to the Clapham Saints, the researcher sought to determine the relationship between Christian commitment and engagement in human trafficking political matters

Using a Genetic Algorithm to Find Molecules with Good Docking Scores

A graph-based genetic algorithm (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package, starting from randomly chosen molecules from the ZINC database, for four different targets: Bacillus subtilis chorismate mutase (CM), human β2-adrenergic G protein-coupled receptor (β2AR), the DDR1 kinase domain (DDR1), and β-cyclodextrin (BCD). By the combined use of functional group filters and a score modifier based on a heuristic synthetic accessibility (SA) score our approach identifies between ca 500 and 6,000 structurally diverse molecules with scores better than known binders by screening a total of 400,000 molecules starting from 8,000 randomly selected molecules from the ZINC database. Screening 250,000 molecules from the ZINC database identifies significantly more molecules with better docking scores than known binders, with the exception of CM, where the conventional screening approach only identifies 60 compounds compared to 511 with GA+Filter+SA. In the case of β2AR and DDR1, the GA+Filter+SA approach finds significantly more molecules with docking scores lower than −9.0 and −10.0. The GA+Filters+SA docking methodology is thus effective in generating a large and diverse set of synthetically accessible molecules with very good docking scores for a particular target. An early incarnation of the GA+Filter+SA approach was used to identify potential binders to the COVID-19 main protease and submitted to the early stages of the COVID Moonshot project, a crowd-sourced initiative to accelerate the development of a COVID antiviral

Evaluating the Predictivity of Virtual Screening for Abl Kinase Inhibitors to Hinder Drug Resistance

Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual screening methods for the leukemia target kinase ABL1 and its drug-resistant mutant ABL1-T315I. ‘Dual active’ inhibitors against both targets were grouped together with inactive ligands chosen from different decoy sets and tested with virtual screening approaches with and without explicit use of target structures (docking). We show how various scoring functions and choice of inactive ligand sets influence overall and early enrichment of the libraries. Although ligand-based methods, for example principal component analyses of chemical properties, can distinguish some decoy sets from active compounds, the addition of target structural information via docking improves enrichment, and explicit consideration of multiple target conformations (i.e. types I and II) achieves best enrichment of active versus inactive ligands, even without assuming knowledge of the binding mode. We believe that this study can be extended to other therapeutically important kinases in prospective virtual screening studies