Reaction Path Averaging: Characterizing the Structural Response of the DNA Double Helix to Electron Transfer

A polarizable environment, prominently the solvent, responds to electronic changes in biomolecules rapidly. The knowledge of conformational relaxation of the biomolecule itself, however, may be scarce or missing. In this work, we describe in detail the structural changes in DNA undergoing electron transfer between two adjacent nucleobases. We employ an approach based on averaging of tens to hundreds of thousands of nonequilibrium trajectories generated with molecular dynamics simulation, and a reduction of dimensionality suitable for DNA. We show that the conformational response of the DNA proceeds along a single collective coordinate that represents the relative orientation of two consecutive base pairs, namely, a combination of helical parameters shift and tilt. The structure of DNA relaxes on time scales reaching nanoseconds, contributing marginally to the relaxation of energies, which is dominated by the modes of motion of the aqueous solvent. The concept of reaction path averaging (RPA), conveniently exploited in this context, makes it possible to filter out any undesirable noise from the nonequilibrium data, and is applicable to any chemical process in general.Comment: 45 pages, 20 figures, published, added Supplementary informatio

Dynamics and Driving Forces of Macromolecular Complexes

Many functions in living cells are governed by macromolecular complexes. To fully describe the underlying mechanisms, they have to be understood at atomic level. The present study combines data obtained by X-ray crystallography and cryo-electron microscopy (cryo-EM) with molecular dynamics (MD) simulations. Two functions of macromolecular complexes, the downregulation of neurotransmitter release by the SNARE protein complex under oxidative stress and the translocation of transfer RNAs (tRNAs) through the ribosome during protein synthesis, were investigated. First, the hypothesis that oxidation of two cysteines on linker of the SNARE protein SNAP-25B and consequent disulfide bond formation shortens this linker sufficiently to hinder complex formation was tested. For this purpose, MD simulations of the SNARE complex with and without the disulfide bond were compared. Disulfide bond formation lead to conformational changes of the linker and of three central hydrophobic layers necessary to form the SNARE complex. Previously, mutations of residues contributing to these layers have been shown to reduce neurotransmitter release, suggesting that the stability of these layers is crucial for complex formation. The results from the simulations agree with the hypothesis that disulfide bond formation leads to a destabilization of the SNARE complex thus rendering it dysfunctional. This mechanism is interpreted as a chemomechanical regulation to shut down neurotransmitter release under oxidative stress, which has been linked to neurodegenerative diseases. In a second part I investigated the ribosome, where after peptide bond formation, bound tRNAs translocate by more than 7 nm to adjacent binding sites (A and P to P and E), accompanied by large-scale conformational motions (L1-stalk, intersubunit rotation) of the ribosome. By combining existing cryo-EM reconstructions of translocation intermediates with high resolution crystal structures, we obtained 13 near-atomic resolution structures. Subsequently, MD simulations of were carried out for each intermediate state. The obtained dynamics within these states allowed to estimate transition rates between states for motions of the L1-stalk, tRNAs and intersubunit rotations. Rapid motions of the L1-stalk and the small (30S) subunit on sub-microsecond timescales were revealed, whereas tRNA motions were seen to be rate-limiting for most transitions. By calculating the interaction free energy between L1-stalk and tRNA, molecular forces were derived showing that the L1-stalk is actively pulling the tRNA from P to E binding site, thereby overcoming barriers for the tRNA motion. Further, ribosomal proteins L5 and L16 guide the tRNAs by 'sliding' and 'stepping' mechanisms involving key protein-tRNA contacts. This explains how tRNA binding affinity is kept sufficiently constant to allow rapid translocation despite large-scale displacements. Translocation is accompanied by rotations of the 30S ribosomal subunit of more than 20 degrees relative to the large (50S) subunit. For each translocation intermediate, the affinity of the two subunits with each other must be finely tuned enabling such conformational flexibility while maintaining integrity of the ribosomal complex. Analyzing the trajectories at residue level reveals two classes of intersubunit contact interactions: i) persistent residue contacts which are independent of 30S rotation and primarily located close to the axis of rotation. ii) contacts that are formed and ruptured depending on the rotation angle, seen mainly on the periphery. Strikingly, also these rotation specific contacts substantially contribute to the overall stability of the ribosomal assembly and are expected to maintain a constant interaction energy with low barriers for rotation. The simulations reveal that upon removal of tRNAs peripheral contacts are weakened and, in turn, intersubunit rotation angles decrease, in agreement with cryo-EM analysis of tRNA depleted ribosomes. The identified mechanisms for lowering free energy barriers and for fine-tuning affinities might have developed similarly in other macromolecular complexes

Research reports: 1991 NASA/ASEE Summer Faculty Fellowship Program

The basic objectives of the programs, which are in the 28th year of operation nationally, are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA Centers. The faculty fellows spent 10 weeks at MSFC engaged in a research project compatible with their interests and background and worked in collaboration with a NASA/MSFC colleague. This is a compilation of their research reports for summer 1991

Estimation of in-situ fluid properties from the combined interpretation of nuclear, dielectric, optical, and magnetic resonance measurements

During the last few decades, the quantification of hydrocarbon pore volume from borehole measurements has been widely studied for reservoir descriptions. Relatively less effort has been devoted to estimating in-situ fluid properties because (1) acquiring fluid samples is expensive, (2) reservoir fluids are a complex mixture of various miscible and non-miscible phases, and (3) they depend on environmental factors such as temperature and pressure. This dissertation investigates the properties of fluid mixtures based on various manifestations of their electromagnetic properties from the MHz to the THz frequency ranges. A variety of fluids, including water, alcohol, alkane, aromatics, cyclics, ether, and their mixtures, are analyzed with both laboratory experiments and numerical simulations. A new method is introduced to quantify in-situ hydrocarbon properties from borehole nuclear measurements. The inversion-based estimation method allows depth-continuous assessment of compositional gradients at in-situ conditions and provides thermodynamically consistent interpretations of reservoir fluids that depend greatly on phase behavior. Applications of this interpretation method to measurements acquired in two field examples, including one in a gas-oil transition zone, yielded reliable and verifiable hydrocarbon compositions. Dielectric properties of polar liquid mixtures were analyzed in the frequency range from 20 MHz to 20 GHz at ambient conditions. The Havriliak-Negami (HN) model was adapted for the estimation of dielectric permittivity and relaxation time. These experimental dielectric properties were compared to Molecular Dynamics (MD) simulations. Additionally, thermodynamic properties, including excess enthalpy, density, number of hydrogen bonds, and effective self-diffusion coefficient, were computed to cross-validate experimental results. Properties predicted from MD simulations are in excellent agreement with experimental measurements. The three most common optical spectroscopy techniques, i.e. Near Infrared (NIR), Infrared, and Raman, were applied for the estimation of compositions and physical properties of liquid mixtures. Several analytical techniques, including Principal Component Analysis (PCA), Radial Basis Functions (RBF), Partial Least-Squares Regression (PLSR), and Artificial Neural Networks (ANN), were separately implemented for each spectrum to build correlations between spectral data and properties of liquid mixtures. Results show that the proposed methods yield prediction errors from 1.5% to 22.2% smaller than those obtained with standard multivariate methods. Furthermore, the errors can be decreased by combining NIR, Infrared, and Raman spectroscopy measurements. Lastly, the ¹H NMR longitudinal relaxation properties of various liquid mixtures were examined with the objective of detecting individual components. Relaxation times and diffusion coefficients obtained via MD simulations for these mixtures are in agreement with experimental data. Also, the ¹H-¹H dipole-dipole relaxations for fluid mixtures were decomposed into the relaxations emanate from the intramolecular and intermolecular interactions. The quantification of intermolecular interactions between the same molecules and different molecules reveals how much each component contributes to the total NMR longitudinal relaxation of the mixture as well as the level of interactions between different fluids. Both experimental and numerical simulation results documented in this dissertation indicate that selecting measurement techniques that can capture the physical property of interest and maximize the physical contrasts between different components is important for reliable and accurate in-situ fluid identificationPetroleum and Geosystems Engineerin

Event-Based Visual-Inertial Odometry Using Smart Features

Event-based cameras are a novel type of visual sensor that operate under a unique paradigm, providing asynchronous data on the log-level changes in light intensity for individual pixels. This hardware-level approach to change detection allows these cameras to achieve ultra-wide dynamic range and high temporal resolution. Furthermore, the advent of convolutional neural networks (CNNs) has led to state-of-the-art navigation solutions that now rival or even surpass human engineered algorithms. The advantages offered by event cameras and CNNs make them excellent tools for visual odometry (VO). This document presents the implementation of a CNN trained to detect and describe features within an image as well as the implementation of an event-based visual-inertial odometry (EVIO) pipeline, which estimates a vehicle\u27s 6-degrees-offreedom (DOF) pose using an affixed event-based camera with an integrated inertial measurement unit (IMU). The front-end of this pipeline utilizes a neural network for generating image frames from asynchronous event camera data. These frames are fed into a multi-state constraint Kalman filter (MSCKF) back-end that uses the output of the developed CNN to perform measurement updates. The EVIO pipeline was tested on a selection from the Event-Camera Dataset [1], and on a dataset collected from a fixed-wing unmanned aerial vehicle (UAV) flight test conducted by the Autonomy and Navigation Technology (ANT) Center

Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation