Elastic scattering of longitudinally polarized electrons from helium-4: A measurement of G(E)(S) at Q2 = 0.1 (GeV/c)2

We have performed the first measurement of the parity-violating asymmetry in the elastic scattering of longitudinally polarized electrons from 4He. The kinematics chosen (Q 2 = 0.1 (GeV/c)2) provide a direct sensitivity to the strange electric form factor GsE with negligible contributions from competing effects. This experiment was performed in June 2004 and July-September 2005 in Hall A at Jefferson Lab. This work represents the experimental setup and analysis of the 2004 dataset.;The final statistical precision, from the combined datasets, put stringent requirements on the systematic errors that normalize the asymmetry (e.g. Q2, beam polarization, backgrounds). The experimental and analysis techniques, presented in this thesis, resulted in a 12.9% relative measure of the parity-violating asymmetry for the 2004 dataset, and a 4.1% relative measure for the 2005 dataset (the most precise measurement of a parity-violating asymmetry ever obtained).;The 2004 measured result, APV = 6.72 +/- 0.84 (stat) +/- 0.21 (syst) ppm, allows for the extraction of the electric strange form factor: GsE (Q2 = 0.1) = -0.038 +/- 0.042 (stat) +/- 0.010 (syst). When combined with results from previous experiments, at nearly the same kinematics, a clear picture of the contribution of strange quarks to the nucleon\u27s electric and magnetic form factors emerges

A novel graph-based method for targeted ligand-protein fitting

A thesis submitted to the Faculty of Creative Arts, Technologies & Science, University of Bedfordshire, in partial & fulfilment of the requirements for the degree of Master of Philosophy.The determination of protein binding sites and ligand -protein fitting are key to understanding the functionality of proteins, from revealing which ligand classes can bind or the optimal ligand for a given protein, such as protein/ drug interactions. There is a need for novel generic computational approaches for representation of protein-ligand interactions and the subsequent prediction of hitherto unknown interactions in proteins where the ligand binding sites are experimentally uncharacterised. The TMSite algorithms read in existing PDB structural data and isolate binding sites regions and identifies conserved features in functionally related proteins (proteins that bind the same ligand). The Boundary Cubes method for surface representation was applied to the modified PDB file allowing the creation of graphs for proteins and ligands that could be compared and caused no loss of geometric data. A method is included for describing binding site features of individual ligands conserved in terms of spatial relationships allowed identification of 3D motifs, named fingerprints, which could be searched for in other protein structures. This method combine with a modification of the pocket algorithm allows reduced search areas for graph matching. The methods allow isolation of the binding site from a complexed protein PDB file, identification of conserved features among the binding sites of individual ligand types, and search for these features in sequence data. In terms of spatial conservation create a fingerprint ofthe binding site that can be sought in other proteins of/mown structure, identifYing putative binding sites. The approach offers a novel and generic method for the identification of putative ligand binding sites for proteins for which there is no prior detailed structural characterisation of protein/ ligand interactions. It is unique in being able to convert PDB data into graphs, ready for comparison and thus fitting of ligand to protein with consideration of chemical charge and in the future other chemica! properties

Data-Driven Rational Drug Design

Vast amount of experimental data in structural biology has been generated, collected and accumulated in the last few decades. This rich dataset is an invaluable mine of knowledge, from which deep insights can be obtained and practical applications can be developed. To achieve that goal, we must be able to manage such Big Data\u27\u27 in science and investigate them expertly. Molecular docking is a field that can prominently make use of the large structural biology dataset. As an important component of rational drug design, molecular docking is used to perform large-scale screening of putative associations between small organic molecules and their pharmacologically relevant protein targets. Given a small molecule (ligand), a molecular docking program simulates its interaction with the target protein, and reports the probable conformation of the protein-ligand complex, and the relative binding affinity compared against other candidate ligands. This dissertation collects my contributions in several aspects of molecular docking. My early contribution focused on developing a novel metric to quantify the structural similarity between two protein-ligand complexes. Benchmarks show that my metric addressed several issues associated with the conventional metric. Furthermore, I extended the functionality of this metric to cross different systems, effectively utilizing the data at the proteome level. After developing the novel metric, I formulated a scoring function that can extract the biological information of the complex, integrate it with the physics components, and finally enhance the performance. Through collaboration, I implemented my model into an ultra-fast, adaptive program, which can take advantage of a range of modern parallel architectures and handle the demanding data processing tasks in large scale molecular docking applications

Mu2e Technical Design Report

The Mu2e experiment at Fermilab will search for charged lepton flavor violation via the coherent conversion process mu- N --> e- N with a sensitivity approximately four orders of magnitude better than the current world's best limits for this process. The experiment's sensitivity offers discovery potential over a wide array of new physics models and probes mass scales well beyond the reach of the LHC. We describe herein the preliminary design of the proposed Mu2e experiment. This document was created in partial fulfillment of the requirements necessary to obtain DOE CD-2 approval.Comment: compressed file, 888 pages, 621 figures, 126 tables; full resolution available at http://mu2e.fnal.gov; corrected typo in background summary, Table 3.

NASA Tech Briefs, November 1993

Topics covered: Advanced Manufacturing; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Graphene channels interfaced with distributed quantum dots

Previous research has elucidated the remarkable electrical and optical characteristics of graphene and pointed to the various applications of graphene-based devices. One of such applications is electro-optical graphene-based elements. In this work, the optoelectronic properties of field-effect transistors are explored. These are composed of surface graphene guides, which are interfaced with an array of individual semiconductor quantum dots. The graphene guide also serves as a channel for the field-effect transistor (FET) while the dots provide for fluorescence markers. They may be placed either within the capacitor formed between the graphene and the gate electrode, or on top of the graphene. Electrical characteristics under white light illumination and the device’€™s photoluminescence (PL) properties at various biasing conditions are studied. The graphene’s channel conductivity as a function of gate bias and drain-source bias under illumination are obtained. A minimum in source-drain current signifies the Dirac point. Under a low intensity of white light, the photocurrent changes signs as a function of gate bias, which suggests that the photocurrent may have originated from the graphene channel rather than the QDs. Negative differential photo-conductance is observed under illumination at large negative gate voltages. Changes in the fluorescence are noted as a function of both the drain-source and gate-source potentials. The fluorescence is more pronounced when the incident or the emission wavelengths are coupled to surface modes. Luminescence lifetimes and linewidths from an array of individual quantum dots (QDs) that are either interfaced with graphene surface guides or dispersed on aluminum electrodes are studied. The observed fluorescence quenching is consistent with screening by charge carriers. Fluorescence quenching is typically mentioned as a sign that chromophores are interfaced with a conductive surface (metal or graphene). The QDs interfaced with the metal film indeed exhibits shorter lifetime and line-broadening compared to QDs on a dielectric substrates but not necessarily fluorescence quenching; the latter may be impacted by molecular concentration, reflectivity considerations and conductor imperfections

The LUX-ZEPLIN (LZ) Experiment

We describe the design and assembly of the LUX-ZEPLIN experiment, a direct detection search for cosmic WIMP dark matter particles. The centerpiece of the experiment is a large liquid xenon time projection chamber sensitive to low energy nuclear recoils. Rejection of backgrounds is enhanced by a Xe skin veto detector and by a liquid scintillator Outer Detector loaded with gadolinium for efficient neutron capture and tagging. LZ is located in the Davis Cavern at the 4850' level of the Sanford Underground Research Facility in Lead, South Dakota, USA. We describe the major subsystems of the experiment and its key design features and requirements

Computational studies of drug-binding kinetics

The drug-receptor binding kinetics are defined by the rate at which a given drug associates with and dissociates from its binding site on its macromolecular receptor. The lead optimization stage of drug discovery programs usually emphasizes optimizing the affinity (as described by the equilibrium dissociation constant, Kd) of a drug which depends on the strength of its binding to a specific target. Since affinity is optimized under equilibrium conditions, it does not always ensures higher potency in vivo. There has been a growing consensus that, in addition to Kd, kinetic parameters (kon and koff ) should be optimized to improve the chances of a good clinical outcome. However, current understanding of the physicochemical features that contribute to differences in binding kinetics is limited. Experimental methods that are used to determine kinetic parameters for drug binding and unbinding are often time consuming and labor-intensive. Therefore, robust, high-throughput in silico methods are needed to predict binding kinetic parameters and to explore the mechanistic determinants of drug-protein binding. As the experimental data on drug-binding kinetics is continuously growing and the number of crystallographic structures of ligand-receptor complexes is also increasing, methods to compute three dimensional (3D) Quantitative-Structure-Kinetics relationships (QSKRs) offer great potential for predicting kinetic rate constants for new compounds. COMparative BINding Energy(COMBINE) analysis is one example of such approach that was developed to derive target-specific scoring functions based on molecular mechanics calculations. It has been used extensively to predict properties such as binding affinity, target selectivity, and substrate specificity. In this thesis, I made the first application of COMBINE analysis to derive Quantitative Structure-Kinetics Relationships (QSKRs) for the dissociation rates. I obtained models for koff of inhibitors of HIV-1 protease and heat shock protein 90 (HSP90) with very good predictive power and identified the key ligand-receptor interactions that contribute to the variance in binding kinetics. With technological and methodological advances, the use of all-atom unbiased Molecular Dynamics (MD) simulations can allow sampling upto the millisecond timescale and investigation of the kinetic profile of drug binding and unbinding to a receptor. However, the residence times of drug-receptor complexes are usually longer than the timescales that are feasible to simulate using conventional molecular dynamics techniques. Enhanced sampling methods can allow faster sampling of protein and ligand dynamics, thereby resulting in application of MD techniques to study longer timescale processes. I have evaluated the application of Tau-Random Acceleration Molecular Dynamics (Tau-RAMD), an enhanced sampling method based on MD, to compute the relative residence times of a series of compounds binding to Haspin kinase. A good correlation (R2 = 0.86) was observed between the computed residence times and the experimental residence times of these compounds. I also performed interaction energy calculations, both at the quantum chemical level and at the molecular mechanics level, to explain the experimental observation that the residence times of kinase inhibitors can be prolonged by introducing halogen-aromatic pi interactions between halogen atoms of inhibitors and aromatic residues at the binding site of kinases. I determined different energetic contributions to this highly polar and directional halogen-bonding interaction by partitioning the total interaction energy calculated at the quantum-chemical level into its constituent energy components. It was observed that the major contribution to this interaction energy comes from the correlation energy which describes second-order intermolecular dispersion interactions and the correlation corrections to the Hartree-Fock energy. In addition, a protocol to determine diffusional kon rates of low molecular weight compounds from Brownian Dynamics (BD) simulations of protein-ligand association was established using SDA 7 software. The widely studied test case of benzamidine binding to trypsin was used to evaluate a set of parameters and a robust set of optimal parameters was determined that should be generally applicable for computing the diffusional association rate constants of a wide range of protein-ligand binding pairs. I validated this protocol on inhibitors of several targets with varying complexity such as Human Coagulation Factor Xa, Haspin kinase and N1 Neuraminidase, and the computed diffusional association rate constants were compared with the experiments. I contributed to the development of a toolbox of computational methods: KBbox (http://kbbox.h-its.org/toolbox/), which provides information about various computational methods to study molecular binding kinetics, and different computational tools that employ them. It was developed to guide researchers on the use of the different computational and simulation approaches available to compute the kinetic parameters of drug-protein binding