Switchable host-guest systems on surfaces.

PubMed

Yang, Ying-Wei; Sun, Yu-Long; Song, Nan

2014-07-15

CONSPECTUS: For device miniaturization, nanotechnology follows either the "top-down" approach scaling down existing larger-scale devices or the "bottom-up' approach assembling the smallest possible building blocks to functional nanoscale entities. For synthetic nanodevices, self-assembly on surfaces is a superb method to achieve useful functions and enable their interactions with the surrounding world. Consequently, adaptability and responsiveness to external stimuli are other prerequisites for their successful operation. Mechanically interlocked molecules such as rotaxanes and catenanes, and their precursors, that is, molecular switches and supramolecular switches including pseudorotaxanes, are molecular machines or prototypes of machines capable of mechanical motion induced by chemical signals, biological inputs, light or redox processes as the external stimuli. Switching of these functional host-guest systems on surfaces becomes a fundamental requirement for artificial molecular machines to work, mimicking the molecular machines in nature, such as proteins and their assemblies operating at dynamic interfaces such as the surfaces of cell membranes. Current research endeavors in material science and technology are focused on developing either a new class of materials or materials with novel/multiple functionalities by shifting host-guest chemistry from solution phase to surfaces. In this Account, we present our most recent attempts of building monolayers of rotaxanes/pseudorotaxanes on surfaces, providing stimuli-induced macroscopic effects and further understanding on the switchable host-guest systems at interfaces. Biocompatible versions of molecular machines based on synthetic macrocycles, such as cucurbiturils, pillararenes, calixarenes, and cyclodextrins, have been employed to form self-assembled monolayers of gates on the surfaces of mesoporous silica nanoparticles to regulate the controlled release of cargo/drug molecules under a range of external stimuli, such as light, pH variations, competitive binding, and enzyme. Rotaxanes have also been assembled onto the surfaces of gold nanodisks and microcantilevers to realize active molecular plasmonics and synthetic molecular actuators for device fabrication and function. Pillararenes have been successfully used to control and aid the synthesis of gold nanoparticles, semiconducting quantum dots, and magnetic nanoparticles. The resulting organic-inorganic hydrid nanomaterials have been successfully used for controlled self-assembly, herbicide sensing and detection, pesticide removal, and so forth, taking advantage of the selective binding of pillarenes toward target molecules. Cyclodextrins have also been successfully functionalized onto the surface of gold nanoparticles to serve as recycling extractors for C60. Many interesting prototypes of nanodevices based on synthetic macrocycles and their host-guest chemistry have been constructed and served for different potential applications. This Account will be a summary of the efforts made mainly by us, and others, on the host-guest chemistry of synthetic macrocyclic compounds on the surfaces of different solid supports.

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.

PubMed

Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D; Duvenaud, David; Maclaurin, Dougal; Blood-Forsythe, Martin A; Chae, Hyun Sik; Einzinger, Markus; Ha, Dong-Gwang; Wu, Tony; Markopoulos, Georgios; Jeon, Soonok; Kang, Hosuk; Miyazaki, Hiroshi; Numata, Masaki; Kim, Sunghan; Huang, Wenliang; Hong, Seong Ik; Baldo, Marc; Adams, Ryan P; Aspuru-Guzik, Alán

2016-10-01

Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

NASA Astrophysics Data System (ADS)

Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D.; Duvenaud, David; MacLaurin, Dougal; Blood-Forsythe, Martin A.; Chae, Hyun Sik; Einzinger, Markus; Ha, Dong-Gwang; Wu, Tony; Markopoulos, Georgios; Jeon, Soonok; Kang, Hosuk; Miyazaki, Hiroshi; Numata, Masaki; Kim, Sunghan; Huang, Wenliang; Hong, Seong Ik; Baldo, Marc; Adams, Ryan P.; Aspuru-Guzik, Alán

2016-10-01

Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.

70% efficiency of bistate molecular machines explained by information theory, high dimensional geometry and evolutionary convergence.

PubMed

Schneider, Thomas D

2010-10-01

The relationship between information and energy is key to understanding biological systems. We can display the information in DNA sequences specifically bound by proteins by using sequence logos, and we can measure the corresponding binding energy. These can be compared by noting that one of the forms of the second law of thermodynamics defines the minimum energy dissipation required to gain one bit of information. Under the isothermal conditions that molecular machines function this is [Formula in text] joules per bit (kB is Boltzmann's constant and T is the absolute temperature). Then an efficiency of binding can be computed by dividing the information in a logo by the free energy of binding after it has been converted to bits. The isothermal efficiencies of not only genetic control systems, but also visual pigments are near 70%. From information and coding theory, the theoretical efficiency limit for bistate molecular machines is ln 2=0.6931. Evolutionary convergence to maximum efficiency is limited by the constraint that molecular states must be distinct from each other. The result indicates that natural molecular machines operate close to their information processing maximum (the channel capacity), and implies that nanotechnology can attain this goal.

70% efficiency of bistate molecular machines explained by information theory, high dimensional geometry and evolutionary convergence

PubMed Central

Schneider, Thomas D.

2010-01-01

The relationship between information and energy is key to understanding biological systems. We can display the information in DNA sequences specifically bound by proteins by using sequence logos, and we can measure the corresponding binding energy. These can be compared by noting that one of the forms of the second law of thermodynamics defines the minimum energy dissipation required to gain one bit of information. Under the isothermal conditions that molecular machines function this is joules per bit ( is Boltzmann's constant and T is the absolute temperature). Then an efficiency of binding can be computed by dividing the information in a logo by the free energy of binding after it has been converted to bits. The isothermal efficiencies of not only genetic control systems, but also visual pigments are near 70%. From information and coding theory, the theoretical efficiency limit for bistate molecular machines is ln 2 = 0.6931. Evolutionary convergence to maximum efficiency is limited by the constraint that molecular states must be distinct from each other. The result indicates that natural molecular machines operate close to their information processing maximum (the channel capacity), and implies that nanotechnology can attain this goal. PMID:20562221

Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools.

PubMed

Price, Jeffrey H; Goodacre, Angela; Hahn, Klaus; Hodgson, Louis; Hunter, Edward A; Krajewski, Stanislaw; Murphy, Robert F; Rabinovich, Andrew; Reed, John C; Heynen, Susanne

2002-01-01

Cellular behavior is complex. Successfully understanding systems at ever-increasing complexity is fundamental to advances in modern science and unraveling the functional details of cellular behavior is no exception. We present a collection of prospectives to provide a glimpse of the techniques that will aid in collecting, managing and utilizing information on complex cellular processes via molecular imaging tools. These include: 1) visualizing intracellular protein activity with fluorescent markers, 2) high throughput (and automated) imaging of multilabeled cells in statistically significant numbers, and 3) machine intelligence to analyze subcellular image localization and pattern. Although not addressed here, the importance of combining cell-image-based information with detailed molecular structure and ligand-receptor binding models cannot be overlooked. Advanced molecular imaging techniques have the potential to impact cellular diagnostics for cancer screening, clinical correlations of tissue molecular patterns for cancer biology, and cellular molecular interactions for accelerating drug discovery. The goal of finally understanding all cellular components and behaviors will be achieved by advances in both instrumentation engineering (software and hardware) and molecular biochemistry. Copyright 2002 Wiley-Liss, Inc.

Motor proteins and molecular motors: how to operate machines at the nanoscale.

PubMed

Kolomeisky, Anatoly B

2013-11-20

Several classes of biological molecules that transform chemical energy into mechanical work are known as motor proteins or molecular motors. These nanometer-sized machines operate in noisy stochastic isothermal environments, strongly supporting fundamental cellular processes such as the transfer of genetic information, transport, organization and functioning. In the past two decades motor proteins have become a subject of intense research efforts, aimed at uncovering the fundamental principles and mechanisms of molecular motor dynamics. In this review, we critically discuss recent progress in experimental and theoretical studies on motor proteins. Our focus is on analyzing fundamental concepts and ideas that have been utilized to explain the non-equilibrium nature and mechanisms of molecular motors.

High-pressure microscopy for tracking dynamic properties of molecular machines.

PubMed

Nishiyama, Masayoshi

2017-12-01

High-pressure microscopy is one of the powerful techniques to visualize the effects of hydrostatic pressures on research targets. It could be used for monitoring the pressure-induced changes in the structure and function of molecular machines in vitro and in vivo. This review focuses on the dynamic properties of the assemblies and machines, analyzed by means of high-pressure microscopy measurement. We developed a high-pressure microscope that is optimized both for the best image formation and for the stability to hydrostatic pressure up to 150 MPa. Application of pressure could change polymerization and depolymerization processes of the microtubule cytoskeleton, suggesting a modulation of the intermolecular interaction between tubulin molecules. A novel motility assay demonstrated that high hydrostatic pressure induces counterclockwise (CCW) to clockwise (CW) reversals of the Escherichia coli flagellar motor. The present techniques could be extended to study how molecular machines in complicated systems respond to mechanical stimuli. Copyright © 2017 Elsevier B.V. All rights reserved.

Nanotube Heterojunctions and Endo-Fullerenes for Nanoelectronics

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Menon, M.; Andriotis, Antonis; Cho, K.; Park, Jun; Biegel, Bryan A. (Technical Monitor)

2002-01-01

Topics discussed include: (1) Light-Weight Multi-Functional Materials: Nanomechanics; Nanotubes and Composites; Thermal/Chemical/Electrical Characterization; (2) Biomimetic/Revolutionary Concepts: Evolutionary Computing and Sensing; Self-Heating Materials; (3) Central Computing System: Molecular Electronics; Materials for Quantum Bits; and (4) Molecular Machines.

Synthetic Ion Channels and DNA Logic Gates as Components of Molecular Robots.

PubMed

Kawano, Ryuji

2018-02-19

A molecular robot is a next-generation biochemical machine that imitates the actions of microorganisms. It is made of biomaterials such as DNA, proteins, and lipids. Three prerequisites have been proposed for the construction of such a robot: sensors, intelligence, and actuators. This Minireview focuses on recent research on synthetic ion channels and DNA computing technologies, which are viewed as potential candidate components of molecular robots. Synthetic ion channels, which are embedded in artificial cell membranes (lipid bilayers), sense ambient ions or chemicals and import them. These artificial sensors are useful components for molecular robots with bodies consisting of a lipid bilayer because they enable the interface between the inside and outside of the molecular robot to function as gates. After the signal molecules arrive inside the molecular robot, they can operate DNA logic gates, which perform computations. These functions will be integrated into the intelligence and sensor sections of molecular robots. Soon, these molecular machines will be able to be assembled to operate as a mass microrobot and play an active role in environmental monitoring and in vivo diagnosis or therapy. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fundamental Characteristics of AAA+ Protein Family Structure and Function.

PubMed

Miller, Justin M; Enemark, Eric J

2016-01-01

Many complex cellular events depend on multiprotein complexes known as molecular machines to efficiently couple the energy derived from adenosine triphosphate hydrolysis to the generation of mechanical force. Members of the AAA+ ATPase superfamily (ATPases Associated with various cellular Activities) are critical components of many molecular machines. AAA+ proteins are defined by conserved modules that precisely position the active site elements of two adjacent subunits to catalyze ATP hydrolysis. In many cases, AAA+ proteins form a ring structure that translocates a polymeric substrate through the central channel using specialized loops that project into the central channel. We discuss the major features of AAA+ protein structure and function with an emphasis on pivotal aspects elucidated with archaeal proteins.

AAA+ Machines of Protein Destruction in Mycobacteria.

PubMed

Alhuwaider, Adnan Ali H; Dougan, David A

2017-01-01

The bacterial cytosol is a complex mixture of macromolecules (proteins, DNA, and RNA), which collectively are responsible for an enormous array of cellular tasks. Proteins are central to most, if not all, of these tasks and as such their maintenance (commonly referred to as protein homeostasis or proteostasis) is vital for cell survival during normal and stressful conditions. The two key aspects of protein homeostasis are, (i) the correct folding and assembly of proteins (coupled with their delivery to the correct cellular location) and (ii) the timely removal of unwanted or damaged proteins from the cell, which are performed by molecular chaperones and proteases, respectively. A major class of proteins that contribute to both of these tasks are the AAA+ (ATPases associated with a variety of cellular activities) protein superfamily. Although much is known about the structure of these machines and how they function in the model Gram-negative bacterium Escherichia coli , we are only just beginning to discover the molecular details of these machines and how they function in mycobacteria. Here we review the different AAA+ machines, that contribute to proteostasis in mycobacteria. Primarily we will focus on the recent advances in the structure and function of AAA+ proteases, the substrates they recognize and the cellular pathways they control. Finally, we will discuss the recent developments related to these machines as novel drug targets.

Graph Kernels for Molecular Similarity.

PubMed

Rupp, Matthias; Schneider, Gisbert

2010-04-12

Molecular similarity measures are important for many cheminformatics applications like ligand-based virtual screening and quantitative structure-property relationships. Graph kernels are formal similarity measures defined directly on graphs, such as the (annotated) molecular structure graph. Graph kernels are positive semi-definite functions, i.e., they correspond to inner products. This property makes them suitable for use with kernel-based machine learning algorithms such as support vector machines and Gaussian processes. We review the major types of kernels between graphs (based on random walks, subgraphs, and optimal assignments, respectively), and discuss their advantages, limitations, and successful applications in cheminformatics. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias

We introduce a fingerprint representation of molecules based on a Fourier series of atomic radial distribution functions. This fingerprint is unique (except for chirality), continuous, and differentiable with respect to atomic coordinates and nuclear charges. It is invariant with respect to translation, rotation, and nuclear permutation, and requires no preconceived knowledge about chemical bonding, topology, or electronic orbitals. As such, it meets many important criteria for a good molecular representation, suggesting its usefulness for machine learning models of molecular properties trained across chemical compound space. To assess the performance of this new descriptor, we have trained machine learning models ofmore » molecular enthalpies of atomization for training sets with up to 10 k organic molecules, drawn at random from a published set of 134 k organic molecules with an average atomization enthalpy of over 1770 kcal/mol. We validate the descriptor on all remaining molecules of the 134 k set. For a training set of 10 k molecules, the fingerprint descriptor achieves a mean absolute error of 8.0 kcal/mol. This is slightly worse than the performance attained using the Coulomb matrix, another popular alternative, reaching 6.2 kcal/mol for the same training and test sets. (c) 2015 Wiley Periodicals, Inc.« less

Highly ordered molecular rotor matrix on a nanopatterned template: titanyl phthalocyanine molecules on FeO/Pt(111).

PubMed

Lu, Shuangzan; Huang, Min; Qin, Zhihui; Yu, Yinghui; Guo, Qinmin; Cao, Gengyu

2018-08-03

Molecular rotors, motors and gears play important roles in artificial molecular machines, in which rotor and motor matrices are highly desirable for large-scale bottom-up fabrication of molecular machines. Here we demonstrate the fabrication of a highly ordered molecular rotor matrix by depositing nonplanar dipolar titanyl phthalocyanine (TiOPc, C 32 H 16 N 8 OTi) molecules on a Moiré patterned dipolar FeO/Pt(111) substrate. TiOPc molecules with O atoms pointing outwards from the substrate (upward) or towards the substrate (downward) are alternatively adsorbed on the fcc sites by strong lateral confinement. The adsorbed molecules, i.e. two kinds of molecular rotors, show different scanning tunneling microscopy images, thermal stabilities and rotational characteristics. Density functional theory calculations clarify that TiOPc molecules anchoring upwards with high adsorption energies correspond to low-rotational-rate rotors, while those anchoring downwards with low adsorption energies correspond to high-rotational-rate rotors. A robust rotor matrix fully occupied by low-rate rotors is fabricated by depositing molecules on the substrate at elevated temperature. Such a paradigm opens up a promising route to fabricate functional molecular rotor matrices, driven motor matrices and even gear groups on solid substrates.

Rotary ATPases

PubMed Central

Stewart, Alastair G.; Sobti, Meghna; Harvey, Richard P.; Stock, Daniela

2013-01-01

Rotary ATPases are molecular rotary motors involved in biological energy conversion. They either synthesize or hydrolyze the universal biological energy carrier adenosine triphosphate. Recent work has elucidated the general architecture and subunit compositions of all three sub-types of rotary ATPases. Composite models of the intact F-, V- and A-type ATPases have been constructed by fitting high-resolution X-ray structures of individual subunits or sub-complexes into low-resolution electron densities of the intact enzymes derived from electron cryo-microscopy. Electron cryo-tomography has provided new insights into the supra-molecular arrangement of eukaryotic ATP synthases within mitochondria and mass-spectrometry has started to identify specifically bound lipids presumed to be essential for function. Taken together these molecular snapshots show that nano-scale rotary engines have much in common with basic design principles of man made machines from the function of individual “machine elements” to the requirement of the right “fuel” and “oil” for different types of motors. PMID:23369889

Well-tempered metadynamics as a tool for characterizing multi-component, crystalline molecular machines.

PubMed

Ilott, Andrew J; Palucha, Sebastian; Hodgkinson, Paul; Wilson, Mark R

2013-10-10

The well-tempered, smoothly converging form of the metadynamics algorithm has been implemented in classical molecular dynamics simulations and used to obtain an estimate of the free energy surface explored by the molecular rotations in the plastic crystal, octafluoronaphthalene. The biased simulations explore the full energy surface extremely efficiently, more than 4 orders of magnitude faster than unbiased molecular dynamics runs. The metadynamics collective variables used have also been expanded to include the simultaneous orientations of three neighboring octafluoronaphthalene molecules. Analysis of the resultant three-dimensional free energy surface, which is sampled to a very high degree despite its significant complexity, demonstrates that there are strong correlations between the molecular orientations. Although this correlated motion is of limited applicability in terms of exploiting dynamical motion in octafluoronaphthalene, the approach used is extremely well suited to the investigation of the function of crystalline molecular machines.

Fundamental Characteristics of AAA+ Protein Family Structure and Function

PubMed Central

2016-01-01

Many complex cellular events depend on multiprotein complexes known as molecular machines to efficiently couple the energy derived from adenosine triphosphate hydrolysis to the generation of mechanical force. Members of the AAA+ ATPase superfamily (ATPases Associated with various cellular Activities) are critical components of many molecular machines. AAA+ proteins are defined by conserved modules that precisely position the active site elements of two adjacent subunits to catalyze ATP hydrolysis. In many cases, AAA+ proteins form a ring structure that translocates a polymeric substrate through the central channel using specialized loops that project into the central channel. We discuss the major features of AAA+ protein structure and function with an emphasis on pivotal aspects elucidated with archaeal proteins. PMID:27703410

Powering the programmed nanostructure and function of gold nanoparticles with catenated DNA machines

NASA Astrophysics Data System (ADS)

Elbaz, Johann; Cecconello, Alessandro; Fan, Zhiyuan; Govorov, Alexander O.; Willner, Itamar

2013-06-01

DNA nanotechnology is a rapidly developing research area in nanoscience. It includes the development of DNA machines, tailoring of DNA nanostructures, application of DNA nanostructures for computing, and more. Different DNA machines were reported in the past and DNA-guided assembly of nanoparticles represents an active research effort in DNA nanotechnology. Several DNA-dictated nanoparticle structures were reported, including a tetrahedron, a triangle or linear nanoengineered nanoparticle structures; however, the programmed, dynamic reversible switching of nanoparticle structures and, particularly, the dictated switchable functions emerging from the nanostructures, are missing elements in DNA nanotechnology. Here we introduce DNA catenane systems (interlocked DNA rings) as molecular DNA machines for the programmed, reversible and switchable arrangement of different-sized gold nanoparticles. We further demonstrate that the machine-powered gold nanoparticle structures reveal unique emerging switchable spectroscopic features, such as plasmonic coupling or surface-enhanced fluorescence.

POLYSHIFT Communications Software for the Connection Machine System CM-200

DOE PAGES

George, William; Brickner, Ralph G.; Johnsson, S. Lennart

1994-01-01

We describe the use and implementation of a polyshift function PSHIFT for circular shifts and end-offs shifts. Polyshift is useful in many scientific codes using regular grids, such as finite difference codes in several dimensions, and multigrid codes, molecular dynamics computations, and in lattice gauge physics computations, such as quantum chromodynamics (QCD) calculations. Our implementation of the PSHIFT function on the Connection Machine systems CM-2 and CM-200 offers a speedup of up to a factor of 3–4 compared with CSHIFT when the local data motion within a node is small. The PSHIFT routine is included in the Connection Machine Scientificmore » Software Library (CMSSL).« less

Light-operated machines based on threaded molecular structures.

PubMed

Credi, Alberto; Silvi, Serena; Venturi, Margherita

2014-01-01

Rotaxanes and related species represent the most common implementation of the concept of artificial molecular machines, because the supramolecular nature of the interactions between the components and their interlocked architecture allow a precise control on the position and movement of the molecular units. The use of light to power artificial molecular machines is particularly valuable because it can play the dual role of "writing" and "reading" the system. Moreover, light-driven machines can operate without accumulation of waste products, and photons are the ideal inputs to enable autonomous operation mechanisms. In appropriately designed molecular machines, light can be used to control not only the stability of the system, which affects the relative position of the molecular components but also the kinetics of the mechanical processes, thereby enabling control on the direction of the movements. This step forward is necessary in order to make a leap from molecular machines to molecular motors.

Single molecule thermodynamics in biological motors.

PubMed

Taniguchi, Yuichi; Karagiannis, Peter; Nishiyama, Masayoshi; Ishii, Yoshiharu; Yanagida, Toshio

2007-04-01

Biological molecular machines use thermal activation energy to carry out various functions. The process of thermal activation has the stochastic nature of output events that can be described according to the laws of thermodynamics. Recently developed single molecule detection techniques have allowed each distinct enzymatic event of single biological machines to be characterized providing clues to the underlying thermodynamics. In this study, the thermodynamic properties in the stepping movement of a biological molecular motor have been examined. A single molecule detection technique was used to measure the stepping movements at various loads and temperatures and a range of thermodynamic parameters associated with the production of each forward and backward step including free energy, enthalpy, entropy and characteristic distance were obtained. The results show that an asymmetry in entropy is a primary factor that controls the direction in which the motor will step. The investigation on single molecule thermodynamics has the potential to reveal dynamic properties underlying the mechanisms of how biological molecular machines work.

Machine learning of molecular properties: Locality and active learning

NASA Astrophysics Data System (ADS)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

Trajectories of the ribosome as a Brownian nanomachine

DOE PAGES

Dashti, Ali; Schwander, Peter; Langlois, Robert; ...

2014-11-24

In a Brownian machine, there is a tiny device buffeted by the random motions of molecules in the environment, is capable of exploiting these thermal motions for many of the conformational changes in its work cycle. Such machines are now thought to be ubiquitous, with the ribosome, a molecular machine responsible for protein synthesis, increasingly regarded as prototypical. We present a new analytical approach capable of determining the free-energy landscape and the continuous trajectories of molecular machines from a large number of snapshots obtained by cryogenic electron microscopy. We demonstrate this approach in the context of experimental cryogenic electron microscopemore » images of a large ensemble of nontranslating ribosomes purified from yeast cells. The free-energy landscape is seen to contain a closed path of low energy, along which the ribosome exhibits conformational changes known to be associated with the elongation cycle. This approach allows model-free quantitative analysis of the degrees of freedom and the energy landscape underlying continuous conformational changes in nanomachines, including those important for biological function.« less

Microcompartments and protein machines in prokaryotes.

PubMed

Saier, Milton H

2013-01-01

The prokaryotic cell was once thought of as a 'bag of enzymes' with little or no intracellular compartmentalization. In this view, most reactions essential for life occurred as a consequence of random molecular collisions involving substrates, cofactors and cytoplasmic enzymes. Our current conception of a prokaryote is far from this view. We now consider a bacterium or an archaeon as a highly structured, nonrandom collection of functional membrane-embedded and proteinaceous molecular machines, each of which serves a specialized function. In this article we shall present an overview of such microcompartments including (1) the bacterial cytoskeleton and the apparati allowing DNA segregation during cell division; (2) energy transduction apparati involving light-driven proton pumping and ion gradient-driven ATP synthesis; (3) prokaryotic motility and taxis machines that mediate cell movements in response to gradients of chemicals and physical forces; (4) machines of protein folding, secretion and degradation; (5) metabolosomes carrying out specific chemical reactions; (6) 24-hour clocks allowing bacteria to coordinate their metabolic activities with the daily solar cycle, and (7) proteinaceous membrane compartmentalized structures such as sulfur granules and gas vacuoles. Membrane-bound prokaryotic organelles were considered in a recent Journal of Molecular Microbiology and Biotechnology written symposium concerned with membranous compartmentalization in bacteria [J Mol Microbiol Biotechnol 2013;23:1-192]. By contrast, in this symposium, we focus on proteinaceous microcompartments. These two symposia, taken together, provide the interested reader with an objective view of the remarkable complexity of what was once thought of as a simple noncompartmentalized cell. Copyright © 2013 S. Karger AG, Basel.

Machine Learning Estimates of Natural Product Conformational Energies

PubMed Central

Rupp, Matthias; Bauer, Matthias R.; Wilcken, Rainer; Lange, Andreas; Reutlinger, Michael; Boeckler, Frank M.; Schneider, Gisbert

2014-01-01

Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. PMID:24453952

Nanomedicine: Tiny Particles and Machines Give Huge Gains

PubMed Central

Tong, Sheng; Fine, Eli J.; Lin, Yanni; Cradick, Thomas J.; Bao, Gang

2014-01-01

Nanomedicine is an emerging field that integrates nanotechnology, biomolecular engineering, life sciences and medicine; it is expected to produce major breakthroughs in medical diagnostics and therapeutics. Nano-scale structures and devices are compatible in size with proteins and nucleic acids in living cells. Therefore, the design, characterization and application of nano-scale probes, carriers and machines may provide unprecedented opportunities for achieving a better control of biological processes, and drastic improvements in disease detection, therapy, and prevention. Recent advances in nanomedicine include the development of nanoparticle-based probes for molecular imaging, nano-carriers for drug/gene delivery, multi-functional nanoparticles for theranostics, and molecular machines for biological and medical studies. This article provides an overview of the nanomedicine field, with an emphasis on nanoparticles for imaging and therapy, as well as engineered nucleases for genome editing. The challenges in translating nanomedicine approaches to clinical applications are discussed. PMID:24297494

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

NASA Astrophysics Data System (ADS)

Gastegger, M.; Schwiedrzik, L.; Bittermann, M.; Berzsenyi, F.; Marquetand, P.

2018-06-01

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the undesirable scaling of the latter with an increasing number of different elements in a chemical system. The performance of these two descriptors is compared using them as inputs in high-dimensional neural network potentials (HDNNPs), employing the molecular structures and associated enthalpies of the 133 855 molecules containing up to five different elements reported in the QM9 database as reference data. A substantially smaller number of wACSFs than ACSFs is needed to obtain a comparable spatial resolution of the molecular structures. At the same time, this smaller set of wACSFs leads to a significantly better generalization performance in the machine learning potential than the large set of conventional ACSFs. Furthermore, we show that the intrinsic parameters of the descriptors can in principle be optimized with a genetic algorithm in a highly automated manner. For the wACSFs employed here, we find however that using a simple empirical parametrization scheme is sufficient in order to obtain HDNNPs with high accuracy.

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

DOE PAGES

Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

2015-06-04

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

Signalling networks and dynamics of allosteric transitions in bacterial chaperonin GroEL: implications for iterative annealing of misfolded proteins.

PubMed

Thirumalai, D; Hyeon, Changbong

2018-06-19

Signal transmission at the molecular level in many biological complexes occurs through allosteric transitions. Allostery describes the responses of a complex to binding of ligands at sites that are spatially well separated from the binding region. We describe the structural perturbation method, based on phonon propagation in solids, which can be used to determine the signal-transmitting allostery wiring diagram (AWD) in large but finite-sized biological complexes. Application to the bacterial chaperonin GroEL-GroES complex shows that the AWD determined from structures also drives the allosteric transitions dynamically. From both a structural and dynamical perspective these transitions are largely determined by formation and rupture of salt-bridges. The molecular description of allostery in GroEL provides insights into its function, which is quantitatively described by the iterative annealing mechanism. Remarkably, in this complex molecular machine, a deep connection is established between the structures, reaction cycle during which GroEL undergoes a sequence of allosteric transitions, and function, in a self-consistent manner.This article is part of a discussion meeting issue 'Allostery and molecular machines'. © 2018 The Author(s).

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

PubMed Central

2015-01-01

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

Protein function in precision medicine: deep understanding with machine learning.

PubMed

Rost, Burkhard; Radivojac, Predrag; Bromberg, Yana

2016-08-01

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both. © 2016 Federation of European Biochemical Societies.

Transitioning NWChem to the Next Generation of Manycore Machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bylaska, Eric J.; Apra, Edoardo; Kowalski, Karol

The NorthWest Chemistry (NWChem) modeling software is a popular molecular chemistry simulation software that was designed from the start to work on massively parallel processing supercomputers[6, 28, 49]. It contains an umbrella of modules that today includes Self Consistent Field (SCF), second order Mller-Plesset perturbation theory (MP2), Coupled Cluster, multi-conguration selfconsistent eld (MCSCF), selected conguration interaction (CI), tensor contraction engine (TCE) many body methods, density functional theory (DFT), time-dependent density functional theory (TDDFT), real time time-dependent density functional theory, pseudopotential plane-wave density functional theory (PSPW), band structure (BAND), ab initio molecular dynamics, Car-Parrinello molecular dynamics, classical molecular dynamics (MD), QM/MM,more » AIMD/MM, GIAO NMR, COSMO, COSMO-SMD, and RISM solvation models, free energy simulations, reaction path optimization, parallel in time, among other capabilities[ 22]. Moreover new capabilities continue to be added with each new release.« less

Stereodivergent synthesis with a programmable molecular machine

NASA Astrophysics Data System (ADS)

Kassem, Salma; Lee, Alan T. L.; Leigh, David A.; Marcos, Vanesa; Palmer, Leoni I.; Pisano, Simone

2017-09-01

It has been convincingly argued that molecular machines that manipulate individual atoms, or highly reactive clusters of atoms, with Ångström precision are unlikely to be realized. However, biological molecular machines routinely position rather less reactive substrates in order to direct chemical reaction sequences, from sequence-specific synthesis by the ribosome to polyketide synthases, where tethered molecules are passed from active site to active site in multi-enzyme complexes. Artificial molecular machines have been developed for tasks that include sequence-specific oligomer synthesis and the switching of product chirality, a photo-responsive host molecule has been described that is able to mechanically twist a bound molecular guest, and molecular fragments have been selectively transported in either direction between sites on a molecular platform through a ratchet mechanism. Here we detail an artificial molecular machine that moves a substrate between different activating sites to achieve different product outcomes from chemical synthesis. This molecular robot can be programmed to stereoselectively produce, in a sequential one-pot operation, an excess of any one of four possible diastereoisomers from the addition of a thiol and an alkene to an α,β-unsaturated aldehyde in a tandem reaction process. The stereodivergent synthesis includes diastereoisomers that cannot be selectively synthesized through conventional iminium-enamine organocatalysis. We anticipate that future generations of programmable molecular machines may have significant roles in chemical synthesis and molecular manufacturing.

Crystal Orientation Effect on the Subsurface Deformation of Monocrystalline Germanium in Nanometric Cutting.

PubMed

Lai, Min; Zhang, Xiaodong; Fang, Fengzhou

2017-12-01

Molecular dynamics simulations of nanometric cutting on monocrystalline germanium are conducted to investigate the subsurface deformation during and after nanometric cutting. The continuous random network model of amorphous germanium is established by molecular dynamics simulation, and its characteristic parameters are extracted to compare with those of the machined deformed layer. The coordination number distribution and radial distribution function (RDF) show that the machined surface presents the similar amorphous state. The anisotropic subsurface deformation is studied by nanometric cutting on the (010), (101), and (111) crystal planes of germanium, respectively. The deformed structures are prone to extend along the 110 slip system, which leads to the difference in the shape and thickness of the deformed layer on various directions and crystal planes. On machined surface, the greater thickness of subsurface deformed layer induces the greater surface recovery height. In order to get the critical thickness limit of deformed layer on machined surface of germanium, the optimized cutting direction on each crystal plane is suggested according to the relevance of the nanometric cutting to the nanoindentation.

REVIEWS OF TOPICAL PROBLEMS Molecular energy transducers of the living cell. Proton ATP synthase: a rotating molecular motor

NASA Astrophysics Data System (ADS)

Romanovsky, Yurii M.; Tikhonov, Alexander N.

2010-12-01

The free energy released upon the enzymatic hydrolysis of adenosine triphosphate (ATP) is the main source of energy for the functioning of the living cell and all multicellular organisms. The overwhelming majority of ATP molecules are formed by proton ATP synthases, which are the smallest macromolecular electric motors in Nature. This paper reviews the modern concepts of the molecular structure and functioning of the proton ATP synthase, and real-time biophysical experiments on the rotation of the 'rotor' of this macromolecular motor. Some mathematical models describing the operation of this nanosized macromolecular machine are described.

Allocating dissipation across a molecular machine cycle to maximize flux

PubMed Central

Brown, Aidan I.; Sivak, David A.

2017-01-01

Biomolecular machines consume free energy to break symmetry and make directed progress. Nonequilibrium ATP concentrations are the typical free energy source, with one cycle of a molecular machine consuming a certain number of ATP, providing a fixed free energy budget. Since evolution is expected to favor rapid-turnover machines that operate efficiently, we investigate how this free energy budget can be allocated to maximize flux. Unconstrained optimization eliminates intermediate metastable states, indicating that flux is enhanced in molecular machines with fewer states. When maintaining a set number of states, we show that—in contrast to previous findings—the flux-maximizing allocation of dissipation is not even. This result is consistent with the coexistence of both “irreversible” and reversible transitions in molecular machine models that successfully describe experimental data, which suggests that, in evolved machines, different transitions differ significantly in their dissipation. PMID:29073016

[Molecular imaging; current status and future prospects in USA].

PubMed

Kobayashi, Hisataka

2007-02-01

The goal of this review is to introduce the definition, current status, and future prospects of the molecular imaging, which has recently been a hot topic in medicine and the biological science in USA. In vivo imaging methods to visualize the molecular events and functions in organs or animals/humans are overviewed and discussed especially in combinations of imaging modalities (machines) and contrast agents(chemicals) used in the molecular imaging. Next, the close relationship between the molecular imaging and the nanotechnology, an important part of nanomedicine, is stressed from the aspect of united multidisciplinary sciences such as physics, chemistry, biology, and medicine.

Molecular mimicry between protein and tRNA.

PubMed

Nakamura, Y

2001-01-01

Mimicry is a sophisticated development in animals, fish, and plants that allows them to fool others by imitating a shape or color for diverse purposes, such as to prey, evade, lure, pollinate, or threaten. This is not restricted to the macro-world, but extends to the micro-world as molecular mimicry. Recent advances in structural and molecular biology uncovered a set of translation factors that resembles a tRNA shape and, in one case, even mimics a tRNA function for deciphering the genetic code. Nature must have evolved this art of molecular mimicry between protein and ribonucleic acid by using different protein structures until the translation factors sat in the cockpit of a ribosome machine, on behalf of tRNA, and achieved diverse actions. Structural, functional, and evolutionary aspects of molecular mimicry will be discussed.

In vitro assembly of semi-artificial molecular machine and its use for detection of DNA damage.

PubMed

Minchew, Candace L; Didenko, Vladimir V

2012-01-11

Naturally occurring bio-molecular machines work in every living cell and display a variety of designs. Yet the development of artificial molecular machines centers on devices capable of directional motion, i.e. molecular motors, and on their scaled-down mechanical parts (wheels, axels, pendants etc). This imitates the macro-machines, even though the physical properties essential for these devices, such as inertia and momentum conservation, are not usable in the nanoworld environments. Alternative designs, which do not follow the mechanical macromachines schemes and use mechanisms developed in the evolution of biological molecules, can take advantage of the specific conditions of the nanoworld. Besides, adapting actual biological molecules for the purposes of nano-design reduces potential dangers the nanotechnology products may pose. Here we demonstrate the assembly and application of one such bio-enabled construct, a semi-artificial molecular device which combines a naturally-occurring molecular machine with artificial components. From the enzymology point of view, our construct is a designer fluorescent enzyme-substrate complex put together to perform a specific useful function. This assembly is by definition a molecular machine, as it contains one. Yet, its integration with the engineered part - fluorescent dual hairpin - re-directs it to a new task of labeling DNA damage. Our construct assembles out of a 32-mer DNA and an enzyme vaccinia topoisomerase I (VACC TOPO). The machine then uses its own material to fabricate two fluorescently labeled detector units (Figure 1). One of the units (green fluorescence) carries VACC TOPO covalently attached to its 3'end and another unit (red fluorescence) is a free hairpin with a terminal 3'OH. The units are short-lived and quickly reassemble back into the original construct, which subsequently recleaves. In the absence of DNA breaks these two units continuously separate and religate in a cyclic manner. In tissue sections with DNA damage, the topoisomerase-carrying detector unit selectively attaches to blunt-ended DNA breaks with 5'OH (DNase II-type breaks), fluorescently labeling them. The second, enzyme-free hairpin formed after oligonucleotide cleavage, will ligate to a 5'PO(4) blunt-ended break (DNase I-type breaks), if T4 DNA ligase is present in the solution. When T4 DNA ligase is added to a tissue section or a solution containing DNA with 5'PO(4) blunt-ended breaks, the ligase reacts with 5'PO(4) DNA ends, forming semi-stable enzyme-DNA complexes. The blunt ended hairpins will interact with these complexes releasing ligase and covalently linking hairpins to DNA, thus labeling 5'PO(4) blunt-ended DNA breaks. This development exemplifies a new practical approach to the design of molecular machines and provides a useful sensor for detection of apoptosis and DNA damage in fixed cells and tissues. Copyright © 2012 Journal of Visualized Experiments

Machine learning of accurate energy-conserving molecular force fields.

PubMed

Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E; Poltavsky, Igor; Schütt, Kristof T; Müller, Klaus-Robert

2017-05-01

Using conservation of energy-a fundamental property of closed classical and quantum mechanical systems-we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol -1 for energies and 1 kcal mol -1 Å̊ -1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

NASA Astrophysics Data System (ADS)

Huang, Bing; von Lilienfeld, O. Anatole

2016-10-01

The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation. Inspired by the postulates of quantum mechanics, we introduce a hierarchy of representations which meet uniqueness and target similarity criteria. To systematically control target similarity, we simply rely on interatomic many body expansions, as implemented in universal force-fields, including Bonding, Angular (BA), and higher order terms. Addition of higher order contributions systematically increases similarity to the true potential energy and predictive accuracy of the resulting ML models. We report numerical evidence for the performance of BAML models trained on molecular properties pre-calculated at electron-correlated and density functional theory level of theory for thousands of small organic molecules. Properties studied include enthalpies and free energies of atomization, heat capacity, zero-point vibrational energies, dipole-moment, polarizability, HOMO/LUMO energies and gap, ionization potential, electron affinity, and electronic excitations. After training, BAML predicts energies or electronic properties of out-of-sample molecules with unprecedented accuracy and speed.

Machine learning of accurate energy-conserving molecular force fields

PubMed Central

Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.; Poltavsky, Igor; Schütt, Kristof T.; Müller, Klaus-Robert

2017-01-01

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å̊−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods. PMID:28508076

In vitro molecular machine learning algorithm via symmetric internal loops of DNA.

PubMed

Lee, Ji-Hoon; Lee, Seung Hwan; Baek, Christina; Chun, Hyosun; Ryu, Je-Hwan; Kim, Jin-Woo; Deaton, Russell; Zhang, Byoung-Tak

2017-08-01

Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules. Copyright © 2017. Published by Elsevier B.V.

Transitioning NWChem to the Next Generation of Manycore Machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bylaska, Eric J.; Apra, E; Kowalski, Karol

The NorthWest chemistry (NWChem) modeling software is a popular molecular chemistry simulation software that was designed from the start to work on massively parallel processing supercomputers [1-3]. It contains an umbrella of modules that today includes self-consistent eld (SCF), second order Møller-Plesset perturbation theory (MP2), coupled cluster (CC), multiconguration self-consistent eld (MCSCF), selected conguration interaction (CI), tensor contraction engine (TCE) many body methods, density functional theory (DFT), time-dependent density functional theory (TDDFT), real-time time-dependent density functional theory, pseudopotential plane-wave density functional theory (PSPW), band structure (BAND), ab initio molecular dynamics (AIMD), Car-Parrinello molecular dynamics (MD), classical MD, hybrid quantum mechanicsmore » molecular mechanics (QM/MM), hybrid ab initio molecular dynamics molecular mechanics (AIMD/MM), gauge independent atomic orbital nuclear magnetic resonance (GIAO NMR), conductor like screening solvation model (COSMO), conductor-like screening solvation model based on density (COSMO-SMD), and reference interaction site model (RISM) solvation models, free energy simulations, reaction path optimization, parallel in time, among other capabilities [4]. Moreover, new capabilities continue to be added with each new release.« less

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.

PubMed

Kim, Sungjin; Jinich, Adrián; Aspuru-Guzik, Alán

2017-04-24

We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using multiple-type-as opposed to single-type-descriptors, we obtain more relevant features for machine learning. Following the principle of "wisdom of the crowds", the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, by employing multiple kernels-more than one kernel function for a set of the input descriptors-MultiDK exploits nonlinear relations between molecular structure and properties better than a linear regression approach. The multiple kernels consist of a Tanimoto similarity kernel and a linear kernel for a set of binary descriptors and a set of nonbinary descriptors, respectively. Using MultiDK, we achieve an average performance of r 2 = 0.92 with a test set of molecules for solubility prediction. We also extend MultiDK to predict pH-dependent solubility and apply it to a set of quinone molecules with different ionizable functional groups to assess their performance as flow battery electrolytes.

Nanoscale swimmers: hydrodynamic interactions and propulsion of molecular machines

NASA Astrophysics Data System (ADS)

Sakaue, T.; Kapral, R.; Mikhailov, A. S.

2010-06-01

Molecular machines execute nearly regular cyclic conformational changes as a result of ligand binding and product release. This cyclic conformational dynamics is generally non-reciprocal so that under time reversal a different sequence of machine conformations is visited. Since such changes occur in a solvent, coupling to solvent hydrodynamic modes will generally result in self-propulsion of the molecular machine. These effects are investigated for a class of coarse grained models of protein machines consisting of a set of beads interacting through pair-wise additive potentials. Hydrodynamic effects are incorporated through a configuration-dependent mobility tensor, and expressions for the propulsion linear and angular velocities, as well as the stall force, are obtained. In the limit where conformational changes are small so that linear response theory is applicable, it is shown that propulsion is exponentially small; thus, propulsion is nonlinear phenomenon. The results are illustrated by computations on a simple model molecular machine.

Towards a molecular logic machine

NASA Astrophysics Data System (ADS)

Remacle, F.; Levine, R. D.

2001-06-01

Finite state logic machines can be realized by pump-probe spectroscopic experiments on an isolated molecule. The most elaborate setup, a Turing machine, can be programmed to carry out a specific computation. We argue that a molecule can be similarly programmed, and provide examples using two photon spectroscopies. The states of the molecule serve as the possible states of the head of the Turing machine and the physics of the problem determines the possible instructions of the program. The tape is written in an alphabet that allows the listing of the different pump and probe signals that are applied in a given experiment. Different experiments using the same set of molecular levels correspond to different tapes that can be read and processed by the same head and program. The analogy to a Turing machine is not a mechanical one and is not completely molecular because the tape is not part of the molecular machine. We therefore also discuss molecular finite state machines, such as sequential devices, for which the tape is not part of the machine. Nonmolecular tapes allow for quite long input sequences with a rich alphabet (at the level of 7 bits) and laser pulse shaping experiments provide concrete examples. Single molecule spectroscopies show that a single molecule can be repeatedly cycled through a logical operation.

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

PubMed

Ramakrishnan, Raghunathan; Dral, Pavlo O; Rupp, Matthias; von Lilienfeld, O Anatole

2015-05-12

Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k isomers of C7H10O2 we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree-Fock methods, at the computational cost of Hartree-Fock, and we establish a qualitative relationship between molecular entropy and electron correlation. The transferability of our approach is demonstrated, using semiempirical quantum chemistry and machine learning models trained on 1 and 10% of 134k organic molecules, to reproduce enthalpies of all remaining molecules at density functional theory level of accuracy.

Molecular Rotors as Switches

PubMed Central

Xue, Mei; Wang, Kang L.

2012-01-01

The use of a functional molecular unit acting as a state variable provides an attractive alternative for the next generations of nanoscale electronics. It may help overcome the limits of conventional MOSFETd due to their potential scalability, low-cost, low variability, and highly integratable characteristics as well as the capability to exploit bottom-up self-assembly processes. This bottom-up construction and the operation of nanoscale machines/devices, in which the molecular motion can be controlled to perform functions, have been studied for their functionalities. Being triggered by external stimuli such as light, electricity or chemical reagents, these devices have shown various functions including those of diodes, rectifiers, memories, resonant tunnel junctions and single settable molecular switches that can be electronically configured for logic gates. Molecule-specific electronic switching has also been reported for several of these device structures, including nanopores containing oligo(phenylene ethynylene) monolayers, and planar junctions incorporating rotaxane and catenane monolayers for the construction and operation of complex molecular machines. A specific electrically driven surface mounted molecular rotor is described in detail in this review. The rotor is comprised of a monolayer of redox-active ligated copper compounds sandwiched between a gold electrode and a highly-doped P+ Si. This electrically driven sandwich-type monolayer molecular rotor device showed an on/off ratio of approximately 104, a read window of about 2.5 V, and a retention time of greater than 104 s. The rotation speed of this type of molecular rotor has been reported to be in the picosecond timescale, which provides a potential of high switching speed applications. Current-voltage spectroscopy (I-V) revealed a temperature-dependent negative differential resistance (NDR) associated with the device. The analysis of the device I–V characteristics suggests the source of the observed switching effects to be the result of the redox-induced ligand rotation around the copper metal center and this attribution of switching is consistent with the observed temperature dependence of the switching behavior as well as the proposed energy diagram of the device. The observed resistance switching shows the potential for future non-volatile memories and logic devices applications. This review will discuss the progress and provide a perspective of molecular motion for nanoelectronics and other applications.

Microcompartments and Protein Machines in Prokaryotes

PubMed Central

Saier, Milton H.

2013-01-01

The prokaryotic cell was once thought of as a “bag of enzymes” with little or no intracellular compartmentalization. In this view, most reactions essential for life occurred as a consequence of random molecular collisions involving substrates, cofactors and cytoplasmic enzymes. Our current conception of a prokaryote is far from this view. We now consider a bacterium or an archaeon as a highly structured, non-random collection of functional membrane-embedded and proteinaceous molecular machines, each of which serves a specialized function. In this article we shall present an overview of such microcompartments including (i) the bacterial cytoskeleton and the apparati allowing DNA segregation during cells division, (ii) energy transduction apparati involving light-driven proton pumping and ion gradient-driven ATP synthesis, (iii) prokaryotic motility and taxis machines that mediate cell movements in response to gradients of chemicals and physical forces, (iv) machines of protein folding, secretion and degradation, (v) metabolasomes carrying out specific chemical reactions, (vi) 24 hour clocks allowing bacteria to coordinate their metabolic activities with the daily solar cycle and (vii) proteinaceous membrane compartmentalized structures such as sulfur granules and gas vacuoles. Membrane-bounded prokaryotic organelles were considered in a recent JMMB written symposium concerned with membraneous compartmentalization in bacteria [Saier and Bogdanov, 2013]. By contrast, in this symposium, we focus on proteinaceous microcompartments. These two symposia, taken together, provide the interested reader with an objective view of the remarkable complexity of what was once thought of as a simple non-compartmentalized cell. PMID:23920489

Enzyme Immobilization: Nanobiotechnology: Putting Molecular Machines to Work

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

2009-04-01

Describes, in general terms, the concepts of high-throughput protein expression coupled with immobilizations in functionalized nanoporous materials to carry out multiple kinds of diverse reactions. The animations also illustrate that immobilized enzymes potentially can refold inactive proteins. Transcripts of videos available upon request

Mitochondrial AAA proteases--towards a molecular understanding of membrane-bound proteolytic machines.

PubMed

Gerdes, Florian; Tatsuta, Takashi; Langer, Thomas

2012-01-01

Mitochondrial AAA proteases play an important role in the maintenance of mitochondrial proteostasis. They regulate and promote biogenesis of mitochondrial proteins by acting as processing enzymes and ensuring the selective turnover of misfolded proteins. Impairment of AAA proteases causes pleiotropic defects in various organisms including neurodegeneration in humans. AAA proteases comprise ring-like hexameric complexes in the mitochondrial inner membrane and are functionally conserved from yeast to man, but variations are evident in the subunit composition of orthologous enzymes. Recent structural and biochemical studies revealed how AAA proteases degrade their substrates in an ATP dependent manner. Intersubunit coordination of the ATP hydrolysis leads to an ordered ATP hydrolysis within the AAA ring, which ensures efficient substrate dislocation from the membrane and translocation to the proteolytic chamber. In this review, we summarize recent findings on the molecular mechanisms underlying the versatile functions of mitochondrial AAA proteases and their relevance to those of the other AAA+ machines. Copyright © 2011 Elsevier B.V. All rights reserved.

Development of a machine learning potential for graphene

NASA Astrophysics Data System (ADS)

Rowe, Patrick; Csányi, Gábor; Alfè, Dario; Michaelides, Angelos

2018-02-01

We present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data—and amongst the empirical potentials themselves—the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org].

Putting Humpty-Dumpty Together: Clustering the Functional Dynamics of Single Biomolecular Machines Such as the Spliceosome.

PubMed

Rohlman, C E; Blanco, M R; Walter, N G

2016-01-01

The spliceosome is a biomolecular machine that, in all eukaryotes, accomplishes site-specific splicing of introns from precursor messenger RNAs (pre-mRNAs) with high fidelity. Operating at the nanometer scale, where inertia and friction have lost the dominant role they play in the macroscopic realm, the spliceosome is highly dynamic and assembles its active site around each pre-mRNA anew. To understand the structural dynamics underlying the molecular motors, clocks, and ratchets that achieve functional accuracy in the yeast spliceosome (a long-standing model system), we have developed single-molecule fluorescence resonance energy transfer (smFRET) approaches that report changes in intra- and intermolecular interactions in real time. Building on our work using hidden Markov models (HMMs) to extract kinetic and conformational state information from smFRET time trajectories, we recognized that HMM analysis of individual state transitions as independent stochastic events is insufficient for a biomolecular machine as complex as the spliceosome. In this chapter, we elaborate on the recently developed smFRET-based Single-Molecule Cluster Analysis (SiMCAn) that dissects the intricate conformational dynamics of a pre-mRNA through the splicing cycle in a model-free fashion. By leveraging hierarchical clustering techniques developed for Bioinformatics, SiMCAn efficiently analyzes large datasets to first identify common molecular behaviors. Through a second level of clustering based on the abundance of dynamic behaviors exhibited by defined functional intermediates that have been stalled by biochemical or genetic tools, SiMCAn then efficiently assigns pre-mRNA FRET states and transitions to specific splicing complexes, with the potential to find heretofore undescribed conformations. SiMCAn thus arises as a general tool to analyze dynamic cellular machines more broadly. © 2016 Elsevier Inc. All rights reserved.

Molecular sled sequences are common in mammalian proteins.

PubMed

Xiong, Kan; Blainey, Paul C

2016-03-18

Recent work revealed a new class of molecular machines called molecular sleds, which are small basic molecules that bind and slide along DNA with the ability to carry cargo along DNA. Here, we performed biochemical and single-molecule flow stretching assays to investigate the basis of sliding activity in molecular sleds. In particular, we identified the functional core of pVIc, the first molecular sled characterized; peptide functional groups that control sliding activity; and propose a model for the sliding activity of molecular sleds. We also observed widespread DNA binding and sliding activity among basic polypeptide sequences that implicate mammalian nuclear localization sequences and many cell penetrating peptides as molecular sleds. These basic protein motifs exhibit weak but physiologically relevant sequence-nonspecific DNA affinity. Our findings indicate that many mammalian proteins contain molecular sled sequences and suggest the possibility that substantial undiscovered sliding activity exists among nuclear mammalian proteins. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Revisiting the Central Dogma One Molecule at a Time

PubMed Central

Bustamante, Carlos; Cheng, Wei; Meija, Yara

2011-01-01

The faithful relay and timely expression of genetic information depend on specialized molecular machines, many of which function as nucleic acid translocases. The emergence over the last decade of single-molecule fluorescence detection and manipulation techniques with nm and Å resolution, and their application to the study of nucleic acid translocases are painting an increasingly sharp picture of the inner workings of these machines, the dynamics and coordination of their moving parts, their thermodynamic efficiency, and the nature of their transient intermediates. Here we present an overview of the main results arrived at by the application of single-molecule methods to the study of the main machines of the central dogma. PMID:21335233

Molecular machines open cell membranes

NASA Astrophysics Data System (ADS)

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G.; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B.; Robinson, Jacob T.; Wang, Gufeng; Pal, Robert; Tour, James M.

2017-08-01

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

Molecular machines open cell membranes.

PubMed

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B; Robinson, Jacob T; Wang, Gufeng; Pal, Robert; Tour, James M

2017-08-30

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

One or 4 h of "in-house" reconditioning by machine perfusion after cold storage improve reperfusion parameters in porcine kidneys.

PubMed

Gallinat, Anja; Efferz, Patrik; Paul, Andreas; Minor, Thomas

2014-11-01

In-house machine perfusion after cold storage (hypothermic reconditioning) has been proposed as convenient tool to improve kidney graft function. This study investigated the role of machine perfusion duration for early reperfusion parameters in porcine kidneys. Kidney function after cold preservation (4 °C, 18 h) and subsequent reconditioning by one or 4 h of pulsatile, nonoxygenated hypothermic machine perfusion (HMP) was studied in an isolated kidney perfusion model in pigs (n = 6, respectively) and compared with simply cold-stored grafts (CS). Compared with CS alone, one or 4 h of subsequent HMP similarly and significantly improved renal flow and kidney function (clearance and sodium reabsorption) upon warm reperfusion, along with reduced perfusate concentrations of endothelin-1 and increased vascular release of nitric oxide. Molecular effects of HMP comprised a significant (vs CS) mRNA increase in the endothelial transcription factor KLF2 and lower expression of endothelin that were observed already at the end of one-hour HMP after CS. Reconditioning of cold-stored kidneys is possible, even if clinical logistics only permit one hour of therapy, while limited extension of the overall storage time by in-house machine perfusion might also allow for postponing of transplantation from night to early day work. © 2014 Steunstichting ESOT.

Irrelevance of the Power Stroke for the Directionality, Stopping Force, and Optimal Efficiency of Chemically Driven Molecular Machines

PubMed Central

Astumian, R. Dean

2015-01-01

A simple model for a chemically driven molecular walker shows that the elastic energy stored by the molecule and released during the conformational change known as the power-stroke (i.e., the free-energy difference between the pre- and post-power-stroke states) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Further, the apportionment of the dependence on the externally applied force between the forward and reverse rate constants of the power-stroke (or indeed among all rate constants) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Arguments based on the principle of microscopic reversibility demonstrate that this result is general for all chemically driven molecular machines, and even more broadly that the relative energies of the states of the motor have no role in determining the directionality, stopping force, or optimal efficiency of the machine. Instead, the directionality, stopping force, and optimal efficiency are determined solely by the relative heights of the energy barriers between the states. Molecular recognition—the ability of a molecular machine to discriminate between substrate and product depending on the state of the machine—is far more important for determining the intrinsic directionality and thermodynamics of chemo-mechanical coupling than are the details of the internal mechanical conformational motions of the machine. In contrast to the conclusions for chemical driving, a power-stroke is very important for the directionality and efficiency of light-driven molecular machines and for molecular machines driven by external modulation of thermodynamic parameters. PMID:25606678

Physics Meets Biology (LBNL Summer Lecture Series)

ScienceCinema

Chu, Steven

2018-05-09

Summer Lecture Series 2006: If scientists could take advantage of the awesomely complex and beautiful functioning of biology's natural molecular machines, their potential for application in many disciplines would be incalculable. Nobel Laureate and Director of the Lawrence Berkeley National Laboratory Steve Chu explores Possible solutions to global warming and its consequences.

Solid surface vs. liquid surface: nanoarchitectonics, molecular machines, and DNA origami.

PubMed

Ariga, Katsuhiko; Mori, Taizo; Nakanishi, Waka; Hill, Jonathan P

2017-09-13

The investigation of molecules and materials at interfaces is critical for the accumulation of new scientific insights and technological advances in the chemical and physical sciences. Immobilization on solid surfaces permits the investigation of different properties of functional molecules or materials with high sensitivity and high spatial resolution. Liquid surfaces also present important media for physicochemical innovation and insight based on their great flexibility and dynamicity, rapid diffusion of molecular components for mixing and rearrangements, as well as drastic spatial variation in the prevailing dielectric environment. Therefore, a comparative discussion of the relative merits of the properties of materials when positioned at solid or liquid surfaces would be informative regarding present-to-future developments of surface-based technologies. In this perspective article, recent research examples of nanoarchitectonics, molecular machines, DNA nanotechnology, and DNA origami are compared with respect to the type of surface used, i.e. solid surfaces vs. liquid surfaces, for future perspectives of interfacial physics and chemistry.

Mesoscale imaging with cryo-light and X-rays: Larger than molecular machines, smaller than a cell: Mesoscale imaging with cryo-light and X-rays

DOE PAGES

Ekman, Axel A.; Chen, Jian-Hua; Guo, Jessica; ...

2016-11-14

In the context of cell biology, the term mesoscale describes length scales ranging from that of an individual cell, down to the size of the molecular machines. In this spatial regime, small building blocks self-organise to form large, functional structures. A comprehensive set of rules governing mesoscale self-organisation has not been established, making the prediction of many cell behaviours difficult, if not impossible. Our knowledge of mesoscale biology comes from experimental data, in particular, imaging. Here, we explore the application of soft X-ray tomography (SXT) to imaging the mesoscale, and describe the structural insights this technology can generate. We alsomore » discuss how SXT imaging is complemented by the addition of correlative fluorescence data measured from the same cell. This combination of two discrete imaging modalities produces a 3D view of the cell that blends high-resolution structural information with precise molecular localisation data.« less

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules.

PubMed

Pronobis, Wiktor; Tkatchenko, Alexandre; Müller, Klaus-Robert

2018-06-12

Machine learning (ML) based prediction of molecular properties across chemical compound space is an important and alternative approach to efficiently estimate the solutions of highly complex many-electron problems in chemistry and physics. Statistical methods represent molecules as descriptors that should encode molecular symmetries and interactions between atoms. Many such descriptors have been proposed; all of them have advantages and limitations. Here, we propose a set of general two-body and three-body interaction descriptors which are invariant to translation, rotation, and atomic indexing. By adapting the successfully used kernel ridge regression methods of machine learning, we evaluate our descriptors on predicting several properties of small organic molecules calculated using density-functional theory. We use two data sets. The GDB-7 set contains 6868 molecules with up to 7 heavy atoms of type CNO. The GDB-9 set is composed of 131722 molecules with up to 9 heavy atoms containing CNO. When trained on 5000 random molecules, our best model achieves an accuracy of 0.8 kcal/mol (on the remaining 1868 molecules of GDB-7) and 1.5 kcal/mol (on the remaining 126722 molecules of GDB-9) respectively. Applying a linear regression model on our novel many-body descriptors performs almost equal to a nonlinear kernelized model. Linear models are readily interpretable: a feature importance ranking measure helps to obtain qualitative and quantitative insights on the importance of two- and three-body molecular interactions for predicting molecular properties computed with quantum-mechanical methods.

MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a

PubMed Central

Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.; Gomes, Joseph; Geniesse, Caleb; Pappu, Aneesh S.; Leswing, Karl

2017-01-01

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm. PMID:29629118

Efficient diagonalization of the sparse matrices produced within the framework of the UK R-matrix molecular codes

NASA Astrophysics Data System (ADS)

Galiatsatos, P. G.; Tennyson, J.

2012-11-01

The most time consuming step within the framework of the UK R-matrix molecular codes is that of the diagonalization of the inner region Hamiltonian matrix (IRHM). Here we present the method that we follow to speed up this step. We use shared memory machines (SMM), distributed memory machines (DMM), the OpenMP directive based parallel language, the MPI function based parallel language, the sparse matrix diagonalizers ARPACK and PARPACK, a variation for real symmetric matrices of the official coordinate sparse matrix format and finally a parallel sparse matrix-vector product (PSMV). The efficient application of the previous techniques rely on two important facts: the sparsity of the matrix is large enough (more than 98%) and in order to get back converged results we need a small only part of the matrix spectrum.

In vitro labeling strategies for in cellulo fluorescence microscopy of single ribonucleoprotein machines.

PubMed

Custer, Thomas C; Walter, Nils G

2017-07-01

RNA plays a fundamental, ubiquitous role as either substrate or functional component of many large cellular complexes-"molecular machines"-used to maintain and control the readout of genetic information, a functional landscape that we are only beginning to understand. The cellular mechanisms for the spatiotemporal organization of the plethora of RNAs involved in gene expression are particularly poorly understood. Intracellular single-molecule fluorescence microscopy provides a powerful emerging tool for probing the pertinent mechanistic parameters that govern cellular RNA functions, including those of protein coding messenger RNAs (mRNAs). Progress has been hampered, however, by the scarcity of efficient high-yield methods to fluorescently label RNA molecules without the need to drastically increase their molecular weight through artificial appendages that may result in altered behavior. Herein, we employ T7 RNA polymerase to body label an RNA with a cyanine dye, as well as yeast poly(A) polymerase to strategically place multiple 2'-azido-modifications for subsequent fluorophore labeling either between the body and tail or randomly throughout the tail. Using a combination of biochemical and single-molecule fluorescence microscopy approaches, we demonstrate that both yeast poly(A) polymerase labeling strategies result in fully functional mRNA, whereas protein coding is severely diminished in the case of body labeling. © 2016 The Protein Society.

Kernel approach to molecular similarity based on iterative graph similarity.

PubMed

Rupp, Matthias; Proschak, Ewgenij; Schneider, Gisbert

2007-01-01

Similarity measures for molecules are of basic importance in chemical, biological, and pharmaceutical applications. We introduce a molecular similarity measure defined directly on the annotated molecular graph, based on iterative graph similarity and optimal assignments. We give an iterative algorithm for the computation of the proposed molecular similarity measure, prove its convergence and the uniqueness of the solution, and provide an upper bound on the required number of iterations necessary to achieve a desired precision. Empirical evidence for the positive semidefiniteness of certain parametrizations of our function is presented. We evaluated our molecular similarity measure by using it as a kernel in support vector machine classification and regression applied to several pharmaceutical and toxicological data sets, with encouraging results.

An artificial molecular machine that builds an asymmetric catalyst

NASA Astrophysics Data System (ADS)

De Bo, Guillaume; Gall, Malcolm A. Y.; Kuschel, Sonja; De Winter, Julien; Gerbaux, Pascal; Leigh, David A.

2018-05-01

Biomolecular machines perform types of complex molecular-level tasks that artificial molecular machines can aspire to. The ribosome, for example, translates information from the polymer track it traverses (messenger RNA) to the new polymer it constructs (a polypeptide)1. The sequence and number of codons read determines the sequence and number of building blocks incorporated into the biomachine-synthesized polymer. However, neither control of sequence2,3 nor the transfer of length information from one polymer to another (which to date has only been accomplished in man-made systems through template synthesis)4 is easily achieved in the synthesis of artificial macromolecules. Rotaxane-based molecular machines5-7 have been developed that successively add amino acids8-10 (including β-amino acids10) to a growing peptide chain by the action of a macrocycle moving along a mono-dispersed oligomeric track derivatized with amino-acid phenol esters. The threaded macrocycle picks up groups that block its path and links them through successive native chemical ligation reactions11 to form a peptide sequence corresponding to the order of the building blocks on the track. Here, we show that as an alternative to translating sequence information, a rotaxane molecular machine can transfer the narrow polydispersity of a leucine-ester-derivatized polystyrene chain synthesized by atom transfer radical polymerization12 to a molecular-machine-made homo-leucine oligomer. The resulting narrow-molecular-weight oligomer folds to an α-helical secondary structure13 that acts as an asymmetric catalyst for the Juliá-Colonna epoxidation14,15 of chalcones.

Comparison of subsurface damages on mono-crystalline silicon between traditional nanoscale machining and laser-assisted nanoscale machining via molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Dai, Houfu; Li, Shaobo; Chen, Genyu

2018-01-01

Molecular dynamics is employed to compare nanoscale traditional machining (TM) with laser-assisted machining (LAM). LAM is that the workpiece is locally heated by an intense laser beam prior to material removal. We have a comprehensive comparison between LAM and TM in terms of atomic trajectories, phase transformation, radial distribution function, chips, temperature distribution, number of atoms in different temperature, grinding temperature, grinding force, friction coefficient and atomic potential energy. It can be found that there is a decrease of atoms with five and six nearest neighbors, and LAM generates more chips than that in the TM. It indicates that LAM reduces the subsurface damage of workpiece, gets a better-qualified ground surface and improves the material removal rate. Moreover, laser energy makes the materials fully softened before being removed, the number of atoms with temperature above 500 K is increased, and the average temperature of workpiece higher and faster to reach the equilibrium in LAM. It means that LAM has an absolute advantage in machining materials and greatly reduces the material resistance. Not only the tangential force (Fx) and the normal force (Fy) but also friction coefficients become smaller as laser heating reduces the strength and hardness of the material in LAM. These results show that LAM is a promising technique since it can get a better-qualified workpiece surface with larger material removal rates, less grinding force and lower friction coefficient.

Single molecule detection, thermal fluctuation and life

PubMed Central

YANAGIDA, Toshio; ISHII, Yoshiharu

2017-01-01

Single molecule detection has contributed to our understanding of the unique mechanisms of life. Unlike artificial man-made machines, biological molecular machines integrate thermal noises rather than avoid them. For example, single molecule detection has demonstrated that myosin motors undergo biased Brownian motion for stepwise movement and that single protein molecules spontaneously change their conformation, for switching to interactions with other proteins, in response to thermal fluctuation. Thus, molecular machines have flexibility and efficiency not seen in artificial machines. PMID:28190869

Learning surface molecular structures via machine vision

NASA Astrophysics Data System (ADS)

Ziatdinov, Maxim; Maksov, Artem; Kalinin, Sergei V.

2017-08-01

Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision. In many technologically relevant atomic and/or molecular systems, however, the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature. One of the critical issues, therefore, lies in being able to accurately identify (`read out') all the individual building blocks in different atomic/molecular architectures, as well as more complex patterns that these blocks may form, on a scale of hundreds and thousands of individual atomic/molecular units. Here we employ machine vision to read and recognize complex molecular assemblies on surfaces. Specifically, we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements. We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorder-property relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale, and elucidate reaction pathway involving molecular conformation changes. The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural, electronic, and magnetic orders in different condensed matter systems.

An autonomous chemically fuelled small-molecule motor

NASA Astrophysics Data System (ADS)

Wilson, Miriam R.; Solà, Jordi; Carlone, Armando; Goldup, Stephen M.; Lebrasseur, Nathalie; Leigh, David A.

2016-06-01

Molecular machines are among the most complex of all functional molecules and lie at the heart of nearly every biological process. A number of synthetic small-molecule machines have been developed, including molecular muscles, synthesizers, pumps, walkers, transporters and light-driven and electrically driven rotary motors. However, although biological molecular motors are powered by chemical gradients or the hydrolysis of adenosine triphosphate (ATP), so far there are no synthetic small-molecule motors that can operate autonomously using chemical energy (that is, the components move with net directionality as long as a chemical fuel is present). Here we describe a system in which a small molecular ring (macrocycle) is continuously transported directionally around a cyclic molecular track when powered by irreversible reactions of a chemical fuel, 9-fluorenylmethoxycarbonyl chloride. Key to the design is that the rate of reaction of this fuel with reactive sites on the cyclic track is faster when the macrocycle is far from the reactive site than when it is near to it. We find that a bulky pyridine-based catalyst promotes carbonate-forming reactions that ratchet the displacement of the macrocycle away from the reactive sites on the track. Under reaction conditions where both attachment and cleavage of the 9-fluorenylmethoxycarbonyl groups occur through different processes, and the cleavage reaction occurs at a rate independent of macrocycle location, net directional rotation of the molecular motor continues for as long as unreacted fuel remains. We anticipate that autonomous chemically fuelled molecular motors will find application as engines in molecular nanotechnology.

Hydrodynamic collective effects of active protein machines in solution and lipid bilayers

PubMed Central

Mikhailov, Alexander S.; Kapral, Raymond

2015-01-01

The cytoplasm and biomembranes in biological cells contain large numbers of proteins that cyclically change their shapes. They are molecular machines that can function as molecular motors or carry out various other tasks in the cell. Many enzymes also undergo conformational changes within their turnover cycles. We analyze the advection effects that nonthermal fluctuating hydrodynamic flows induced by active proteins have on other passive molecules in solution or membranes. We show that the diffusion constants of passive particles are enhanced substantially. Furthermore, when gradients of active proteins are present, a chemotaxis-like drift of passive particles takes place. In lipid bilayers, the effects are strongly nonlocal, so that active inclusions in the entire membrane contribute to local diffusion enhancement and the drift. All active proteins in a biological cell or in a membrane contribute to such effects and all passive particles, and the proteins themselves, will be subject to them. PMID:26124140

Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density

NASA Astrophysics Data System (ADS)

Seino, Junji; Kageyama, Ryo; Fujinami, Mikito; Ikabata, Yasuhiro; Nakai, Hiromi

2018-06-01

A semi-local kinetic energy density functional (KEDF) was constructed based on machine learning (ML). The present scheme adopts electron densities and their gradients up to third-order as the explanatory variables for ML and the Kohn-Sham (KS) kinetic energy density as the response variable in atoms and molecules. Numerical assessments of the present scheme were performed in atomic and molecular systems, including first- and second-period elements. The results of 37 conventional KEDFs with explicit formulae were also compared with those of the ML KEDF with an implicit formula. The inclusion of the higher order gradients reduces the deviation of the total kinetic energies from the KS calculations in a stepwise manner. Furthermore, our scheme with the third-order gradient resulted in the closest kinetic energies to the KS calculations out of the presented functionals.

Machine learning molecular dynamics for the simulation of infrared spectra.

PubMed

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

Machine Learning of Accurate Energy-Conserving Molecular Force Fields

NASA Astrophysics Data System (ADS)

Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel; Poltavsky, Igor; Schütt, Kristof; Müller, Klaus-Robert; GDML Collaboration

Efficient and accurate access to the Born-Oppenheimer potential energy surface (PES) is essential for long time scale molecular dynamics (MD) simulations. Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio MD trajectories (AIMD). The GDML implementation is able to reproduce global potential-energy surfaces of intermediate-size molecules with an accuracy of 0.3 kcal/mol for energies and 1 kcal/mol/Å for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, malonaldehyde, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative MD simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.

Molecular-Sized DNA or RNA Sequencing Machine | NCI Technology Transfer Center | TTC

Cancer.gov

The National Cancer Institute's Gene Regulation and Chromosome Biology Laboratory is seeking statements of capability or interest from parties interested in collaborative research to co-develop a molecular-sized DNA or RNA sequencing machine.

Advances in molecular dynamics simulation of ultra-precision machining of hard and brittle materials

NASA Astrophysics Data System (ADS)

Guo, Xiaoguang; Li, Qiang; Liu, Tao; Kang, Renke; Jin, Zhuji; Guo, Dongming

2017-03-01

Hard and brittle materials, such as silicon, SiC, and optical glasses, are widely used in aerospace, military, integrated circuit, and other fields because of their excellent physical and chemical properties. However, these materials display poor machinability because of their hard and brittle properties. Damages such as surface micro-crack and subsurface damage often occur during machining of hard and brittle materials. Ultra-precision machining is widely used in processing hard and brittle materials to obtain nanoscale machining quality. However, the theoretical mechanism underlying this method remains unclear. This paper provides a review of present research on the molecular dynamics simulation of ultra-precision machining of hard and brittle materials. The future trends in this field are also discussed.

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

PubMed Central

Dührkop, Kai; Shen, Huibin; Meusel, Marvin; Rousu, Juho; Böcker, Sebastian

2015-01-01

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin. PMID:26392543

A machine learning approach for ranking clusters of docked protein‐protein complexes by pairwise cluster comparison

PubMed Central

Pfeiffenberger, Erik; Chaleil, Raphael A.G.; Moal, Iain H.

2017-01-01

ABSTRACT Reliable identification of near‐native poses of docked protein–protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein–protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descriptors is trained. The protocol is based on first locally enriching clusters with additional poses, the clusters are then characterized using features describing the distribution of molecular descriptors within the cluster, which are combined into a pairwise cluster comparison model to discriminate near‐native from incorrect clusters. The results show that our approach is able to identify clusters containing near‐native protein–protein complexes. In addition, we present an analysis of the descriptors with respect to their power to discriminate near native from incorrect clusters and how data transformations and recursive feature elimination can improve the ranking performance. Proteins 2017; 85:528–543. © 2016 Wiley Periodicals, Inc. PMID:27935158

Machine Phase Fullerene Nanotechnology: 1996

NASA Technical Reports Server (NTRS)

Globus, Al; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

NASA has used exotic materials for spacecraft and experimental aircraft to good effect for many decades. In spite of many advances, transportation to space still costs about $10,000 per pound. Drexler has proposed a hypothetical nanotechnology based on diamond and investigated the properties of such molecular systems. These studies and others suggest enormous potential for aerospace systems. Unfortunately, methods to realize diamonoid nanotechnology are at best highly speculative. Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically relatively accessible and of great aerospace interest. Machine phase materials are (hypothetical) materials consisting entirely or in large part of microscopic machines. In a sense, most living matter fits this definition. To begin investigation of fullerene nanotechnology, we used molecular dynamics to study the properties of carbon nanotube based gears and gear/shaft configurations. Experiments on C60 and quantum calculations suggest that benzyne may react with carbon nanotubes to form gear teeth. Han has computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Results suggest that rotation can be converted to rotating or linear motion, and linear motion may be converted into rotation. Preliminary results suggest that these mechanical systems can be cooled by a helium atmosphere. Furthermore, Deepak has successfully simulated using helical electric fields generated by a laser to power fullerene gears once a positive and negative charge have been added to form a dipole. Even with mechanical motion, cooling, and power; creating a viable nanotechnology requires support structures, computer control, a system architecture, a variety of components, and some approach to manufacture. Additional information is contained within the original extended abstract.

Machine Learning Force Field Parameters from Ab Initio Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Ying; Li, Hui; Pickard, Frank C.

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. The results were further improved by introducing an offset factor duringmore » the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.« less

Laser cutting: influence on morphological and physicochemical properties of polyhydroxybutyrate.

PubMed

Lootz, D; Behrend, D; Kramer, S; Freier, T; Haubold, A; Benkiesser, G; Schmitz, K P; Becher, B

2001-09-01

Polyhydroxybutyrate (PHB) is a biocompatible and resorbable implant material. For these reasons, it has been used for the fabrication of temporary stents, bone plates, nails and screws (Peng et al. Biomaterials 1996;17:685). In some cases, the brittle mechanical properties of PHB homopolymer limit its application. A typical plasticizer, triethylcitrate (TEC), was used to overcome such limitations by making the material more pliable. In the past few years, CO2-laser cutting of PHB was used in the manufacturing of small medical devices such as stents. Embrittlement of plasticized PHB tubes has been observed, after laser machining. Consequently, the physicochemical and morphological properties of laser-processed surfaces and cut edges of plasticized polymer samples were examined to determine the extent of changes in polymer properties as a result of laser machining. These studies included determination of the depth of the laser-induced heat affected zone by polariscopy of thin polymer sections. Molecular weight changes and changes in the TEC content as a function of distance from the laser-cut edge were determined. In a preliminary test, the cellular response to the processed material was investigated by cell culture study of L929 mouse fibroblasts on laser-machined surfaces. The heat-affected zone was readily classified into four different regions with a total depth of about 60 to 100 microm (Klamp, Master Thesis, University of Rostock, 1998). These results correspond well with the chemical analysis and molecular weight measurements. Furthermore, it was found that cells grew preferentially on the laser-machined area. These findings have significant implications for the manufacture of medical implants from PHB by laser machining.

Predicting activity approach based on new atoms similarity kernel function.

PubMed

Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella

2015-07-01

Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods. Copyright © 2015 Elsevier Inc. All rights reserved.

Biological Moleculars: Have Most of Our Problems Already Been Solved?

NASA Technical Reports Server (NTRS)

Downey, James P.; Rose, M. Franklin (Technical Monitor)

2000-01-01

Evolution has resulted in biological machinery that engineers have great reason to envy and at present can only poorly mimic. This is not just a curiosity as biological systems perform many functions that are desired industrial processes. Examples include photosynthesis, chemosynthesis, energy storage, low temperature chemical conversion, reproducible manufacture of chemical compounds, etc. The bases of biological machinery are the proteins and nucleic acids that comprise living organisms. Each molecule functions as a part of a biological machine. In many cases the molecule can be properly regarded as a stand alone machine of its own. Concepts and methods for harnessing the power of biological molecules exist but are often overlooked in the industrial world. Some are old and appear crude but are quite effective, e.g. the fermentation of grains and fruits. Currently, there is a revolution in progress regarding the harnessing biological processes. These include techniques such as genetic manipulation via polymerase chain reaction, forced evolution also known as evolution in a test tube, determination of molecular structure, and combinatorial chemistry. The following is a brief discussion on how these processes are performed and how they may relate to industrial and aerospace processes.

Harnessing Reversible Electronic Energy Transfer: From Molecular Dyads to Molecular Machines.

PubMed

Denisov, Sergey A; Yu, Shinlin; Pozzo, Jean-Luc; Jonusauskas, Gediminas; McClenaghan, Nathan D

2016-06-17

Reversible electronic energy transfer (REET) may be instilled in bi-/multichromophoric molecule-based systems, following photoexcitation, upon judicious structural integration of matched chromophores. This leads to a new set of photophysical properties for the ensemble, which can be fully characterized by steady-state and time-resolved spectroscopic methods. Herein, we take a comprehensive look at progress in the development of this type of supermolecule in the last five years, which has seen systems evolve from covalently tethered dyads to synthetic molecular machines, exemplified by two different pseudorotaxanes. Indeed, REET holds promise in the control of movement in molecular machines, their assembly/disassembly, as well as in charge separation. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Learning surface molecular structures via machine vision

DOE PAGES

Ziatdinov, Maxim; Maksov, Artem; Kalinin, Sergei V.

2017-08-10

Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision. In many technologically relevant atomic and/or molecular systems, however, the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature. One of the critical issues, therefore, lies in being able to accurately identify (‘read out’) all the individual building blocks in different atomic/molecular architectures, as well as more complex patterns that these blocks may form, on a scale of hundreds andmore » thousands of individual atomic/molecular units. Here we employ machine vision to read and recognize complex molecular assemblies on surfaces. Specifically, we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements. We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorder-property relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale, and elucidate reaction pathway involving molecular conformation changes. Here, the method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural, electronic, and magnetic orders in different condensed matter systems.« less

Learning surface molecular structures via machine vision

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ziatdinov, Maxim; Maksov, Artem; Kalinin, Sergei V.

Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision. In many technologically relevant atomic and/or molecular systems, however, the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature. One of the critical issues, therefore, lies in being able to accurately identify (‘read out’) all the individual building blocks in different atomic/molecular architectures, as well as more complex patterns that these blocks may form, on a scale of hundreds andmore » thousands of individual atomic/molecular units. Here we employ machine vision to read and recognize complex molecular assemblies on surfaces. Specifically, we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements. We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorder-property relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale, and elucidate reaction pathway involving molecular conformation changes. Here, the method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural, electronic, and magnetic orders in different condensed matter systems.« less

A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking.

PubMed

Ballester, Pedro J; Mitchell, John B O

2010-05-01

Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions. We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score's performance was shown to improve dramatically with training set size and hence the future availability of more high-quality structural and interaction data is expected to lead to improved versions of RF-Score. pedro.ballester@ebi.ac.uk; jbom@st-andrews.ac.uk Supplementary data are available at Bioinformatics online.

A surface-bound molecule that undergoes optically biased Brownian rotation.

PubMed

Hutchison, James A; Uji-i, Hiroshi; Deres, Ania; Vosch, Tom; Rocha, Susana; Müller, Sibylle; Bastian, Andreas A; Enderlein, Jörg; Nourouzi, Hassan; Li, Chen; Herrmann, Andreas; Müllen, Klaus; De Schryver, Frans; Hofkens, Johan

2014-02-01

Developing molecular systems with functions analogous to those of macroscopic machine components, such as rotors, gyroscopes and valves, is a long-standing goal of nanotechnology. However, macroscopic analogies go only so far in predicting function in nanoscale environments, where friction dominates over inertia. In some instances, ratchet mechanisms have been used to bias the ever-present random, thermally driven (Brownian) motion and drive molecular diffusion in desired directions. Here, we visualize the motions of surface-bound molecular rotors using defocused fluorescence imaging, and observe the transition from hindered to free Brownian rotation by tuning medium viscosity. We show that the otherwise random rotations can be biased by the polarization of the excitation light field, even though the associated optical torque is insufficient to overcome thermal fluctuations. The biased rotation is attributed instead to a fluctuating-friction mechanism in which photoexcitation of the rotor strongly inhibits its diffusion rate.

Simulations of molecular self-assembled monolayers on surfaces: packing structures, formation processes and functions tuned by intermolecular and interfacial interactions.

PubMed

Wen, Jin; Li, Wei; Chen, Shuang; Ma, Jing

2016-08-17

Surfaces modified with a functional molecular monolayer are essential for the fabrication of nano-scale electronics or machines with novel physical, chemical, and/or biological properties. Theoretical simulation based on advanced quantum chemical and classical models is at present a necessary tool in the development, design, and understanding of the interfacial nanostructure. The nanoscale surface morphology, growth processes, and functions are controlled by not only the electronic structures (molecular energy levels, dipole moments, polarizabilities, and optical properties) of building units but also the subtle balance between intermolecular and interfacial interactions. The switchable surfaces are also constructed by introducing stimuli-responsive units like azobenzene derivatives. To bridge the gap between experiments and theoretical models, opportunities and challenges for future development of modelling of ferroelectricity, entropy, and chemical reactions of surface-supported monolayers are also addressed. Theoretical simulations will allow us to obtain important and detailed information about the structure and dynamics of monolayer modified interfaces, which will guide the rational design and optimization of dynamic interfaces to meet challenges of controlling optical, electrical, and biological functions.

Interlocking Mechanism between Molecular Gears Attached to Surfaces.

PubMed

Zhao, Rundong; Zhao, Yan-Ling; Qi, Fei; Hermann, Klaus E; Zhang, Rui-Qin; Van Hove, Michel A

2018-03-27

While molecular machines play an increasingly significant role in nanoscience research and applications, there remains a shortage of investigations and understanding of the molecular gear (cogwheel), which is an indispensable and fundamental component to drive a larger correlated molecular machine system. Employing ab initio calculations, we investigate model systems consisting of molecules adsorbed on metal or graphene surfaces, ranging from very simple triple-arm gears such as PF 3 and NH 3 to larger multiarm gears based on carbon rings. We explore in detail the transmission of slow rotational motion from one gear to the next by these relatively simple molecules, so as to isolate and reveal the mechanisms of the relevant intermolecular interactions. Several characteristics of molecular gears are discussed, in particular the flexibility of the arms and the slipping and skipping between interlocking arms of adjacent gears, which differ from familiar macroscopic rigid gears. The underlying theoretical concepts suggest strongly that other analogous structures may also exhibit similar behavior which may inspire future exploration in designing large correlated molecular machines.

Coupling of kinesin ATP turnover to translocation and microtubule regulation: one engine, many machines.

PubMed

Friel, Claire T; Howard, Jonathon

2012-12-01

The cycle of ATP turnover is integral to the action of motor proteins. Here we discuss how variation in this cycle leads to variation of function observed amongst members of the kinesin superfamily of microtubule associated motor proteins. Variation in the ATP turnover cycle among superfamily members can tune the characteristic kinesin motor to one of the range of microtubule-based functions performed by kinesins. The speed at which ATP is hydrolysed affects the speed of translocation. The ratio of rate constants of ATP turnover in relation to association and dissociation from the microtubule influence the processivity of translocation. Variation in the rate-limiting step of the cycle can reverse the way in which the motor domain interacts with the microtubule producing non-motile kinesins. Because the ATP turnover cycle is not fully understood for the majority of kinesins, much work remains to show how the kinesin engine functions in such a wide variety of molecular machines.

Automatic classification of protein structures using physicochemical parameters.

PubMed

Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

2014-09-01

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

RNA Polymerase Structure, Function, Regulation, Dynamics, Fidelity, and Roles in GENE EXPRESSION | Center for Cancer Research

Cancer.gov

Multi-subunit RNA polymerases (RNAP) are ornate molecular machines that translocate on a DNA template as they generate a complementary RNA chain. RNAPs are highly conserved in evolution among eukarya, eubacteria, archaea, and some viruses. As such, multi-subunit RNAPs appear to be an irreplaceable advance in the evolution of complex life on earth. Because of their stepwise

Physical Principles of Discrete Hierarchies Formation in Protein Macromolecules

NASA Astrophysics Data System (ADS)

Malyshko, E. V.; Tverdislov, V. A.

2017-11-01

A model for chiral periodicity with alternating chiral sense in hierarchies of protein and nucleic acid structures is proposed and substantiated. Regular alternation of the chirality sense is revealed in transitions from the lowest to higher levels of structural-functional organization in proteins where it is L-D-L-D. The stratification principle combines the ideas of biomacromolecules folding and molecular biological machines.

Architectonics: Design of Molecular Architecture for Functional Applications.

PubMed

Avinash, M B; Govindaraju, Thimmaiah

2018-02-20

The term architectonics has its roots in the architectural and philosophical (as early as 1600s) literature that refers to "the theory of structure" and "the structure of theory", respectively. The concept of architectonics has been adapted to advance the field of molecular self-assembly and termed as molecular architectonics. In essence, the methodology of organizing molecular units in the required and controlled configurations to develop advanced functional systems for materials and biological applications comprises the field of molecular architectonics. This concept of designing noncovalent systems enables to focus on different functional aspects of designer molecules for biological and nonbiological applications and also strengthens our efforts toward the mastery over the art of controlled molecular self-assemblies. Programming complex molecular interactions and assemblies for specific functions has been one of the most challenging tasks in the modern era. Meticulously ordered molecular assemblies can impart remarkable developments in several areas spanning energy, health, and environment. For example, the well-defined nano-, micro-, and macroarchitectures of functional molecules with specific molecular ordering possess potential applications in flexible electronics, photovoltaics, photonic crystals, microreactors, sensors, drug delivery, biomedicine, and superhydrophobic coatings, among others. The functional molecular architectures having unparalleled properties are widely evident in various designs of Nature. By drawing inspirations from Nature, intended molecular architectures can be designed and developed to harvest various functions, as there is an inexhaustible resource and scope. In this Account, we present exquisite designer molecules developed by our group and others with an objective to master the art of molecular recognition and self-assembly for functional applications. We demonstrate the tailor-ability of molecular self-assemblies by employing biomolecules like amino acids and nucleobases as auxiliaries. Naphthalenediimide (NDI), perylenediimide (PDI), and few other molecular systems serve as functional modules. The effects of stereochemistry and minute structural modifications in the molecular designs on the supramolecular interactions, and construction of self-assembled zero-dimensional (OD), one-dimensional (1D), and two-dimensional (2D) nano- and microarchitectures like particles, spheres, cups, bowls, fibers, belts, helical belts, supercoiled helices, sheets, fractals, and honeycomb-like arrays are discussed in extensive detail. Additionally, we present molecular systems that showcase the elegant designs of coassembly, templated assembly, hierarchical assembly, transient self-assembly, chiral denaturation, retentive helical memory, self-replication, supramolecular regulation, supramolecular speciation, supernon linearity, dynamic pathway complexity, supramolecular heterojunction, living supramolecular polymerization, and molecular machines. Finally, we describe the molecular engineering principles learnt over the years that have led to several applications, namely, organic electronics, self-cleaning, high-mechanical strength, and tissue engineering.

Portraying the Expression Landscapes of B-Cell Lymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes

PubMed Central

Hopp, Lydia; Lembcke, Kathrin; Binder, Hans; Wirth, Henry

2013-01-01

We present an analytic framework based on Self-Organizing Map (SOM) machine learning to study large scale patient data sets. The potency of the approach is demonstrated in a case study using gene expression data of more than 200 mature aggressive B-cell lymphoma patients. The method portrays each sample with individual resolution, characterizes the subtypes, disentangles the expression patterns into distinct modules, extracts their functional context using enrichment techniques and enables investigation of the similarity relations between the samples. The method also allows to detect and to correct outliers caused by contaminations. Based on our analysis, we propose a refined classification of B-cell Lymphoma into four molecular subtypes which are characterized by differential functional and clinical characteristics. PMID:24833231

Synthesis of single-molecule nanocars.

PubMed

Vives, Guillaume; Tour, James M

2009-03-17

The drive to miniaturize devices has led to a variety of molecular machines inspired by macroscopic counterparts such as molecular motors, switches, shuttles, turnstiles, barrows, elevators, and nanovehicles. Such nanomachines are designed for controlled mechanical motion and the transport of nanocargo. As researchers miniaturize devices, they can consider two complementary approaches: (1) the "top-down" approach, which reduces the size of macroscopic objects to reach an equivalent microscopic entity using photolithography and related techniques and (2) the "bottom-up" approach, which builds functional microscopic or nanoscopic entities from molecular building blocks. The top-down approach, extensively used by the semiconductor industry, is nearing its scaling limits. On the other hand, the bottom-up approach takes advantage of the self-assembly of smaller molecules into larger networks by exploiting typically weak molecular interactions. But self-assembly alone will not permit complex assembly. Using nanomachines, we hope to eventually consider complex, enzyme-like directed assembly. With that ultimate goal, we are currently exploring the control of nanomachines that would provide a basis for the future bottom-up construction of complex systems. This Account describes the synthesis of a class of molecular machines that resemble macroscopic vehicles. We designed these so-called nanocars for study at the single-molecule level by scanning probe microscopy (SPM). The vehicles have a chassis connected to wheel-terminated axles and convert energy inputs such as heat, electric fields, or light into controlled motion on a surface, ultimately leading to transport of nanocargo. At first, we used C(60) fullerenes as wheels, which allowed the demonstration of a directional rolling mechanism of a nanocar on a gold surface by STM. However, because of the low solubility of the fullerene nanocars and the incompatibility of fullerenes with photochemical processes, we developed new p-carborane- and ruthenium-based wheels with greater solubility in organic solvents. Although fullerene wheels must be attached in the final synthetic step, p-carborane- and ruthenium-based wheels do not inhibit organometallic coupling reactions, which allows a more convergent synthesis of molecular machines. We also prepared functional nanotrucks for the transport of atoms and molecules, as well as self-assembling nanocars and nanotrains. Although engineering challenges such as movement over long distance and non-atomically flat surfaces remain, the greatest current research challenge is imaging. The detailed study of nanocars requires complementary single molecule imaging techniques such as STM, AFM, TEM, or single-molecule fluorescence microscopy. Further developments in engineering and synthesis could lead to enzyme-like manipulation and assembly of atoms and small molecules in nonbiological environments.

Ribosomes are optimized for autocatalytic production

NASA Astrophysics Data System (ADS)

Reuveni, Shlomi; Ehrenberg, Måns; Paulsson, Johan

2017-07-01

Many fine-scale features of ribosomes have been explained in terms of function, revealing a molecular machine that is optimized for error-correction, speed and control. Here we demonstrate mathematically that many less well understood, larger-scale features of ribosomes—such as why a few ribosomal RNA molecules dominate the mass and why the ribosomal protein content is divided into 55-80 small, similarly sized segments—speed up their autocatalytic production.

Molecular switch-like regulation in motor proteins.

PubMed

Tafoya, Sara; Bustamante, Carlos

2018-06-19

Motor proteins are powered by nucleotide hydrolysis and exert mechanical work to carry out many fundamental biological tasks. To ensure their correct and efficient performance, the motors' activities are allosterically regulated by additional factors that enhance or suppress their NTPase activity. Here, we review two highly conserved mechanisms of ATP hydrolysis activation and repression operating in motor proteins-the glutamate switch and the arginine finger-and their associated regulatory factors. We examine the implications of these regulatory mechanisms in proteins that are formed by multiple ATPase subunits. We argue that the regulatory mechanisms employed by motor proteins display features similar to those described in small GTPases, which require external regulatory elements, such as dissociation inhibitors, exchange factors and activating proteins, to switch the protein's function 'on' and 'off'. Likewise, similar regulatory roles are taken on by the motor's substrate, additional binding factors, and even adjacent subunits in multimeric complexes. However, in motor proteins, more than one regulatory factor and the two mechanisms described here often underlie the machine's operation. Furthermore, ATPase regulation takes place throughout the motor's cycle, which enables a more complex function than the binary 'active' and 'inactive' states.This article is part of a discussion meeting issue 'Allostery and molecular machines'. © 2018 The Author(s).

Computational Nanotechnology at NASA Ames Research Center, 1996

NASA Technical Reports Server (NTRS)

Globus, Al; Bailey, David; Langhoff, Steve; Pohorille, Andrew; Levit, Creon; Chancellor, Marisa K. (Technical Monitor)

1996-01-01

Some forms of nanotechnology appear to have enormous potential to improve aerospace and computer systems; computational nanotechnology, the design and simulation of programmable molecular machines, is crucial to progress. NASA Ames Research Center has begun a computational nanotechnology program including in-house work, external research grants, and grants of supercomputer time. Four goals have been established: (1) Simulate a hypothetical programmable molecular machine replicating itself and building other products. (2) Develop molecular manufacturing CAD (computer aided design) software and use it to design molecular manufacturing systems and products of aerospace interest, including computer components. (3) Characterize nanotechnologically accessible materials of aerospace interest. Such materials may have excellent strength and thermal properties. (4) Collaborate with experimentalists. Current in-house activities include: (1) Development of NanoDesign, software to design and simulate a nanotechnology based on functionalized fullerenes. Early work focuses on gears. (2) A design for high density atomically precise memory. (3) Design of nanotechnology systems based on biology. (4) Characterization of diamonoid mechanosynthetic pathways. (5) Studies of the laplacian of the electronic charge density to understand molecular structure and reactivity. (6) Studies of entropic effects during self-assembly. Characterization of properties of matter for clusters up to sizes exhibiting bulk properties. In addition, the NAS (NASA Advanced Supercomputing) supercomputer division sponsored a workshop on computational molecular nanotechnology on March 4-5, 1996 held at NASA Ames Research Center. Finally, collaborations with Bill Goddard at CalTech, Ralph Merkle at Xerox Parc, Don Brenner at NCSU (North Carolina State University), Tom McKendree at Hughes, and Todd Wipke at UCSC are underway.

Controlled clockwise and anticlockwise rotational switching of a molecular motor.

PubMed

Perera, U G E; Ample, F; Kersell, H; Zhang, Y; Vives, G; Echeverria, J; Grisolia, M; Rapenne, G; Joachim, C; Hla, S-W

2013-01-01

The design of artificial molecular machines often takes inspiration from macroscopic machines. However, the parallels between the two systems are often only superficial, because most molecular machines are governed by quantum processes. Previously, rotary molecular motors powered by light and chemical energy have been developed. In electrically driven motors, tunnelling electrons from the tip of a scanning tunnelling microscope have been used to drive the rotation of a simple rotor in a single direction and to move a four-wheeled molecule across a surface. Here, we show that a stand-alone molecular motor adsorbed on a gold surface can be made to rotate in a clockwise or anticlockwise direction by selective inelastic electron tunnelling through different subunits of the motor. Our motor is composed of a tripodal stator for vertical positioning, a five-arm rotor for controlled rotations, and a ruthenium atomic ball bearing connecting the static and rotational parts. The directional rotation arises from sawtooth-like rotational potentials, which are solely determined by the internal molecular structure and are independent of the surface adsorption site.

Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations.

PubMed

Wu, Jingheng; Shen, Lin; Yang, Weitao

2017-10-28

Ab initio quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulation is a useful tool to calculate thermodynamic properties such as potential of mean force for chemical reactions but intensely time consuming. In this paper, we developed a new method using the internal force correction for low-level semiempirical QM/MM molecular dynamics samplings with a predefined reaction coordinate. As a correction term, the internal force was predicted with a machine learning scheme, which provides a sophisticated force field, and added to the atomic forces on the reaction coordinate related atoms at each integration step. We applied this method to two reactions in aqueous solution and reproduced potentials of mean force at the ab initio QM/MM level. The saving in computational cost is about 2 orders of magnitude. The present work reveals great potentials for machine learning in QM/MM simulations to study complex chemical processes.

Biological proton pumping in an oscillating electric field.

PubMed

Kim, Young C; Furchtgott, Leon A; Hummer, Gerhard

2009-12-31

Time-dependent external perturbations provide powerful probes of the function of molecular machines. Here we study biological proton pumping in an oscillating electric field. The protein cytochrome c oxidase is the main energy transducer in aerobic life, converting chemical energy into an electric potential by pumping protons across a membrane. With the help of master-equation descriptions that recover the key thermodynamic and kinetic properties of this biological "fuel cell," we show that the proton pumping efficiency and the electronic currents in steady state depend significantly on the frequency and amplitude of the applied field, allowing us to distinguish between different microscopic mechanisms of the machine. A spectral analysis reveals dominant reaction steps consistent with an electron-gated pumping mechanism.

A comparison of machine learning and Bayesian modelling for molecular serotyping.

PubMed

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological insights, which we illustrate with an example.

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective

PubMed Central

Lötsch, Jörn; Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred

2017-01-01

Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence. PMID:28848388

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

PubMed

Wang, Yong-Cui; Wang, Yong; Yang, Zhi-Xia; Deng, Nai-Yang

2011-06-20

Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism. In this paper, we introduce two key improvements in predicting enzyme function within the machine learning framework. One is to introduce the efficient sequence encoding methods for representing given proteins. The second one is to develop a structure-based prediction method with low computational complexity. In particular, we propose to use the conjoint triad feature (CTF) to represent the given protein sequences by considering not only the composition of amino acids but also the neighbor relationships in the sequence. Then we develop a support vector machine (SVM)-based method, named as SVMHL (SVM for hierarchy labels), to output enzyme function by fully considering the hierarchical structure of EC. The experimental results show that our SVMHL with the CTF outperforms SVMHL with the amino acid composition (AAC) feature both in predictive accuracy and Matthew's correlation coefficient (MCC). In addition, SVMHL with the CTF obtains the accuracy and MCC ranging from 81% to 98% and 0.82 to 0.98 when predicting the first three EC digits on a low-homologous enzyme dataset. We further demonstrate that our method outperforms the methods which do not take account of hierarchical relationship among enzyme categories and alternative methods which incorporate prior knowledge about inter-class relationships. Our structure-based prediction model, SVMHL with the CTF, reduces the computational complexity and outperforms the alternative approaches in enzyme function prediction. Therefore our new method will be a useful tool for enzyme function prediction community.

Molecular graph convolutions: moving beyond fingerprints

NASA Astrophysics Data System (ADS)

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Molecular graph convolutions: moving beyond fingerprints.

PubMed

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Machine Learning Helps Identify CHRONO as a Circadian Clock Component

PubMed Central

Venkataraman, Anand; Ramanathan, Chidambaram; Kavakli, Ibrahim H.; Hughes, Michael E.; Baggs, Julie E.; Growe, Jacqueline; Liu, Andrew C.; Kim, Junhyong; Hogenesch, John B.

2014-01-01

Over the last decades, researchers have characterized a set of “clock genes” that drive daily rhythms in physiology and behavior. This arduous work has yielded results with far-reaching consequences in metabolic, psychiatric, and neoplastic disorders. Recent attempts to expand our understanding of circadian regulation have moved beyond the mutagenesis screens that identified the first clock components, employing higher throughput genomic and proteomic techniques. In order to further accelerate clock gene discovery, we utilized a computer-assisted approach to identify and prioritize candidate clock components. We used a simple form of probabilistic machine learning to integrate biologically relevant, genome-scale data and ranked genes on their similarity to known clock components. We then used a secondary experimental screen to characterize the top candidates. We found that several physically interact with known clock components in a mammalian two-hybrid screen and modulate in vitro cellular rhythms in an immortalized mouse fibroblast line (NIH 3T3). One candidate, Gene Model 129, interacts with BMAL1 and functionally represses the key driver of molecular rhythms, the BMAL1/CLOCK transcriptional complex. Given these results, we have renamed the gene CHRONO (computationally highlighted repressor of the network oscillator). Bi-molecular fluorescence complementation and co-immunoprecipitation demonstrate that CHRONO represses by abrogating the binding of BMAL1 to its transcriptional co-activator CBP. Most importantly, CHRONO knockout mice display a prolonged free-running circadian period similar to, or more drastic than, six other clock components. We conclude that CHRONO is a functional clock component providing a new layer of control on circadian molecular dynamics. PMID:24737000

Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks.

PubMed

Havugimana, Pierre C; Hu, Pingzhao; Emili, Andrew

2017-10-01

Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and for functional annotation of previously uncharacterized proteins (via guilt-by-association or -correlation). After a decade in the field, we felt it timely to document our own experiences in the systematic analysis of protein interaction networks. Areas covered: Researchers worldwide have contributed innovative experimental and computational approaches that have driven the rapidly evolving field of 'functional proteomics'. These include mass spectrometry-based methods to characterize macromolecular complexes on a global-scale and sophisticated data analysis tools - most notably machine learning - that allow for the generation of high-quality protein association maps. Expert commentary: Here, we recount some key lessons learned, with an emphasis on successful workflows, and challenges, arising from our own and other groups' ongoing efforts to generate, interpret and report proteome-scale interaction networks in increasingly diverse biological contexts.

Tribology and energy efficiency: from molecules to lubricated contacts to complete machines.

PubMed

Taylor, Robert Ian

2012-01-01

The impact of lubricants on energy efficiency is considered. Molecular details of base oils used in lubricants can have a great impact on the lubricant's physical properties which will affect the energy efficiency performance of a lubricant. In addition, molecular details of lubricant additives can result in significant differences in measured friction coefficients for machine elements operating in the mixed/boundary lubrication regime. In single machine elements, these differences will result in lower friction losses, and for complete systems (such as cars, trucks, hydraulic circuits, industrial gearboxes etc.) lower fuel consumption or lower electricity consumption can result.

A charge-driven molecular water pump.

PubMed

Gong, Xiaojing; Li, Jingyuan; Lu, Hangjun; Wan, Rongzheng; Li, Jichen; Hu, Jun; Fang, Haiping

2007-11-01

Understanding and controlling the transport of water across nanochannels is of great importance for designing novel molecular devices, machines and sensors and has wide applications, including the desalination of seawater. Nanopumps driven by electric or magnetic fields can transport ions and magnetic quanta, but water is charge-neutral and has no magnetic moment. On the basis of molecular dynamics simulations, we propose a design for a molecular water pump. The design uses a combination of charges positioned adjacent to a nanopore and is inspired by the structure of channels in the cellular membrane that conduct water in and out of the cell (aquaporins). The remarkable pumping ability is attributed to the charge dipole-induced ordering of water confined in the nanochannels, where water can be easily driven by external fields in a concerted fashion. These findings may provide possibilities for developing water transport devices that function without osmotic pressure or a hydrostatic pressure gradient.

Engineering the vibrational coherence of vision into a synthetic molecular device.

PubMed

Gueye, Moussa; Manathunga, Madushanka; Agathangelou, Damianos; Orozco, Yoelvis; Paolino, Marco; Fusi, Stefania; Haacke, Stefan; Olivucci, Massimo; Léonard, Jérémie

2018-01-22

The light-induced double-bond isomerization of the visual pigment rhodopsin operates a molecular-level optomechanical energy transduction, which triggers a crucial protein structure change. In fact, rhodopsin isomerization occurs according to a unique, ultrafast mechanism that preserves mode-specific vibrational coherence all the way from the reactant excited state to the primary photoproduct ground state. The engineering of such an energy-funnelling function in synthetic compounds would pave the way towards biomimetic molecular machines capable of achieving optimum light-to-mechanical energy conversion. Here we use resonance and off-resonance vibrational coherence spectroscopy to demonstrate that a rhodopsin-like isomerization operates in a biomimetic molecular switch in solution. Furthermore, by using quantum chemical simulations, we show why the observed coherent nuclear motion critically depends on minor chemical modifications capable to induce specific geometric and electronic effects. This finding provides a strategy for engineering vibrationally coherent motions in other synthetic systems.

PSB27: A thylakoid protein enabling Arabidopsis to adapt to changing light intensity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hou, Xin; Garcia, Veder J.; Buchanan, Bob B.

Project Title: Immunophilins in the assembly and maintenance of photosynthetic electron transport chain in Arabidopsis Applicant: The Regents of the University of California PI: Sheng Luan, University of California at Berkeley Photosynthetic light energy conversion entails coordinated function of complex molecular machines that capture and convert light energy into chemical forms through photosynthetic electron transport chain. Each molecular machine, such as photosystem II (PSII), may consist of dozens of protein subunits and small molecule cofactors. Despite advanced understanding of the structure and function of these complexes, little is known about “How individual proteins and cofactors assemble into a functional machinemore » and how do these molecular machines maintain their structure and function under a highly hazardous lumenal environment.” Our studies on immunophilins have unexpectedly contributed to the understanding of this question. Originally defined as cellular receptors for immunosuppressants, immunophilins have been discovered in a wide range of organisms from bacteria, fungi, plants, to animals. Immunophilins function in protein folding processes as chaperones and foldases. Arabidopsis genome encodes ca. 50 immunophilins. The most striking finding is that 16 immunophilin members are targeted to chloroplast thylakoid lumen, by far the largest group in the lumenal proteome. What is the function of immunophilins in the thylakoid lumen? Our studies have demonstrated critical roles for several immunophilins in the biogenesis and maintenance of photosynthetic complexes such as PSII. These studies have made a critical link between immunophilins and the assembly of photosynthetic machines and thus opened up a new area of research in photosynthesis. Our goal is to dissect the roles of immunophilins and their partners in the assembly and maintenance of the photosynthetic electron transport chain. The specific objectives for this funding period will be: 1. To dissect the mechanism of action for CYP38. Our studies suggest an “autoinhibitory” model for CYP38 action. We plan to test this model and determine the mechanistic details for CYP38 function by biochemical and genetic procedures. 2. To determine the mechanism of action of FKBP20-2/FKBP16-3. Studies on FBKP16-3 and FKBP20-2 suggest that they may work together as a redox-dependent duo in PSII assembly. We will test this hypothesis using biochemical and genetic tools. 3. To determine the function of lumenal FKBPs by multi-gene mutagenesis approach. Using new genetic knockout (KO) procedures especially CRISPR/CAS9 system, we plan to generate multi-gene KO models that will likely provide vital information on those FKBPs with functional redundancy. 4. Functional analysis of thylakoid lumen network. We will focus our effort on objectives 1-3. Objective 4 represents a long-term goal to establish a more comprehensive network of proteins in the thylakoid lumen and their function in the assembly and maintenance of photosynthetic complexes. I hypothesize that the 16 immunophilin-type chaperones and foldases in the thylakoid lumen constitute an “assembly line” for the various components in the photosynthetic electron transport chain. Understanding this assembly line will enable further engineering of more efficient photosynthetic machines to capture more light energy and enhance plant productivity. Furthermore, it is envisioned that such molecular “assembly line” may be rebuilt to produce artificial photosynthesis in vitro or in non-photosynthetic organisms. These are highly relevant to the mission of “Photosynthetic Systems” and “Physical Biosciences” programs to “enhance our understanding of energy capture, conversion, and/or storage.” During the funding period, we have performed a number of experiments under the proposed objectives and obtained several new results. We have found in Objective 1 that CYP38 is associated to the thylokoid membrane with a larger complex that might be PSII supercomplex. Under objective 2, we have found that FKBP16-2 interacted with PSB27 that was further pursuited and published a research article in PNAS (attached). Under Objective 3, we have identified several mutants of other FKBPs in the thyalkoid lumen that should be further studied if future funding is available. Under Objective 4, we have started to build a network of lumenal proteins that play a number of roles in photosynthesis. For example, the CYP37 and CYP28 are linked to chloroplast signaling to nucleus, critical for controlling plant response to high light and adaptation to climate change. Unfortunately these studies have been terminated due to funding shortage.« less

Computational Nanotechnology of Materials, Devices, and Machines: Carbon Nanotubes

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Kwak, Dolhan (Technical Monitor)

2000-01-01

The mechanics and chemistry of carbon nanotubes have relevance for their numerous electronic applications. Mechanical deformations such as bending and twisting affect the nanotube's conductive properties, and at the same time they possess high strength and elasticity. Two principal techniques were utilized including the analysis of large scale classical molecular dynamics on a shared memory architecture machine and a quantum molecular dynamics methodology. In carbon based electronics, nanotubes are used as molecular wires with topological defects which are mediated through various means. Nanotubes can be connected to form junctions.

Micro-machined calorimetric biosensors

DOEpatents

Doktycz, Mitchel J.; Britton, Jr., Charles L.; Smith, Stephen F.; Oden, Patrick I.; Bryan, William L.; Moore, James A.; Thundat, Thomas G.; Warmack, Robert J.

2002-01-01

A method and apparatus are provided for detecting and monitoring micro-volumetric enthalpic changes caused by molecular reactions. Micro-machining techniques are used to create very small thermally isolated masses incorporating temperature-sensitive circuitry. The thermally isolated masses are provided with a molecular layer or coating, and the temperature-sensitive circuitry provides an indication when the molecules of the coating are involved in an enthalpic reaction. The thermally isolated masses may be provided singly or in arrays and, in the latter case, the molecular coatings may differ to provide qualitative and/or quantitative assays of a substance.

LOSITAN: a workbench to detect molecular adaptation based on a Fst-outlier method.

PubMed

Antao, Tiago; Lopes, Ana; Lopes, Ricardo J; Beja-Pereira, Albano; Luikart, Gordon

2008-07-28

Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user. Here we present LOSITAN, a selection detection workbench based on a well evaluated Fst-outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral Fst), provides data import and export functions, iterative contour smoothing and generation of graphics in a easy to use graphical user interface. LOSITAN is able to use modern multi-core processor architectures by locally parallelizing fdist, reducing computation time by half in current dual core machines and with almost linear performance gains in machines with more cores. LOSITAN makes selection detection feasible to a much wider range of users, even for large population genomic datasets, by both providing an easy to use interface and essential functionality to complete the whole selection detection process.

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.

PubMed

Hansen, Katja; Montavon, Grégoire; Biegler, Franziska; Fazli, Siamac; Rupp, Matthias; Scheffler, Matthias; von Lilienfeld, O Anatole; Tkatchenko, Alexandre; Müller, Klaus-Robert

2013-08-13

The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.

A machine learning approach to computer-aided molecular design

NASA Astrophysics Data System (ADS)

Bolis, Giorgio; Di Pace, Luigi; Fabrocini, Filippo

1991-12-01

Preliminary results of a machine learning application concerning computer-aided molecular design applied to drug discovery are presented. The artificial intelligence techniques of machine learning use a sample of active and inactive compounds, which is viewed as a set of positive and negative examples, to allow the induction of a molecular model characterizing the interaction between the compounds and a target molecule. The algorithm is based on a twofold phase. In the first one — the specialization step — the program identifies a number of active/inactive pairs of compounds which appear to be the most useful in order to make the learning process as effective as possible and generates a dictionary of molecular fragments, deemed to be responsible for the activity of the compounds. In the second phase — the generalization step — the fragments thus generated are combined and generalized in order to select the most plausible hypothesis with respect to the sample of compounds. A knowledge base concerning physical and chemical properties is utilized during the inductive process.

Molecular graph convolutions: moving beyond fingerprints

PubMed Central

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-01-01

Molecular “fingerprints” encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement. PMID:27558503

Molecular analyses of the principal components of response strength.

PubMed Central

Killeen, Peter R; Hall, Scott S; Reilly, Mark P; Kettle, Lauren C

2002-01-01

Killeen and Hall (2001) showed that a common factor called strength underlies the key dependent variables of response probability, latency, and rate, and that overall response rate is a good predictor of strength. In a search for the mechanisms that underlie those correlations, this article shows that (a) the probability of responding on a trial is a two-state Markov process; (b) latency and rate of responding can be described in terms of the probability and period of stochastic machines called clocked Bernoulli modules, and (c) one such machine, the refractory Poisson process, provides a functional relation between the probability of observing a response during any epoch and the rate of responding. This relation is one of proportionality at low rates and curvilinearity at higher rates. PMID:12216975

Biological proton pumping in an oscillating electric field

PubMed Central

Kim, Young C.; Furchtgott, Leon A.; Hummer, Gerhard

2010-01-01

Time-dependent external perturbations provide powerful probes of the function of molecular machines. Here we study biological proton pumping in an oscillating electric field. The protein cytochrome c oxidase is the main energy transducer in aerobic life, converting chemical energy into an electric potential by pumping protons across a membrane. With the help of master-equation descriptions that recover the key thermodynamic and kinetic properties of this biological “fuel cell,” we show that the proton pumping efficiency and the electronic currents in steady state both depend significantly and distinctly on the frequency and amplitude of the applied field, allowing us to distinguish between different microscopic mechanisms of the machine. A spectral analysis reveals dominant kinetic modes that show reaction steps consistent with an electron-gated pumping mechanism. PMID:20366348

Impact of mutations on the allosteric conformational equilibrium

PubMed Central

Weinkam, Patrick; Chen, Yao Chi; Pons, Jaume; Sali, Andrej

2012-01-01

Allostery in a protein involves effector binding at an allosteric site that changes the structure and/or dynamics at a distant, functional site. In addition to the chemical equilibrium of ligand binding, allostery involves a conformational equilibrium between one protein substate that binds the effector and a second substate that less strongly binds the effector. We run molecular dynamics simulations using simple, smooth energy landscapes to sample specific ligand-induced conformational transitions, as defined by the effector-bound and unbound protein structures. These simulations can be performed using our web server: http://salilab.org/allosmod/. We then develop a set of features to analyze the simulations and capture the relevant thermodynamic properties of the allosteric conformational equilibrium. These features are based on molecular mechanics energy functions, stereochemical effects, and structural/dynamic coupling between sites. Using a machine-learning algorithm on a dataset of 10 proteins and 179 mutations, we predict both the magnitude and sign of the allosteric conformational equilibrium shift by the mutation; the impact of a large identifiable fraction of the mutations can be predicted with an average unsigned error of 1 kBT. With similar accuracy, we predict the mutation effects for an 11th protein that was omitted from the initial training and testing of the machine-learning algorithm. We also assess which calculated thermodynamic properties contribute most to the accuracy of the prediction. PMID:23228330

ATP Synthase Diseases of Mitochondrial Genetic Origin

PubMed Central

Dautant, Alain; Meier, Thomas; Hahn, Alexander; Tribouillard-Tanvier, Déborah; di Rago, Jean-Paul; Kucharczyk, Roza

2018-01-01

Devastating human neuromuscular disorders have been associated to defects in the ATP synthase. This enzyme is found in the inner mitochondrial membrane and catalyzes the last step in oxidative phosphorylation, which provides aerobic eukaryotes with ATP. With the advent of structures of complete ATP synthases, and the availability of genetically approachable systems such as the yeast Saccharomyces cerevisiae, we can begin to understand these molecular machines and their associated defects at the molecular level. In this review, we describe what is known about the clinical syndromes induced by 58 different mutations found in the mitochondrial genes encoding membrane subunits 8 and a of ATP synthase, and evaluate their functional consequences with respect to recently described cryo-EM structures. PMID:29670542

Free-energy simulations reveal molecular mechanism for functional switch of a DNA helicase

PubMed Central

Ma, Wen; Whitley, Kevin D; Schulten, Klaus

2018-01-01

Helicases play key roles in genome maintenance, yet it remains elusive how these enzymes change conformations and how transitions between different conformational states regulate nucleic acid reshaping. Here, we developed a computational technique combining structural bioinformatics approaches and atomic-level free-energy simulations to characterize how the Escherichia coli DNA repair enzyme UvrD changes its conformation at the fork junction to switch its function from unwinding to rezipping DNA. The lowest free-energy path shows that UvrD opens the interface between two domains, allowing the bound ssDNA to escape. The simulation results predict a key metastable 'tilted' state during ssDNA strand switching. By simulating FRET distributions with fluorophores attached to UvrD, we show that the new state is supported quantitatively by single-molecule measurements. The present study deciphers key elements for the 'hyper-helicase' behavior of a mutant and provides an effective framework to characterize directly structure-function relationships in molecular machines. PMID:29664402

Free-energy simulations reveal molecular mechanism for functional switch of a DNA helicase.

PubMed

Ma, Wen; Whitley, Kevin D; Chemla, Yann R; Luthey-Schulten, Zaida; Schulten, Klaus

2018-04-17

Helicases play key roles in genome maintenance, yet it remains elusive how these enzymes change conformations and how transitions between different conformational states regulate nucleic acid reshaping. Here, we developed a computational technique combining structural bioinformatics approaches and atomic-level free-energy simulations to characterize how the Escherichia coli DNA repair enzyme UvrD changes its conformation at the fork junction to switch its function from unwinding to rezipping DNA. The lowest free-energy path shows that UvrD opens the interface between two domains, allowing the bound ssDNA to escape. The simulation results predict a key metastable 'tilted' state during ssDNA strand switching. By simulating FRET distributions with fluorophores attached to UvrD, we show that the new state is supported quantitatively by single-molecule measurements. The present study deciphers key elements for the 'hyper-helicase' behavior of a mutant and provides an effective framework to characterize directly structure-function relationships in molecular machines. © 2018, Ma et al.

Machine learning landscapes and predictions for patient outcomes

NASA Astrophysics Data System (ADS)

Das, Ritankar; Wales, David J.

2017-07-01

The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. In this contribution, we test the predictions obtained by fitting to single measurements, and then to combinations of between 2 and 10 different patient medical data items. The effect of including measurements over different time intervals from the 48 h period in question is analysed, and the most recent values are found to be the most important. We also compare results obtained for neural networks as a function of the number of hidden nodes, and for different values of a regularization parameter. The predictions are compared with an alternative convex fitting function, and a strong correlation is observed. The dependence of these results on the patients randomly selected for training and testing decreases systematically with the size of the database available. The machine learning landscapes defined by neural network fits in this investigation have single-funnel character, which probably explains why it is relatively straightforward to obtain the global minimum solution, or a fit that behaves similarly to this optimal parameterization.

EffectorP: predicting fungal effector proteins from secretomes using machine learning.

PubMed

Sperschneider, Jana; Gardiner, Donald M; Dodds, Peter N; Tini, Francesco; Covarelli, Lorenzo; Singh, Karam B; Manners, John M; Taylor, Jennifer M

2016-04-01

Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N-terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine-rich, which suffers from poor accuracy. We present EffectorP which pioneers the application of machine learning to fungal effector prediction. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EffectorP is powerful when combined with in planta expression data for predicting high-priority effector candidates. EffectorP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EffectorP is available at http://effectorp.csiro.au. © 2015 CSIRO New Phytologist © 2015 New Phytologist Trust.

Molecular Machine Powered Surface Programmatic Chain Reaction for Highly Sensitive Electrochemical Detection of Protein.

PubMed

Zhu, Jing; Gan, Haiying; Wu, Jie; Ju, Huangxian

2018-04-17

A bipedal molecular machine powered surface programmatic chain reaction was designed for electrochemical signal amplification and highly sensitive electrochemical detection of protein. The bipedal molecular machine was built through aptamer-target specific recognition for the binding of one target protein with two DNA probes, which hybridized with surface-tethered hairpin DNA 1 (H1) via proximity effect to expose the prelocked toehold domain of H1 for the hybridization of ferrocene-labeled hairpin DNA 2 (H2-Fc). The toehold-mediated strand displacement reaction brought the electrochemical signal molecule Fc close to the electrode and meanwhile released the bipedal molecular machine to traverse the sensing surface by the surface programmatic chain reaction. Eventually, a large number of duplex structures of H1-H2 with ferrocene groups facing to the electrode were formed on the sensor surface to generate an amplified electrochemical signal. Using thrombin as a model target, this method showed a linear detection range from 2 pM to 20 nM with a detection limit of 0.76 pM. The proposed detection strategy was enzyme-free and allowed highly sensitive and selective detection of a variety of protein targets by using corresponding DNA-based affinity probes, showing potential application in bioanalysis.

Specificity of interactions among the DNA-packaging machine components of T4-related bacteriophages.

PubMed

Gao, Song; Rao, Venigalla B

2011-02-04

Tailed bacteriophages use powerful molecular motors to package the viral genome into a preformed capsid. Packaging at a rate of up to ∼2000 bp/s and generating a power density twice that of an automobile engine, the phage T4 motor is the fastest and most powerful reported to date. Central to DNA packaging are dynamic interactions among the packaging components, capsid (gp23), portal (gp20), motor (gp17, large "terminase"), and regulator (gp16, small terminase), leading to precise orchestration of the packaging process, but the mechanisms are poorly understood. Here we analyzed the interactions between small and large terminases of T4-related phages. Our results show that the gp17 packaging ATPase is maximally stimulated by homologous, but not heterologous, gp16. Multiple interaction sites are identified in both gp16 and gp17. The specificity determinants in gp16 are clustered in the diverged N- and C-terminal domains (regions I-III). Swapping of diverged region(s), such as replacing C-terminal RB49 region III with that of T4, switched ATPase stimulation specificity. Two specificity regions, amino acids 37-52 and 290-315, are identified in or near the gp17-ATPase "transmission" subdomain II. gp16 binding at these sites might cause a conformational change positioning the ATPase-coupling residues into the catalytic pocket, triggering ATP hydrolysis. These results lead to a model in which multiple weak interactions between motor and regulator allow dynamic assembly and disassembly of various packaging complexes, depending on the functional state of the packaging machine. This might be a general mechanism for regulation of the phage packaging machine and other complex molecular machines.

Characterizing water-metal interfaces and machine learning potential energy surfaces

NASA Astrophysics Data System (ADS)

Ryczko, Kevin

In this thesis, we first discuss the fundamentals of ab initio electronic structure theory and density functional theory (DFT). We also discuss statistics related to computing thermodynamic averages of molecular dynamics (MD). We then use this theory to analyze and compare the structural, dynamical, and electronic properties of liquid water next to prototypical metals including platinum, graphite, and graphene. Our results are built on Born-Oppenheimer molecular dynamics (BOMD) generated using density functional theory (DFT) which explicitly include van der Waals (vdW) interactions within a first principles approach. All calculations reported use large simulation cells, allowing for an accurate treatment of the water-electrode interfaces. We have included vdW interactions through the use of the optB86b-vdW exchange correlation functional. Comparisons with the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional are also shown. We find an initial peak, due to chemisorption, in the density profile of the liquid water-Pt interface not seen in the liquid water-graphite interface, liquid watergraphene interface, nor interfaces studied previously. To further investigate this chemisorption peak, we also report differences in the electronic structure of single water molecules on both Pt and graphite surfaces. We find that a covalent bond forms between the single water molecule and the platinum surface, but not between the single water molecule and the graphite surface. We also discuss the effects that defects and dopants in the graphite and graphene surfaces have on the structure and dynamics of liquid water. Lastly, we introduce artificial neural networks (ANNs), and demonstrate how they can be used to machine learn electronic structure calculations. As a proof of principle, we show the success of an ANN potential energy surfaces for a dimer molecule with a Lennard-Jones potential.

Dynamic combinatorial libraries: from exploring molecular recognition to systems chemistry.

PubMed

Li, Jianwei; Nowak, Piotr; Otto, Sijbren

2013-06-26

Dynamic combinatorial chemistry (DCC) is a subset of combinatorial chemistry where the library members interconvert continuously by exchanging building blocks with each other. Dynamic combinatorial libraries (DCLs) are powerful tools for discovering the unexpected and have given rise to many fascinating molecules, ranging from interlocked structures to self-replicators. Furthermore, dynamic combinatorial molecular networks can produce emergent properties at systems level, which provide exciting new opportunities in systems chemistry. In this perspective we will highlight some new methodologies in this field and analyze selected examples of DCLs that are under thermodynamic control, leading to synthetic receptors, catalytic systems, and complex self-assembled supramolecular architectures. Also reviewed are extensions of the principles of DCC to systems that are not at equilibrium and may therefore harbor richer functional behavior. Examples include self-replication and molecular machines.

Synthesis of a pH-Sensitive Hetero[4]Rotaxane Molecular Machine that Combines [c2]Daisy and [2]Rotaxane Arrangements.

PubMed

Waelès, Philip; Riss-Yaw, Benjamin; Coutrot, Frédéric

2016-05-10

The synthesis of a novel pH-sensitive hetero[4]rotaxane molecular machine through a self-sorting strategy is reported. The original tetra-interlocked molecular architecture combines a [c2]daisy chain scaffold linked to two [2]rotaxane units. Actuation of the system through pH variation is possible thanks to the specific interactions of the dibenzo-24-crown-8 (DB24C8) macrocycles for ammonium, anilinium, and triazolium molecular stations. Selective deprotonation of the anilinium moieties triggers shuttling of the unsubstituted DB24C8 along the [2]rotaxane units. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Twin target self-amplification-based DNA machine for highly sensitive detection of cancer-related gene.

PubMed

Xu, Huo; Jiang, Yifan; Liu, Dengyou; Liu, Kai; Zhang, Yafeng; Yu, Suhong; Shen, Zhifa; Wu, Zai-Sheng

2018-06-29

The sensitive detection of cancer-related genes is of great significance for early diagnosis and treatment of human cancers, and previous isothermal amplification sensing systems were often based on the reuse of target DNA, the amplification of enzymatic products and the accumulation of reporting probes. However, no reporting probes are able to be transformed into target species and in turn initiate the signal of other probes. Herein we reported a simple, isothermal and highly sensitive homogeneous assay system for tumor suppressor p53 gene detection based on a new autonomous DNA machine, where the signaling probe, molecular beacon (MB), was able to execute the function similar to target DNA besides providing the common signal. In the presence of target p53 gene, the operation of DNA machine can be initiated, and cyclical nucleic acid strand-displacement polymerization (CNDP) and nicking/polymerization cyclical amplification (NPCA) occur, during which the MB was opened by target species and cleaved by restriction endonuclease. In turn, the cleaved fragments could activate the next signaling process as target DNA did. According to the functional similarity, the cleaved fragment was called twin target, and the corresponding fashion to amplify the signal was named twin target self-amplification. Utilizing this newly-proposed DNA machine, the target DNA could be detected down to 0.1 pM with a wide dynamic range (6 orders of magnitude) and single-base mismatched targets were discriminated, indicating a very high assay sensitivity and good specificity. In addition, the DNA machine was not only used to screen the p53 gene in complex biological matrix but also was capable of practically detecting genomic DNA p53 extracted from A549 cell line. This indicates that the proposed DNA machine holds the potential application in biomedical research and early clinical diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

76 FR 81518 - Notice of Issuance of Final Determination Concerning Laser-Based Multi-Function Office Machines

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-28

... Determination Concerning Laser-Based Multi-Function Office Machines AGENCY: U.S. Customs and Border Protection... country of origin of laser-based multi-function office machines. Based upon the facts presented, CBP has... essential character of the laser-based multi-function office machine, and it is at their assembly and...

Molecular, metabolic, and genetic control: An introduction

NASA Astrophysics Data System (ADS)

Tyson, John J.; Mackey, Michael C.

2001-03-01

The living cell is a miniature, self-reproducing, biochemical machine. Like all machines, it has a power supply, a set of working components that carry out its necessary tasks, and control systems that ensure the proper coordination of these tasks. In this Special Issue, we focus on the molecular regulatory systems that control cell metabolism, gene expression, environmental responses, development, and reproduction. As for the control systems in human-engineered machines, these regulatory networks can be described by nonlinear dynamical equations, for example, ordinary differential equations, reaction-diffusion equations, stochastic differential equations, or cellular automata. The articles collected here illustrate (i) a range of theoretical problems presented by modern concepts of cellular regulation, (ii) some strategies for converting molecular mechanisms into dynamical systems, (iii) some useful mathematical tools for analyzing and simulating these systems, and (iv) the sort of results that derive from serious interplay between theory and experiment.

Understanding the role of dynamics in the iron sulfur cluster molecular machine.

PubMed

di Maio, Danilo; Chandramouli, Balasubramanian; Yan, Robert; Brancato, Giuseppe; Pastore, Annalisa

2017-01-01

The bacterial proteins IscS, IscU and CyaY, the bacterial orthologue of frataxin, play an essential role in the biological machine that assembles the prosthetic FeS cluster groups on proteins. They form functionally binary and ternary complexes both in vivo and in vitro. Yet, the mechanism by which they work remains unclear. We carried out extensive molecular dynamics simulations to understand the nature of their interactions and the role of dynamics starting from the crystal structure of a IscS-IscU complex and the experimentally-based model of a ternary IscS-IscU-CyaY complex and used nuclear magnetic resonance to experimentally test the interface. We show that, while being firmly anchored to IscS, IscU has a pivotal motion around the interface. Our results also describe how the catalytic loop of IscS can flip conformation to allow FeS cluster assembly. This motion is hampered in the ternary complex explaining its inhibitory properties in cluster formation. We conclude that the observed 'fluid' IscS-IscU interface provides the binary complex with a functional adaptability exploited in partner recognition and unravels the molecular determinants of the reported inhibitory action of CyaY in the IscS-IscU-CyaY complex explained in terms of the hampering effect on specific IscU-IscS movements. Our study provides the first mechanistic basis to explain how the IscS-IscU complex selects its binding partners and supports the inhibitory role of CyaY in the ternary complex. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Dynamical photo-induced electronic properties of molecular junctions

NASA Astrophysics Data System (ADS)

Beltako, K.; Michelini, F.; Cavassilas, N.; Raymond, L.

2018-03-01

Nanoscale molecular-electronic devices and machines are emerging as promising functional elements, naturally flexible and efficient, for next-generation technologies. A deeper understanding of carrier dynamics in molecular junctions is expected to benefit many fields of nanoelectronics and power devices. We determine time-resolved charge current flowing at the donor-acceptor interface in molecular junctions connected to metallic electrodes by means of quantum transport simulations. The current is induced by the interaction of the donor with a Gaussian-shape femtosecond laser pulse. Effects of the molecular internal coupling, metal-molecule tunneling, and light-donor coupling on photocurrent are discussed. We then define the time-resolved local density of states which is proposed as an efficient tool to describe the absorbing molecule in contact with metallic electrodes. Non-equilibrium reorganization of hybridized molecular orbitals through the light-donor interaction gives rise to two phenomena: the dynamical Rabi shift and the appearance of Floquet-like states. Such insights into the dynamical photoelectronic structure of molecules are of strong interest for ultrafast spectroscopy and open avenues toward the possibility of analyzing and controlling the internal properties of quantum nanodevices with pump-push photocurrent spectroscopy.

Analysis of the morphology, stability, and folding pathways of ring polymers with supramolecular topological constraints using molecular simulation and nonlinear manifold learning

NASA Astrophysics Data System (ADS)

Wang, Jiang; Ferguson, Andrew

Ring polymers offer a wide range of natural and engineered functions and applications, including as circular bacterial DNA, crown ethers for cation chelation, and ``molecular machines'' such as mechanical nanoswitches. The morphology and dynamics of ring polymers are governed by the chemistry and degree of polymerization of the ring, and intramolecular and supramolecular topological constraints such as knots or mechanically-interlocked rings. We perform molecular dynamics simulations of polyethylene ring polymers as a function of degree of polymerization and in different topological states, including a knotted state, catenane state (two interlocked rings), and borromean state (three interlocked rings). Applying nonlinear manifold learning to our all-atom simulation trajectories, we extract low-dimensional free energy surfaces governing the accessible conformational states and their relative thermodynamic stability. The free energy surfaces reveal how degree of polymerization and topological constraints affect the thermally accessible conformations, chiral symmetry breaking, and folding and collapse pathways of the rings, and present a means to rationally engineer ring size and topology to preferentially stabilize particular conformational states.

Single-molecule studies of multi-protein machines

NASA Astrophysics Data System (ADS)

van Oijen, Antoine

2010-03-01

Advances in optical imaging and molecular manipulation techniques have made it possible to observe individual enzymes and record molecular movies that provide new insight into their dynamics and reaction mechanisms. In a biological context, most of these enzymes function in concert with other enzymes in multi-protein complexes, so an important future direction will be the utilization of single-molecule techniques to unravel the orchestration of large macromolecular assemblies. Our group is developing the single-molecule tools that will make it possible to study biochemical pathways of arbitrary complexity at the single-molecule level. I will discuss results of single-molecule experiments on the replisome, the molecular machinery that is responsible for replication of DNA. We stretch individual DNA molecules and use their elastic properties to obtain dynamic information on the proteins that unwind the double helix and copy its genetic information. Furthermore, we visualize fluorescently labeled components of the replisome and thus obtain information on stochiometry and exchange kinetics. This simultaneous observation of catalytic activity and composition allows us to gain deeper insight into the structure-function relationship of the replisome.

Energy Landscapes: From Protein Folding to Molecular Assembly

Science.gov Websites

been used, for example, in DNA origami, in which artificial structures and machines are built in a mechanical processes and eventually to reproduce these in artificial machines. This conference will provide

Hydrogen exchange mass spectrometry of bacteriorhodopsin reveals light-induced changes in the structural dynamics of a biomolecular machine.

PubMed

Pan, Yan; Brown, Leonid; Konermann, Lars

2011-12-21

Many proteins act as molecular machines that are fuelled by a nonthermal energy source. Examples include transmembrane pumps and stator-rotor complexes. These systems undergo cyclic motions (CMs) that are being driven along a well-defined conformational trajectory. Superimposed on these CMs are thermal fluctuations (TFs) that are coupled to stochastic motions of the solvent. Here we explore whether the TFs of a molecular machine are affected by the occurrence of CMs. Bacteriorhodopsin (BR) is a light-driven proton pump that serves as a model system in this study. The function of BR is based on a photocycle that involves trans/cis isomerization of a retinal chromophore, as well as motions of transmembrane helices. Hydrogen/deuterium exchange (HDX) mass spectrometry was used to monitor the TFs of BR, focusing on the monomeric form of the protein. Comparative HDX studies were conducted under illumination and in the dark. The HDX kinetics of BR are dramatically accelerated in the presence of light. The isotope exchange rates and the number of backbone amides involved in EX2 opening transitions increase roughly 2-fold upon illumination. In contrast, light/dark control experiments on retinal-free protein produced no discernible differences. It can be concluded that the extent of TFs in BR strongly depends on photon-driven CMs. The light-induced differences in HDX behavior are ascribed to protein destabilization. Specifically, the thermodynamic stability of the dark-adapted protein is estimated to be 5.5 kJ mol(-1) under the conditions of our work. This value represents the free energy difference between the folded state F and a significantly unfolded conformer U. Illumination reduces the stability of F by 2.2 kJ mol(-1). Mechanical agitation caused by isomerization of the chromophore is transferred to the surrounding protein scaffold, and subsequently, the energy dissipates into the solvent. Light-induced retinal motions therefore act analogously to an internal heat source that promotes the occurrence of TFs. Overall, our data highlight the potential of HDX methods for probing the structural dynamics of molecular machines under "engine on" and "engine off" conditions. © 2011 American Chemical Society

NanoDesign: Concepts and Software for a Nanotechnology Based on Functionalized Fullerenes

NASA Technical Reports Server (NTRS)

Globus, Al; Jaffe, Richard; Chancellor, Marisa K. (Technical Monitor)

1996-01-01

Eric Drexler has proposed a hypothetical nanotechnology based on diamond and investigated the properties of such molecular systems. While attractive, diamonoid nanotechnology is not physically accessible with straightforward extensions of current laboratory techniques. We propose a nanotechnology based on functionalized fullerenes and investigate carbon nanotube based gears with teeth added via a benzyne reaction known to occur with C60. The gears are single-walled carbon nanotubes with appended coenzyme groups for teeth. Fullerenes are in widespread laboratory use and can be functionalized in many ways. Companion papers computationally demonstrate the properties of these gears (they appear to work) and the accessibility of the benzyne/nanotube reaction. This paper describes the molecular design techniques and rationale as well as the software that implements these design techniques. The software is a set of persistent C++ objects controlled by TCL command scripts. The c++/tcl interface is automatically generated by a software system called tcl_c++ developed by the author and described here. The objects keep track of different portions of the molecular machinery to allow different simulation techniques and boundary conditions to be applied as appropriate. This capability has been required to demonstrate (computationally) our gear's feasibility. A new distributed software architecture featuring a WWW universal client, CORBA distributed objects, and agent software is under consideration. The software architecture is intended to eventually enable a widely disbursed group to develop complex simulated molecular machines.

Machine learning in computational docking.

PubMed

Khamis, Mohamed A; Gomaa, Walid; Ahmed, Walaa F

2015-03-01

The objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has gained much popularity and interest over the last few years. Central to this methodology is the notion of computational docking which is the process of predicting the best pose (orientation + conformation) of a small molecule (drug candidate) when bound to a target larger receptor molecule (protein) in order to form a stable complex molecule. In computational docking, a large number of binding poses are evaluated and ranked using a scoring function. The scoring function is a mathematical predictive model that produces a score that represents the binding free energy, and hence the stability, of the resulting complex molecule. Generally, such a function should produce a set of plausible ligands ranked according to their binding stability along with their binding poses. In more practical terms, an effective scoring function should produce promising drug candidates which can then be synthesized and physically screened using high throughput screening process. Therefore, the key to computer-aided drug design is the design of an efficient highly accurate scoring function (using ML techniques). The methods presented in this paper are specifically based on ML techniques. Despite many traditional techniques have been proposed, the performance was generally poor. Only in the last few years started the application of the ML technology in the design of scoring functions; and the results have been very promising. The ML-based techniques are based on various molecular features extracted from the abundance of protein-ligand information in the public molecular databases, e.g., protein data bank bind (PDBbind). In this paper, we present this paradigm shift elaborating on the main constituent elements of the ML approach to molecular docking along with the state-of-the-art research in this area. For instance, the best random forest (RF)-based scoring function on PDBbind v2007 achieves a Pearson correlation coefficient between the predicted and experimentally determined binding affinities of 0.803 while the best conventional scoring function achieves 0.644. The best RF-based ranking power ranks the ligands correctly based on their experimentally determined binding affinities with accuracy 62.5% and identifies the top binding ligand with accuracy 78.1%. We conclude with open questions and potential future research directions that can be pursued in smart computational docking; using molecular features of different nature (geometrical, energy terms, pharmacophore), advanced ML techniques (e.g., deep learning), combining more than one ML models. Copyright © 2015 Elsevier B.V. All rights reserved.

The "Origin-of-Life Reactor" and Reduction of CO2 by H2 in Inorganic Precipitates.

PubMed

Jackson, J Baz

2017-08-01

It has been suggested that inorganic membranes were forerunners of organic membranes at the origin of life. Such membranes, interposed between alkaline fluid in submarine vents and the more acidic Hadean ocean, were thought to house inorganic molecular machines. H + flowed down the pH gradient (ΔpH) from ocean to vent through the molecular machines to drive metabolic reactions for early life. A set of experiments was performed by Herschy et al. (J Mol Evol 79:213-227, 2014) who followed earlier work to construct inorganic precipitate membranes which, they argued, would be transected by a ΔpH. They supposed that inorganic molecular machines might assemble by chance in the precipitate membranes, and be capable of using the ΔpH to drive unfavourable reduction of CO 2 by H 2 to formate and formaldehyde. Indeed, these workers detected both of these compounds in their origin-of-life reaction vessel and contend this was proof of principle for their hypothesis. However, it is shown here by a straightforward calculation that the formate produced was only that which reached on approach to equilibrium without any driving force from ΔpH. We conclude that the reaction was facilitated by isotropic catalysts in the precipitate membrane but not by an anisotropic ΔpH-driven molecular machine.

Learning molecular energies using localized graph kernels.

PubMed

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-21

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Learning molecular energies using localized graph kernels

NASA Astrophysics Data System (ADS)

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-01

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Machine Learning and Network Analysis of Molecular Dynamics Trajectories Reveal Two Chains of Red/Ox-specific Residue Interactions in Human Protein Disulfide Isomerase.

PubMed

Karamzadeh, Razieh; Karimi-Jafari, Mohammad Hossein; Sharifi-Zarchi, Ali; Chitsaz, Hamidreza; Salekdeh, Ghasem Hosseini; Moosavi-Movahedi, Ali Akbar

2017-06-16

The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes. Then, important structural features that exhibit considerable differences in dynamics of redox states are extracted by statistical machine learning methods. Mapping the structural variations to time series of residue interaction networks also provides a holistic representation of the dynamical redox differences. With emphasizing on persistent long-lasting interactions, an approach is proposed that compiled these time series networks to a single dynamic residue interaction network (DRIN). Differential comparison of DRIN in oxidized and reduced states reveals chains of residue interactions that represent potential allosteric paths between catalytic and ligand binding sites of hPDI.

Entity recognition in the biomedical domain using a hybrid approach.

PubMed

Basaldella, Marco; Furrer, Lenz; Tasso, Carlo; Rinaldi, Fabio

2017-11-09

This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. These results are to our knowledge the best reported so far in this particular task.

Double-Layer Mediated Electromechanical Response of Amyloid Fibrils in Liquid Environment

PubMed Central

Nikiforov, M.P.; Thompson, G.L.; Reukov, V.V.; Jesse, S.; Guo, S.; Rodriguez, B.J.; Seal, K.; Vertegel, A.A.; Kalinin, S.V.

2010-01-01

Harnessing electrical bias-induced mechanical motion on the nanometer and molecular scale is a critical step towards understanding the fundamental mechanisms of redox processes and implementation of molecular electromechanical machines. Probing these phenomena in biomolecular systems requires electromechanical measurements be performed in liquid environments. Here we demonstrate the use of band excitation piezoresponse force microscopy for probing electromechanical coupling in amyloid fibrils. The approaches for separating the elastic and electromechanical contributions based on functional fits and multivariate statistical analysis are presented. We demonstrate that in the bulk of the fibril the electromechanical response is dominated by double-layer effects (consistent with shear piezoelectricity of biomolecules), while a number of electromechanically active hot spots possibly related to structural defects are observed. PMID:20088597

Computational assessment of organic photovoltaic candidate compounds

NASA Astrophysics Data System (ADS)

Borunda, Mario; Dai, Shuo; Olivares-Amaya, Roberto; Amador-Bedolla, Carlos; Aspuru-Guzik, Alan

2015-03-01

Organic photovoltaic (OPV) cells are emerging as a possible renewable alternative to petroleum based resources and are needed to meet our growing demand for energy. Although not as efficient as silicon based cells, OPV cells have as an advantage that their manufacturing cost is potentially lower. The Harvard Clean Energy Project, using a cheminformatic approach of pattern recognition and machine learning strategies, has ranked a molecular library of more than 2.6 million candidate compounds based on their performance as possible OPV materials. Here, we present a ranking of the top 1000 molecules for use as photovoltaic materials based on their optical absorption properties obtained via time-dependent density functional theory. This computational search has revealed the molecular motifs shared by the set of most promising molecules.

Metal-centred azaphosphatriptycene gear with a photo- and thermally driven mechanical switching function based on coordination isomerism.

PubMed

Ube, Hitoshi; Yasuda, Yoshihiro; Sato, Hiroyasu; Shionoya, Mitsuhiko

2017-02-08

Metal ions can serve as a centre of molecular motions due to their coordination geometry, reversible bonding nature and external stimuli responsiveness. Such essential features of metal ions have been utilized for metal-mediated molecular machines with the ability to motion switch via metallation/demetallation or coordination number variation at the metal centre; however, motion switching based on the change in coordination geometry remain largely unexplored. Herein, we report a Pt II -centred molecular gear that demonstrates control of rotor engagement and disengagement based on photo- and thermally driven cis-trans isomerization at the Pt II centre. This molecular rotary motion transmitter has been constructed from two coordinating azaphosphatriptycene rotators and one Pt II ion as a stator. Isomerization between an engaged cis-form and a disengaged trans-form is reversibly driven by ultraviolet irradiation and heating. Such a photo- and thermally triggered motional interconversion between engaged/disengaged states on a metal ion would provide a selector switch for more complex interlocking systems.

The brain as a system of nested but partially overlapping networks. Heuristic relevance of the model for brain physiology and pathology.

PubMed

Agnati, L F; Guidolin, D; Fuxe, K

2007-01-01

A new model of the brain organization is proposed. The model is based on the assumption that a global molecular network enmeshes the entire central nervous system. Thus, brain extra-cellular and intra-cellular molecular networks are proposed to communicate at the level of special plasma membrane regions (e.g., the lipid rafts) where horizontal molecular networks can represent input/output regions allowing the cell to have informational exchanges with the extracellular environment. Furthermore, some "pervasive signals" such as field potentials, pressure waves and thermal gradients that affect large parts of the brain cellular and molecular networks are discussed. Finally, at least two learning paradigms are analyzed taking into account the possible role of Volume Transmission: the so-called model of "temporal difference learning" and the "Turing B-unorganised machine". The relevance of this new view of brain organization for a deeper understanding of some neurophysiological and neuropathological aspects of its function is briefly discussed.

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

PubMed Central

Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M.; Da, Xiao; Attiah, Mark; Pigrish, Vadim; Bi, Yingtao; Pal, Sharmistha; Davuluri, Ramana V.; Roccograndi, Laura; Dahmane, Nadia; Martinez-Lage, Maria; Biros, George; Wolf, Ronald L.; Bilello, Michel; O'Rourke, Donald M.; Davatzikos, Christos

2016-01-01

Background MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). Methods One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. Results Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. Conclusions By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood–brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients. PMID:26188015

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

NASA Astrophysics Data System (ADS)

Imbalzano, Giulio; Anelli, Andrea; Giofré, Daniele; Klees, Sinja; Behler, Jörg; Ceriotti, Michele

2018-06-01

Machine learning of atomic-scale properties is revolutionizing molecular modeling, making it possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the costs. The accuracy, speed, and reliability of machine learning potentials, however, depend strongly on the way atomic configurations are represented, i.e., the choice of descriptors used as input for the machine learning method. The raw Cartesian coordinates are typically transformed in "fingerprints," or "symmetry functions," that are designed to encode, in addition to the structure, important properties of the potential energy surface like its invariances with respect to rotation, translation, and permutation of like atoms. Here we discuss automatic protocols to select a number of fingerprints out of a large pool of candidates, based on the correlations that are intrinsic to the training data. This procedure can greatly simplify the construction of neural network potentials that strike the best balance between accuracy and computational efficiency and has the potential to accelerate by orders of magnitude the evaluation of Gaussian approximation potentials based on the smooth overlap of atomic positions kernel. We present applications to the construction of neural network potentials for water and for an Al-Mg-Si alloy and to the prediction of the formation energies of small organic molecules using Gaussian process regression.

Converting conformational changes to electrostatic energy in molecular motors: The energetics of ATP synthase.

PubMed

Strajbl, Marek; Shurki, Avital; Warshel, Arieh

2003-12-09

F1-ATPase is the catalytic component of the ATP synthase molecular machine responsible for most of the uphill synthesis of ATP in living systems. The enormous advances in biochemical and structural studies of this machine provide an opportunity for detailed understanding of the nature of its rotary mechanism. However, further quantitative progress in this direction requires development of reliable ways of translating the observed structural changes to the corresponding energies. This requirement is particularly challenging because we are dealing with a large system that couples major structural changes with a chemical process. The present work provides such a structure-function correlation by using the linear response approximation to describe the rotary mechanism. This approach allows one to evaluate the energy of transitions between different conformational states by considering only the changes in the corresponding electrostatic energies of the ligands. The relevant energetics are also obtained by calculating the linear response approximation-based free energies of transferring the ligands from water to the different sites of F1-ATPase in their different conformational states. We also use the empirical valence bond approach to evaluate the actual free-energy profile for the ATP synthesis in the different conformational states of the system. Integrating the information from the different approaches provides a semiquantitative structure-function correlation for F1-ATPase. It is found that the conformational changes are converted to changes in the electrostatic interaction between the protein and its ligands, which drives the ATP synthesis.

A nanojet: propulsion of a molecular machine by an asymmetric distribution of reaction--products

NASA Astrophysics Data System (ADS)

Liverpool, Tanniemola; Golestanian, Ramin; Ajdari, Armand

2006-03-01

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. Motion of the device is driven by an asymmetric distribution of reaction products. We calculate the propulsive velocity of the device as well as the scale of the velocity fluctuations. We also consider the effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction.

Propulsion of a Molecular Machine by Asymmetric Distribution of Reaction Products

NASA Astrophysics Data System (ADS)

Golestanian, Ramin; Liverpool, Tanniemola B.; Ajdari, Armand

2005-06-01

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. The motion of the device is driven by an asymmetric distribution of reaction products. The propulsive velocity of the device is calculated as well as the scale of the velocity fluctuations. The effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction are addressed.

Propulsion of a molecular machine by asymmetric distribution of reaction products.

PubMed

Golestanian, Ramin; Liverpool, Tanniemola B; Ajdari, Armand

2005-06-10

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. The motion of the device is driven by an asymmetric distribution of reaction products. The propulsive velocity of the device is calculated as well as the scale of the velocity fluctuations. The effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction are addressed.

Chromatin Computation

PubMed Central

Bryant, Barbara

2012-01-01

In living cells, DNA is packaged along with protein and RNA into chromatin. Chemical modifications to nucleotides and histone proteins are added, removed and recognized by multi-functional molecular complexes. Here I define a new computational model, in which chromatin modifications are information units that can be written onto a one-dimensional string of nucleosomes, analogous to the symbols written onto cells of a Turing machine tape, and chromatin-modifying complexes are modeled as read-write rules that operate on a finite set of adjacent nucleosomes. I illustrate the use of this “chromatin computer” to solve an instance of the Hamiltonian path problem. I prove that chromatin computers are computationally universal – and therefore more powerful than the logic circuits often used to model transcription factor control of gene expression. Features of biological chromatin provide a rich instruction set for efficient computation of nontrivial algorithms in biological time scales. Modeling chromatin as a computer shifts how we think about chromatin function, suggests new approaches to medical intervention, and lays the groundwork for the engineering of a new class of biological computing machines. PMID:22567109

Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification.

PubMed

Lee, Eugene K; Tran, David D; Keung, Wendy; Chan, Patrick; Wong, Gabriel; Chan, Camie W; Costa, Kevin D; Li, Ronald A; Khine, Michelle

2017-11-14

Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC)-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS) electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

SMOG 2: A Versatile Software Package for Generating Structure-Based Models.

PubMed

Noel, Jeffrey K; Levi, Mariana; Raghunathan, Mohit; Lammert, Heiko; Hayes, Ryan L; Onuchic, José N; Whitford, Paul C

2016-03-01

Molecular dynamics simulations with coarse-grained or simplified Hamiltonians have proven to be an effective means of capturing the functionally important long-time and large-length scale motions of proteins and RNAs. Originally developed in the context of protein folding, structure-based models (SBMs) have since been extended to probe a diverse range of biomolecular processes, spanning from protein and RNA folding to functional transitions in molecular machines. The hallmark feature of a structure-based model is that part, or all, of the potential energy function is defined by a known structure. Within this general class of models, there exist many possible variations in resolution and energetic composition. SMOG 2 is a downloadable software package that reads user-designated structural information and user-defined energy definitions, in order to produce the files necessary to use SBMs with high performance molecular dynamics packages: GROMACS and NAMD. SMOG 2 is bundled with XML-formatted template files that define commonly used SBMs, and it can process template files that are altered according to the needs of each user. This computational infrastructure also allows for experimental or bioinformatics-derived restraints or novel structural features to be included, e.g. novel ligands, prosthetic groups and post-translational/transcriptional modifications. The code and user guide can be downloaded at http://smog-server.org/smog2.

Metal-organic frameworks with dynamic interlocked components

NASA Astrophysics Data System (ADS)

Vukotic, V. Nicholas; Harris, Kristopher J.; Zhu, Kelong; Schurko, Robert W.; Loeb, Stephen J.

2012-06-01

The dynamics of mechanically interlocked molecules such as rotaxanes and catenanes have been studied in solution as examples of rudimentary molecular switches and machines, but in this medium, the molecules are randomly dispersed and their motion incoherent. As a strategy for achieving a higher level of molecular organization, we have constructed a metal-organic framework material using a [2]rotaxane as the organic linker and binuclear Cu(II) units as the nodes. Activation of the as-synthesized material creates a void space inside the rigid framework that allows the soft macrocyclic ring of the [2]rotaxane to rotate rapidly, unimpeded by neighbouring molecular components. Variable-temperature 13C and 2H solid-state NMR experiments are used to characterize the nature and rate of the dynamic processes occurring inside this unique material. These results provide a blueprint for the future creation of solid-state molecular switches and molecular machines based on mechanically interlocked molecules.

The mismeasure of machine: Synthetic biology and the trouble with engineering metaphors.

PubMed

Boudry, Maarten; Pigliucci, Massimo

2013-12-01

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the "blueprint" of an organism, organisms are "reverse engineered" to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on "designs" that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with "programming" the right "software" would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes-isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms-pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for "engineering" a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic "instructions" is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to "search" for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Sparse Self-Consistent Field Algorithm and Its Parallel Implementation: Application to Density-Functional-Based Tight Binding.

PubMed

Scemama, Anthony; Renon, Nicolas; Rapacioli, Mathias

2014-06-10

We present an algorithm and its parallel implementation for solving a self-consistent problem as encountered in Hartree-Fock or density functional theory. The algorithm takes advantage of the sparsity of matrices through the use of local molecular orbitals. The implementation allows one to exploit efficiently modern symmetric multiprocessing (SMP) computer architectures. As a first application, the algorithm is used within the density-functional-based tight binding method, for which most of the computational time is spent in the linear algebra routines (diagonalization of the Fock/Kohn-Sham matrix). We show that with this algorithm (i) single point calculations on very large systems (millions of atoms) can be performed on large SMP machines, (ii) calculations involving intermediate size systems (1000-100 000 atoms) are also strongly accelerated and can run efficiently on standard servers, and (iii) the error on the total energy due to the use of a cutoff in the molecular orbital coefficients can be controlled such that it remains smaller than the SCF convergence criterion.

The Structural Basis of IKs Ion-Channel Activation: Mechanistic Insights from Molecular Simulations.

PubMed

Ramasubramanian, Smiruthi; Rudy, Yoram

2018-06-05

Relating ion channel (iCh) structural dynamics to physiological function remains a challenge. Current experimental and computational techniques have limited ability to explore this relationship in atomistic detail over physiological timescales. A framework associating iCh structure to function is necessary for elucidating normal and disease mechanisms. We formulated a modeling schema that overcomes the limitations of current methods through applications of artificial intelligence machine learning. Using this approach, we studied molecular processes that underlie human IKs voltage-mediated gating. IKs malfunction underlies many debilitating and life-threatening diseases. Molecular components of IKs that underlie its electrophysiological function include KCNQ1 (a pore-forming tetramer) and KCNE1 (an auxiliary subunit). Simulations, using the IKs structure-function model, reproduced experimentally recorded saturation of gating-charge displacement at positive membrane voltages, two-step voltage sensor (VS) movement shown by fluorescence, iCh gating statistics, and current-voltage relationship. Mechanistic insights include the following: 1) pore energy profile determines iCh subconductance; 2) the entire protein structure, not limited to the pore, contributes to pore energy and channel subconductance; 3) interactions with KCNE1 result in two distinct VS movements, causing gating-charge saturation at positive membrane voltages and current activation delay; and 4) flexible coupling between VS and pore permits pore opening at lower VS positions, resulting in sequential gating. The new modeling approach is applicable to atomistic scale studies of other proteins on timescales of physiological function. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Photonics walking up a human hair

NASA Astrophysics Data System (ADS)

Zeng, Hao; Parmeggiani, Camilla; Martella, Daniele; Wasylczyk, Piotr; Burresi, Matteo; Wiersma, Diederik S.

2016-03-01

While animals have access to sugars as energy source, this option is generally not available to artificial machines and robots. Energy delivery is thus the bottleneck for creating independent robots and machines, especially on micro- and nano- meter length scales. We have found a way to produce polymeric nano-structures with local control over the molecular alignment, which allowed us to solve the above issue. By using a combination of polymers, of which part is optically sensitive, we can create complex functional structures with nanometer accuracy, responsive to light. In particular, this allowed us to realize a structure that can move autonomously over surfaces (it can "walk") using the environmental light as its energy source. The robot is only 60 μm in total length, thereby smaller than any known terrestrial walking species, and it is capable of random, directional walking and rotating on different dry surfaces.

Second Law Violations by Means of a Stratification of Temperature Due to Force Fields

NASA Astrophysics Data System (ADS)

Trupp, Andreas

2002-11-01

In 1868 J.C. Maxwell proved that a perpetual motion machine of the second kind would become possible, if the equilibrium temperature in a vertical column of gas subject to gravity were a function of height. However, Maxwell had claimed that the temperature had to be the same at all points of the column. So did Boltzmann. Their opponent was Loschmidt. He claimed that the equilibrium temperature declined with height, and that a perpetual motion machine of the second kind operating by means of such column was compatible with the second law of thermodynamics. Extending the general idea behind Loschmidt's concept to other force fields, gravity can be replaced by molecular forces acting on molecules that try to escape from the surface of a liquid into the vapor space. Experiments proving the difference of temperature between the liquid and the vapor phase were conducted in the 19th century already.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

PubMed

Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

2018-03-21

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

All tangled up: how cells direct, manage and exploit topoisomerase function

PubMed Central

Vos, Seychelle M.; Tretter, Elsa M.; Schmidt, Bryan H.; Berger, James M.

2015-01-01

Preface Topoisomerases are complex molecular machines that modulate DNA topology to maintain chromosome superstructure and integrity. Although capable of stand-alone activity in vitro, topoisomerases frequently are linked to larger pathways and systems that resolve specific DNA superstructures and intermediates arising from cellular processes such as DNA repair, transcription, replication, and chromosome compaction. Topoisomerase activity is indispensible to cells, but requires the transient breakage of DNA strands. This property has been exploited, often for significant clinical benefit, by various exogenous agents that interfere with cell proliferation. Despite decades of study, surprising findings involving topoisomerases continue to emerge with respect to their cellular function, regulation, and utility as therapeutic targets. PMID:22108601

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

PubMed

Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M; Da, Xiao; Attiah, Mark; Pigrish, Vadim; Bi, Yingtao; Pal, Sharmistha; Davuluri, Ramana V; Roccograndi, Laura; Dahmane, Nadia; Martinez-Lage, Maria; Biros, George; Wolf, Ronald L; Bilello, Michel; O'Rourke, Donald M; Davatzikos, Christos

2016-03-01

MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Can Simple Biophysical Principles Yield Complicated Biological Functions?

NASA Astrophysics Data System (ADS)

Liphardt, Jan

2011-03-01

About once a year, a new regulatory paradigm is discovered in cell biology. As of last count, eukaryotic cells have more than 40 distinct ways of regulating protein concentration and function. Regulatory possibilities include site-specific phosphorylation, epigenetics, alternative splicing, mRNA (re)localization, and modulation of nucleo-cytoplasmic transport. This raises a simple question. Do all the remarkable things cells do, require an intricately choreographed supporting cast of hundreds of molecular machines and associated signaling networks? Alternatively, are there a few simple biophysical principles that can generate apparently very complicated cellular behaviors and functions? I'll discuss two problems, spatial organization of the bacterial chemotaxis system and nucleo-cytoplasmic transport, where the latter might be true. In both cases, the ability to precisely quantify biological organization and function, at the single-molecule level, helped to find signatures of basic biological organizing principles.

Ethane-xenon mixtures under shock conditions

DOE PAGES

Magyar, Rudolph J.; Root, Seth; Mattsson, Thomas; ...

2015-04-22

Mixtures of light elements with heavy elements are important in inertial confinement fusion. We explore the physics of molecular scale mixing through a validation study of equation of state (EOS) properties. Density functional theory molecular dynamics (DFT-MD) at elevated temperature and pressure is used to obtain the thermodynamic state properties of pure xenon, ethane, and various compressed mixture compositions along their principal Hugoniots. In order to validate these simulations, we have performed shock compression experiments using the Sandia Z-Machine. A bond tracking analysis correlates the sharp rise in the Hugoniot curve with the completion of dissociation in ethane. Furthermore, themore » DFT-based simulation results compare well with the experimental data along the principal Hugoniots and are used to provide insight into the dissociation and temperature along the Hugoniots as a function of mixture composition. Interestingly, we find that the compression ratio for complete dissociation is similar for several compositions suggesting a limiting compression for C-C bonded systems.« less

Use of Single-Cysteine Variants for Trapping Transient States in DNA Mismatch Repair.

PubMed

Friedhoff, Peter; Manelyte, Laura; Giron-Monzon, Luis; Winkler, Ines; Groothuizen, Flora S; Sixma, Titia K

2017-01-01

DNA mismatch repair (MMR) is necessary to prevent incorporation of polymerase errors into the newly synthesized DNA strand, as they would be mutagenic. In humans, errors in MMR cause a predisposition to cancer, called Lynch syndrome. The MMR process is performed by a set of ATPases that transmit, validate, and couple information to identify which DNA strand requires repair. To understand the individual steps in the repair process, it is useful to be able to study these large molecular machines structurally and functionally. However, the steps and states are highly transient; therefore, the methods to capture and enrich them are essential. Here, we describe how single-cysteine variants can be used for specific cross-linking and labeling approaches that allow trapping of relevant transient states. Analysis of these defined states in functional and structural studies is instrumental to elucidate the molecular mechanism of this important DNA MMR process. © 2017 Elsevier Inc. All rights reserved.

Coupled Molecular Switching Processes in Ordered Mono- and Multilayers of Stimulus-Responsive Rotaxanes on Gold Surfaces

PubMed Central

2015-01-01

Interfaces provide the structural basis for function as, for example, encountered in nature in the membrane-embedded photosystem or in technology in solar cells. Synthetic functional multilayers of molecules cooperating in a coupled manner can be fabricated on surfaces through layer-by-layer self-assembly. Ordered arrays of stimulus-responsive rotaxanes undergoing well-controlled axle shuttling are excellent candidates for coupled mechanical motion. Such stimulus-responsive surfaces may help integrate synthetic molecular machines in larger systems exhibiting even macroscopic effects or generating mechanical work from chemical energy through cooperative action. The present work demonstrates the successful deposition of ordered mono- and multilayers of chemically switchable rotaxanes on gold surfaces. Rotaxane mono- and multilayers are shown to reversibly switch in a coupled manner between two ordered states as revealed by linear dichroism effects in angle-resolved NEXAFS spectra. Such a concerted switching process is observed only when the surfaces are well packed, while less densely packed surfaces lacking lateral order do not exhibit such effects. PMID:25782057

Learning molecular energies using localized graph kernels

DOE PAGES

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

2017-03-21

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Learning molecular energies using localized graph kernels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Classifying Structures in the ISM with Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

2011-01-01

The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

Protein Science by DNA Sequencing: How Advances in Molecular Biology Are Accelerating Biochemistry.

PubMed

Higgins, Sean A; Savage, David F

2018-01-09

A fundamental goal of protein biochemistry is to determine the sequence-function relationship, but the vastness of sequence space makes comprehensive evaluation of this landscape difficult. However, advances in DNA synthesis and sequencing now allow researchers to assess the functional impact of every single mutation in many proteins, but challenges remain in library construction and the development of general assays applicable to a diverse range of protein functions. This Perspective briefly outlines the technical innovations in DNA manipulation that allow massively parallel protein biochemistry and then summarizes the methods currently available for library construction and the functional assays of protein variants. Areas in need of future innovation are highlighted with a particular focus on assay development and the use of computational analysis with machine learning to effectively traverse the sequence-function landscape. Finally, applications in the fundamentals of protein biochemistry, disease prediction, and protein engineering are presented.

Condensins: universal organizers of chromosomes with diverse functions

PubMed Central

Hirano, Tatsuya

2012-01-01

Condensins are multisubunit protein complexes that play a fundamental role in the structural and functional organization of chromosomes in the three domains of life. Most eukaryotic species have two different types of condensin complexes, known as condensins I and II, that fulfill nonoverlapping functions and are subjected to differential regulation during mitosis and meiosis. Recent studies revealed that the two complexes contribute to a wide variety of interphase chromosome functions, such as gene regulation, recombination, and repair. Also emerging are their cell type- and tissue-specific functions and relevance to human disease. Biochemical and structural analyses of eukaryotic and bacterial condensins steadily uncover the mechanisms of action of this class of highly sophisticated molecular machines. Future studies on condensins will not only enhance our understanding of chromosome architecture and dynamics, but also help address a previously underappreciated yet profound set of questions in chromosome biology. PMID:22855829

STOCK: Structure mapper and online coarse-graining kit for molecular simulations

DOE PAGES

Bevc, Staš; Junghans, Christoph; Praprotnik, Matej

2015-03-15

We present a web toolkit STructure mapper and Online Coarse-graining Kit for setting up coarse-grained molecular simulations. The kit consists of two tools: structure mapping and Boltzmann inversion tools. The aim of the first tool is to define a molecular mapping from high, e.g. all-atom, to low, i.e. coarse-grained, resolution. Using a graphical user interface it generates input files, which are compatible with standard coarse-graining packages, e.g. VOTCA and DL_CGMAP. Our second tool generates effective potentials for coarse-grained simulations preserving the structural properties, e.g. radial distribution functions, of the underlying higher resolution model. The required distribution functions can be providedmore » by any simulation package. Simulations are performed on a local machine and only the distributions are uploaded to the server. The applicability of the toolkit is validated by mapping atomistic pentane and polyalanine molecules to a coarse-grained representation. Effective potentials are derived for systems of TIP3P (transferable intermolecular potential 3 point) water molecules and salt solution. The presented coarse-graining web toolkit is available at http://stock.cmm.ki.si.« less

Computational Nanotechnology of Materials, Electronics and Machines: Carbon Nanotubes

NASA Technical Reports Server (NTRS)

Srivastava, Deepak

2001-01-01

This report presents the goals and research of the Integrated Product Team (IPT) on Devices and Nanotechnology. NASA's needs for this technology are discussed and then related to the research focus of the team. The two areas of focus for technique development are: 1) large scale classical molecular dynamics on a shared memory architecture machine; and 2) quantum molecular dynamics methodology. The areas of focus for research are: 1) nanomechanics/materials; 2) carbon based electronics; 3) BxCyNz composite nanotubes and junctions; 4) nano mechano-electronics; and 5) nano mechano-chemistry.

Remote Photoregulated Ring Gliding in a [2]Rotaxane via a Molecular Effector.

PubMed

Tron, Arnaud; Pianet, Isabelle; Martinez-Cuezva, Alberto; Tucker, James H R; Pisciottani, Luca; Alajarin, Mateo; Berna, Jose; McClenaghan, Nathan D

2017-01-06

A molecular barbiturate messenger, which is reversibly released/captured by a photoswitchable artificial molecular receptor, is shown to act as an effector to control ring gliding on a distant hydrogen-bonding [2]rotaxane. Thus, light-driven chemical communication governing the operation of a remote molecular machine is demonstrated using an information-rich neutral molecule.

Visualization and characterization of individual type III protein secretion machines in live bacteria

PubMed Central

Lara-Tejero, María; Bewersdorf, Jörg; Galán, Jorge E.

2017-01-01

Type III protein secretion machines have evolved to deliver bacterially encoded effector proteins into eukaryotic cells. Although electron microscopy has provided a detailed view of these machines in isolation or fixed samples, little is known about their organization in live bacteria. Here we report the visualization and characterization of the Salmonella type III secretion machine in live bacteria by 2D and 3D single-molecule switching superresolution microscopy. This approach provided access to transient components of this machine, which previously could not be analyzed. We determined the subcellular distribution of individual machines, the stoichiometry of the different components of this machine in situ, and the spatial distribution of the substrates of this machine before secretion. Furthermore, by visualizing this machine in Salmonella mutants we obtained major insights into the machine’s assembly. This study bridges a major resolution gap in the visualization of this nanomachine and may serve as a paradigm for the examination of other bacterially encoded molecular machines. PMID:28533372

Computational Nanotechnology of Molecular Materials, Electronics, and Actuators with Carbon Nanotubes and Fullerenes

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Menon, Madhu; Cho, Kyeongjae; Biegel, Bryan (Technical Monitor)

2001-01-01

The role of computational nanotechnology in developing next generation of multifunctional materials, molecular scale electronic and computing devices, sensors, actuators, and machines is described through a brief review of enabling computational techniques and few recent examples derived from computer simulations of carbon nanotube based molecular nanotechnology.

Clustering the Orion B giant molecular cloud based on its molecular emission.

PubMed

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. A clustering analysis based only on the J = 1 - 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes ( n H ~ 100 cm -3 , ~ 500 cm -3 , and > 1000 cm -3 ), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 - 0 line of HCO + and the N = 1 - 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO + and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO + intensity ratio in UV-illuminated regions. Finer distinctions in density classes ( n H ~ 7 × 10 3 cm -3 ~ 4 × 10 4 cm -3 ) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO + (1 - 0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers.

Nonlinear machine learning in soft materials engineering and design

NASA Astrophysics Data System (ADS)

Ferguson, Andrew

The inherently many-body nature of molecular folding and colloidal self-assembly makes it challenging to identify the underlying collective mechanisms and pathways governing system behavior, and has hindered rational design of soft materials with desired structure and function. Fundamentally, there exists a predictive gulf between the architecture and chemistry of individual molecules or colloids and the collective many-body thermodynamics and kinetics. Integrating machine learning techniques with statistical thermodynamics provides a means to bridge this divide and identify emergent folding pathways and self-assembly mechanisms from computer simulations or experimental particle tracking data. We will survey a few of our applications of this framework that illustrate the value of nonlinear machine learning in understanding and engineering soft materials: the non-equilibrium self-assembly of Janus colloids into pinwheels, clusters, and archipelagos; engineering reconfigurable ''digital colloids'' as a novel high-density information storage substrate; probing hierarchically self-assembling onjugated asphaltenes in crude oil; and determining macromolecular folding funnels from measurements of single experimental observables. We close with an outlook on the future of machine learning in soft materials engineering, and share some personal perspectives on working at this disciplinary intersection. We acknowledge support for this work from a National Science Foundation CAREER Award (Grant No. DMR-1350008) and the Donors of the American Chemical Society Petroleum Research Fund (ACS PRF #54240-DNI6).

Assessing the potential of surface-immobilized molecular logic machines for integration with solid state technology.

PubMed

Dunn, Katherine E; Trefzer, Martin A; Johnson, Steven; Tyrrell, Andy M

2016-08-01

Molecular computation with DNA has great potential for low power, highly parallel information processing in a biological or biochemical context. However, significant challenges remain for the field of DNA computation. New technology is needed to allow multiplexed label-free readout and to enable regulation of molecular state without addition of new DNA strands. These capabilities could be provided by hybrid bioelectronic systems in which biomolecular computing is integrated with conventional electronics through immobilization of DNA machines on the surface of electronic circuitry. Here we present a quantitative experimental analysis of a surface-immobilized OR gate made from DNA and driven by strand displacement. The purpose of our work is to examine the performance of a simple representative surface-immobilized DNA logic machine, to provide valuable information for future work on hybrid bioelectronic systems involving DNA devices. We used a quartz crystal microbalance to examine a DNA monolayer containing approximately 5×10(11)gatescm(-2), with an inter-gate separation of approximately 14nm, and we found that the ensemble of gates took approximately 6min to switch. The gates could be switched repeatedly, but the switching efficiency was significantly degraded on the second and subsequent cycles when the binding site for the input was near to the surface. Otherwise, the switching efficiency could be 80% or better, and the power dissipated by the ensemble of gates during switching was approximately 0.1nWcm(-2), which is orders of magnitude less than the power dissipated during switching of an equivalent array of transistors. We propose an architecture for hybrid DNA-electronic systems in which information can be stored and processed, either in series or in parallel, by a combination of molecular machines and conventional electronics. In this architecture, information can flow freely and in both directions between the solution-phase and the underlying electronics via surface-immobilized DNA machines that provide the interface between the molecular and electronic domains. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation

NASA Astrophysics Data System (ADS)

Li, Xiayue; Curtis, Farren S.; Rose, Timothy; Schober, Christoph; Vazquez-Mayagoitia, Alvaro; Reuter, Karsten; Oberhofer, Harald; Marom, Noa

2018-06-01

We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations, Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases.

Acid/Base and H2PO4(-) Controllable High-Contrast Optical Molecular Switches with a Novel BODIPY Functionalized [2]Rotaxane.

PubMed

Arumugaperumal, Reguram; Srinivasadesikan, Venkatesan; Ramakrishnam Raju, Mandapati V; Lin, Ming-Chang; Shukla, Tarun; Singh, Ravinder; Lin, Hong-Cheu

2015-12-09

A novel multifunctional mechanically interlocked switchable [2]rotaxane R4 containing two molecular stations and rotaxane arms terminated with boron-dipyrromethene (BODIPY) fluorophores and its derivatives were synthesized for the first time by CuAAC click reaction. The shuttling motion of macrocycle between the dibenzylammonium and triazolium recognition sites and the distance dependent photoinduced electron transfer process of R4 is demonstrated by utilizing external chemical stimuli (acid/base). Interestingly, the reversible self-assembly process of R4 was recognized by the acid-base molecular switch strategy. Notably, two symmetrical triazolium groups acted as molecular stations, H2PO4(-) receptors, and H-bonded donors. Both [2]rotaxane R4 and thread R2 demonstrated excellent optical responses and high selectivity toward H2PO4(-) ion. The specific motion and guest-host interactions of mechanically interlocked machines (MIMs) were also further explored by quantum mechanical calculations. The thread R2 also demonstrated to enable the detection of H2PO4(-) in RAW 264.7 cells successfully.

Developing Preservice Teachers' Understanding of Function Using a Vending Machine Metaphor Applet

ERIC Educational Resources Information Center

McCulloch, Allison; Lovett, Jennifer; Edgington, Cyndi

2017-01-01

The purpose of this study is to examine the use of a Vending Machine applet as a cognitive root for the development of preservice teachers understanding of function. The applet was designed to purposefully problematize common misconceptions associated with the algebraic nature of typical function machines. Findings indicated affordances and…

Metabolite identification through multiple kernel learning on fragmentation trees.

PubMed

Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho

2014-06-15

Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.

Molecular machines operating on the nanoscale: from classical to quantum

PubMed Central

2016-01-01

Summary The main physical features and operating principles of isothermal nanomachines in the microworld, common to both classical and quantum machines, are reviewed. Special attention is paid to the dual, constructive role of dissipation and thermal fluctuations, the fluctuation–dissipation theorem, heat losses and free energy transduction, thermodynamic efficiency, and thermodynamic efficiency at maximum power. Several basic models are considered and discussed to highlight generic physical features. This work examines some common fallacies that continue to plague the literature. In particular, the erroneous beliefs that one should minimize friction and lower the temperature for high performance of Brownian machines, and that the thermodynamic efficiency at maximum power cannot exceed one-half are discussed. The emerging topic of anomalous molecular motors operating subdiffusively but very efficiently in the viscoelastic environment of living cells is also discussed. PMID:27335728

Towards better modelling of drug-loading in solid lipid nanoparticles: Molecular dynamics, docking experiments and Gaussian Processes machine learning.

PubMed

Hathout, Rania M; Metwally, Abdelkader A

2016-11-01

This study represents one of the series applying computer-oriented processes and tools in digging for information, analysing data and finally extracting correlations and meaningful outcomes. In this context, binding energies could be used to model and predict the mass of loaded drugs in solid lipid nanoparticles after molecular docking of literature-gathered drugs using MOE® software package on molecularly simulated tripalmitin matrices using GROMACS®. Consequently, Gaussian processes as a supervised machine learning artificial intelligence technique were used to correlate the drugs' descriptors (e.g. M.W., xLogP, TPSA and fragment complexity) with their molecular docking binding energies. Lower percentage bias was obtained compared to previous studies which allows the accurate estimation of the loaded mass of any drug in the investigated solid lipid nanoparticles by just projecting its chemical structure to its main features (descriptors). Copyright © 2016 Elsevier B.V. All rights reserved.

Exploring the Function Space of Deep-Learning Machines

NASA Astrophysics Data System (ADS)

Li, Bo; Saad, David

2018-06-01

The function space of deep-learning machines is investigated by studying growth in the entropy of functions of a given error with respect to a reference function, realized by a deep-learning machine. Using physics-inspired methods we study both sparsely and densely connected architectures to discover a layerwise convergence of candidate functions, marked by a corresponding reduction in entropy when approaching the reference function, gain insight into the importance of having a large number of layers, and observe phase transitions as the error increases.

Just Working with the Cellular Machine: A High School Game for Teaching Molecular Biology

ERIC Educational Resources Information Center

Cardoso, Fernanda Serpa; Dumpel, Renata; Gomes da Silva, Luisa B.; Rodrigues, Carlos R.; Santos, Dilvani O.; Cabral, Lucio Mendes; Castro, Helena C.

2008-01-01

Molecular biology is a difficult comprehension subject due to its high complexity, thus requiring new teaching approaches. Herein, we developed an interdisciplinary board game involving the human immune system response against a bacterial infection for teaching molecular biology at high school. Initially, we created a database with several…

Driving and controlling molecular surface rotors with a terahertz electric field.

PubMed

Neumann, Jan; Gottschalk, Kay E; Astumian, R Dean

2012-06-26

Great progress has been made in the design and synthesis of molecular motors and rotors. Loosely inspired by biomolecular machines such as kinesin and the FoF1 ATPsynthase, these molecules are hoped to provide elements for construction of more elaborate structures that can carry out tasks at the nanoscale corresponding to the tasks accomplished by elementary machines in the macroscopic world. Most of the molecular motors synthesized to date suffer from the drawback that they operate relatively slowly (less than kHz). Here we show by molecular dynamics studies of a diethyl sulfide rotor on a gold(111) surface that a high-frequency oscillating electric field normal to the surface can drive directed rotation at GHz frequencies. The maximum directed rotation rate is 10(10) rotations per second, significantly faster than the rotation of previously reported directional molecular rotors. Understanding the fundamental basis of directed motion of surface rotors is essential for the further development of efficient externally driven artificial rotors. Our results represent a step toward the design of a surface-bound molecular rotary motor with a tunable rotation frequency and direction.

Comprehensive Reactive Receiver Modeling for Diffusive Molecular Communication Systems: Reversible Binding, Molecule Degradation, and Finite Number of Receptors.

PubMed

Ahmadzadeh, Arman; Arjmandi, Hamidreza; Burkovski, Andreas; Schober, Robert

2016-10-01

This paper studies the problem of receiver modeling in molecular communication systems. We consider the diffusive molecular communication channel between a transmitter nano-machine and a receiver nano-machine in a fluid environment. The information molecules released by the transmitter nano-machine into the environment can degrade in the channel via a first-order degradation reaction and those that reach the receiver nano-machine can participate in a reversible bimolecular reaction with receiver receptor proteins. Thereby, we distinguish between two scenarios. In the first scenario, we assume that the entire surface of the receiver is covered by receptor molecules. We derive a closed-form analytical expression for the expected received signal at the receiver, i.e., the expected number of activated receptors on the surface of the receiver. Then, in the second scenario, we consider the case where the number of receptor molecules is finite and the uniformly distributed receptor molecules cover the receiver surface only partially. We show that the expected received signal for this scenario can be accurately approximated by the expected received signal for the first scenario after appropriately modifying the forward reaction rate constant. The accuracy of the derived analytical results is verified by Brownian motion particle-based simulations of the considered environment, where we also show the impact of the effect of receptor occupancy on the derived analytical results.

Structural classification of proteins using texture descriptors extracted from the cellular automata image.

PubMed

Kavianpour, Hamidreza; Vasighi, Mahdi

2017-02-01

Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.

Computational work and time on finite machines.

NASA Technical Reports Server (NTRS)

Savage, J. E.

1972-01-01

Measures of the computational work and computational delay required by machines to compute functions are given. Exchange inequalities are developed for random access, tape, and drum machines to show that product inequalities between storage and time, number of drum tracks and time, number of bits in an address and time, etc., must be satisfied to compute finite functions on bounded machines.

Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles.

PubMed

Deeb, Sally J; Tyanova, Stefka; Hummel, Michael; Schmidt-Supprian, Marc; Cox, Juergen; Mann, Matthias

2015-11-01

Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signaling pathways promises to enhance our understanding of cancer aberrations at the functional level, but this requires accurate and robust tools. Here, we develop a state of the art quantitative mass spectrometric pipeline to characterize formalin-fixed paraffin-embedded tissues of patients with closely related subtypes of diffuse large B-cell lymphoma. We combined a super-SILAC approach with label-free quantification (hybrid LFQ) to address situations where the protein is absent in the super-SILAC standard but present in the patient samples. Shotgun proteomic analysis on a quadrupole Orbitrap quantified almost 9,000 tumor proteins in 20 patients. The quantitative accuracy of our approach allowed the segregation of diffuse large B-cell lymphoma patients according to their cell of origin using both their global protein expression patterns and the 55-protein signature obtained previously from patient-derived cell lines (Deeb, S. J., D'Souza, R. C., Cox, J., Schmidt-Supprian, M., and Mann, M. (2012) Mol. Cell. Proteomics 11, 77-89). Expression levels of individual segregation-driving proteins as well as categories such as extracellular matrix proteins behaved consistently with known trends between the subtypes. We used machine learning (support vector machines) to extract candidate proteins with the highest segregating power. A panel of four proteins (PALD1, MME, TNFAIP8, and TBC1D4) is predicted to classify patients with low error rates. Highly ranked proteins from the support vector analysis revealed differential expression of core signaling molecules between the subtypes, elucidating aspects of their pathobiology. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Molecular Symmetry in Ab Initio Calculations

NASA Astrophysics Data System (ADS)

Madhavan, P. V.; Written, J. L.

1987-05-01

A scheme is presented for the construction of the Fock matrix in LCAO-SCF calculations and for the transformation of basis integrals to LCAO-MO integrals that can utilize several symmetry unique lists of integrals corresponding to different symmetry groups. The algorithm is fully compatible with vector processing machines and is especially suited for parallel processing machines.

Direct Quantum Dynamics Using Grid-Based Wave Function Propagation and Machine-Learned Potential Energy Surfaces.

PubMed

Richings, Gareth W; Habershon, Scott

2017-09-12

We describe a method for performing nuclear quantum dynamics calculations using standard, grid-based algorithms, including the multiconfiguration time-dependent Hartree (MCTDH) method, where the potential energy surface (PES) is calculated "on-the-fly". The method of Gaussian process regression (GPR) is used to construct a global representation of the PES using values of the energy at points distributed in molecular configuration space during the course of the wavepacket propagation. We demonstrate this direct dynamics approach for both an analytical PES function describing 3-dimensional proton transfer dynamics in malonaldehyde and for 2- and 6-dimensional quantum dynamics simulations of proton transfer in salicylaldimine. In the case of salicylaldimine we also perform calculations in which the PES is constructed using Hartree-Fock calculations through an interface to an ab initio electronic structure code. In all cases, the results of the quantum dynamics simulations are in excellent agreement with previous simulations of both systems yet do not require prior fitting of a PES at any stage. Our approach (implemented in a development version of the Quantics package) opens a route to performing accurate quantum dynamics simulations via wave function propagation of many-dimensional molecular systems in a direct and efficient manner.

Sequential allosteric mechanism of ATP hydrolysis by the CCT/TRiC chaperone is revealed through Arrhenius analysis

PubMed Central

Gruber, Ranit; Levitt, Michael; Horovitz, Amnon

2017-01-01

Knowing the mechanism of allosteric switching is important for understanding how molecular machines work. The CCT/TRiC chaperonin nanomachine undergoes ATP-driven conformational changes that are crucial for its folding function. Here, we demonstrate that insight into its allosteric mechanism of ATP hydrolysis can be achieved by Arrhenius analysis. Our results show that ATP hydrolysis triggers sequential ‟conformational waves.” They also suggest that these waves start from subunits CCT6 and CCT8 (or CCT3 and CCT6) and proceed clockwise and counterclockwise, respectively. PMID:28461478

Sequential allosteric mechanism of ATP hydrolysis by the CCT/TRiC chaperone is revealed through Arrhenius analysis.

PubMed

Gruber, Ranit; Levitt, Michael; Horovitz, Amnon

2017-05-16

Knowing the mechanism of allosteric switching is important for understanding how molecular machines work. The CCT/TRiC chaperonin nanomachine undergoes ATP-driven conformational changes that are crucial for its folding function. Here, we demonstrate that insight into its allosteric mechanism of ATP hydrolysis can be achieved by Arrhenius analysis. Our results show that ATP hydrolysis triggers sequential ‟conformational waves." They also suggest that these waves start from subunits CCT6 and CCT8 (or CCT3 and CCT6) and proceed clockwise and counterclockwise, respectively.

Silicon sample holder for molecular beam epitaxy on pre-fabricated integrated circuits

NASA Technical Reports Server (NTRS)

Hoenk, Michael E. (Inventor); Grunthaner, Paula J. (Inventor); Grunthaner, Frank J. (Inventor)

1994-01-01

The sample holder of the invention is formed of the same semiconductor crystal as the integrated circuit on which the molecular beam expitaxial process is to be performed. In the preferred embodiment, the sample holder comprises three stacked micro-machined silicon wafers: a silicon base wafer having a square micro-machined center opening corresponding in size and shape to the active area of a CCD imager chip, a silicon center wafer micro-machined as an annulus having radially inwardly pointing fingers whose ends abut the edges of and center the CCD imager chip within the annulus, and a silicon top wafer micro-machined as an annulus having cantilevered membranes which extend over the top of the CCD imager chip. The micro-machined silicon wafers are stacked in the order given above with the CCD imager chip centered in the center wafer and sandwiched between the base and top wafers. The thickness of the center wafer is about 20% less than the thickness of the CCD imager chip. Preferably, four titanium wires, each grasping the edges of the top and base wafers, compress all three wafers together, flexing the cantilever fingers of the top wafer to accommodate the thickness of the CCD imager chip, acting as a spring holding the CCD imager chip in place.

Discovering rules for protein-ligand specificity using support vector inductive logic programming.

PubMed

Kelley, Lawrence A; Shrimpton, Paul J; Muggleton, Stephen H; Sternberg, Michael J E

2009-09-01

Structural genomics initiatives are rapidly generating vast numbers of protein structures. Comparative modelling is also capable of producing accurate structural models for many protein sequences. However, for many of the known structures, functions are not yet determined, and in many modelling tasks, an accurate structural model does not necessarily tell us about function. Thus, there is a pressing need for high-throughput methods for determining function from structure. The spatial arrangement of key amino acids in a folded protein, on the surface or buried in clefts, is often the determinants of its biological function. A central aim of molecular biology is to understand the relationship between such substructures or surfaces and biological function, leading both to function prediction and to function design. We present a new general method for discovering the features of binding pockets that confer specificity for particular ligands. Using a recently developed machine-learning technique which couples the rule-discovery approach of inductive logic programming with the statistical learning power of support vector machines, we are able to discriminate, with high precision (90%) and recall (86%) between pockets that bind FAD and those that bind NAD on a large benchmark set given only the geometry and composition of the backbone of the binding pocket without the use of docking. In addition, we learn rules governing this specificity which can feed into protein functional design protocols. An analysis of the rules found suggests that key features of the binding pocket may be tied to conformational freedom in the ligand. The representation is sufficiently general to be applicable to any discriminatory binding problem. All programs and data sets are freely available to non-commercial users at http://www.sbg.bio.ic.ac.uk/svilp_ligand/.

The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning.

PubMed

Decherchi, Sergio; Berteotti, Anna; Bottegoni, Giovanni; Rocchia, Walter; Cavalli, Andrea

2015-01-27

The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to study the binding mechanism of a transition state analogue (DADMe-immucillin-H) to the purine nucleoside phosphorylase (PNP) enzyme. Microsecond-long MD simulations allow us to observe several binding events, following different dynamical routes and reaching diverse binding configurations. These simulations are used to estimate kinetic and thermodynamic quantities, such as kon and binding free energy, obtaining a good agreement with available experimental data. In addition, we advance a hypothesis for the slow-onset inhibition mechanism of DADMe-immucillin-H against PNP. Combining extensive MD simulations with machine learning algorithms could therefore be a fruitful approach for capturing key aspects of drug-target recognition and binding.

Understanding bimolecular machines: Theoretical and experimental approaches

NASA Astrophysics Data System (ADS)

Goler, Adam Scott

This dissertation concerns the study of two classes of molecular machines from a physical perspective: enzymes and membrane proteins. Though the functions of these classes of proteins are different, they each represent important test-beds from which new understanding can be developed by the application of different techniques. HIV1 Reverse Transcriptase is an enzyme that performs multiple functions, including reverse transcription of RNA into an RNA/DNA duplex, RNA degradation by the RNaseH domain, and synthesis of dsDNA. These functions allow for the incorporation of the retroviral genes into the host genome. Its catalytic cycle requires repeated large-scale conformational changes fundamental to its mechanism. Motivated by experimental work, these motions were studied theoretically by the application of normal mode analysis. It was observed that the lowest order modes correlate with largest amplitude (low-frequency) motion, which are most likely to be catalytically relevant. Comparisons between normal modes obtained via an elastic network model to those calculated from the essential dynamics of a series of all-atom molecular dynamics simulations show the self-consistency between these calculations. That similar conformational motions are seen between independent theoretical methods reinforces the importance of large-scale subdomain motion for the biochemical action of DNA polymerases in general. Moreover, it was observed that the major subunits of HIV1 Reverse Transcriptase interact quasi-harmonically. The 5HT3A Serotonin receptor and P2X1 receptor, by contrast, are trans-membrane proteins that function as ligand gated ion channels. Such proteins feature a central pore, which allows for the transit of ions necessary for cellular function across a membrane. The pore is opened by the ligation of binding sites on the extracellular portion of different protein subunits. In an attempt to resolve the individual subunits of these membrane proteins beyond the diffraction limit, a super-localization microscope capable of reconstructing super-resolution images was constructed. This novel setup allows for the study of discrete state kinetic mechanisms with spatial resolution good enough to distinguish individual binding sites of these membrane proteins. Further use of this technique may allow for the study of allostery and subunit specific stoichiometry in the presence of agonist or antagonist ligands relevant to pharmacology.

Computational approaches for the classification of seed storage proteins.

PubMed

Radhika, V; Rao, V Sree Hari

2015-07-01

Seed storage proteins comprise a major part of the protein content of the seed and have an important role on the quality of the seed. These storage proteins are important because they determine the total protein content and have an effect on the nutritional quality and functional properties for food processing. Transgenic plants are being used to develop improved lines for incorporation into plant breeding programs and the nutrient composition of seeds is a major target of molecular breeding programs. Hence, classification of these proteins is crucial for the development of superior varieties with improved nutritional quality. In this study we have applied machine learning algorithms for classification of seed storage proteins. We have presented an algorithm based on nearest neighbor approach for classification of seed storage proteins and compared its performance with decision tree J48, multilayer perceptron neural (MLP) network and support vector machine (SVM) libSVM. The model based on our algorithm has been able to give higher classification accuracy in comparison to the other methods.

Unveiling the mechanism of the promising two-dimensional photoswitch - Hemithioindigo

NASA Astrophysics Data System (ADS)

Li, Donglin; Yang, Yonggang; Li, Chaozheng; Liu, Yufang

2018-07-01

The control of internal molecular motions by outside stimuli is a decisive task in the construction of functional molecules and molecular machines. Light-induced intramolecular rotations of photoswitches have attracted increasing research interests because of the high stability and high reversibility of photoswitches. Recently, Henry et al. reported an unprecedented two-dimensional controlled photoswitch, the hemithioindigo (HTI) derivative Z1, whose single bond rotation in dimethyl sulphoxide (DMSO) solvent and double bond rotation in cyclohexane solvent can be induced by visible light (J. Am. Chem. Soc. 2016, 138, 12,219). Here we investigate the intramolecular rotations of the HTI and Z1 in different polar solvents by time-dependent density functional theory (TDDFT) and Nonadiabatic dynamic simulations. Due to the steric hindrance between methyl and thioindigo fragment, the rotations of Z1 in the excited state are obstructed. Interestingly, the HTI exhibits two distinct rotation paths in DMSO and cyclohexane solvents at about 50 fs. The intermolecular hydrogen bonds between HTI and DMSO play an important role in the rotation of HTI in DMSO solvent. Therefore, the HTI is a more promising two-dimensional photoswitch compared with the Z1. Our finding is thus of fundamental importance to understand the mechanisms of this class of photoswitches and design complex molecular behavior.

Defense Logistics Standard Systems Functional Requirements.

DTIC Science & Technology

1987-03-01

Artificial Intelligence - the development of a machine capability to perform functions normally concerned with human intelligence, such as learning , adapting...Basic Data Base Machine Configurations .... ......... D- 18 xx ~ ?f~~~vX PART I: MODELS - DEFENSE LOGISTICS STANDARD SYSTEMS FUNCTIONAL REQUIREMENTS...On-line, Interactive Access. Integrating user input and machine output in a dynamic, real-time, give-and- take process is considered the optimum mode

Biochemistry and Molecular Biology of Flaviviruses.

PubMed

Barrows, Nicholas J; Campos, Rafael K; Liao, Kuo-Chieh; Prasanth, K Reddisiva; Soto-Acosta, Ruben; Yeh, Shih-Chia; Schott-Lerner, Geraldine; Pompon, Julien; Sessions, October M; Bradrick, Shelton S; Garcia-Blanco, Mariano A

2018-04-25

Flaviviruses, such as dengue, Japanese encephalitis, tick-borne encephalitis, West Nile, yellow fever, and Zika viruses, are critically important human pathogens that sicken a staggeringly high number of humans every year. Most of these pathogens are transmitted by mosquitos, and not surprisingly, as the earth warms and human populations grow and move, their geographic reach is increasing. Flaviviruses are simple RNA-protein machines that carry out protein synthesis, genome replication, and virion packaging in close association with cellular lipid membranes. In this review, we examine the molecular biology of flaviviruses touching on the structure and function of viral components and how these interact with host factors. The latter are functionally divided into pro-viral and antiviral factors, both of which, not surprisingly, include many RNA binding proteins. In the interface between the virus and the hosts we highlight the role of a noncoding RNA produced by flaviviruses to impair antiviral host immune responses. Throughout the review, we highlight areas of intense investigation, or a need for it, and potential targets and tools to consider in the important battle against pathogenic flaviviruses.

Activation and desensitization of ionotropic glutamate receptors by selectively triggering pre-existing motions.

PubMed

Krieger, James; Lee, Ji Young; Greger, Ingo H; Bahar, Ivet

2018-02-23

Ionotropic glutamate receptors (iGluRs) are ligand-gated ion channels that are key players in synaptic transmission and plasticity. They are composed of four subunits, each containing four functional domains, the quaternary packing and collective structural dynamics of which are important determinants of their molecular mechanism of function. With the explosion of structural studies on different members of the family, including the structures of activated open channels, the mechanisms of action of these central signaling machines are now being elucidated. We review the current state of computational studies on two major members of the family, AMPA and NMDA receptors, with focus on molecular simulations and elastic network model analyses that have provided insights into the coupled movements of extracellular and transmembrane domains. We describe the newly emerging mechanisms of activation, allosteric signaling and desensitization, as mainly a selective triggering of pre-existing soft motions, as deduced from computational models and analyses that leverage structural data on intact AMPA and NMDA receptors in different states. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Ethane-xenon mixtures under shock conditions

NASA Astrophysics Data System (ADS)

Flicker, Dawn; Magyar, Rudolph; Root, Seth; Cochrane, Kyle; Mattsson, Thomas

2015-06-01

Mixtures of light and heavy elements arise in inertial confinement fusion and planetary science. We present results on the physics of molecular scale mixing through a validation study of equation of state (EOS) properties. Density functional theory molecular dynamics (DFT/QMD) at elevated-temperature and pressure is used to obtain the properties of pure xenon, ethane, and various compressed mixture compositions along their principal Hugoniots. To validate the QMD simulations, we performed high-precision shock compression experiments using Sandia's Z-Machine. A bond tracking analysis of the simulations correlates the sharp rise in the Hugoniot curve with completion of dissociation in ethane. DFT-based simulation results compare well with experimental data and are used to provide insight into the dissociation as a function of mixture composition. Interestingly, we find that the compression ratio for complete dissociation is similar for ethane, Xe-ethane, polymethyl-pentene, and polystyrene, suggesting that a limiting compression exists for C-C bonded systems. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Company, Security Administration under contract DE-AC04-94AL85000.

Self-assembled software and method of overriding software execution

DOEpatents

Bouchard, Ann M.; Osbourn, Gordon C.

2013-01-08

A computer-implemented software self-assembled system and method for providing an external override and monitoring capability to dynamically self-assembling software containing machines that self-assemble execution sequences and data structures. The method provides an external override machine that can be introduced into a system of self-assembling machines while the machines are executing such that the functionality of the executing software can be changed or paused without stopping the code execution and modifying the existing code. Additionally, a monitoring machine can be introduced without stopping code execution that can monitor specified code execution functions by designated machines and communicate the status to an output device.

Metabolome of human gut microbiome is predictive of host dysbiosis.

PubMed

Larsen, Peter E; Dai, Yang

2015-01-01

Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome's interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome-host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

Metabolome of human gut microbiome is predictive of host dysbiosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, Peter E.; Dai, Yang

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Metabolome of human gut microbiome is predictive of host dysbiosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, Peter E.; Dai, Yang

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependentmore » on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Metabolome of human gut microbiome is predictive of host dysbiosis

DOE PAGES

Larsen, Peter E.; Dai, Yang

2015-09-14

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

PubMed

Wan, Cen; Lees, Jonathan G; Minneci, Federico; Orengo, Christine A; Jones, David T

2017-10-01

Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

PubMed

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.

A machine-learned computational functional genomics-based approach to drug classification.

PubMed

Lötsch, Jörn; Ultsch, Alfred

2016-12-01

The public accessibility of "big data" about the molecular targets of drugs and the biological functions of genes allows novel data science-based approaches to pharmacology that link drugs directly with their effects on pathophysiologic processes. This provides a phenotypic path to drug discovery and repurposing. This paper compares the performance of a functional genomics-based criterion to the traditional drug target-based classification. Knowledge discovery in the DrugBank and Gene Ontology databases allowed the construction of a "drug target versus biological process" matrix as a combination of "drug versus genes" and "genes versus biological processes" matrices. As a canonical example, such matrices were constructed for classical analgesic drugs. These matrices were projected onto a toroid grid of 50 × 82 artificial neurons using a self-organizing map (SOM). The distance, respectively, cluster structure of the high-dimensional feature space of the matrices was visualized on top of this SOM using a U-matrix. The cluster structure emerging on the U-matrix provided a correct classification of the analgesics into two main classes of opioid and non-opioid analgesics. The classification was flawless with both the functional genomics and the traditional target-based criterion. The functional genomics approach inherently included the drugs' modulatory effects on biological processes. The main pharmacological actions known from pharmacological science were captures, e.g., actions on lipid signaling for non-opioid analgesics that comprised many NSAIDs and actions on neuronal signal transmission for opioid analgesics. Using machine-learned techniques for computational drug classification in a comparative assessment, a functional genomics-based criterion was found to be similarly suitable for drug classification as the traditional target-based criterion. This supports a utility of functional genomics-based approaches to computational system pharmacology for drug discovery and repurposing.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

A universal strategy for the creation of machine learning-based atomistic force fields

NASA Astrophysics Data System (ADS)

Huan, Tran Doan; Batra, Rohit; Chapman, James; Krishnan, Sridevi; Chen, Lihua; Ramprasad, Rampi

2017-09-01

Emerging machine learning (ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems. In this contribution, we outline a universal strategy to create ML-based atomistic force fields, which can be used to perform high-fidelity molecular dynamics simulations. This scheme involves (1) preparing a big reference dataset of atomic environments and forces with sufficiently low noise, e.g., using density functional theory or higher-level methods, (2) utilizing a generalizable class of structural fingerprints for representing atomic environments, (3) optimally selecting diverse and non-redundant training datasets from the reference data, and (4) proposing various learning approaches to predict atomic forces directly (and rapidly) from atomic configurations. From the atomistic forces, accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory. Based on this strategy, we have created model ML force fields for six elemental bulk solids, including Al, Cu, Ti, W, Si, and C, and show that all of them can reach chemical accuracy. The proposed procedure is general and universal, in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention. Moreover, the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.

The Universal Anaesthesia Machine (UAM): assessment of a new anaesthesia workstation built with global health in mind.

PubMed

de Beer, D A H; Nesbitt, F D; Bell, G T; Rapuleng, A

2017-04-01

The Universal Anaesthesia Machine has been developed as a complete anaesthesia workstation for use in low- and middle-income countries, where the provision of safe general anaesthesia is often compromised by unreliable supply of electricity and anaesthetic gases. We performed a functional and clinical assessment of this anaesthetic machine, with particular reference to novel features and functioning in the intended environment. The Universal Anaesthesia Machine was found to be reliable, safe and consistent across a range of tests during targeted functional testing. © 2016 The Association of Anaesthetists of Great Britain and Ireland.

Functional analysis of an unusual type IV pilus in the Gram‐positive Streptococcus sanguinis

PubMed Central

Gurung, Ishwori; Spielman, Ingrid; Davies, Mark R.; Lala, Rajan; Gaustad, Peter; Biais, Nicolas

2015-01-01

Summary Type IV pili (Tfp), which have been studied extensively in a few Gram‐negative species, are the paradigm of a group of widespread and functionally versatile nano‐machines. Here, we performed the most detailed molecular characterisation of Tfp in a Gram‐positive bacterium. We demonstrate that the naturally competent S treptococcus sanguinis produces retractable Tfp, which like their Gram‐negative counterparts can generate hundreds of piconewton of tensile force and promote intense surface‐associated motility. Tfp power ‘train‐like’ directional motion parallel to the long axis of chains of cells, leading to spreading zones around bacteria grown on plates. However, S . sanguinis  Tfp are not involved in DNA uptake, which is mediated by a related but distinct nano‐machine, and are unusual because they are composed of two pilins in comparable amounts, rather than one as normally seen. Whole genome sequencing identified a locus encoding all the genes involved in Tfp biology in S . sanguinis. A systematic mutational analysis revealed that Tfp biogenesis in S . sanguinis relies on a more basic machinery (only 10 components) than in Gram‐negative species and that a small subset of four proteins dispensable for pilus biogenesis are essential for motility. Intriguingly, one of the piliated mutants that does not exhibit spreading retains microscopic motility but moves sideways, which suggests that the corresponding protein controls motion directionality. Besides establishing S . sanguinis as a useful new model for studying Tfp biology, these findings have important implications for our understanding of these widespread filamentous nano‐machines. PMID:26435398

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.

PubMed

Ashtawy, Hossam M; Mahapatra, Nihar R

2015-01-01

Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when bound to a receptor protein, from among a large set of candidate poses. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited docking power (or ability to successfully identify the correct pose) has been a major impediment to cost-effective drug discovery. Therefore, in this work, we explore a range of novel SFs employing different machine-learning (ML) approaches in conjunction with physicochemical and geometrical features characterizing protein-ligand complexes to predict the native or near-native pose of a ligand docked to a receptor protein's binding site. We assess the docking accuracies of these new ML SFs as well as those of conventional SFs in the context of the 2007 PDBbind benchmark dataset on both diverse and homogeneous (protein-family-specific) test sets. Further, we perform a systematic analysis of the performance of the proposed SFs in identifying native poses of ligands that are docked to novel protein targets. We find that the best performing ML SF has a success rate of 80% in identifying poses that are within 1 Å root-mean-square deviation from the native poses of 65 different protein families. This is in comparison to a success rate of only 70% achieved by the best conventional SF, ASP, employed in the commercial docking software GOLD. In addition, the proposed ML SFs perform better on novel proteins that they were never trained on before. We also observed steady gains in the performance of these scoring functions as the training set size and number of features were increased by considering more protein-ligand complexes and/or more computationally-generated poses for each complex.

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins

PubMed Central

2015-01-01

Background Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when bound to a receptor protein, from among a large set of candidate poses. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited docking power (or ability to successfully identify the correct pose) has been a major impediment to cost-effective drug discovery. Therefore, in this work, we explore a range of novel SFs employing different machine-learning (ML) approaches in conjunction with physicochemical and geometrical features characterizing protein-ligand complexes to predict the native or near-native pose of a ligand docked to a receptor protein's binding site. We assess the docking accuracies of these new ML SFs as well as those of conventional SFs in the context of the 2007 PDBbind benchmark dataset on both diverse and homogeneous (protein-family-specific) test sets. Further, we perform a systematic analysis of the performance of the proposed SFs in identifying native poses of ligands that are docked to novel protein targets. Results and conclusion We find that the best performing ML SF has a success rate of 80% in identifying poses that are within 1 Å root-mean-square deviation from the native poses of 65 different protein families. This is in comparison to a success rate of only 70% achieved by the best conventional SF, ASP, employed in the commercial docking software GOLD. In addition, the proposed ML SFs perform better on novel proteins that they were never trained on before. We also observed steady gains in the performance of these scoring functions as the training set size and number of features were increased by considering more protein-ligand complexes and/or more computationally-generated poses for each complex. PMID:25916860

Radar detection with the Neyman-Pearson criterion using supervised-learning-machines trained with the cross-entropy error

NASA Astrophysics Data System (ADS)

Jarabo-Amores, María-Pilar; la Mata-Moya, David de; Gil-Pita, Roberto; Rosa-Zurera, Manuel

2013-12-01

The application of supervised learning machines trained to minimize the Cross-Entropy error to radar detection is explored in this article. The detector is implemented with a learning machine that implements a discriminant function, which output is compared to a threshold selected to fix a desired probability of false alarm. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition for a discriminant function to be used to approximate the optimum Neyman-Pearson (NP) detector. In this article, the function a supervised learning machine approximates to after being trained to minimize the Cross-Entropy error is obtained. This discriminant function can be used to implement the NP detector, which maximizes the probability of detection, maintaining the probability of false alarm below or equal to a predefined value. Some experiments about signal detection using neural networks are also presented to test the validity of the study.

Predicting competency in automated machine use in an acquired brain injury population using neuropsychological measures.

PubMed

Crowe, Simon F; Mahony, Kate; Jackson, Martin

2004-08-01

The purpose of the current study was to explore whether performance on standardised neuropsychological measures could predict functional ability with automated machines and services among people with an acquired brain injury (ABI). Participants were 45 individuals who met the criteria for mild, moderate or severe ABI and 15 control participants matched on demographic variables including age- and education. Each participant was required to complete a battery of neuropsychological tests, as well as performing three automated service delivery tasks: a transport automated ticketing machine, an automated teller machine (ATM) and an automated telephone service. The results showed consistently high relationship between the neuropsychological measures, both as single predictors and in combination, and level of competency with the automated machines. Automated machines are part of a relatively new phenomena in service delivery and offer an ecologically valid functional measure of performance that represents a true indication of functional disability.

Computational Nanotechnology Molecular Electronics, Materials and Machines

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Biegel, Bryan A. (Technical Monitor)

2002-01-01

This presentation covers research being performed on computational nanotechnology, carbon nanotubes and fullerenes at the NASA Ames Research Center. Topics cover include: nanomechanics of nanomaterials, nanotubes and composite materials, molecular electronics with nanotube junctions, kinky chemistry, and nanotechnology for solid-state quantum computers using fullerenes.

The Molecular Industrial Revolution: Automated Synthesis of Small Molecules.

PubMed

Trobe, Melanie; Burke, Martin D

2018-04-09

Today we are poised for a transition from the highly customized crafting of specific molecular targets by hand to the increasingly general and automated assembly of different types of molecules with the push of a button. Creating machines that are capable of making many different types of small molecules on demand, akin to that which has been achieved on the macroscale with 3D printers, is challenging. Yet important progress is being made toward this objective with two complementary approaches: 1) Automation of customized synthesis routes to different targets by machines that enable the use of many reactions and starting materials, and 2) automation of generalized platforms that make many different targets using common coupling chemistry and building blocks. Continued progress in these directions has the potential to shift the bottleneck in molecular innovation from synthesis to imagination, and thereby help drive a new industrial revolution on the molecular scale. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantum Monte Carlo for large chemical systems: implementing efficient strategies for petascale platforms and beyond.

PubMed

Scemama, Anthony; Caffarel, Michel; Oseret, Emmanuel; Jalby, William

2013-04-30

Various strategies to implement efficiently quantum Monte Carlo (QMC) simulations for large chemical systems are presented. These include: (i) the introduction of an efficient algorithm to calculate the computationally expensive Slater matrices. This novel scheme is based on the use of the highly localized character of atomic Gaussian basis functions (not the molecular orbitals as usually done), (ii) the possibility of keeping the memory footprint minimal, (iii) the important enhancement of single-core performance when efficient optimization tools are used, and (iv) the definition of a universal, dynamic, fault-tolerant, and load-balanced framework adapted to all kinds of computational platforms (massively parallel machines, clusters, or distributed grids). These strategies have been implemented in the QMC=Chem code developed at Toulouse and illustrated with numerical applications on small peptides of increasing sizes (158, 434, 1056, and 1731 electrons). Using 10-80 k computing cores of the Curie machine (GENCI-TGCC-CEA, France), QMC=Chem has been shown to be capable of running at the petascale level, thus demonstrating that for this machine a large part of the peak performance can be achieved. Implementation of large-scale QMC simulations for future exascale platforms with a comparable level of efficiency is expected to be feasible. Copyright © 2013 Wiley Periodicals, Inc.

Mechanically Compliant Electronic Materials for Wearable Photovoltaics and Human-Machine Interfaces

NASA Astrophysics Data System (ADS)

O'Connor, Timothy Francis, III

Applications of stretchable electronic materials for human-machine interfaces are described herein. Intrinsically stretchable organic conjugated polymers and stretchable electronic composites were used to develop stretchable organic photovoltaics (OPVs), mechanically robust wearable OPVs, and human-machine interfaces for gesture recognition, American Sign Language Translation, haptic control of robots, and touch emulation for virtual reality, augmented reality, and the transmission of touch. The stretchable and wearable OPVs comprise active layers of poly-3-alkylthiophene:phenyl-C61-butyric acid methyl ester (P3AT:PCBM) and transparent conductive electrodes of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) and devices could only be fabricated through a deep understanding of the connection between molecular structure and the co-engineering of electronic performance with mechanical resilience. The talk concludes with the use of composite piezoresistive sensors two smart glove prototypes. The first integrates stretchable strain sensors comprising a carbon-elastomer composite, a wearable microcontroller, low energy Bluetooth, and a 6-axis accelerometer/gyroscope to construct a fully functional gesture recognition glove capable of wirelessly translating American Sign Language to text on a cell phone screen. The second creates a system for the haptic control of a 3D printed robot arm, as well as the transmission of touch and temperature information.

Clustering the Orion B giant molecular cloud based on its molecular emission

PubMed Central

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H.; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R.; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2017-01-01

Context Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). Aims We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. Methods We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. Results A clustering analysis based only on the J = 1 – 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes (nH ~ 100 cm−3, ~ 500 cm−3, and > 1000 cm−3), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 − 0 line of HCO+ and the N = 1 − 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO+ and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO+ intensity ratio in UV-illuminated regions. Finer distinctions in density classes (nH ~ 7 × 103 cm−3 ~ 4 × 104 cm−3) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO+ (1 – 0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Conclusions Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers. PMID:29456256

First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

DOE PAGES

von Lilienfeld, O. Anatole

2013-02-26

A well-defined notion of chemical compound space (CCS) is essential for gaining rigorous control of properties through variation of elemental composition and atomic configurations. Here, we give an introduction to an atomistic first principles perspective on CCS. First, CCS is discussed in terms of variational nuclear charges in the context of conceptual density functional and molecular grand-canonical ensemble theory. Thereafter, we revisit the notion of compound pairs, related to each other via “alchemical” interpolations involving fractional nuclear charges in the electronic Hamiltonian. We address Taylor expansions in CCS, property nonlinearity, improved predictions using reference compound pairs, and the ounce-of-gold prizemore » challenge to linearize CCS. Finally, we turn to machine learning of analytical structure property relationships in CCS. Here, these relationships correspond to inferred, rather than derived through variational principle, solutions of the electronic Schrödinger equation.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ekman, Axel A.; Chen, Jian-Hua; Guo, Jessica

In the context of cell biology, the term mesoscale describes length scales ranging from that of an individual cell, down to the size of the molecular machines. In this spatial regime, small building blocks self-organise to form large, functional structures. A comprehensive set of rules governing mesoscale self-organisation has not been established, making the prediction of many cell behaviours difficult, if not impossible. Our knowledge of mesoscale biology comes from experimental data, in particular, imaging. Here, we explore the application of soft X-ray tomography (SXT) to imaging the mesoscale, and describe the structural insights this technology can generate. We alsomore » discuss how SXT imaging is complemented by the addition of correlative fluorescence data measured from the same cell. This combination of two discrete imaging modalities produces a 3D view of the cell that blends high-resolution structural information with precise molecular localisation data.« less

Calculation and Visualization of Atomistic Mechanical Stresses in Nanomaterials and Biomolecules

PubMed Central

Gilson, Michael K.

2014-01-01

Many biomolecules have machine-like functions, and accordingly are discussed in terms of mechanical properties like force and motion. However, the concept of stress, a mechanical property that is of fundamental importance in the study of macroscopic mechanics, is not commonly applied in the biomolecular context. We anticipate that microscopical stress analyses of biomolecules and nanomaterials will provide useful mechanistic insights and help guide molecular design. To enable such applications, we have developed Calculator of Atomistic Mechanical Stress (CAMS), an open-source software package for computing atomic resolution stresses from molecular dynamics (MD) simulations. The software also enables decomposition of stress into contributions from bonded, nonbonded and Generalized Born potential terms. CAMS reads GROMACS topology and trajectory files, which are easily generated from AMBER files as well; and time-varying stresses may be animated and visualized in the VMD viewer. Here, we review relevant theory and present illustrative applications. PMID:25503996

Centromeric Heterochromatin: The Primordial Segregation Machine

PubMed Central

Bloom, Kerry S.

2014-01-01

Centromeres are specialized domains of heterochromatin that provide the foundation for the kinetochore. Centromeric heterochromatin is characterized by specific histone modifications, a centromere-specific histone H3 variant (CENP-A), and the enrichment of cohesin, condensin, and topo-isomerase II. Centromere DNA varies orders of magnitude in size from 125 bp (budding yeast) to several megabases (human). In metaphase, sister kinetochores on the surface of replicated chromosomes face away from each other, where they establish microtubule attachment and bi-orientation. Despite the disparity in centromere size, the distance between separated sister kinetochores is remarkably conserved (approximately 1 μm) throughout phylogeny. The centromere functions as a molecular spring that resists microtubule-based extensional forces in mitosis. This review explores the physical properties of DNA in order to understand how the molecular spring is built and how it contributes to the fidelity of chromosome segregation. PMID:25251850

Mitochondria and the non-genetic origins of cell-to-cell variability: More is different.

PubMed

Guantes, Raúl; Díaz-Colunga, Juan; Iborra, Francisco J

2016-01-01

Gene expression activity is heterogeneous in a population of isogenic cells. Identifying the molecular basis of this variability will improve our understanding of phenomena like tumor resistance to drugs, virus infection, or cell fate choice. The complexity of the molecular steps and machines involved in transcription and translation could introduce sources of randomness at many levels, but a common constraint to most of these processes is its energy dependence. In eukaryotic cells, most of this energy is provided by mitochondria. A clonal population of cells may show a large variability in the number and functionality of mitochondria. Here, we discuss how differences in the mitochondrial content of each cell contribute to heterogeneity in gene products. Changes in the amount of mitochondria can also entail drastic alterations of a cell's gene expression program, which ultimately leads to phenotypic diversity. Also watch the Video Abstract. © 2015 WILEY Periodicals, Inc.

Calculation and visualization of atomistic mechanical stresses in nanomaterials and biomolecules.

PubMed

Fenley, Andrew T; Muddana, Hari S; Gilson, Michael K

2014-01-01

Many biomolecules have machine-like functions, and accordingly are discussed in terms of mechanical properties like force and motion. However, the concept of stress, a mechanical property that is of fundamental importance in the study of macroscopic mechanics, is not commonly applied in the biomolecular context. We anticipate that microscopical stress analyses of biomolecules and nanomaterials will provide useful mechanistic insights and help guide molecular design. To enable such applications, we have developed Calculator of Atomistic Mechanical Stress (CAMS), an open-source software package for computing atomic resolution stresses from molecular dynamics (MD) simulations. The software also enables decomposition of stress into contributions from bonded, nonbonded and Generalized Born potential terms. CAMS reads GROMACS topology and trajectory files, which are easily generated from AMBER files as well; and time-varying stresses may be animated and visualized in the VMD viewer. Here, we review relevant theory and present illustrative applications.

GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies.

PubMed

Boudard, Mélanie; Bernauer, Julie; Barth, Dominique; Cohen, Johanne; Denise, Alain

2015-01-01

Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is predicting the spatial arrangement of the various structural elements of RNA. As RNA folding is generally hierarchical, methods involving coarse-grained models hold great promise for this purpose. We present here a novel coarse-grained method for sampling, based on game theory and knowledge-based potentials. This strategy, GARN (Game Algorithm for RNa sampling), is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints. GARN is available from: http://garn.lri.fr/.

Molecular dynamics simulation of membrane in room temperature ionic liquids

NASA Astrophysics Data System (ADS)

Theng, Soong Guan; Jumbri, Khairulazhar bin; Wirzal, Mohd Dzul Hakim

2017-10-01

The polyvinylidene difluoride (PVDF) membrane has been a popular material in membrane separation process. In this work, molecular dynamic simulation was done on the PVDF membrane with 100 wt% IL and 50 wt% IL in GROningen MAchine for Chemical Simulations (GROMACS). The results was evaluated based on potential energy, root mean square deviation (RMSD) and radial distribution function (RDF). The stability and interaction of PVDF were evaluated. Results reveal that PVDF has a stronger interaction to [C2bim]+ cation compared to water and bromine anion. Both potential energy and RMSD were lower when the weight percentage of IL is higher. This indicates that the IL is able to stabilize the PVDF structure. RMSD reveals that [C2bim]+ cation is dominant at short distance (less than 1 nm), indicating that strong interaction of cation to PVDF. This understanding of the behavior of PVDF-IL could be used as a reference for future development of stronger membrane.

Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.

PubMed

Fernandez, Michael; Abreu, Jose I; Shi, Hongqing; Barnard, Amanda S

2016-11-14

The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gaps of 622 computationally optimized graphene nanoflakes were mapped to topological autocorrelation vectors using machine learning techniques. Machine learning modeling revealed that the most relevant correlations appear at topological distances in the range of 1 to 42 with prediction accuracy higher than 80%. The data-driven model can statistically discriminate between graphene nanoflakes with different energy gaps on the basis of their molecular topology.

RNA structures as mediators of neurological diseases and as drug targets

PubMed Central

Bernat, Viachaslau; Disney, Matthew D.

2015-01-01

RNAs adopt diverse folded structures that are essential for function and thus play critical roles in cellular biology. A striking example of this is the ribosome, a complex, three-dimensionally folded macromolecular machine that orchestrates protein synthesis. Advances in RNA biochemistry, structural and molecular biology, and bioinformatics have revealed other non-coding RNAs whose functions are dictated by their structure. It is not surprising that aberrantly folded RNA structures contribute to disease. In this review, we provide a brief introduction into RNA structural biology and then describe how RNA structures function in cells and cause or contribute to neurological disease. Finally, we highlight successful applications of rational design principles to provide chemical probes and lead compounds targeting structured RNAs. Based on several examples of well-characterized RNA-driven neurological disorders, we demonstrate how designed small molecules can facilitate study of RNA dysfunction, elucidating previously unknown roles for RNA in disease, and provide lead therapeutics. PMID:26139368

GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction.

PubMed

Curtis, Farren; Li, Xiayue; Rose, Timothy; Vázquez-Mayagoitia, Álvaro; Bhattacharya, Saswata; Ghiringhelli, Luca M; Marom, Noa

2018-04-10

We present the implementation of GAtor, a massively parallel, first-principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several subpopulations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low-energy minima, including those located in disconnected, hard to reach regions of the potential energy landscape. The best structures generated are re-relaxed and re-ranked using a hierarchy of increasingly accurate DFT functionals and dispersion methods. GAtor is applied to a chemically diverse set of four past blind test targets, characterized by different types of intermolecular interactions. The experimentally observed structures and other low-energy structures are found for all four targets. In particular, for Target II, 5-cyano-3-hydroxythiophene, the top ranked putative crystal structure is a Z' = 2 structure with P1̅ symmetry and a scaffold packing motif, which has not been reported previously.

Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

PubMed

Munteanu, Cristian R; Gonzalez-Diaz, Humberto; Garcia, Rafael; Loza, Mabel; Pazos, Alejandro

2015-01-01

The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.

Redox control of molecular motion in switchable artificial nanoscale devices.

PubMed

Credi, Alberto; Semeraro, Monica; Silvi, Serena; Venturi, Margherita

2011-03-15

The design, synthesis, and operation of molecular-scale systems that exhibit controllable motions of their component parts is a topic of great interest in nanoscience and a fascinating challenge of nanotechnology. The development of this kind of species constitutes the premise to the construction of molecular machines and motors, which in a not-too-distant future could find applications in fields such as materials science, information technology, energy conversion, diagnostics, and medicine. In the past 25 years the development of supramolecular chemistry has enabled the construction of an interesting variety of artificial molecular machines. These devices operate via electronic and molecular rearrangements and, like the macroscopic counterparts, they need energy to work as well as signals to communicate with the operator. Here we outline the design principles at the basis of redox switching of molecular motion in artificial nanodevices. Redox processes, chemically, electrically, or photochemically induced, can indeed supply the energy to bring about molecular motions. Moreover, in the case of electrically and photochemically induced processes, electrochemical and photochemical techniques can be used to read the state of the system, and thus to control and monitor the operation of the device. Some selected examples are also reported to describe the most representative achievements in this research area.

Machine learnt bond order potential to model metal-organic (Co-C) heterostructures.

PubMed

Narayanan, Badri; Chan, Henry; Kinaci, Alper; Sen, Fatih G; Gray, Stephen K; Chan, Maria K Y; Sankaranarayanan, Subramanian K R S

2017-11-30

A fundamental understanding of the inter-relationships between structure, morphology, atomic scale dynamics, chemistry, and physical properties of mixed metallic-covalent systems is essential to design novel functional materials for applications in flexible nano-electronics, energy storage and catalysis. To achieve such knowledge, it is imperative to develop robust and computationally efficient atomistic models that describe atomic interactions accurately within a single framework. Here, we present a unified Tersoff-Brenner type bond order potential (BOP) for a Co-C system, trained against lattice parameters, cohesive energies, equation of state, and elastic constants of different crystalline phases of cobalt as well as orthorhombic Co 2 C derived from density functional theory (DFT) calculations. The independent BOP parameters are determined using a combination of supervised machine learning (genetic algorithms) and local minimization via the simplex method. Our newly developed BOP accurately describes the structural, thermodynamic, mechanical, and surface properties of both the elemental components as well as the carbide phases, in excellent accordance with DFT calculations and experiments. Using our machine-learnt BOP potential, we performed large-scale molecular dynamics simulations to investigate the effect of metal/carbon concentration on the structure and mechanical properties of porous architectures obtained via self-assembly of cobalt nanoparticles and fullerene molecules. Such porous structures have implications in flexible electronics, where materials with high electrical conductivity and low elastic stiffness are desired. Using unsupervised machine learning (clustering), we identify the pore structure, pore-distribution, and metallic conduction pathways in self-assembled structures at different C/Co ratios. We find that as the C/Co ratio increases, the connectivity between the Co nanoparticles becomes limited, likely resulting in low electrical conductivity; on the other hand, such C-rich hybrid structures are highly flexible (i.e., low stiffness). The BOP model developed in this work is a valuable tool to investigate atomic scale processes, structure-property relationships, and temperature/pressure response of Co-C systems, as well as design organic-inorganic hybrid structures with a desired set of properties.

Methods For Self-Organizing Software

DOEpatents

Bouchard, Ann M.; Osbourn, Gordon C.

2005-10-18

A method for dynamically self-assembling and executing software is provided, containing machines that self-assemble execution sequences and data structures. In addition to ordered functions calls (found commonly in other software methods), mutual selective bonding between bonding sites of machines actuates one or more of the bonding machines. Two or more machines can be virtually isolated by a construct, called an encapsulant, containing a population of machines and potentially other encapsulants that can only bond with each other. A hierarchical software structure can be created using nested encapsulants. Multi-threading is implemented by populations of machines in different encapsulants that are interacting concurrently. Machines and encapsulants can move in and out of other encapsulants, thereby changing the functionality. Bonding between machines' sites can be deterministic or stochastic with bonding triggering a sequence of actions that can be implemented by each machine. A self-assembled execution sequence occurs as a sequence of stochastic binding between machines followed by their deterministic actuation. It is the sequence of bonding of machines that determines the execution sequence, so that the sequence of instructions need not be contiguous in memory.

Amp: A modular approach to machine learning in atomistic simulations

NASA Astrophysics Data System (ADS)

Khorshidi, Alireza; Peterson, Andrew A.

2016-10-01

Electronic structure calculations, such as those employing Kohn-Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning techniques can provide accurate potentials that can match the quality of electronic structure calculations, provided sufficient training data. These potentials can then be used to rapidly simulate large and long time-scale phenomena at similar quality to the parent electronic structure approach. Machine-learning potentials usually take a bias-free mathematical form and can be readily developed for a wide variety of systems. Electronic structure calculations have favorable properties-namely that they are noiseless and targeted training data can be produced on-demand-that make them particularly well-suited for machine learning. This paper discusses our modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package (Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems. Potentials developed through the atom-centered approach are simultaneously applicable for systems with various sizes. Interpolation can be enhanced by introducing custom descriptors of the local environment. We demonstrate this in the current work for Gaussian-type, bispectrum, and Zernike-type descriptors. Amp has an intuitive and modular structure with an interface through the python scripting language yet has parallelizable fortran components for demanding tasks; it is designed to integrate closely with the widely used Atomic Simulation Environment (ASE), which makes it compatible with a wide variety of commercial and open-source electronic structure codes. We finally demonstrate that the neural network model inside Amp can accurately interpolate electronic structure energies as well as forces of thousands of multi-species atomic systems.

Development of plasma chemical vaporization machining

NASA Astrophysics Data System (ADS)

Mori, Yuzo; Yamauchi, Kazuto; Yamamura, Kazuya; Sano, Yasuhisa

2000-12-01

Conventional machining processes, such as turning, grinding, or lapping are still applied for many materials including functional ones. But those processes are accompanied with the formation of a deformed layer, so that machined surfaces cannot perform their original functions. In order to avoid such points, plasma chemical vaporization machining (CVM) has been developed. Plasma CVM is a chemical machining method using neutral radicals, which are generated by the atmospheric pressure plasma. By using a rotary electrode for generation of plasma, a high density of neutral radicals was formed, and we succeeded in obtaining high removal rate of several microns to several hundred microns per minute for various functional materials such as fused silica, single crystal silicon, molybdenum, tungsten, silicon carbide, and diamond. Especially, a high removal rate equal to lapping in the mechanical machining of fused silica and silicon was realized. 1.4 nm (p-v) was obtained as a surface roughness in the case of machining a silicon wafer. The defect density of a silicon wafer surface polished by various machining method was evaluated by the surface photo voltage spectroscopy. As a result, the defect density of the surface machined by plasma CVM was under 1/100 in comparison with the surface machined by mechanical polishing and argon ion sputtering, and very low defect density which was equivalent to the chemical etched surface was realized. A numerically controlled CVM machine for x-ray mirror fabrication is detailed in the accompanying article in this issue.

Recent Advances in Conotoxin Classification by Using Machine Learning Methods.

PubMed

Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao

2017-06-25

Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.

PubMed

You, Zhu-Hong; Li, Shuai; Gao, Xin; Luo, Xin; Ji, Zhen

2014-01-01

Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.

Reverse engineering of wörner type drilling machine structure.

NASA Astrophysics Data System (ADS)

Wibowo, A.; Belly, I.; llhamsyah, R.; Indrawanto; Yuwana, Y.

2018-03-01

A product design needs to be modified based on the conditions of production facilities and existing resource capabilities without reducing the functional aspects of the product itself. This paper describes the reverse engineering process of the main structure of the wörner type drilling machine to obtain a machine structure design that can be made by resources with limited ability by using simple processes. Some structural, functional and the work mechanism analyzes have been performed to understand the function and role of each basic components. The process of dismantling of the drilling machine and measuring each of the basic components was performed to obtain sets of the geometry and size data of each component. The geometric model of each structure components and the machine assembly were built to facilitate the simulation process and machine performance analysis that refers to ISO standard of drilling machine. The tolerance stackup analysis also performed to determine the type and value of geometrical and dimensional tolerances, which could affect the ease of the components to be manufactured and assembled

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

Origin of acoustic emission produced during single point machining

NASA Astrophysics Data System (ADS)

Heiple, C. R.; Carpenter, S. H.; Armentrout, D. L.

1991-05-01

Acoustic emission was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Acoustic emission produced during tensile and compressive deformation of these alloys has been previously characterized as a function of heat treatment. Heat treatments which increase the strength of 4340 steel increase the amount of acoustic emission produced during deformation, while heat treatments which increase the strength of Ti-6Al-4V decrease the amount of acoustic emission produced during deformation. If chip deformation were the primary source of acoustic emission during single point machining, then opposite trends in the level of acoustic emission produced during machining as a function of material strength would be expected for these two alloys. Trends in rms acoustic emission level with increasing strength were similar for both alloys, demonstrating that chip deformation is not a major source of acoustic emission in single point machining. Acoustic emission has also been monitored as a function of machining parameters on 6061-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, lead, and teflon. The data suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of acoustic emission. Changes in acoustic emission with tool wear were strongly material dependent.

Development of an Empirical Model for Optimization of Machining Parameters to Minimize Power Consumption

NASA Astrophysics Data System (ADS)

Kant Garg, Girish; Garg, Suman; Sangwan, K. S.

2018-04-01

The manufacturing sector consumes huge energy demand and the machine tools used in this sector have very less energy efficiency. Selection of the optimum machining parameters for machine tools is significant for energy saving and for reduction of environmental emission. In this work an empirical model is developed to minimize the power consumption using response surface methodology. The experiments are performed on a lathe machine tool during the turning of AISI 6061 Aluminum with coated tungsten inserts. The relationship between the power consumption and machining parameters is adequately modeled. This model is used for formulation of minimum power consumption criterion as a function of optimal machining parameters using desirability function approach. The influence of machining parameters on the energy consumption has been found using the analysis of variance. The validation of the developed empirical model is proved using the confirmation experiments. The results indicate that the developed model is effective and has potential to be adopted by the industry for minimum power consumption of machine tools.

Study of Effect of Impacting Direction on Abrasive Nanometric Cutting Process with Molecular Dynamics

NASA Astrophysics Data System (ADS)

Li, Junye; Meng, Wenqing; Dong, Kun; Zhang, Xinming; Zhao, Weihong

2018-01-01

Abrasive flow polishing plays an important part in modern ultra-precision machining. Ultrafine particles suspended in the medium of abrasive flow removes the material in nanoscale. In this paper, three-dimensional molecular dynamics (MD) simulations are performed to investigate the effect of impacting direction on abrasive cutting process during abrasive flow polishing. The molecular dynamics simulation software Lammps was used to simulate the cutting of single crystal copper with SiC abrasive grains at different cutting angles (0o-45o). At a constant friction coefficient, we found a direct relation between cutting angle and cutting force, which ultimately increases the number of dislocation during abrasive flow machining. Our theoretical study reveal that a small cutting angle is beneficial for improving surface quality and reducing internal defects in the workpiece. However, there is no obvious relationship between cutting angle and friction coefficient.

Study of Effect of Impacting Direction on Abrasive Nanometric Cutting Process with Molecular Dynamics.

PubMed

Li, Junye; Meng, Wenqing; Dong, Kun; Zhang, Xinming; Zhao, Weihong

2018-01-11

Abrasive flow polishing plays an important part in modern ultra-precision machining. Ultrafine particles suspended in the medium of abrasive flow removes the material in nanoscale. In this paper, three-dimensional molecular dynamics (MD) simulations are performed to investigate the effect of impacting direction on abrasive cutting process during abrasive flow polishing. The molecular dynamics simulation software Lammps was used to simulate the cutting of single crystal copper with SiC abrasive grains at different cutting angles (0 o -45 o ). At a constant friction coefficient, we found a direct relation between cutting angle and cutting force, which ultimately increases the number of dislocation during abrasive flow machining. Our theoretical study reveal that a small cutting angle is beneficial for improving surface quality and reducing internal defects in the workpiece. However, there is no obvious relationship between cutting angle and friction coefficient.

A Study of Multifunctional Document Centers that Are Accessible to People Who Are Visually Impaired

ERIC Educational Resources Information Center

Huffman, Lee A.; Uslan, Mark M.; Burton, Darren M.; Eghtesadi, Caesar

2009-01-01

The capabilities of modern photocopy machines have advanced beyond the simple duplication of documents. In addition to the standard functions of copying, collating, and stapling, such machines can be a part of telecommunication networks and provide printing, scanning, faxing, and e-mailing functions. No longer just copy machines, these devices are…

Molecular dynamic simulation for nanometric cutting of single-crystal face-centered cubic metals.

PubMed

Huang, Yanhua; Zong, Wenjun

2014-01-01

In this work, molecular dynamics simulations are performed to investigate the influence of material properties on the nanometric cutting of single crystal copper and aluminum with a diamond cutting tool. The atomic interactions in the two metallic materials are modeled by two sets of embedded atom method (EAM) potential parameters. Simulation results show that although the plastic deformation of the two materials is achieved by dislocation activities, the deformation behavior and related physical phenomena, such as the machining forces, machined surface quality, and chip morphology, are significantly different for different materials. Furthermore, the influence of material properties on the nanometric cutting has a strong dependence on the operating temperature.

Amazing structure of respirasome: unveiling the secrets of cell respiration.

PubMed

Guo, Runyu; Gu, Jinke; Wu, Meng; Yang, Maojun

2016-12-01

Respirasome, a huge molecular machine that carries out cellular respiration, has gained growing attention since its discovery, because respiration is the most indispensable biological process in almost all living creatures. The concept of respirasome has renewed our understanding of the respiratory chain organization, and most recently, the structure of respirasome solved by Yang's group from Tsinghua University (Gu et al. Nature 237(7622):639-643, 2016) firstly presented the detailed interactions within this huge molecular machine, and provided important information for drug design and screening. However, the study of cellular respiration went through a long history. Here, we briefly showed the detoured history of respiratory chain investigation, and then described the amazing structure of respirasome.

Tools and procedures for visualization of proteins and other biomolecules.

PubMed

Pan, Lurong; Aller, Stephen G

2015-04-01

Protein, peptides, and nucleic acids are biomolecules that drive biological processes in living organisms. An enormous amount of structural data for a large number of these biomolecules has been described with atomic precision in the form of structural "snapshots" that are freely available in public repositories. These snapshots can help explain how the biomolecules function, the nature of interactions between multi-molecular complexes, and even how small-molecule drugs can modulate the biomolecules for clinical benefits. Furthermore, these structural snapshots serve as inputs for sophisticated computer simulations to turn the biomolecules into moving, "breathing" molecular machines for understanding their dynamic properties in real-time computer simulations. In order for the researcher to take advantage of such a wealth of structural data, it is necessary to gain competency in the use of computer molecular visualization tools for exploring the structures and visualizing three-dimensional spatial representations. Here, we present protocols for using two common visualization tools--the Web-based Jmol and the stand-alone PyMOL package--as well as a few examples of other popular tools. Copyright © 2015 John Wiley & Sons, Inc.

Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

PubMed

Liu, Zhi-Ping; Chen, Luonan

2016-01-01

Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

Machine learning of molecular electronic properties in chemical compound space

NASA Astrophysics Data System (ADS)

Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand; Vazquez-Mayagoitia, Alvaro; Hansen, Katja; Tkatchenko, Alexandre; Müller, Klaus-Robert; Anatole von Lilienfeld, O.

2013-09-01

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e. nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a ‘quantum machine’ is similar, and sometimes superior, to modern quantum-chemical methods—at negligible computational cost.

48 CFR 52.223-13 - Acquisition of EPEAT®-Registered Imaging Equipment.

Code of Federal Regulations, 2014 CFR

2014-10-01

...) Facsimile machine (fax machine)—A commercially available imaging product whose primary functions are... available imaging product with a sole function of the production of hard copy duplicates from graphic hard... functionally integrated components, that performs two or more of the core functions of copying, printing...

Role of oxygen functional groups in reduced graphene oxide for lubrication

PubMed Central

Gupta, Bhavana; Kumar, Niranjan; Panda, Kalpataru; Kanan, Vigneshwaran; Joshi, Shailesh; Visoly-Fisher, Iris

2017-01-01

Functionalized and fully characterized graphene-based lubricant additives are potential 2D materials for energy-efficient tribological applications in machine elements, especially at macroscopic contacts. Two different reduced graphene oxide (rGO) derivatives, terminated by hydroxyl and epoxy-hydroxyl groups, were prepared and blended with two different molecular weights of polyethylene glycol (PEG) for tribological investigation. Epoxy-hydroxyl-terminated rGO dispersed in PEG showed significantly smaller values of the friction coefficient. In this condition, PEG chains intercalate between the functionalized graphene sheets, and shear can take place between the PEG and rGO sheets. However, the friction coefficient was unaffected when hydroxyl-terminated rGO was coupled with PEG. This can be explained by the strong coupling between graphene sheets through hydroxyl units, causing the interaction of PEG with the rGO to be non- effective for lubrication. On the other hand, antiwear properties of hydroxyl-terminated rGO were significantly enhanced compared to epoxy-hydroxyl functionalized rGO due to the integrity of graphene sheet clusters. PMID:28344337

Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct.

PubMed

Funk, Christopher S; Kahanda, Indika; Ben-Hur, Asa; Verspoor, Karin M

2015-01-01

Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature and analyze their impact in the context of a structured output support vector machine model, GOstruct. We find that even simple literature based features are useful for predicting human protein function (F-max: Molecular Function =0.408, Biological Process =0.461, Cellular Component =0.608). One advantage of using literature features is their ability to offer easy verification of automated predictions. We find through manual inspection of misclassifications that some false positive predictions could be biologically valid predictions based upon support extracted from the literature. Additionally, we present a "medium-throughput" pipeline that was used to annotate a large subset of co-mentions; we suggest that this strategy could help to speed up the rate at which proteins are curated.

Evaluation of molecular brain changes associated with environmental stress in rodent models compared to human major depressive disorder: A proteomic systems approach.

PubMed

Cox, David Alan; Gottschalk, Michael Gerd; Stelzhammer, Viktoria; Wesseling, Hendrik; Cooper, Jason David; Bahn, Sabine

2016-11-25

Rodent models of major depressive disorder (MDD) are indispensable when screening for novel treatments, but assessing their translational relevance with human brain pathology has proved difficult. Using a novel systems approach, proteomics data obtained from post-mortem MDD anterior prefrontal cortex tissue (n = 12) and matched controls (n = 23) were compared with equivalent data from three commonly used preclinical models exposed to environmental stressors (chronic mild stress, prenatal stress and social defeat). Functional pathophysiological features associated with depression-like behaviour were identified in these models through enrichment of protein-protein interaction networks. A cross-species comparison evaluated which model(s) represent human MDD pathology most closely. Seven functional domains associated with MDD and represented across at least two models such as "carbohydrate metabolism and cellular respiration" were identified. Through statistical evaluation using kernel-based machine learning techniques, the social defeat model was found to represent MDD brain changes most closely for four of the seven domains. This is the first study to apply a method for directly evaluating the relevance of the molecular pathology of multiple animal models to human MDD on the functional level. The methodology and findings outlined here could help to overcome translational obstacles of preclinical psychiatric research.

[Fragments Woven into a Whole: A New Approach to the Diagnosis of Cancer through Mass Spectrometry and Machine-Learning].

PubMed

Takeda, Sen; Yoshimura, Kentaro; Tanabe, Kunio

2015-09-01

Conventionally, a definitive diagnosis of cancer is derived from histopathological diagnostics based on morphological criteria that are difficult to standardize on a quantifiable basis. On the other hand, while molecular tumor markers and blood biochemical profiles give quantitative values evaluated by objective criteria, these parameters are usually generated by deductive methods such as peak extraction. Therefore, some of the data that may contain useful information on specimens are discarded. To overcome the disadvantages of these methods, we have developed a new approach by employing both mass spectrometry and machine-learning for cancer diagnosis. Probe electrospray ionization (PESI) is a derivative of electrospray ionization that uses a fine acupuncture needle as a sample picker as well as an ion emitter for mass spectrometry. This method enables us to ionize very small tissue samples up to a few pico liters in the presence of physiological concentrations of inorganic salts, without the need for any sample pretreatment. Moreover, as this technique makes it possible to ionize all components with minimal suppression effects, we can retrieve much more molecular information from specimens. To make the most of data enriched with lipid compounds and substances with lower molecular weights such as carbohydrates, we employed machine-learning named the dual penalized logistic regression machine (dPLRM). This method is completely different from pattern-matching in that it discriminates categories by projecting the spectral data into a mathematical space with very high dimensions, where final judgment is made. We are now approaching the final clinical trial to validate the usefulness of our system.

Future developments in brain-machine interface research.

PubMed

Lebedev, Mikhail A; Tate, Andrew J; Hanson, Timothy L; Li, Zheng; O'Doherty, Joseph E; Winans, Jesse A; Ifft, Peter J; Zhuang, Katie Z; Fitzsimmons, Nathan A; Schwarz, David A; Fuller, Andrew M; An, Je Hi; Nicolelis, Miguel A L

2011-01-01

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Scheduling Jobs with Variable Job Processing Times on Unrelated Parallel Machines

PubMed Central

Zhang, Guang-Qian; Wang, Jian-Jun; Liu, Ya-Jing

2014-01-01

m unrelated parallel machines scheduling problems with variable job processing times are considered, where the processing time of a job is a function of its position in a sequence, its starting time, and its resource allocation. The objective is to determine the optimal resource allocation and the optimal schedule to minimize a total cost function that dependents on the total completion (waiting) time, the total machine load, the total absolute differences in completion (waiting) times on all machines, and total resource cost. If the number of machines is a given constant number, we propose a polynomial time algorithm to solve the problem. PMID:24982933

Quantum dynamics of light-driven chiral molecular motors.

PubMed

Yamaki, Masahiro; Nakayama, Shin-ichiro; Hoki, Kunihito; Kono, Hirohiko; Fujimura, Yuichi

2009-03-21

The results of theoretical studies on quantum dynamics of light-driven molecular motors with internal rotation are presented. Characteristic features of chiral motors driven by a non-helical, linearly polarized electric field of light are explained on the basis of symmetry argument. The rotational potential of the chiral motor is characterized by a ratchet form. The asymmetric potential determines the directional motion: the rotational direction is toward the gentle slope of the asymmetric potential. This direction is called the intuitive direction. To confirm the unidirectional rotational motion, results of quantum dynamical calculations of randomly-oriented molecular motors are presented. A theoretical design of the smallest light-driven molecular machine is presented. The smallest chiral molecular machine has an optically driven engine and a running propeller on its body. The mechanisms of transmission of driving forces from the engine to the propeller are elucidated by using a quantum dynamical treatment. The results provide a principle for control of optically-driven molecular bevel gears. Temperature effects are discussed using the density operator formalism. An effective method for ultrafast control of rotational motions in any desired direction is presented with the help of a quantum control theory. In this method, visible or UV light pulses are applied to drive the motor via an electronic excited state. A method for driving a large molecular motor consisting of an aromatic hydrocarbon is presented. The molecular motor is operated by interactions between the induced dipole of the molecular motor and the electric field of light pulses.

Diverse Effects, Complex Causes: Children Use Information about Machines' Functional Diversity to Infer Internal Complexity

ERIC Educational Resources Information Center

Ahl, Richard E.; Keil, Frank C.

2017-01-01

Four studies explored the abilities of 80 adults and 180 children (4-9 years), from predominantly middle-class families in the Northeastern United States, to use information about machines' observable functional capacities to infer their internal, "hidden" mechanistic complexity. Children as young as 4 and 5 years old used machines'…

Molecular Nanotechnology and Designs of Future

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

Reviewing the status of current approaches and future projections, as already published in the scientific journals and books, the talk will summarize the direction in which computational and experimental molecular nanotechnologies are progressing. Examples of nanotechnological approach to the concepts of design and simulation of atomically precise materials in a variety of interdisciplinary areas will be presented. The concepts of hypothetical molecular machines and assemblers as explained in Drexler's and Merckle's already published work and Han et. al's WWW distributed molecular gears will be explained.

DNA Bipedal Motor Achieves a Large Number of Steps Due to Operation Using Microfluidics-Based Interface.

PubMed

Tomov, Toma E; Tsukanov, Roman; Glick, Yair; Berger, Yaron; Liber, Miran; Avrahami, Dorit; Gerber, Doron; Nir, Eyal

2017-04-25

Realization of bioinspired molecular machines that can perform many and diverse operations in response to external chemical commands is a major goal in nanotechnology, but current molecular machines respond to only a few sequential commands. Lack of effective methods for introduction and removal of command compounds and low efficiencies of the reactions involved are major reasons for the limited performance. We introduce here a user interface based on a microfluidics device and single-molecule fluorescence spectroscopy that allows efficient introduction and removal of chemical commands and enables detailed study of the reaction mechanisms involved in the operation of synthetic molecular machines. The microfluidics provided 64 consecutive DNA strand commands to a DNA-based motor system immobilized inside the microfluidics, driving a bipedal walker to perform 32 steps on a DNA origami track. The microfluidics enabled removal of redundant strands, resulting in a 6-fold increase in processivity relative to an identical motor operated without strand removal and significantly more operations than previously reported for user-controlled DNA nanomachines. In the motor operated without strand removal, redundant strands interfere with motor operation and reduce its performance. The microfluidics also enabled computer control of motor direction and speed. Furthermore, analysis of the reaction kinetics and motor performance in the absence of redundant strands, made possible by the microfluidics, enabled accurate modeling of the walker processivity. This enabled identification of dynamic boundaries and provided an explanation, based on the "trap state" mechanism, for why the motor did not perform an even larger number of steps. This understanding is very important for the development of future motors with significantly improved performance. Our universal interface enables two-way communication between user and molecular machine and, relying on concepts similar to that of solid-phase synthesis, removes limitations on the number of external stimuli. This interface, therefore, is an important step toward realization of reliable, processive, reproducible, and useful externally controlled DNA nanomachines.

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity.

PubMed

Papaleo, Elena

2015-01-01

In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

Complementary uses of small angle X-ray scattering and X-ray crystallography.

PubMed

Pillon, Monica C; Guarné, Alba

2017-11-01

Most proteins function within networks and, therefore, protein interactions are central to protein function. Although stable macromolecular machines have been extensively studied, dynamic protein interactions remain poorly understood. Small-angle X-ray scattering probes the size, shape and dynamics of proteins in solution at low resolution and can be used to study samples in a large range of molecular weights. Therefore, it has emerged as a powerful technique to study the structure and dynamics of biomolecular systems and bridge fragmented information obtained using high-resolution techniques. Here we review how small-angle X-ray scattering can be combined with other structural biology techniques to study protein dynamics. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman. Copyright © 2017 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liphardt, Jan

In April 1953, Watson and Crick largely defined the program of 20th century biology: obtaining the blueprint of life encoded in the DNA. Fifty years later, in 2003, the sequencing of the human genome was completed. Like any major scientific breakthrough, the sequencing of the human genome raised many more questions than it answered. I'll brief you on some of the big open problems in cell and developmental biology, and I'll explain why approaches, tools, and ideas from the physical sciences are currently reshaping biological research. Super-resolution light microscopies are revealing the intricate spatial organization of cells, single-molecule methods showmore » how molecular machines function, and new probes are clarifying the role of mechanical forces in cell and tissue function. At the same time, Physics stands to gain beautiful new problems in soft condensed matter, quantum mechanics, and non-equilibrium thermodynamics.« less

The Intersection of Physics and Biology

ScienceCinema

Liphardt, Jan

2017-12-22

In April 1953, Watson and Crick largely defined the program of 20th century biology: obtaining the blueprint of life encoded in the DNA. Fifty years later, in 2003, the sequencing of the human genome was completed. Like any major scientific breakthrough, the sequencing of the human genome raised many more questions than it answered. I'll brief you on some of the big open problems in cell and developmental biology, and I'll explain why approaches, tools, and ideas from the physical sciences are currently reshaping biological research. Super-resolution light microscopies are revealing the intricate spatial organization of cells, single-molecule methods show how molecular machines function, and new probes are clarifying the role of mechanical forces in cell and tissue function. At the same time, Physics stands to gain beautiful new problems in soft condensed matter, quantum mechanics, and non-equilibrium thermodynamics.

Origin of acoustic emission produced during single point machining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heiple, C.R,.; Carpenter, S.H.; Armentrout, D.L.

1991-01-01

Acoustic emission was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Acoustic emission produced during tensile and compressive deformation of these alloys has been previously characterized as a function of heat treatment. Heat treatments which increase the strength of 4340 steel increase the amount of acoustic emission produced during deformation, while heat treatments which increase the strength of Ti-6Al-4V decrease the amount of acoustic emission produced during deformation. If chip deformation were the primary source of acoustic emission during single point machining, then opposite trends in the level of acoustic emissionmore » produced during machining as a function of material strength would be expected for these two alloys. Trends in rms acoustic emission level with increasing strength were similar for both alloys, demonstrating that chip deformation is not a major source of acoustic emission in single point machining. Acoustic emission has also been monitored as a function of machining parameters on 6061-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, lead, and teflon. The data suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of acoustic emission. Changes in acoustic emission with tool wear were strongly material dependent. 21 refs., 19 figs., 4 tabs.« less

Single-Molecule Real-Time 3D Imaging of the Transcription Cycle by Modulation Interferometry.

PubMed

Wang, Guanshi; Hauver, Jesse; Thomas, Zachary; Darst, Seth A; Pertsinidis, Alexandros

2016-12-15

Many essential cellular processes, such as gene control, employ elaborate mechanisms involving the coordination of large, multi-component molecular assemblies. Few structural biology tools presently have the combined spatial-temporal resolution and molecular specificity required to capture the movement, conformational changes, and subunit association-dissociation kinetics, three fundamental elements of how such intricate molecular machines work. Here, we report a 3D single-molecule super-resolution imaging study using modulation interferometry and phase-sensitive detection that achieves <2 nm axial localization precision, well below the few-nanometer-sized individual protein components. To illustrate the capability of this technique in probing the dynamics of complex macromolecular machines, we visualize the movement of individual multi-subunit E. coli RNA polymerases through the complete transcription cycle, dissect the kinetics of the initiation-elongation transition, and determine the fate of σ 70 initiation factors during promoter escape. Modulation interferometry sets the stage for single-molecule studies of several hitherto difficult-to-investigate multi-molecular transactions that underlie genome regulation. Copyright © 2016 Elsevier Inc. All rights reserved.

The Harvard Clean Energy Project: High-throughput screening of organic photovoltaic materials using cheminformatics, machine learning, and pattern recognition

NASA Astrophysics Data System (ADS)

Olivares-Amaya, Roberto; Hachmann, Johannes; Amador-Bedolla, Carlos; Daly, Aidan; Jinich, Adrian; Atahan-Evrenk, Sule; Boixo, Sergio; Aspuru-Guzik, Alán

2012-02-01

Organic photovoltaic devices have emerged as competitors to silicon-based solar cells, currently reaching efficiencies of over 9% and offering desirable properties for manufacturing and installation. We study conjugated donor polymers for high-efficiency bulk-heterojunction photovoltaic devices with a molecular library motivated by experimental feasibility. We use quantum mechanics and a distributed computing approach to explore this vast molecular space. We will detail the screening approach starting from the generation of the molecular library, which can be easily extended to other kinds of molecular systems. We will describe the screening method for these materials which ranges from descriptor models, ubiquitous in the drug discovery community, to eventually reaching first principles quantum chemistry methods. We will present results on the statistical analysis, based principally on machine learning, specifically partial least squares and Gaussian processes. Alongside, clustering methods and the use of the hypergeometric distribution reveal moieties important for the donor materials and allow us to quantify structure-property relationships. These efforts enable us to accelerate materials discovery in organic photovoltaics through our collaboration with experimental groups.

Legionellosis Outbreak Associated with Asphalt Paving Machine, Spain, 2009

PubMed Central

Fenollar, José; Escribano, Isabel; González-Candelas, Fernando

2010-01-01

From 1999 through 2005 in Alcoi, Spain, incidence of legionellosis was continually high. Over the next 4 years, incidence was lower, but an increase in July 2009 led health authorities to declare an epidemic outbreak. A molecular epidemiology investigation showed that the allelic profiles for all Legionella pneumophila samples from the 2009 outbreak patients were the same, thus pointing to a common genetic origin for their infections, and that they were identical to that of the organism that had caused the previous outbreaks. Spatial-temporal and sequence-based typing analyses indicated a milling machine used in street asphalt repaving and its water tank as the most likely sources. As opposed to other machines used for street cleaning, the responsible milling machine used water from a natural spring. When the operation of this machine was prohibited and cleaning measures were adopted, infections ceased. PMID:20735921

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

PubMed

Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

2017-12-26

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

Power training using pneumatic machines vs. plate-loaded machines to improve muscle power in older adults.

PubMed

Balachandran, Anoop T; Gandia, Kristine; Jacobs, Kevin A; Streiner, David L; Eltoukhy, Moataz; Signorile, Joseph F

2017-11-01

Power training has been shown to be more effective than conventional resistance training for improving physical function in older adults; however, most trials have used pneumatic machines during training. Considering that the general public typically has access to plate-loaded machines, the effectiveness and safety of power training using plate-loaded machines compared to pneumatic machines is an important consideration. The purpose of this investigation was to compare the effects of high-velocity training using pneumatic machines (Pn) versus standard plate-loaded machines (PL). Independently-living older adults, 60years or older were randomized into two groups: pneumatic machine (Pn, n=19) and plate-loaded machine (PL, n=17). After 12weeks of high-velocity training twice per week, groups were analyzed using an intention-to-treat approach. Primary outcomes were lower body power measured using a linear transducer and upper body power using medicine ball throw. Secondary outcomes included lower and upper body muscle muscle strength, the Physical Performance Battery (PPB), gallon jug test, the timed up-and-go test, and self-reported function using the Patient Reported Outcomes Measurement Information System (PROMIS) and an online video questionnaire. Outcome assessors were blinded to group membership. Lower body power significantly improved in both groups (Pn: 19%, PL: 31%), with no significant difference between the groups (Cohen's d=0.4, 95% CI (-1.1, 0.3)). Upper body power significantly improved only in the PL group, but showed no significant difference between the groups (Pn: 3%, PL: 6%). For balance, there was a significant difference between the groups favoring the Pn group (d=0.7, 95% CI (0.1, 1.4)); however, there were no statistically significant differences between groups for PPB, gallon jug transfer, muscle muscle strength, timed up-and-go or self-reported function. No serious adverse events were reported in either of the groups. Pneumatic and plate-loaded machines were effective in improving lower body power and physical function in older adults. The results suggest that power training can be safely and effectively performed by older adults using either pneumatic or plate-loaded machines. Copyright © 2017 Elsevier Inc. All rights reserved.

Artificial Dipolar Molecular Rotors

NASA Astrophysics Data System (ADS)

Horansky, R. D.; Magnera, T. F.; Price, J. C.; Michl, J.

Rotors are present in almost every macroscopic machine, converting rotational motion into energy of other forms, or converting other forms of energy into rotation. Rotation may be transmitted via belts or gears, converted into linear motion by various linkages, or used to drive propellers to produce fluid motion. Examples of macroscopic rotors include engines which couple to combustible energy sources, windmills which couple to air flows, and most generators of electricity. A key feature of these objects is the presence of a part with rotational freedom relative to a stationary frame. In this chapter we discuss the miniaturization of rotary machines all the way to the molecular scale, where chemical groups form the rotary and stationary parts. For a recent review of molecules with rotary and stationary parts see [1].

50 GFlops molecular dynamics on the Connection Machine 5

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lomdahl, P.S.; Tamayo, P.; Groenbech-Jensen, N.

1993-12-31

The authors present timings and performance numbers for a new short range three dimensional (3D) molecular dynamics (MD) code, SPaSM, on the Connection Machine-5 (CM-5). They demonstrate that runs with more than 10{sup 8} particles are now possible on massively parallel MIMD computers. To the best of their knowledge this is at least an order of magnitude more particles than what has previously been reported. Typical production runs show sustained performance (including communication) in the range of 47--50 GFlops on a 1024 node CM-5 with vector units (VUs). The speed of the code scales linearly with the number of processorsmore » and with the number of particles and shows 95% parallel efficiency in the speedup.« less

Implementation of the force decomposition machine for molecular dynamics simulations.

PubMed

Borštnik, Urban; Miller, Benjamin T; Brooks, Bernard R; Janežič, Dušanka

2012-09-01

We present the design and implementation of the force decomposition machine (FDM), a cluster of personal computers (PCs) that is tailored to running molecular dynamics (MD) simulations using the distributed diagonal force decomposition (DDFD) parallelization method. The cluster interconnect architecture is optimized for the communication pattern of the DDFD method. Our implementation of the FDM relies on standard commodity components even for networking. Although the cluster is meant for DDFD MD simulations, it remains general enough for other parallel computations. An analysis of several MD simulation runs on both the FDM and a standard PC cluster demonstrates that the FDM's interconnect architecture provides a greater performance compared to a more general cluster interconnect. Copyright © 2012 Elsevier Inc. All rights reserved.

Steps in the bacterial flagellar motor.

PubMed

Mora, Thierry; Yu, Howard; Sowa, Yoshiyuki; Wingreen, Ned S

2009-10-01

The bacterial flagellar motor is a highly efficient rotary machine used by many bacteria to propel themselves. It has recently been shown that at low speeds its rotation proceeds in steps. Here we propose a simple physical model, based on the storage of energy in protein springs, that accounts for this stepping behavior as a random walk in a tilted corrugated potential that combines torque and contact forces. We argue that the absolute angular position of the rotor is crucial for understanding step properties and show this hypothesis to be consistent with the available data, in particular the observation that backward steps are smaller on average than forward steps. We also predict a sublinear speed versus torque relationship for fixed load at low torque, and a peak in rotor diffusion as a function of torque. Our model provides a comprehensive framework for understanding and analyzing stepping behavior in the bacterial flagellar motor and proposes novel, testable predictions. More broadly, the storage of energy in protein springs by the flagellar motor may provide useful general insights into the design of highly efficient molecular machines.

Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions.

PubMed

Li, Yang; Yang, Jianyi

2017-04-24

The prediction of protein-ligand binding affinity has recently been improved remarkably by machine-learning-based scoring functions. For example, using a set of simple descriptors representing the atomic distance counts, the RF-Score improves the Pearson correlation coefficient to about 0.8 on the core set of the PDBbind 2007 database, which is significantly higher than the performance of any conventional scoring function on the same benchmark. A few studies have been made to discuss the performance of machine-learning-based methods, but the reason for this improvement remains unclear. In this study, by systemically controlling the structural and sequence similarity between the training and test proteins of the PDBbind benchmark, we demonstrate that protein structural and sequence similarity makes a significant impact on machine-learning-based methods. After removal of training proteins that are highly similar to the test proteins identified by structure alignment and sequence alignment, machine-learning-based methods trained on the new training sets do not outperform the conventional scoring functions any more. On the contrary, the performance of conventional functions like X-Score is relatively stable no matter what training data are used to fit the weights of its energy terms.

Confinement of Aggregation-Induced Emission Molecular Rotors in Ultrathin Two-Dimensional Porous Organic Nanosheets for Enhanced Molecular Recognition.

PubMed

Dong, Jinqiao; Li, Xu; Zhang, Kang; Di Yuan, Yi; Wang, Yuxiang; Zhai, Linzhi; Liu, Guoliang; Yuan, Daqiang; Jiang, Jianwen; Zhao, Dan

2018-03-21

Despite the rapid development of molecular rotors over the past decade, it still remains a huge challenge to understand their confined behavior in ultrathin two-dimensional (2D) nanomaterials for molecular recognition. Here, we report an all-carbon, 2D π-conjugated aromatic polymer, named NUS-25, containing flexible tetraphenylethylene (TPE) units as aggregation-induced emission (AIE) molecular rotors. NUS-25 bulk powder can be easily exfoliated into micrometer-sized lamellar freestanding nanosheets with a thickness of 2-5 nm. The dynamic behavior of the TPE rotors is partially restricted through noncovalent interactions in the ultrathin 2D nanosheets, which is proved by comparative experimental studies including AIE characteristics, size-selective molecular recognition, and theoretical calculations of rotary energy barrier. Because of the partially restricted TPE rotors, NUS-25 nanosheets are highly fluorescent. This property allows NUS-25 nanosheets to be used as a chemical sensor for the specific detection of acenaphthylene among a series of polycyclic aromatic hydrocarbons (PAHs) via fluorescent quenching mechanism. Further investigations show that NUS-25 nanosheets have much higher sensitivity and selectivity than their stacked bulk powder and other similar polymers containing dynamic TPE rotors. The highly efficient molecular recognition can be attributed to the photoinduced electron transfer (PET) from NUS-25 nanosheets to acenaphthylene, which is investigated by time-resolved photoluminescence measurements (TRPL), excitation and emission spectra, and density functional theory (DFT) calculations. Our findings demonstrate that confinement of AIE molecular rotors in 2D nanomaterials can enhance the molecular recognition. We anticipate that the material design strategy demonstrated in this study will inspire the development of other ultrathin 2D nanomaterials equipped with smart molecular machines for various applications.

Future developments in brain-machine interface research

PubMed Central

Lebedev, Mikhail A; Tate, Andrew J; Hanson, Timothy L; Li, Zheng; O'Doherty, Joseph E; Winans, Jesse A; Ifft, Peter J; Zhuang, Katie Z; Fitzsimmons, Nathan A; Schwarz, David A; Fuller, Andrew M; An, Je Hi; Nicolelis, Miguel A L

2011-01-01

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition. PMID:21779720

Anaesthesia machine: checklist, hazards, scavenging.

PubMed

Goneppanavar, Umesh; Prabhu, Manjunath

2013-09-01

From a simple pneumatic device of the early 20(th) century, the anaesthesia machine has evolved to incorporate various mechanical, electrical and electronic components to be more appropriately called anaesthesia workstation. Modern machines have overcome many drawbacks associated with the older machines. However, addition of several mechanical, electronic and electric components has contributed to recurrence of some of the older problems such as leak or obstruction attributable to newer gadgets and development of newer problems. No single checklist can satisfactorily test the integrity and safety of all existing anaesthesia machines due to their complex nature as well as variations in design among manufacturers. Human factors have contributed to greater complications than machine faults. Therefore, better understanding of the basics of anaesthesia machine and checking each component of the machine for proper functioning prior to use is essential to minimise these hazards. Clear documentation of regular and appropriate servicing of the anaesthesia machine, its components and their satisfactory functioning following servicing and repair is also equally important. Trace anaesthetic gases polluting the theatre atmosphere can have several adverse effects on the health of theatre personnel. Therefore, safe disposal of these gases away from the workplace with efficiently functioning scavenging system is necessary. Other ways of minimising atmospheric pollution such as gas delivery equipment with negligible leaks, low flow anaesthesia, minimal leak around the airway equipment (facemask, tracheal tube, laryngeal mask airway, etc.) more than 15 air changes/hour and total intravenous anaesthesia should also be considered.

Anaesthesia Machine: Checklist, Hazards, Scavenging

PubMed Central

Goneppanavar, Umesh; Prabhu, Manjunath

2013-01-01

From a simple pneumatic device of the early 20th century, the anaesthesia machine has evolved to incorporate various mechanical, electrical and electronic components to be more appropriately called anaesthesia workstation. Modern machines have overcome many drawbacks associated with the older machines. However, addition of several mechanical, electronic and electric components has contributed to recurrence of some of the older problems such as leak or obstruction attributable to newer gadgets and development of newer problems. No single checklist can satisfactorily test the integrity and safety of all existing anaesthesia machines due to their complex nature as well as variations in design among manufacturers. Human factors have contributed to greater complications than machine faults. Therefore, better understanding of the basics of anaesthesia machine and checking each component of the machine for proper functioning prior to use is essential to minimise these hazards. Clear documentation of regular and appropriate servicing of the anaesthesia machine, its components and their satisfactory functioning following servicing and repair is also equally important. Trace anaesthetic gases polluting the theatre atmosphere can have several adverse effects on the health of theatre personnel. Therefore, safe disposal of these gases away from the workplace with efficiently functioning scavenging system is necessary. Other ways of minimising atmospheric pollution such as gas delivery equipment with negligible leaks, low flow anaesthesia, minimal leak around the airway equipment (facemask, tracheal tube, laryngeal mask airway, etc.) more than 15 air changes/hour and total intravenous anaesthesia should also be considered. PMID:24249887

Fault detection in rotating machines with beamforming: Spatial visualization of diagnosis features

NASA Astrophysics Data System (ADS)

Cardenas Cabada, E.; Leclere, Q.; Antoni, J.; Hamzaoui, N.

2017-12-01

Rotating machines diagnosis is conventionally related to vibration analysis. Sensors are usually placed on the machine to gather information about its components. The recorded signals are then processed through a fault detection algorithm allowing the identification of the failing part. This paper proposes an acoustic-based diagnosis method. A microphone array is used to record the acoustic field radiated by the machine. The main advantage over vibration-based diagnosis is that the contact between the sensors and the machine is no longer required. Moreover, the application of acoustic imaging makes possible the identification of the sources of acoustic radiation on the machine surface. The display of information is then spatially continuous while the accelerometers only give it discrete. Beamforming provides the time-varying signals radiated by the machine as a function of space. Any fault detection tool can be applied to the beamforming output. Spectral kurtosis, which highlights the impulsiveness of a signal as function of frequency, is used in this study. The combination of spectral kurtosis with acoustic imaging makes possible the mapping of the impulsiveness as a function of space and frequency. The efficiency of this approach lays on the source separation in the spatial and frequency domains. These mappings make possible the localization of such impulsive sources. The faulty components of the machine have an impulsive behavior and thus will be highlighted on the mappings. The study presents experimental validations of the method on rotating machines.

Clustering the Orion B giant molecular cloud based on its molecular emission

NASA Astrophysics Data System (ADS)

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H.; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R.; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

Context. Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). Aims: We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. Methods: We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional probability density function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. Results: A clustering analysis based only on the J = 1-0 lines of three isotopologues of CO proves sufficient to reveal distinct density/column density regimes (nH 100 cm-3, 500 cm-3, and >1000 cm-3), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1-0 line of HCO+ and the N = 1-0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO+ and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO+ intensity ratio in UV-illuminated regions. Finer distinctions in density classes (nH 7 × 103 cm-3, 4 × 104 cm-3) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO+ (1-0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Conclusions: Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers. Data products associated with this paper are available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/610/A12 and at http://www.iram.fr/ pety/ORION-B

Functional assignment to JEV proteins using SVM.

PubMed

Sahoo, Ganesh Chandra; Dikhit, Manas Ranjan; Das, Pradeep

2008-01-01

Identification of different protein functions facilitates a mechanistic understanding of Japanese encephalitis virus (JEV) infection and opens novel means for drug development. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to Japanese encephalitis virus protein. Our study from SVMProt and available JE virus sequences suggests that structural and nonstructural proteins of JEV genome possibly belong to diverse protein functions, are expected to occur in the life cycle of JE virus. Protein functions common to both structural and non-structural proteins are iron-binding, metal-binding, lipid-binding, copper-binding, transmembrane, outer membrane, channels/Pores - Pore-forming toxins (proteins and peptides) group of proteins. Non-structural proteins perform functions like actin binding, zinc-binding, calcium-binding, hydrolases, Carbon-Oxygen Lyases, P-type ATPase, proteins belonging to major facilitator family (MFS), secreting main terminal branch (MTB) family, phosphotransfer-driven group translocators and ATP-binding cassette (ABC) family group of proteins. Whereas structural proteins besides belonging to same structural group of proteins (capsid, structural, envelope), they also perform functions like nuclear receptor, antibiotic resistance, RNA-binding, DNA-binding, magnesium-binding, isomerase (intra-molecular), oxidoreductase and participate in type II (general) secretory pathway (IISP).

Functional assignment to JEV proteins using SVM

PubMed Central

Sahoo, Ganesh Chandra; Dikhit, Manas Ranjan; Das, Pradeep

2008-01-01

Identification of different protein functions facilitates a mechanistic understanding of Japanese encephalitis virus (JEV) infection and opens novel means for drug development. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to Japanese encephalitis virus protein. Our study from SVMProt and available JE virus sequences suggests that structural and nonstructural proteins of JEV genome possibly belong to diverse protein functions, are expected to occur in the life cycle of JE virus. Protein functions common to both structural and non-structural proteins are iron-binding, metal-binding, lipid-binding, copper-binding, transmembrane, outer membrane, channels/Pores - Pore-forming toxins (proteins and peptides) group of proteins. Non-structural proteins perform functions like actin binding, zinc-binding, calcium-binding, hydrolases, Carbon-Oxygen Lyases, P-type ATPase, proteins belonging to major facilitator family (MFS), secreting main terminal branch (MTB) family, phosphotransfer-driven group translocators and ATP-binding cassette (ABC) family group of proteins. Whereas structural proteins besides belonging to same structural group of proteins (capsid, structural, envelope), they also perform functions like nuclear receptor, antibiotic resistance, RNA-binding, DNA-binding, magnesium-binding, isomerase (intra-molecular), oxidoreductase and participate in type II (general) secretory pathway (IISP). PMID:19052658

Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase.

PubMed

Singh, Nidhi; Shah, Priyanka; Dwivedi, Hemlata; Mishra, Shikha; Tripathi, Renu; Sahasrabuddhe, Amogh A; Siddiqi, Mohammad Imran

2016-11-15

N-Myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking substrate proteins to membrane locations, stabilization and protein-protein interactions. It is a studied and validated pre-clinical drug target for fungal and parasitic infections. In the present study, a machine learning approach, docking studies and CoMFA analysis have been integrated with the objective of translation of knowledge into a pipelined workflow towards the identification of putative hits through the screening of large compound libraries. In the proposed pipeline, the reported parasitic NMT inhibitors have been used to develop predictive machine learning classification models. Simultaneously, a TbNMT complex model was generated to establish the relationship between the binding mode of the inhibitors for LmNMT and TbNMT through molecular dynamics simulation studies. A 3D-QSAR model was developed and used to predict the activity of the proposed hits in the subsequent step. The hits classified as active based on the machine learning model were assessed as the potential anti-trypanosomal NMT inhibitors through molecular docking studies, predicted activity using a QSAR model and visual inspection. In the final step, the proposed pipeline was validated through in vitro experiments. A total of seven hits have been proposed and tested in vitro for evaluation of dual inhibitory activity against Leishmania donovani and Trypanosoma brucei. Out of these five compounds showed significant inhibition against both of the organisms. The common topmost active compound SEW04173 belongs to a pyrazole carboxylate scaffold and is anticipated to enrich the chemical space with enhanced potency through optimization.

Energy-free machine learning force field for aluminum.

PubMed

Kruglov, Ivan; Sergeev, Oleg; Yanilkin, Alexey; Oganov, Artem R

2017-08-17

We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The method was applied to crystalline and liquid aluminum and uranium at different temperatures and densities, and showed the highest accuracy among different published potentials. Phonon density of states, entropy and melting temperature of aluminum were calculated using this machine learning potential. The results are in excellent agreement with experimental data and results of full ab initio calculations.

Computational Nanotechnology of Molecular Materials, Electronics and Machines

NASA Technical Reports Server (NTRS)

Srivastava, D.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

This viewgraph presentation covers carbon nanotubes, their characteristics, and their potential future applications. The presentation include predictions on the development of nanostructures and their applications, the thermal characteristics of carbon nanotubes, mechano-chemical effects upon carbon nanotubes, molecular electronics, and models for possible future nanostructure devices. The presentation also proposes a neural model for signal processing.

Looking west at Machine Shop (Bldg. 163) south bay interior. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Looking west at Machine Shop (Bldg. 163) south bay interior. Note the Shaw 15-ton bridge crane. This portion of the building housed machine tools and locomotive component repair functions that supported the erecting shop operations - Atchison, Topeka, Santa Fe Railroad, Albuquerque Shops, Machine Shop, 908 Second Street, Southwest, Albuquerque, Bernalillo County, NM

Characteristics for electrochemical machining with nanoscale voltage pulses.

PubMed

Lee, E S; Back, S Y; Lee, J T

2009-06-01

Electrochemical machining has traditionally been used in highly specialized fields, such as those of the aerospace and defense industries. It is now increasingly being applied in other industries, where parts with difficult-to-cut material, complex geometry and tribology, and devices of nanoscale and microscale are required. Electric characteristic plays a principal function role in and chemical characteristic plays an assistant function role in electrochemical machining. Therefore, essential parameters in electrochemical machining can be described current density, machining time, inter-electrode gap size, electrolyte, electrode shape etc. Electrochemical machining provides an economical and effective method for machining high strength, high tension and heat-resistant materials into complex shapes such as turbine blades of titanium and aluminum alloys. The application of nanoscale voltage pulses between a tool electrode and a workpiece in an electrochemical environment allows the three-dimensional machining of conducting materials with sub-micrometer precision. In this study, micro probe are developed by electrochemical etching and micro holes are manufactured using these micro probe as tool electrodes. Micro holes and microgroove can be accurately achieved by using nanoscale voltages pulses.

An experimental study of the putative mechanism of a synthetic autonomous rotary DNA nanomotor

NASA Astrophysics Data System (ADS)

Dunn, K. E.; Leake, M. C.; Wollman, A. J. M.; Trefzer, M. A.; Johnson, S.; Tyrrell, A. M.

2017-03-01

DNA has been used to construct a wide variety of nanoscale molecular devices. Inspiration for such synthetic molecular machines is frequently drawn from protein motors, which are naturally occurring and ubiquitous. However, despite the fact that rotary motors such as ATP synthase and the bacterial flagellar motor play extremely important roles in nature, very few rotary devices have been constructed using DNA. This paper describes an experimental study of the putative mechanism of a rotary DNA nanomotor, which is based on strand displacement, the phenomenon that powers many synthetic linear DNA motors. Unlike other examples of rotary DNA machines, the device described here is designed to be capable of autonomous operation after it is triggered. The experimental results are consistent with operation of the motor as expected, and future work on an enhanced motor design may allow rotation to be observed at the single-molecule level. The rotary motor concept presented here has potential applications in molecular processing, DNA computing, biosensing and photonics.

Teaching about the U.S. Constitution through Metaphor: Government as a Machine.

ERIC Educational Resources Information Center

Mills, Randy K.

1988-01-01

Briefly reviews theories of brain hemisphere functions and draws implications for social studies instruction. Maintains that the metaphor aids the development of understanding because it connects right and left brain functions. Provides a learning activity based on the metaphor of the U.S. government functioning as a machine. (BSR)

Implementing Molecular Dynamics for Hybrid High Performance Computers - 1. Short Range Forces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, W Michael; Wang, Peng; Plimpton, Steven J

The use of accelerators such as general-purpose graphics processing units (GPGPUs) have become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power requirements. Hybrid high performance computers, machines with more than one type of floating-point processor, are now becoming more prevalent due to these advantages. In this work, we discuss several important issues in porting a large molecular dynamics code for use on parallel hybrid machines - 1) choosing a hybrid parallel decomposition that works on central processing units (CPUs) with distributed memory and accelerator cores with shared memory,more » 2) minimizing the amount of code that must be ported for efficient acceleration, 3) utilizing the available processing power from both many-core CPUs and accelerators, and 4) choosing a programming model for acceleration. We present our solution to each of these issues for short-range force calculation in the molecular dynamics package LAMMPS. We describe algorithms for efficient short range force calculation on hybrid high performance machines. We describe a new approach for dynamic load balancing of work between CPU and accelerator cores. We describe the Geryon library that allows a single code to compile with both CUDA and OpenCL for use on a variety of accelerators. Finally, we present results on a parallel test cluster containing 32 Fermi GPGPUs and 180 CPU cores.« less

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems. PMID:28097128

RNA Structures as Mediators of Neurological Diseases and as Drug Targets.

PubMed

Bernat, Viachaslau; Disney, Matthew D

2015-07-01

RNAs adopt diverse folded structures that are essential for function and thus play critical roles in cellular biology. A striking example of this is the ribosome, a complex, three-dimensionally folded macromolecular machine that orchestrates protein synthesis. Advances in RNA biochemistry, structural and molecular biology, and bioinformatics have revealed other non-coding RNAs whose functions are dictated by their structure. It is not surprising that aberrantly folded RNA structures contribute to disease. In this Review, we provide a brief introduction into RNA structural biology and then describe how RNA structures function in cells and cause or contribute to neurological disease. Finally, we highlight successful applications of rational design principles to provide chemical probes and lead compounds targeting structured RNAs. Based on several examples of well-characterized RNA-driven neurological disorders, we demonstrate how designed small molecules can facilitate the study of RNA dysfunction, elucidating previously unknown roles for RNA in disease, and provide lead therapeutics. Copyright © 2015 Elsevier Inc. All rights reserved.

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System

PubMed Central

Adelson, David; Brown, Fred; Chaudhri, Naeem

2017-01-01

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice. PMID:28812013

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System.

PubMed

Banjar, Haneen; Adelson, David; Brown, Fred; Chaudhri, Naeem

2017-01-01

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.

Mechanical transduction via a single soft polymer

NASA Astrophysics Data System (ADS)

Hou, Ruizheng; Wang, Nan; Bao, Weizhu; Wang, Zhisong

2018-04-01

Molecular machines from biology and nanotechnology often depend on soft structures to perform mechanical functions, but the underlying mechanisms and advantages or disadvantages over rigid structures are not fully understood. We report here a rigorous study of mechanical transduction along a single soft polymer based on exact solutions to the realistic three-dimensional wormlike-chain model and augmented with analytical relations derived from simpler polymer models. The results reveal surprisingly that a soft polymer with vanishingly small persistence length below a single chemical bond still transduces biased displacement and mechanical work up to practically significant amounts. This "soft" approach possesses unique advantages over the conventional wisdom of rigidity-based transduction, and potentially leads to a unified mechanism for effective allosterylike transduction and relay of mechanical actions, information, control, and molecules from one position to another in molecular devices and motors. This study also identifies an entropy limit unique to the soft transduction, and thereby suggests a possibility of detecting higher efficiency for kinesin motor and mutants in future experiments.

Effect of Ring Strain on the Charge Transport of a Robust Norbornadiene–Quadricyclane-Based Molecular Photoswitch

PubMed Central

2017-01-01

Integrating functional molecules into single-molecule devices is a key step toward the realization of future computing machines based on the smallest possible components. In this context, photoswitching molecules that can make a transition between high and low conductivity in response to light are attractive candidates. Here we present the synthesis and conductance properties of a new type of robust molecular photothermal switch based on the norbornadiene (NB)–quadricyclane (QC) system. The transport through the molecule in the ON state is dominated by a pathway through the π-conjugated system, which is no longer available when the system is switched to the OFF state. Interestingly, in the OFF state we find that the same pathway contributes only 12% to the transport properties. We attribute this observation to the strained tetrahedral geometry of the QC. These results challenge the prevailing assumption that current will simply flow through the shortest through-bond path in a molecule. PMID:28408968

Avatar DNA Nanohybrid System in Chip-on-a-Phone

NASA Astrophysics Data System (ADS)

Park, Dae-Hwan; Han, Chang Jo; Shul, Yong-Gun; Choy, Jin-Ho

2014-05-01

Long admired for informational role and recognition function in multidisciplinary science, DNA nanohybrids have been emerging as ideal materials for molecular nanotechnology and genetic information code. Here, we designed an optical machine-readable DNA icon on microarray, Avatar DNA, for automatic identification and data capture such as Quick Response and ColorZip codes. Avatar icon is made of telepathic DNA-DNA hybrids inscribed on chips, which can be identified by camera of smartphone with application software. Information encoded in base-sequences can be accessed by connecting an off-line icon to an on-line web-server network to provide message, index, or URL from database library. Avatar DNA is then converged with nano-bio-info-cogno science: each building block stands for inorganic nanosheets, nucleotides, digits, and pixels. This convergence could address item-level identification that strengthens supply-chain security for drug counterfeits. It can, therefore, provide molecular-level vision through mobile network to coordinate and integrate data management channels for visual detection and recording.

Resonant antenna probes for tip-enhanced infrared near-field microscopy.

PubMed

Huth, Florian; Chuvilin, Andrey; Schnell, Martin; Amenabar, Iban; Krutokhvostov, Roman; Lopatin, Sergei; Hillenbrand, Rainer

2013-03-13

We report the development of infrared-resonant antenna probes for tip-enhanced optical microscopy. We employ focused-ion-beam machining to fabricate high-aspect ratio gold cones, which replace the standard tip of a commercial Si-based atomic force microscopy cantilever. Calculations show large field enhancements at the tip apex due to geometrical antenna resonances in the cones, which can be precisely tuned throughout a broad spectral range from visible to terahertz frequencies by adjusting the cone length. Spectroscopic analysis of these probes by electron energy loss spectroscopy, Fourier transform infrared spectroscopy, and Fourier transform infrared near-field spectroscopy corroborates their functionality as resonant antennas and verifies the broad tunability. By employing the novel probes in a scattering-type near-field microscope and imaging a single tobacco mosaic virus (TMV), we experimentally demonstrate high-performance mid-infrared nanoimaging of molecular absorption. Our probes offer excellent perspectives for optical nanoimaging and nanospectroscopy, pushing the detection and resolution limits in many applications, including nanoscale infrared mapping of organic, molecular, and biological materials, nanocomposites, or nanodevices.

In Silico Investigation of Traditional Chinese Medicine Compounds to Inhibit Human Histone Deacetylase 2 for Patients with Alzheimer's Disease

PubMed Central

Hung, Tzu-Chieh; Lee, Wen-Yuan; Chen, Kuen-Bao; Chan, Yueh-Chiu; Lee, Cheng-Chun

2014-01-01

Human histone deacetylase 2 (HDAC2) has been identified as being associated with Alzheimer's disease (AD), a neuropathic degenerative disease. In this study, we screen the world's largest Traditional Chinese Medicine (TCM) database for natural compounds that may be useful as lead compounds in the search for inhibitors of HDAC2 function. The technique of molecular docking was employed to select the ten top TCM candidates. We used three prediction models, multiple linear regression (MLR), support vector machine (SVM), and the Bayes network toolbox (BNT), to predict the bioactivity of the TCM candidates. Molecular dynamics simulation provides the protein-ligand interactions of compounds. The bioactivity predictions of pIC50 values suggest that the TCM candidatesm, (−)-Bontl ferulate, monomethylcurcumin, and ningposides C, have a greater effect on HDAC2 inhibition. The structure variation caused by the hydrogen bonds and hydrophobic interactions between protein-ligand interactions indicates that these compounds have an inhibitory effect on the protein. PMID:25045700

Genome-wide mutant profiling predicts the mechanism of a Lipid II binding antibiotic.

PubMed

Santiago, Marina; Lee, Wonsik; Fayad, Antoine Abou; Coe, Kathryn A; Rajagopal, Mithila; Do, Truc; Hennessen, Fabienne; Srisuknimit, Veerasak; Müller, Rolf; Meredith, Timothy C; Walker, Suzanne

2018-06-01

Identifying targets of antibacterial compounds remains a challenging step in the development of antibiotics. We have developed a two-pronged functional genomics approach to predict mechanism of action that uses mutant fitness data from antibiotic-treated transposon libraries containing both upregulation and inactivation mutants. We treated a Staphylococcus aureus transposon library containing 690,000 unique insertions with 32 antibiotics. Upregulation signatures identified from directional biases in insertions revealed known molecular targets and resistance mechanisms for the majority of these. Because single-gene upregulation does not always confer resistance, we used a complementary machine-learning approach to predict the mechanism from inactivation mutant fitness profiles. This approach suggested the cell wall precursor Lipid II as the molecular target of the lysocins, a mechanism we have confirmed. We conclude that docking to membrane-anchored Lipid II precedes the selective bacteriolysis that distinguishes these lytic natural products, showing the utility of our approach for nominating the antibiotic mechanism of action.

Avatar DNA Nanohybrid System in Chip-on-a-Phone

PubMed Central

Park, Dae-Hwan; Han, Chang Jo; Shul, Yong-Gun; Choy, Jin-Ho

2014-01-01

Long admired for informational role and recognition function in multidisciplinary science, DNA nanohybrids have been emerging as ideal materials for molecular nanotechnology and genetic information code. Here, we designed an optical machine-readable DNA icon on microarray, Avatar DNA, for automatic identification and data capture such as Quick Response and ColorZip codes. Avatar icon is made of telepathic DNA-DNA hybrids inscribed on chips, which can be identified by camera of smartphone with application software. Information encoded in base-sequences can be accessed by connecting an off-line icon to an on-line web-server network to provide message, index, or URL from database library. Avatar DNA is then converged with nano-bio-info-cogno science: each building block stands for inorganic nanosheets, nucleotides, digits, and pixels. This convergence could address item-level identification that strengthens supply-chain security for drug counterfeits. It can, therefore, provide molecular-level vision through mobile network to coordinate and integrate data management channels for visual detection and recording. PMID:24824876

Automatic Selection of Order Parameters in the Analysis of Large Scale Molecular Dynamics Simulations.

PubMed

Sultan, Mohammad M; Kiss, Gert; Shukla, Diwakar; Pande, Vijay S

2014-12-09

Given the large number of crystal structures and NMR ensembles that have been solved to date, classical molecular dynamics (MD) simulations have become powerful tools in the atomistic study of the kinetics and thermodynamics of biomolecular systems on ever increasing time scales. By virtue of the high-dimensional conformational state space that is explored, the interpretation of large-scale simulations faces difficulties not unlike those in the big data community. We address this challenge by introducing a method called clustering based feature selection (CB-FS) that employs a posterior analysis approach. It combines supervised machine learning (SML) and feature selection with Markov state models to automatically identify the relevant degrees of freedom that separate conformational states. We highlight the utility of the method in the evaluation of large-scale simulations and show that it can be used for the rapid and automated identification of relevant order parameters involved in the functional transitions of two exemplary cell-signaling proteins central to human disease states.

The Transporter Classification Database: recent advances.

PubMed

Saier, Milton H; Yen, Ming Ren; Noto, Keith; Tamang, Dorjee G; Elkan, Charles

2009-01-01

The Transporter Classification Database (TCDB), freely accessible at http://www.tcdb.org, is a relational database containing sequence, structural, functional and evolutionary information about transport systems from a variety of living organisms, based on the International Union of Biochemistry and Molecular Biology-approved transporter classification (TC) system. It is a curated repository for factual information compiled largely from published references. It uses a functional/phylogenetic system of classification, and currently encompasses about 5000 representative transporters and putative transporters in more than 500 families. We here describe novel software designed to support and extend the usefulness of TCDB. Our recent efforts render it more user friendly, incorporate machine learning to input novel data in a semiautomatic fashion, and allow analyses that are more accurate and less time consuming. The availability of these tools has resulted in recognition of distant phylogenetic relationships and tremendous expansion of the information available to TCDB users.

The pilus usher controls protein interactions via domain masking and is functional as an oligomer.

PubMed

Werneburg, Glenn T; Henderson, Nadine S; Portnoy, Erica B; Sarowar, Samema; Hultgren, Scott J; Li, Huilin; Thanassi, David G

2015-07-01

The chaperone-usher (CU) pathway assembles organelles termed pili or fimbriae in Gram-negative bacteria. Type 1 pili expressed by uropathogenic Escherichia coli are prototypical structures assembled by the CU pathway. Biogenesis of pili by the CU pathway requires a periplasmic chaperone and an outer-membrane protein termed the usher (FimD). We show that the FimD C-terminal domains provide the high-affinity substrate-binding site but that these domains are masked in the resting usher. Domain masking requires the FimD plug domain, which serves as a switch controlling usher activation. We demonstrate that usher molecules can act in trans for pilus biogenesis, providing conclusive evidence for a functional usher oligomer. These results reveal mechanisms by which molecular machines such as the usher regulate and harness protein-protein interactions and suggest that ushers may interact in a cooperative manner during pilus assembly in bacteria.

The Pilus Usher Controls Protein Interactions via Domain Masking and is Functional as an Oligomer

PubMed Central

Werneburg, Glenn T.; Henderson, Nadine S.; Portnoy, Erica B.; Sarowar, Samema; Hultgren, Scott J.; Li, Huilin; Thanassi, David G.

2015-01-01

The chaperone-usher (CU) pathway assembles organelles termed pili or fimbriae in Gram-negative bacteria. Type 1 pili expressed by uropathogenic Escherichia coli are prototypical structures assembled by the CU pathway. Biogenesis of pili by the CU pathway requires a periplasmic chaperone and an outer membrane protein termed the usher (FimD). We show that the FimD C-terminal domains provide the high-affinity substrate binding site, but that these domains are masked in the resting usher. Domain masking requires the FimD plug domain, which serves as a switch controlling usher activation. We demonstrate that usher molecules can act in trans for pilus biogenesis, providing conclusive evidence for a functional usher oligomer. These results reveal mechanisms by which molecular machines such as the usher regulate and harness protein-protein interactions, and suggest that ushers may interact in a cooperative manner during pilus assembly in bacteria. PMID:26052892

New Insights Into “Plant Memories”

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanbonmatsu, Karissa

A special stretch of ribonucleic acid (RNA) called COOLAIR is revealing its inner structure and function to scientists, displaying a striking resemblance to an RNA molecular machine, territory previously understood to be limited to the cells’ protein factory (the ‘ribosome’) and not a skill set given to mere strings of RNA. “We are uncovering the nuts and bolts of plant memories,” said Karissa Sanbonmatsu of Los Alamos National Laboratory, lead author on a new article this week in the journal Cell Reports. In the past 5 years or so, material in the cell known as “junk DNA” had actually turnedmore » out not to be junk at all. Instead, it was shown to produce RNA molecules that play key roles in the development of organs in the embryo, as well as affecting cancer, brain function and plant biology.« less

Computing Prediction and Functional Analysis of Prokaryotic Propionylation.

PubMed

Wang, Li-Na; Shi, Shao-Ping; Wen, Ping-Ping; Zhou, Zhi-You; Qiu, Jian-Ding

2017-11-27

Identification and systematic analysis of candidates for protein propionylation are crucial steps for understanding its molecular mechanisms and biological functions. Although several proteome-scale methods have been performed to delineate potential propionylated proteins, the majority of lysine-propionylated substrates and their role in pathological physiology still remain largely unknown. By gathering various databases and literatures, experimental prokaryotic propionylation data were collated to be trained in a support vector machine with various features via a three-step feature selection method. A novel online tool for seeking potential lysine-propionylated sites (PropSeek) ( http://bioinfo.ncu.edu.cn/PropSeek.aspx ) was built. Independent test results of leave-one-out and n-fold cross-validation were similar to each other, showing that PropSeek is a stable and robust predictor with satisfying performance. Meanwhile, analyses of Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathways, and protein-protein interactions implied a potential role of prokaryotic propionylation in protein synthesis and metabolism.

PAREMD: A parallel program for the evaluation of momentum space properties of atoms and molecules

NASA Astrophysics Data System (ADS)

Meena, Deep Raj; Gadre, Shridhar R.; Balanarayan, P.

2018-03-01

The present work describes a code for evaluating the electron momentum density (EMD), its moments and the associated Shannon information entropy for a multi-electron molecular system. The code works specifically for electronic wave functions obtained from traditional electronic structure packages such as GAMESS and GAUSSIAN. For the momentum space orbitals, the general expression for Gaussian basis sets in position space is analytically Fourier transformed to momentum space Gaussian basis functions. The molecular orbital coefficients of the wave function are taken as an input from the output file of the electronic structure calculation. The analytic expressions of EMD are evaluated over a fine grid and the accuracy of the code is verified by a normalization check and a numerical kinetic energy evaluation which is compared with the analytic kinetic energy given by the electronic structure package. Apart from electron momentum density, electron density in position space has also been integrated into this package. The program is written in C++ and is executed through a Shell script. It is also tuned for multicore machines with shared memory through OpenMP. The program has been tested for a variety of molecules and correlated methods such as CISD, Møller-Plesset second order (MP2) theory and density functional methods. For correlated methods, the PAREMD program uses natural spin orbitals as an input. The program has been benchmarked for a variety of Gaussian basis sets for different molecules showing a linear speedup on a parallel architecture.

Assessing heterogeneity in oligomeric AAA+ machines.

PubMed

Sysoeva, Tatyana A

2017-03-01

ATPases Associated with various cellular Activities (AAA+ ATPases) are molecular motors that use the energy of ATP binding and hydrolysis to remodel their target macromolecules. The majority of these ATPases form ring-shaped hexamers in which the active sites are located at the interfaces between neighboring subunits. Structural changes initiate in an active site and propagate to distant motor parts that interface and reshape the target macromolecules, thereby performing mechanical work. During the functioning cycle, the AAA+ motor transits through multiple distinct states. Ring architecture and placement of the catalytic sites at the intersubunit interfaces allow for a unique level of coordination among subunits of the motor. This in turn results in conformational differences among subunits and overall asymmetry of the motor ring as it functions. To date, a large amount of structural information has been gathered for different AAA+ motors, but even for the most characterized of them only a few structural states are known and the full mechanistic cycle cannot be yet reconstructed. Therefore, the first part of this work will provide a broad overview of what arrangements of AAA+ subunits have been structurally observed focusing on diversity of ATPase oligomeric ensembles and heterogeneity within the ensembles. The second part of this review will concentrate on methods that assess structural and functional heterogeneity among subunits of AAA+ motors, thus bringing us closer to understanding the mechanism of these fascinating molecular motors.

Operations and thermodynamics of an artificial rotary molecular motor in solution.

PubMed

Moro, Lorenzo; di Giosia, Matteo; Calvaresi, Matteo; Bakalis, Evangelos; Zerbetto, Francesco

2014-06-23

A general framework is provided that makes possible the estimation of time-dependent properties of a stochastic system moving far from equilibrium. The process is investigated and discussed in general terms of nonequilibrium thermodynamics. The approach is simple and can be exploited to gain insight into the dynamics of any molecular-level machine. As a case study, the dynamics of an artificial molecular rotary motor, similar to the inversion of a helix, which drives the motor from a metastable state to equilibrium, are examined. The energy path that the motor walks was obtained from the results of atomistic calculations. The motor undergoes unidirectional rotation and its entropy, internal energy, free energy, and net exerted force are given as a function of time, starting from the solution of Smoluchowski's equation. The rather low value of the organization index, that is, the ratio of the work done by the particle against friction during the unidirectional motion per available free energy, reveals that the motion is mainly subject to randomness, and the amount of energy converted to heat due to the directional motion is very small. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cell-Type-Specific Predictive Network Yields Novel Insights into Mouse Embryonic Stem Cell Self-Renewal and Cell Fate

PubMed Central

Dowell, Karen G.; Simons, Allen K.; Wang, Zack Z.; Yun, Kyuson; Hibbs, Matthew A.

2013-01-01

Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC) self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org) to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation. PMID:23468881

Hospital-acquired listeriosis linked to a persistently contaminated milkshake machine.

PubMed

Mazengia, E; Kawakami, V; Rietberg, K; Kay, M; Wyman, P; Skilton, C; Aberra, A; Boonyaratanakornkit, J; Limaye, A P; Pergam, S A; Whimbey, E; Olsen-Scribner, R J; Duchin, J S

2017-04-01

One case of hospital-acquired listeriosis was linked to milkshakes produced in a commercial-grade shake freezer machine. This machine was found to be contaminated with a strain of Listeria monocytogenes epidemiologically and molecularly linked to a contaminated pasteurized, dairy-based ice cream product at the same hospital a year earlier, despite repeated cleaning and sanitizing. Healthcare facilities should be aware of the potential for prolonged Listeria contamination of food service equipment. In addition, healthcare providers should consider counselling persons who have an increased risk for Listeria infections regarding foods that have caused Listeria infections. The prevalence of persistent Listeria contamination of commercial-grade milkshake machines in healthcare facilities and the risk associated with serving dairy-based ice cream products to hospitalized patients at increased risk for invasive L. monocytogenes infections should be further evaluated.

Functional language and data flow architectures

NASA Technical Reports Server (NTRS)

Ercegovac, M. D.; Patel, D. R.; Lang, T.

1983-01-01

This is a tutorial article about language and architecture approaches for highly concurrent computer systems based on the functional style of programming. The discussion concentrates on the basic aspects of functional languages, and sequencing models such as data-flow, demand-driven and reduction which are essential at the machine organization level. Several examples of highly concurrent machines are described.

Impact of equalizing currents on losses and torque ripples in electrical machines with fractional slot concentrated windings

NASA Astrophysics Data System (ADS)

Toporkov, D. M.; Vialcev, G. B.

2017-10-01

The implementation of parallel branches is a commonly used manufacturing method of the realizing of fractional slot concentrated windings in electrical machines. If the rotor eccentricity is enabled in a machine with parallel branches, the equalizing currents can arise. The simulation approach of the equalizing currents in parallel branches of an electrical machine winding based on magnetic field calculation by using Finite Elements Method is discussed in the paper. The high accuracy of the model is provided by the dynamic improvement of the inductances in the differential equation system describing a machine. The pre-computed table flux linkage functions are used for that. The functions are the dependences of the flux linkage of parallel branches on the branches currents and rotor position angle. The functions permit to calculate self-inductances and mutual inductances by partial derivative. The calculated results obtained for the electric machine specimen are presented. The results received show that the adverse combination of design solutions and the rotor eccentricity leads to a high value of the equalizing currents and windings heating. Additional torque ripples also arise. The additional ripples harmonic content is not similar to the cogging torque or ripples caused by the rotor eccentricity.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

The Cooling and Lubrication Performance of Graphene Platelets in Micro-Machining Environments

NASA Astrophysics Data System (ADS)

Chu, Bryan

The research presented in this thesis is aimed at investigating the use of graphene platelets (GPL) to address the challenges of excessive tool wear, reduced part quality, and high specific power consumption encountered in micro-machining processes. There are two viable methods of introducing GPL into micro-machining environments, viz., the embedded delivery method, where the platelets are embedded into the part being machined, and the external delivery method, where graphene is carried into the cutting zone by jetting or atomizing a carrier fluid. The study involving the embedded delivery method is focused on the micro-machining performance of hierarchical graphene composites. The results of this study show that the presence of graphene in the epoxy matrix improves the machinability of the composite. In general, the tool wear, cutting forces, surface roughness, and extent of delamination are all seen to be lower for the hierarchical composite when compared to the conventional two-phase glass fiber composite. These improvements are attributed to the fact that graphene platelets improve the thermal conductivity of the matrix, provide lubrication at the tool-chip interface and also improve the interface strength between the glass fibers and the matrix. The benefits of graphene are seen to also carry over to the external delivery method. The platelets provide improved cooling and lubrication performance to both environmentally-benign cutting fluids as well as to semi-synthetic cutting fluids used in micro-machining. The cutting performance is seen to be a function of the geometry (i.e., lateral size and thickness) and extent of oxygen-functionalization of the platelet. Ultrasonically exfoliated platelets (with 2--3 graphene layers and lowest in-solution characteristic lateral length of 120 nm) appear to be the most favorable for micro-machining applications. Even at the lowest concentration of 0.1 wt%, they are capable of providing a 51% reduction in the cutting temperature and a 25% reduction in the surface roughness value over that of the baseline semi-synthetic cutting fluid. For the thermally-reduced platelets (with 4--8 graphene layers and in-solution characteristic lateral length of 562--2780 nm), a concentration of 0.2 wt% appears to be optimal. An investigation into the impingement dynamics of the graphene-laden colloidal solutions on a heated substrate reveals that the most important criterion dictating their machining performance is their ability to form uniform, submicron thick films of the platelets upon evaporation of the carrier fluid. As such, the characterization of the residual platelet film left behind on a heated substrate may be an effective technique for evaluating different graphene colloidal solutions for cutting fluids applications in micromachining. Graphene platelets have also recently been shown to reduce the aggressive chemical wear of diamond tools during the machining of transition metal alloys. However, the specific mechanisms responsible for this improvement are currently unknown. The modeling work presented in this thesis uses molecular dynamics techniques to shed light on the wear mitigation mechanisms that are active during the diamond cutting of steel when in the presence of graphene platelets. The dual mechanisms responsible for graphene-induced chemical wear mitigation are: 1) The formation of a physical barrier between the metal and tool atoms, preventing graphitization; and 2) The preferential transfer of carbon from the graphene platelet rather than from the diamond tool. The results of the simulations also provide new insight into the behavior of the 2D graphene platelets in the cutting zone, specifically illustrating the mechanisms of cleaving and interlayer sliding in graphene platelets under the high pressures in cutting zones.

Ship’s Stores Automation Modernization (SSAM) - Phase I Report.

DTIC Science & Technology

1982-05-01

Fleet Accounting and Disbursing Center FI0o.1 Function 10 sub-function 1 GFS General Fund Survey GMO Game Machine Operator ICR Inventory Control...PERFORMERS: SSO, BSO, Food Service Officer (FSO), RSO, highest ranking Supply Officer (SO), Commanding Officer (CO) DESCRIPTION: Issues to Enlisted Dining...also counted from vending machines by the VMO and from the game machines by the GMO and turned over to the CA. The procedure is the same. The collection

Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study.

PubMed

Lima, Clodoaldo A M; Coelho, André L V

2011-10-01

We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely, Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). Copyright © 2011 Elsevier B.V. All rights reserved.

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

PubMed Central

Boucher, Benjamin; Lee, Anna Y.; Hallett, Michael; Jenna, Sarah

2016-01-01

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

Understanding of anesthesia machine function is enhanced with a transparent reality simulation.

PubMed

Fischler, Ira S; Kaschub, Cynthia E; Lizdas, David E; Lampotang, Samsun

2008-01-01

Photorealistic simulations may provide efficient transfer of certain skills to the real system, but by being opaque may fail to encourage deeper learning of the structure and function of the system. Schematic simulations that are more abstract, with less visual fidelity but make system structure and function transparent, may enhance deeper learning and optimize retention and transfer of learning. We compared learning effectiveness of these 2 modes of externalizing the output of a common simulation engine (the Virtual Anesthesia Machine, VAM) that models machine function and dynamics and responds in real time to user interventions such as changes in gas flow or ventilation. Undergraduate students (n = 39) and medical students (n = 35) were given a single, 1-hour guided learning session with either a Transparent or an Opaque version of the VAM simulation. The following day, the learners' knowledge of machine components, function, and dynamics was tested. The Transparent-VAM groups scored higher than the Opaque-VAM groups on a set of multiple-choice questions concerning conceptual knowledge about anesthesia machines (P = 0.009), provided better and more complete explanations of component function (P = 0.003), and were more accurate in remembering and inferring cause-and-effect dynamics of the machine and relations among components (P = 0.003). Although the medical students outperformed undergraduates on all measures, a similar pattern of benefits for the Transparent VAM was observed for these 2 groups. Schematic simulations that transparently allow learners to visualize, and explore, underlying system dynamics and relations among components may provide a more effective mental model for certain systems. This may lead to a deeper understanding of how the system works, and therefore, we believe, how to detect and respond to potentially adverse situations.

Compact friction and wear machine

NASA Astrophysics Data System (ADS)

Hannigan, James W.; Schwarz, Ricardo B.

1988-08-01

We have developed a compact ring-on-ring wear machine that measures the friction coefficient between large area surfaces as a function of time, normal stress, and sliding velocity. The machine measures the temperature of the sliding surfaces and collects the wear debris.

Molecular Machines Determining the Fate of Endocytosed Synaptic Vesicles in Nerve Terminals

PubMed Central

Fassio, Anna; Fadda, Manuela; Benfenati, Fabio

2016-01-01

The cycle of a synaptic vesicle (SV) within the nerve terminal is a step-by-step journey with the final goal of ensuring the proper synaptic strength under changing environmental conditions. The SV cycle is a precisely regulated membrane traffic event in cells and, because of this, a plethora of membrane-bound and cytosolic proteins are devoted to assist SVs in each step of the journey. The cycling fate of endocytosed SVs determines both the availability for subsequent rounds of release and the lifetime of SVs in the terminal and is therefore crucial for synaptic function and plasticity. Molecular players that determine the destiny of SVs in nerve terminals after a round of exo-endocytosis are largely unknown. Here we review the functional role in SV fate of phosphorylation/dephosphorylation of SV proteins and of small GTPases acting on membrane trafficking at the synapse, as they are emerging as key molecules in determining the recycling route of SVs within the nerve terminal. In particular, we focus on: (i) the cyclin-dependent kinase-5 (cdk5) and calcineurin (CN) control of the recycling pool of SVs; (ii) the role of small GTPases of the Rab and ADP-ribosylation factor (Arf) families in defining the route followed by SV in their nerve terminal cycle. These regulatory proteins together with their synaptic regulators and effectors, are molecular nanomachines mediating homeostatic responses in synaptic plasticity and potential targets of drugs modulating the efficiency of synaptic transmission. PMID:27242505

Molecular Machines Determining the Fate of Endocytosed Synaptic Vesicles in Nerve Terminals.

PubMed

Fassio, Anna; Fadda, Manuela; Benfenati, Fabio

2016-01-01

The cycle of a synaptic vesicle (SV) within the nerve terminal is a step-by-step journey with the final goal of ensuring the proper synaptic strength under changing environmental conditions. The SV cycle is a precisely regulated membrane traffic event in cells and, because of this, a plethora of membrane-bound and cytosolic proteins are devoted to assist SVs in each step of the journey. The cycling fate of endocytosed SVs determines both the availability for subsequent rounds of release and the lifetime of SVs in the terminal and is therefore crucial for synaptic function and plasticity. Molecular players that determine the destiny of SVs in nerve terminals after a round of exo-endocytosis are largely unknown. Here we review the functional role in SV fate of phosphorylation/dephosphorylation of SV proteins and of small GTPases acting on membrane trafficking at the synapse, as they are emerging as key molecules in determining the recycling route of SVs within the nerve terminal. In particular, we focus on: (i) the cyclin-dependent kinase-5 (cdk5) and calcineurin (CN) control of the recycling pool of SVs; (ii) the role of small GTPases of the Rab and ADP-ribosylation factor (Arf) families in defining the route followed by SV in their nerve terminal cycle. These regulatory proteins together with their synaptic regulators and effectors, are molecular nanomachines mediating homeostatic responses in synaptic plasticity and potential targets of drugs modulating the efficiency of synaptic transmission.

National Synchrotron Light Source annual report 1991

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hulbert, S.L.; Lazarz, N.M.

1992-04-01

This report discusses the following research conducted at NSLS: atomic and molecular science; energy dispersive diffraction; lithography, microscopy and tomography; nuclear physics; UV photoemission and surface science; x-ray absorption spectroscopy; x-ray scattering and crystallography; x-ray topography; workshop on surface structure; workshop on electronic and chemical phenomena at surfaces; workshop on imaging; UV FEL machine reviews; VUV machine operations; VUV beamline operations; VUV storage ring parameters; x-ray machine operations; x-ray beamline operations; x-ray storage ring parameters; superconducting x-ray lithography source; SXLS storage ring parameters; the accelerator test facility; proposed UV-FEL user facility at the NSLS; global orbit feedback systems; and NSLSmore » computer system.« less

The Molecular Industrial Revolution: Automated Synthesis of Small Molecules

PubMed Central

Trobe, Melanie; Burke, Martin D.

2018-01-01

The eighteenth and nineteenth centuries marked a sweeping transition from manual to automated manufacturing on the macroscopic scale. This enabled an unmatched period of human innovation that helped drive the Industrial Revolution. The impact on society was transformative, ultimately yielding substantial improvements in living conditions and lifespan in many parts of the world. During the same time period, the first manual syntheses of organic molecules was achieved. Now, two centuries later, we are poised for an analogous transition from highly customized crafting of specific molecular targets by hand to the increasingly general and automated assembly of many different types of molecules with the push of a button. Automation of customized small molecule synthesis pathways is already enabling safer, more reproducible, and readily scalable production of specific targets, and general machines now exist for the synthesis of a wide range of different peptides, oligonucleotides, and oligosaccharides. Creating general machines that are similarly capable of making many different types of small molecules on-demand, akin to that which has been achieved on the macroscopic scale with 3D printers, has proven to be substantially more challenging. Yet important progress is being made toward this potentially transformative objective with two complementary approaches: (1) automation of customized synthesis routes to different targets via machines that enable use of many different reactions and starting materials, and (2) automation of generalized platforms that make many different targets using common coupling chemistry and building blocks. Continued progress in these exciting directions has the potential to shift the bottleneck in molecular innovation from synthesis to imagination, and thereby help drive a new industrial revolution on the molecular scale. PMID:29513400

Artificial Intelligence/Robotics Applications to Navy Aircraft Maintenance.

DTIC Science & Technology

1984-06-01

other automatic machinery such as presses, molding machines , and numerically-controlled machine tools, just as people do. A-36...Robotics Technologies 3 B. Relevant AI Technologies 4 1. Expert Systems 4 2. Automatic Planning 4 3. Natural Language 5 4. Machine Vision...building machines that imitate human behavior. Artificial intelligence is concerned with the functions of the brain, whereas robotics include, in

MACHINE COOLANT WASTE REDUCTION BY OPTIMIZING COOLANT LIFE

EPA Science Inventory

Machine shops use coolants to improve the life and function of machine tools. hese coolants become contaminated with oils with use, and this contamination can lead to growth of anaerobic bacteria and shortened coolant life. his project investigated methods to extend coolant life ...

Role of the α clamp in the protein translocation mechanism of anthrax toxin

PubMed Central

Brown, Michael J.; Thoren, Katie L.; Krantz, Bryan A.

2015-01-01

Membrane-embedded molecular machines are utilized to move water-soluble proteins across these barriers. Anthrax toxin forms one such machine through the self-assembly of its three component proteins—protective antigen (PA), lethal factor (LF), and edema factor (EF). Upon endocytosis into host cells, acidification of the endosome induces PA to form a membrane-inserted channel, which unfolds LF and EF and translocates them into the host cytosol. Translocation is driven by the proton motive force, comprised of the chemical potential, the proton-gradient (ΔpH), and the membrane potential (ΔΨ). A crystal structure of the lethal toxin core complex revealed an “α clamp” structure that binds to substrate helices nonspecifically. Here we test the hypothesis that through the recognition of unfolding helical structure the α clamp can accelerate the rate of translocation. We produced a synthetic PA mutant in which an α helix was crosslinked into the α clamp to block its function. This synthetic construct impairs translocation by raising a yet uncharacterized translocation barrier shown to be much less force dependent than the known unfolding barrier. We also report that the α clamp more stably binds substrates that can form helices than those, such as polyproline, that cannot. Hence the α clamp recognizes substrates by a general shape-complementarity mechanism. Substrates that are incapable of forming compact secondary structure (due to the introduction of a polyproline track) are severely deficient for translocation. Therefore, the α clamp and its recognition of helical structure in the translocating substrate play key roles in the molecular mechanism of protein translocation. PMID:26344833

Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning.

PubMed

Xiao, Jiajie; Melvin, Ryan L; Salsbury, Freddie R

2018-03-02

Thrombin is a key component for chemotherapeutic and antithrombotic therapy development. As the physiologic and pathologic roles of the light chain still remain vague, here, we continue previous efforts to understand the impacts of the disease-associated single deletion of LYS9 in the light chain. By combining supervised and unsupervised machine learning methodologies and more traditional structural analyses on data from 10 μs molecular dynamics simulations, we show that the conformational ensemble of the ΔK9 mutant is significantly perturbed. Our analyses consistently indicate that LYS9 deletion destabilizes both the catalytic cleft and regulatory functional regions and result in some conformational changes that occur in tens to hundreds of nanosecond scaled motions. We also reveal that the two forms of thrombin each prefer a distinct binding mode of a Na + ion. We expand our understanding of previous experimental observations and shed light on the mechanisms of the LYS9 deletion associated bleeding disorder by providing consistent but more quantitative and detailed structural analyses than early studies in literature. With a novel application of supervised learning, i.e. the decision tree learning on the hydrogen bonding features in the wild-type and ΔK9 mutant forms of thrombin, we predict that seven pairs of critical hydrogen bonding interactions are significant for establishing distinct behaviors of wild-type thrombin and its ΔK9 mutant form. Our calculations indicate the LYS9 in the light chain has both localized and long-range allosteric effects on thrombin, supporting the opinion that light chain has an important role as an allosteric effector.

Applications of Database Machines in Library Systems.

ERIC Educational Resources Information Center

Salmon, Stephen R.

1984-01-01

Characteristics and advantages of database machines are summarized and their applications to library functions are described. The ability to attach multiple hosts to the same database and flexibility in choosing operating and database management systems for different functions without loss of access to common database are noted. (EJS)

Manipulating Slot Machine Preference in Problem Gamblers through Contextual Control

ERIC Educational Resources Information Center

Nastally, Becky L.; Dixon, Mark R.; Jackson, James W.

2010-01-01

Pathological and nonpathological gamblers completed a task that assessed preference among 2 concurrently available slot machines. Subsequent assessments of choice were conducted after various attempts to transfer contextual functions associated with irrelevant characteristics of the slot machines. Results indicated that the nonproblem gambling…

Army Research Laboratory S&T Campaign Plans 2015-2035

DTIC Science & Technology

2014-09-01

addressing propagation effects, impact of wind noise on detection range and angular accuracy. Use of vector sensors is one approach for potential advances...for the future warfighter, such as new protective and responsive materials, sensors , and munitions. Polymer Chemistry explores the molecular-level...ultimately enable the design and development of novel materials, molecular sensors , and nanoscale machines that exploit the exceptional capabilities of

Hybrid organic-inorganic rotaxanes and molecular shuttles.

PubMed

Lee, Chin-Fa; Leigh, David A; Pritchard, Robin G; Schultz, David; Teat, Simon J; Timco, Grigore A; Winpenny, Richard E P

2009-03-19

The tetravalency of carbon and its ability to form covalent bonds with itself and other elements enables large organic molecules with complex structures, functions and dynamics to be constructed. The varied electronic configurations and bonding patterns of inorganic elements, on the other hand, can impart diverse electronic, magnetic, catalytic and other useful properties to molecular-level structures. Some hybrid organic-inorganic materials that combine features of both chemistries have been developed, most notably metal-organic frameworks, dense and extended organic-inorganic frameworks and coordination polymers. Metal ions have also been incorporated into molecules that contain interlocked subunits, such as rotaxanes and catenanes, and structures in which many inorganic clusters encircle polymer chains have been described. Here we report the synthesis of a series of discrete rotaxane molecules in which inorganic and organic structural units are linked together mechanically at the molecular level. Structural units (dialkyammonium groups) in dumb-bell-shaped organic molecules template the assembly of essentially inorganic 'rings' about 'axles' to form rotaxanes consisting of various numbers of rings and axles. One of the rotaxanes behaves as a 'molecular shuttle': the ring moves between two binding sites on the axle in a large-amplitude motion typical of some synthetic molecular machine systems. The architecture of the rotaxanes ensures that the electronic, magnetic and paramagnetic characteristics of the inorganic rings-properties that could make them suitable as qubits for quantum computers-can influence, and potentially be influenced by, the organic portion of the molecule.

Deep learning in pharmacogenomics: from gene regulation to patient stratification.

PubMed

Kalinin, Alexandr A; Higgins, Gerald A; Reamaroon, Narathip; Soroushmehr, Sayedmohammadreza; Allyn-Feuer, Ari; Dinov, Ivo D; Najarian, Kayvan; Athey, Brian D

2018-05-01

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

The tail sheath structure of bacteriophage T4: a molecular machine for infecting bacteria

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aksyuk, Anastasia A.; Leiman, Petr G.; Kurochkina, Lidia P.

2009-07-22

The contractile tail of bacteriophage T4 is a molecular machine that facilitates very high viral infection efficiency. Its major component is a tail sheath, which contracts during infection to less than half of its initial length. The sheath consists of 138 copies of the tail sheath protein, gene product (gp) 18, which surrounds the central non-contractile tail tube. The contraction of the sheath drives the tail tube through the outer membrane, creating a channel for the viral genome delivery. A crystal structure of about three quarters of gp18 has been determined and was fitted into cryo-electron microscopy reconstructions of themore » tail sheath before and after contraction. It was shown that during contraction, gp18 subunits slide over each other with no apparent change in their structure.« less

Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

PubMed Central

Garcia Lopez, Sebastian; Kim, Philip M.

2014-01-01

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

PubMed

de Ávila, Maurício Boff; Xavier, Mariana Morrone; Pintro, Val Oliveira; de Azevedo, Walter Filgueira

2017-12-09

Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC 50 ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores. Copyright © 2017 Elsevier Inc. All rights reserved.

A mechanical Turing machine: blueprint for a biomolecular computer

PubMed Central

Shapiro, Ehud

2012-01-01

We describe a working mechanical device that embodies the theoretical computing machine of Alan Turing, and as such is a universal programmable computer. The device operates on three-dimensional building blocks by applying mechanical analogues of polymer elongation, cleavage and ligation, movement along a polymer, and control by molecular recognition unleashing allosteric conformational changes. Logically, the device is not more complicated than biomolecular machines of the living cell, and all its operations are part of the standard repertoire of these machines; hence, a biomolecular embodiment of the device is not infeasible. If implemented, such a biomolecular device may operate in vivo, interacting with its biochemical environment in a program-controlled manner. In particular, it may ‘compute’ synthetic biopolymers and release them into its environment in response to input from the environment, a capability that may have broad pharmaceutical and biological applications. PMID:22649583

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1989-01-01

In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved to converge to the minimum of a cost function. Finally, simulations will show the effectiveness of a variety of search techniques for the intelligent machine.

Simple Machines. Physical Science in Action[TM]. Schlessinger Science Library. [Videotape].

ERIC Educational Resources Information Center

2000

In today's world, kids are aware that there are machines all around them. What they may not realize is that the function of all machines is to make work easier in some way. Simple Machines uses engaging visuals and colorful graphics to explain the concept of work and how humans use certain basic tools to help get work done. Students will learn…

AIM for Allostery: Using the Ising Model to Understand Information Processing and Transmission in Allosteric Biomolecular Systems.

PubMed

LeVine, Michael V; Weinstein, Harel

2015-05-01

In performing their biological functions, molecular machines must process and transmit information with high fidelity. Information transmission requires dynamic coupling between the conformations of discrete structural components within the protein positioned far from one another on the molecular scale. This type of biomolecular "action at a distance" is termed allostery . Although allostery is ubiquitous in biological regulation and signal transduction, its treatment in theoretical models has mostly eschewed quantitative descriptions involving the system's underlying structural components and their interactions. Here, we show how Ising models can be used to formulate an approach to allostery in a structural context of interactions between the constitutive components by building simple allosteric constructs we termed Allosteric Ising Models (AIMs). We introduce the use of AIMs in analytical and numerical calculations that relate thermodynamic descriptions of allostery to the structural context, and then show that many fundamental properties of allostery, such as the multiplicative property of parallel allosteric channels, are revealed from the analysis of such models. The power of exploring mechanistic structural models of allosteric function in more complex systems by using AIMs is demonstrated by building a model of allosteric signaling for an experimentally well-characterized asymmetric homodimer of the dopamine D2 receptor.

Functional odor classification through a medicinal chemistry approach.

PubMed

Poivet, Erwan; Tahirova, Narmin; Peterlin, Zita; Xu, Lu; Zou, Dong-Jing; Acree, Terry; Firestein, Stuart

2018-02-01

Crucial for any hypothesis about odor coding is the classification and prediction of sensory qualities in chemical compounds. The relationship between perceptual quality and molecular structure has occupied olfactory scientists throughout the 20th century, but details of the mechanism remain elusive. Odor molecules are typically organic compounds of low molecular weight that may be aliphatic or aromatic, may be saturated or unsaturated, and may have diverse functional polar groups. However, many molecules conforming to these characteristics are odorless. One approach recently used to solve this problem was to apply machine learning strategies to a large set of odors and human classifiers in an attempt to find common and unique chemical features that would predict a chemical's odor. We use an alternative method that relies more on the biological responses of olfactory sensory neurons and then applies the principles of medicinal chemistry, a technique widely used in drug discovery. We demonstrate the effectiveness of this strategy through a classification for esters, an important odorant for the creation of flavor in wine. Our findings indicate that computational approaches that do not account for biological responses will be plagued by both false positives and false negatives and fail to provide meaningful mechanistic data. However, the two approaches used in tandem could resolve many of the paradoxes in odor perception.

Man-Machine Integrated Design and Analysis System (MIDAS): Functional Overview

NASA Technical Reports Server (NTRS)

Corker, Kevin; Neukom, Christian

1998-01-01

Included in the series of screen print-outs illustrates the structure and function of the Man-Machine Integrated Design and Analysis System (MIDAS). Views into the use of the system and editors are featured. The use-case in this set of graphs includes the development of a simulation scenario.

Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-06-28

A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is superior by almost an order of magnitude over the ANN method in terms of the stability, generality, and robustness of the final model. The LS-SVM model needs a much smaller numbers of samples (a much smaller sample set) to make accurate prediction results. Potential energy surface (PES) approximations for molecular dynamics (MD) studies are discussed as a promising application for the LS-SVM calibration approach. This journal is © the Owner Societies 2011

Machine-Aided Indexing at NASA.

ERIC Educational Resources Information Center

Silvester, June P.; And Others

1994-01-01

Describes the National Aeronautics and Space Administration (NASA) Lexical Dictionary (NLD), a machine-aided indexing system used online at the NASA Center for AeroSpace Information (CASI). The functions of NLD system components are described in detail, and production and quality benefits resulting from machine-aided indexing at CASI are…

49 CFR 236.771 - Machine, control.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Machine, control. 236.771 Section 236.771..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.771 Machine, control. An assemblage of manually operated devices for controlling the functions of a traffic...

49 CFR 236.771 - Machine, control.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false Machine, control. 236.771 Section 236.771..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.771 Machine, control. An assemblage of manually operated devices for controlling the functions of a traffic...

49 CFR 236.771 - Machine, control.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Machine, control. 236.771 Section 236.771..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.771 Machine, control. An assemblage of manually operated devices for controlling the functions of a traffic...

49 CFR 236.771 - Machine, control.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Machine, control. 236.771 Section 236.771..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.771 Machine, control. An assemblage of manually operated devices for controlling the functions of a traffic...

Bacteria as computers making computers

PubMed Central

Danchin, Antoine

2009-01-01

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments. PMID:19016882

Virtual network computing: cross-platform remote display and collaboration software.

PubMed

Konerding, D E

1999-04-01

VNC (Virtual Network Computing) is a computer program written to address the problem of cross-platform remote desktop/application display. VNC uses a client/server model in which an image of the desktop of the server is transmitted to the client and displayed. The client collects mouse and keyboard input from the user and transmits them back to the server. The VNC client and server can run on Windows 95/98/NT, MacOS, and Unix (including Linux) operating systems. VNC is multi-user on Unix machines (any number of servers can be run are unrelated to the primary display of the computer), while it is effectively single-user on Macintosh and Windows machines (only one server can be run, displaying the contents of the primary display of the server). The VNC servers can be configured to allow more than one client to connect at one time, effectively allowing collaboration through the shared desktop. I describe the function of VNC, provide details of installation, describe how it achieves its goal, and evaluate the use of VNC for molecular modelling. VNC is an extremely useful tool for collaboration, instruction, software development, and debugging of graphical programs with remote users.

Bacteria as computers making computers.

PubMed

Danchin, Antoine

2009-01-01

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments.

Interlocking Molecular Gear Chains Built on Surfaces.

PubMed

Zhao, Rundong; Qi, Fei; Zhao, Yan-Ling; Hermann, Klaus E; Zhang, Rui-Qin; Van Hove, Michel A

2018-05-17

Periodic chains of molecular gears in which molecules couple with each other and rotate on surfaces have been previously explored by us theoretically using ab initio simulation tools. On the basis of the knowledge and experience gained about the interactions between neighboring molecular gears, we here explore the transmission of rotational motion and energy over larger distances, namely, through a longer chain of gear-like passive "slave" molecules. Such microscopic gears exhibit quite different behaviors compared to rigid cogwheels in the macroscopic world due to their structural flexibility affecting intermolecular interaction. Here, we investigate the capabilities of such gear chains and reveal the mechanisms of the transmission process in terms of both quantum-level density functional theory (DFT) and simple classical mechanics. We find that the transmission of rotation along gear chains depends strongly on the gear-gear distance: short distances can cause tilting of gears and even irregular "creep-then-jump" (or "stick-slip") motion or expulsion of gears; long gear-gear distances cause weak coupling between gears, slipping and skipping. More importantly, for transmission of rotation at intermediate gear-gear distances, our modeling clearly exhibits the relative roles of several important factors: flexibility of gear arms, axles, and supports, as well as resulting rotational delays, slippages, and thermal and other effects. These studies therefore allow better informed design of future molecular machine components involving motors, gears, axles, etc.

Hidden Markov models and other machine learning approaches in computational molecular biology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baldi, P.

1995-12-31

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In thismore » tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.« less

Knowledge Acquisition, Knowledge Programming, and Knowledge Refinement.

ERIC Educational Resources Information Center

Hayes-Roth, Frederick; And Others

This report describes the principal findings and recommendations of a 2-year Rand research project on machine-aided knowledge acquisition and discusses the transfer of expertise from humans to machines, as well as the functions of planning, debugging, knowledge refinement, and autonomous machine learning. The relative advantages of humans and…

The IBM PC as an Online Search Machine--Part 2: Physiology for Searchers.

ERIC Educational Resources Information Center

Kolner, Stuart J.

1985-01-01

Enumerates "hardware problems" associated with use of the IBM personal computer as an online search machine: purchase of machinery, unpacking of parts, and assembly into a properly functioning computer. Components that allow transformations of computer into a search machine (combination boards, printer, modem) and diagnostics software…

National Synchrotron Light Source annual report 1991. Volume 1, October 1, 1990--September 30, 1991

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hulbert, S.L.; Lazarz, N.M.

1992-04-01

This report discusses the following research conducted at NSLS: atomic and molecular science; energy dispersive diffraction; lithography, microscopy and tomography; nuclear physics; UV photoemission and surface science; x-ray absorption spectroscopy; x-ray scattering and crystallography; x-ray topography; workshop on surface structure; workshop on electronic and chemical phenomena at surfaces; workshop on imaging; UV FEL machine reviews; VUV machine operations; VUV beamline operations; VUV storage ring parameters; x-ray machine operations; x-ray beamline operations; x-ray storage ring parameters; superconducting x-ray lithography source; SXLS storage ring parameters; the accelerator test facility; proposed UV-FEL user facility at the NSLS; global orbit feedback systems; and NSLSmore » computer system.« less

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

NASA Astrophysics Data System (ADS)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

Electric machine and current source inverter drive system

DOEpatents

Hsu, John S

2014-06-24

A drive system includes an electric machine and a current source inverter (CSI). This integration of an electric machine and an inverter uses the machine's field excitation coil for not only flux generation in the machine but also for the CSI inductor. This integration of the two technologies, namely the U machine motor and the CSI, opens a new chapter for the component function integration instead of the traditional integration by simply placing separate machine and inverter components in the same housing. Elimination of the CSI inductor adds to the CSI volumetric reduction of the capacitors and the elimination of PMs for the motor further improve the drive system cost, weight, and volume.

AceCloud: Molecular Dynamics Simulations in the Cloud.

PubMed

Harvey, M J; De Fabritiis, G

2015-05-26

We present AceCloud, an on-demand service for molecular dynamics simulations. AceCloud is designed to facilitate the secure execution of large ensembles of simulations on an external cloud computing service (currently Amazon Web Services). The AceCloud client, integrated into the ACEMD molecular dynamics package, provides an easy-to-use interface that abstracts all aspects of interaction with the cloud services. This gives the user the experience that all simulations are running on their local machine, minimizing the learning curve typically associated with the transition to using high performance computing services.

A computer architecture for intelligent machines

NASA Technical Reports Server (NTRS)

Lefebvre, D. R.; Saridis, G. N.

1992-01-01

The theory of intelligent machines proposes a hierarchical organization for the functions of an autonomous robot based on the principle of increasing precision with decreasing intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed. The authors present a computer architecture that implements the lower two levels of the intelligent machine. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Execution-level controllers for motion and vision systems are briefly addressed, as well as the Petri net transducer software used to implement coordination-level functions. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

The human role in space: Technology, economics and optimization

NASA Technical Reports Server (NTRS)

Hall, S. B. (Editor)

1985-01-01

Man-machine interactions in space are explored in detail. The role and the degree of direct involvement of humans that will be required in future space missions are investigated. An attempt is made to establish valid criteria for allocating functional activities between humans and machines and to provide insight into the technological requirements, economics, and benefits of the human presence in space. Six basic categories of man-machine interactions are considered: manual, supported, augmented, teleoperated, supervised, and independent. Appendices are included which provide human capability data, project analyses, activity timeline profiles and data sheets for 37 generic activities, support equipment and human capabilities required in these activities, and cumulative costs as a function of activity for seven man-machine modes.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Application of ARAMIS capabilities to space project functional elements

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities and their related ground support functions are studied, so that informed decisions can be made on which aspects of ARAMIS to develop. The specific tasks which will be required by future space project tasks are identified and the relative merits of these options are evaluated. The ARAMIS options defined and researched span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Function library programming to support B89 evaluation of Sheffield Apollo RS50 DCC (Direct Computer Control) CMM (Coordinate Measuring Machine)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, R.N.

1990-02-28

The Inspection Shop at Lawrence Livermore Lab recently purchased a Sheffield Apollo RS50 Direct Computer Control Coordinate Measuring Machine. The performance of the machine was specified to conform to B89 standard which relies heavily upon using the measuring machine in its intended manner to verify its accuracy (rather than parametric tests). Although it would be possible to use the interactive measurement system to perform these tasks, a more thorough and efficient job can be done by creating Function Library programs for certain tasks which integrate Hewlett-Packard Basic 5.0 language and calls to proprietary analysis and machine control routines. This combinationmore » provides efficient use of the measuring machine with a minimum of keyboard input plus an analysis of the data with respect to the B89 Standard rather than a CMM analysis which would require subsequent interpretation. This paper discusses some characteristics of the Sheffield machine control and analysis software and my use of H-P Basic language to create automated measurement programs to support the B89 performance evaluation of the CMM. 1 ref.« less

Kinesin Motor Enzymology: Chemistry, Structure, and Physics of Nanoscale Molecular Machines.

PubMed

Cochran, J C

2015-09-01

Molecular motors are enzymes that convert chemical potential energy into controlled kinetic energy for mechanical work inside cells. Understanding the biophysics of these motors is essential for appreciating life as well as apprehending diseases that arise from motor malfunction. This review focuses on kinesin motor enzymology with special emphasis on the literature that reports the chemistry, structure and physics of several different kinesin superfamily members.

Biomimetic molecular design tools that learn, evolve, and adapt.

PubMed

Winkler, David A

2017-01-01

A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known "S curve", with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.

Biomimetic molecular design tools that learn, evolve, and adapt

PubMed Central

2017-01-01

A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine. PMID:28694872

Elucidating the ensemble of functionally-relevant transitions in protein systems with a robotics-inspired method.

PubMed

Molloy, Kevin; Shehu, Amarda

2013-01-01

Many proteins tune their biological function by transitioning between different functional states, effectively acting as dynamic molecular machines. Detailed structural characterization of transition trajectories is central to understanding the relationship between protein dynamics and function. Computational approaches that build on the Molecular Dynamics framework are in principle able to model transition trajectories at great detail but also at considerable computational cost. Methods that delay consideration of dynamics and focus instead on elucidating energetically-credible conformational paths connecting two functionally-relevant structures provide a complementary approach. Effective sampling-based path planning methods originating in robotics have been recently proposed to produce conformational paths. These methods largely model short peptides or address large proteins by simplifying conformational space. We propose a robotics-inspired method that connects two given structures of a protein by sampling conformational paths. The method focuses on small- to medium-size proteins, efficiently modeling structural deformations through the use of the molecular fragment replacement technique. In particular, the method grows a tree in conformational space rooted at the start structure, steering the tree to a goal region defined around the goal structure. We investigate various bias schemes over a progress coordinate for balance between coverage of conformational space and progress towards the goal. A geometric projection layer promotes path diversity. A reactive temperature scheme allows sampling of rare paths that cross energy barriers. Experiments are conducted on small- to medium-size proteins of length up to 214 amino acids and with multiple known functionally-relevant states, some of which are more than 13Å apart of each-other. Analysis reveals that the method effectively obtains conformational paths connecting structural states that are significantly different. A detailed analysis on the depth and breadth of the tree suggests that a soft global bias over the progress coordinate enhances sampling and results in higher path diversity. The explicit geometric projection layer that biases the exploration away from over-sampled regions further increases coverage, often improving proximity to the goal by forcing the exploration to find new paths. The reactive temperature scheme is shown effective in increasing path diversity, particularly in difficult structural transitions with known high-energy barriers.

Light-powered autonomous and directional molecular motion of a dissipative self-assembling system

NASA Astrophysics Data System (ADS)

Ragazzon, Giulio; Baroncini, Massimo; Silvi, Serena; Venturi, Margherita; Credi, Alberto

2015-01-01

Biomolecular motors convert energy into directed motion and operate away from thermal equilibrium. The development of dynamic chemical systems that exploit dissipative (non-equilibrium) processes is a challenge in supramolecular chemistry and a premise for the realization of artificial nanoscale motors. Here, we report the relative unidirectional transit of a non-symmetric molecular axle through a macrocycle powered solely by light. The molecular machine rectifies Brownian fluctuations by energy and information ratchet mechanisms and can repeat its working cycle under photostationary conditions. The system epitomizes the conceptual and practical elements forming the basis of autonomous light-powered directed motion with a minimalist molecular design.

Understanding the undelaying mechanism of HA-subtyping in the level of physic-chemical characteristics of protein.

PubMed

Ebrahimi, Mansour; Aghagolzadeh, Parisa; Shamabadi, Narges; Tahmasebi, Ahmad; Alsharifi, Mohammed; Adelson, David L; Hemmatzadeh, Farhid; Ebrahimie, Esmaeil

2014-01-01

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.

Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

PubMed Central

Ebrahimi, Mansour; Aghagolzadeh, Parisa; Shamabadi, Narges; Tahmasebi, Ahmad; Alsharifi, Mohammed; Adelson, David L.

2014-01-01

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics. PMID:24809455

A microcomputer network for control of a continuous mining machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schiffbauer, W.H.

1993-12-31

This report details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines and installed on a continuous mining machine. The network consists of microcomputers that are connected together via a single twisted-pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers, in conjunction with the appropriate sensors, provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and control the continuous miningmore » machine. Because of the network`s generic structure, it can be installed on most mining machines.« less

Method and apparatus for characterizing and enhancing the functional performance of machine tools

DOEpatents

Barkman, William E; Babelay, Jr., Edwin F; Smith, Kevin Scott; Assaid, Thomas S; McFarland, Justin T; Tursky, David A; Woody, Bethany; Adams, David

2013-04-30

Disclosed are various systems and methods for assessing and improving the capability of a machine tool. The disclosure applies to machine tools having at least one slide configured to move along a motion axis. Various patterns of dynamic excitation commands are employed to drive the one or more slides, typically involving repetitive short distance displacements. A quantification of a measurable merit of machine tool response to the one or more patterns of dynamic excitation commands is typically derived for the machine tool. Examples of measurable merits of machine tool performance include workpiece surface finish, and the ability to generate chips of the desired length.

Safety Features in Anaesthesia Machine

PubMed Central

Subrahmanyam, M; Mohan, S

2013-01-01

Anaesthesia is one of the few sub-specialties of medicine, which has quickly adapted technology to improve patient safety. This application of technology can be seen in patient monitoring, advances in anaesthesia machines, intubating devices, ultrasound for visualisation of nerves and vessels, etc., Anaesthesia machines have come a long way in the last 100 years, the improvements being driven both by patient safety as well as functionality and economy of use. Incorporation of safety features in anaesthesia machines and ensuring that a proper check of the machine is done before use on a patient ensures patient safety. This review will trace all the present safety features in the machine and their evolution. PMID:24249880

Multiple-basin energy landscapes for large-amplitude conformational motions of proteins: Structure-based molecular dynamics simulations

PubMed Central

Okazaki, Kei-ichi; Koga, Nobuyasu; Takada, Shoji; Onuchic, Jose N.; Wolynes, Peter G.

2006-01-01

Biomolecules often undergo large-amplitude motions when they bind or release other molecules. Unlike macroscopic machines, these biomolecular machines can partially disassemble (unfold) and then reassemble (fold) during such transitions. Here we put forward a minimal structure-based model, the “multiple-basin model,” that can directly be used for molecular dynamics simulation of even very large biomolecular systems so long as the endpoints of the conformational change are known. We investigate the model by simulating large-scale motions of four proteins: glutamine-binding protein, S100A6, dihydrofolate reductase, and HIV-1 protease. The mechanisms of conformational transition depend on the protein basin topologies and change with temperature near the folding transition. The conformational transition rate varies linearly with driving force over a fairly large range. This linearity appears to be a consequence of partial unfolding during the conformational transition. PMID:16877541

Newton Methods for Large Scale Problems in Machine Learning

ERIC Educational Resources Information Center

Hansen, Samantha Leigh

2014-01-01

The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

Ex-vivo machine perfusion for kidney preservation.

PubMed

Hamar, Matyas; Selzner, Markus

2018-06-01

Machine perfusion is a novel strategy to decrease preservation injury, improve graft assessment, and increase organ acceptance for transplantation. This review summarizes the current advances in ex-vivo machine-based kidney preservation technologies over the last year. Ex-vivo perfusion technologies, such as hypothermic and normothermic machine perfusion and controlled oxygenated rewarming, have gained high interest in the field of organ preservation. Keeping kidney grafts functionally and metabolically active during the preservation period offers a unique chance for viability assessment, reconditioning, and organ repair. Normothermic ex-vivo kidney perfusion has been recently translated into clinical practice. Preclinical results suggest that prolonged warm perfusion appears superior than a brief end-ischemic reconditioning in terms of renal function and injury. An established standardized protocol for continuous warm perfusion is still not available for human grafts. Ex-vivo machine perfusion represents a superior organ preservation method over static cold storage. There is still an urgent need for the optimization of the perfusion fluid and machine technology and to identify the optimal indication in kidney transplantation. Recent research is focusing on graft assessment and therapeutic strategies.

Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.

PubMed

Madsen, Kristoffer H; Krohne, Laerke G; Cai, Xin-Lu; Wang, Yi; Chan, Raymond C K

2018-03-15

Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and generalization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical parametric mapping, parcellation, complex network analysis, and decomposition methods, as well as classification with a special focus on support vector classification and deep learning. We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives.

A Second-Generation Device for Automated Training and Quantitative Behavior Analyses of Molecularly-Tractable Model Organisms

PubMed Central

Blackiston, Douglas; Shomrat, Tal; Nicolas, Cindy L.; Granata, Christopher; Levin, Michael

2010-01-01

A deep understanding of cognitive processes requires functional, quantitative analyses of the steps leading from genetics and the development of nervous system structure to behavior. Molecularly-tractable model systems such as Xenopus laevis and planaria offer an unprecedented opportunity to dissect the mechanisms determining the complex structure of the brain and CNS. A standardized platform that facilitated quantitative analysis of behavior would make a significant impact on evolutionary ethology, neuropharmacology, and cognitive science. While some animal tracking systems exist, the available systems do not allow automated training (feedback to individual subjects in real time, which is necessary for operant conditioning assays). The lack of standardization in the field, and the numerous technical challenges that face the development of a versatile system with the necessary capabilities, comprise a significant barrier keeping molecular developmental biology labs from integrating behavior analysis endpoints into their pharmacological and genetic perturbations. Here we report the development of a second-generation system that is a highly flexible, powerful machine vision and environmental control platform. In order to enable multidisciplinary studies aimed at understanding the roles of genes in brain function and behavior, and aid other laboratories that do not have the facilities to undergo complex engineering development, we describe the device and the problems that it overcomes. We also present sample data using frog tadpoles and flatworms to illustrate its use. Having solved significant engineering challenges in its construction, the resulting design is a relatively inexpensive instrument of wide relevance for several fields, and will accelerate interdisciplinary discovery in pharmacology, neurobiology, regenerative medicine, and cognitive science. PMID:21179424

Capturing Structural Snapshots during Photochemical Reactions with Ultrafast Raman Spectroscopy: From Materials Transformation to Biosensor Responses.

PubMed

Fang, Chong; Tang, Longteng; Oscar, Breland G; Chen, Cheng

2018-06-21

Chemistry studies the composition, structure, properties, and transformation of matter. A mechanistic understanding of the pertinent processes is required to translate fundamental knowledge into practical applications. The current development of ultrafast Raman as a powerful time-resolved vibrational technique, particularly femtosecond stimulated Raman spectroscopy (FSRS), has shed light on the structure-energy-function relationships of various photosensitive systems. This Perspective reviews recent work incorporating optical innovations, including the broad-band up-converted multicolor array (BUMA) into a tunable FSRS setup, and demonstrates its resolving power to watch metal speciation and photolysis, leading to high-quality thin films, and fluorescence modulation of chimeric protein biosensors for calcium ion imaging. We discuss advantages of performing FSRS in the mixed time-frequency domain and present strategies to delineate mechanisms by tracking low-frequency modes and systematically modifying chemical structures with specific functional groups. These unique insights at the chemical-bond level have started to enable the rational design and precise control of functional molecular machines in optical, materials, energy, and life sciences.

Long-range coupling between ATP-binding and lever-arm regions in myosin via dielectric allostery

NASA Astrophysics Data System (ADS)

Sato, Takato; Ohnuki, Jun; Takano, Mitsunori

2017-12-01

A protein molecule is a dielectric substance, so the binding of a ligand is expected to induce dielectric response in the protein molecule, considering that ligands are charged or polar in general. We previously reported that binding of adenosine triphosphate (ATP) to molecular motor myosin actually induces such a dielectric response in myosin due to the net negative charge of ATP. By this dielectric response, referred to as "dielectric allostery," spatially separated two regions in myosin, the ATP-binding region and the actin-binding region, are allosterically coupled. In this study, from the statistically stringent analyses of the extensive molecular dynamics simulation data obtained in the ATP-free and the ATP-bound states, we show that there exists the dielectric allostery that transmits the signal of ATP binding toward the distant lever-arm region. The ATP-binding-induced electrostatic potential change observed on the surface of the main domain induced a movement of the converter subdomain from which the lever arm extends. The dielectric response was found to be caused by an underlying large-scale concerted rearrangement of the electrostatic bond network, in which highly conserved charged/polar residues are involved. Our study suggests the importance of the dielectric property for molecular machines in exerting their function.

ATOMIC RESOLUTION CRYO ELECTRON MICROSCOPY OF MACROMOLECULAR COMPLEXES

PubMed Central

ZHOU, Z. HONG

2013-01-01

Single-particle cryo electron microscopy (cryoEM) is a technique for determining three-dimensional (3D) structures from projection images of molecular complexes preserved in their “native,” noncrystalline state. Recently, atomic or near-atomic resolution structures of several viruses and protein assemblies have been determined by single-particle cryoEM, allowing ab initio atomic model building by following the amino acid side chains or nucleic acid bases identifiable in their cryoEM density maps. In particular, these cryoEM structures have revealed extended arms contributing to molecular interactions that are otherwise not resolved by the conventional structural method of X-ray crystallography at similar resolutions. High-resolution cryoEM requires careful consideration of a number of factors, including proper sample preparation to ensure structural homogeneity, optimal configuration of electron imaging conditions to record high-resolution cryoEM images, accurate determination of image parameters to correct image distortions, efficient refinement and computation to reconstruct a 3D density map, and finally appropriate choice of modeling tools to construct atomic models for functional interpretation. This progress illustrates the power of cryoEM and ushers it into the arsenal of structural biology, alongside conventional techniques of X-ray crystallography and NMR, as a major tool (and sometimes the preferred one) for the studies of molecular interactions in supramolecular assemblies or machines. PMID:21501817

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Soft 2D nanoarchitectonics

NASA Astrophysics Data System (ADS)

Ariga, Katsuhiko; Watanabe, Shun; Mori, Taizo; Takeya, Jun

2018-04-01

Nanoarchitectonics is a new paradigm to combine and unify nanotechnology with other sciences and technologies, such as supramolecular chemistry, self-assembly, self-organization, materials technology for manipulation of the size of material objects, and even biotechnology for hybridization with bio-components. The nanoarchitectonic concept leads to the synergistic combination of various methodologies in materials production, including atomic/molecular-level control, self-organization, and field-controlled organization. The focus of this review is on soft 2D nanoarchitectonics. Scientific views on soft 2D nanomaterials are not fully established compared with those on rigid 2D materials. Here, we collect recent examples of 2D nanoarchitectonic constructions of functional materials and systems with soft components. These examples are selected according to the following three categories on the basis of 2D spatial density and motional freedom: (i) well-packed and oriented organic 2D materials with rational design of component molecules and device applications, (ii) well-defined assemblies with 2D porous structures as 2D network materials, and (iii) 2D control of molecular machines and receptors on the basis of certain motional freedom confined in two dimensions.

Nuclear Quantum Effects in H+ and OH- Diffusion Along Confined Water Wires from Ab Initio Path Integral Molecular Dyanmics

NASA Astrophysics Data System (ADS)

Rossi, Mariana; Ceriotti, Michele; Manolopoulos, David

Diffusion of H+ and OH- along water wires provides an efficient mechanism for charge transport that is exploited by biological systems and shows promise in technological applications. However, what is lacking for a better control and design of these systems is a thorough theoretical understanding of the diffusion process at the atomic scale. Here we consider H+ and OH- in finite water wires using density functional theory. We employ machine learning techniques to identify the charged species, thus obtaining an agnostic definition of the charge. We employ thermostated ring polymer molecular dynamics and extract a ``universal'' diffusion coefficient from simulations with different wire sizes by considering Langevin dynamics on the potential of mean force of the charged species. In the classical case, diffusion coefficients depend significantly on the potential energy surface, in particular on how dispersion forces modulate O-O distances. NQEs, however, make the diffusion less sensitive to the underlying potential and geometry of the wire, presumably making them more robust to environment fluctuations.

Transmission of chirality through space and across length scales

NASA Astrophysics Data System (ADS)

Morrow, Sarah M.; Bissette, Andrew J.; Fletcher, Stephen P.

2017-05-01

Chirality is a fundamental property and vital to chemistry, biology, physics and materials science. The ability to use asymmetry to operate molecular-level machines or macroscopically functional devices, or to give novel properties to materials, may address key challenges at the heart of the physical sciences. However, how chirality at one length scale can be translated to asymmetry at a different scale is largely not well understood. In this Review, we discuss systems where chiral information is translated across length scales and through space. A variety of synthetic systems involve the transmission of chiral information between the molecular-, meso- and macroscales. We show how fundamental stereochemical principles may be used to design and understand nanoscale chiral phenomena and highlight important recent advances relevant to nanotechnology. The survey reveals that while the study of stereochemistry on the nanoscale is a rich and dynamic area, our understanding of how to control and harness it and dial-up specific properties is still in its infancy. The long-term goal of controlling nanoscale chirality promises to be an exciting journey, revealing insight into biological mechanisms and providing new technologies based on dynamic physical properties.

Binding Affinity prediction with Property Encoded Shape Distribution signatures

PubMed Central

Das, Sourav; Krein, Michael P.

2010-01-01

We report the use of the molecular signatures known as “Property-Encoded Shape Distributions” (PESD) together with standard Support Vector Machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This “PESD-SVM” method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore – a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (ΔH/-TΔS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach and further improvement in accuracy would require accounting for these components rigorously. PMID:20095526

Creation of smart composites using an embroidery machine

NASA Astrophysics Data System (ADS)

Torii, Nobuhiro; Oka, Kosuke; Ikeda, Tadashige

2016-04-01

A smart composite with functional fibers and reinforcement fibers optimally placed with an embroidery machine was created. Fiber orientation affects mechanical properties of composite laminates significantly. Accordingly, if the fibers can be placed along a desired curved path, fiber reinforced plastic (FRP) structures can be designed more lightly and more sophisticatedly. To this end a tailored fiber placement method using the embroidery machine have been studied. To add functions to the FRP structures, shape memory alloy (SMA) wires were placed as functional fibers. First, for a certain purpose the paths of the reinforcement fibers and the SMA wires were simultaneously optimized in analysis. Next, the reinforcement fibers and tubes with the SMA wires were placed on fabrics by using the embroidery machine and this fabric was impregnated with resin by using the vacuum assisted resin transfer molding method. This smart composite was activated by applying voltage to the SMA wires. Fundamental properties of the smart composite were examined and the feasibility of the proposed creation method was shown.

Microcomputer network for control of a continuous mining machine. Information circular/1993

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schiffbauer, W.H.

1993-01-01

The paper details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines, and installed on a Joy 14 continuous mining machine. The network consists of microcomputers that are connected together via a single twisted pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers in conjunction with the appropriate sensors provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and controlmore » the continuous mining machine. Although the network was installed on a Joy 14 continuous mining machine, its use extends beyond it. Its generic structure lends itself to installation onto most mining machine types.« less

Machine learning for many-body physics: The case of the Anderson impurity model

DOE PAGES

Arsenault, Louis-François; Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole; ...

2014-10-31

We applied machine learning methods in order to find the Green's function of the Anderson impurity model, a basic model system of quantum many-body condensed-matter physics. Furthermore, different methods of parametrizing the Green's function are investigated; a representation in terms of Legendre polynomials is found to be superior due to its limited number of coefficients and its applicability to state of the art methods of solution. The dependence of the errors on the size of the training set is determined. Our results indicate that a machine learning approach to dynamical mean-field theory may be feasible.

Machine learning for many-body physics: The case of the Anderson impurity model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arsenault, Louis-François; Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole

We applied machine learning methods in order to find the Green's function of the Anderson impurity model, a basic model system of quantum many-body condensed-matter physics. Furthermore, different methods of parametrizing the Green's function are investigated; a representation in terms of Legendre polynomials is found to be superior due to its limited number of coefficients and its applicability to state of the art methods of solution. The dependence of the errors on the size of the training set is determined. Our results indicate that a machine learning approach to dynamical mean-field theory may be feasible.

DFT modeling of chemistry on the Z machine

NASA Astrophysics Data System (ADS)

Mattsson, Thomas

2013-06-01

Density Functional Theory (DFT) has proven remarkably accurate in predicting properties of matter under shock compression for a wide-range of elements and compounds: from hydrogen to xenon via water. Materials where chemistry plays a role are of particular interest for many applications. For example the deep interiors of Neptune, Uranus, and hundreds of similar exoplanets are composed of molecular ices of carbon, hydrogen, oxygen, and nitrogen at pressures of several hundred GPa and temperatures of many thousand Kelvin. High-quality thermophysical experimental data and high-fidelity simulations including chemical reaction are necessary to constrain planetary models over a large range of conditions. As examples of where chemical reactions are important, and demonstration of the high fidelity possible for these both structurally and chemically complex systems, we will discuss shock- and re-shock of liquid carbon dioxide (CO2) in the range 100 to 800 GPa, shock compression of the hydrocarbon polymers polyethylene (PE) and poly(4-methyl-1-pentene) (PMP), and finally simulations of shock compression of glow discharge polymer (GDP) including the effects of doping with germanium. Experimental results from Sandia's Z machine have time and again validated the DFT simulations at extreme conditions and the combination of experiment and DFT provide reliable data for evaluating existing and constructing future wide-range equations of state models for molecular compounds like CO2 and polymers like PE, PMP, and GDP. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Company, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships.

PubMed

Janet, Jon Paul; Kulik, Heather J

2017-11-22

Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the molecular representation becomes a critical ingredient in ML model predictive accuracy. We introduce a series of revised autocorrelation functions (RACs) that encode relationships of the heuristic atomic properties (e.g., size, connectivity, and electronegativity) on a molecular graph. We alter the starting point, scope, and nature of the quantities evaluated in standard ACs to make these RACs amenable to inorganic chemistry. On an organic molecule set, we first demonstrate superior standard AC performance to other presently available topological descriptors for ML model training, with mean unsigned errors (MUEs) for atomization energies on set-aside test molecules as low as 6 kcal/mol. For inorganic chemistry, our RACs yield 1 kcal/mol ML MUEs on set-aside test molecules in spin-state splitting in comparison to 15-20× higher errors for feature sets that encode whole-molecule structural information. Systematic feature selection methods including univariate filtering, recursive feature elimination, and direct optimization (e.g., random forest and LASSO) are compared. Random-forest- or LASSO-selected subsets 4-5× smaller than the full RAC set produce sub- to 1 kcal/mol spin-splitting MUEs, with good transferability to metal-ligand bond length prediction (0.004-5 Å MUE) and redox potential on a smaller data set (0.2-0.3 eV MUE). Evaluation of feature selection results across property sets reveals the relative importance of local, electronic descriptors (e.g., electronegativity, atomic number) in spin-splitting and distal, steric effects in redox potential and bond lengths.

The Impact Of Surface Shape Of Chip-Breaker On Machined Surface

NASA Astrophysics Data System (ADS)

Šajgalík, Michal; Czán, Andrej; Martinček, Juraj; Varga, Daniel; Hemžský, Pavel; Pitela, David

2015-12-01

Machined surface is one of the most used indicators of workpiece quality. But machined surface is influenced by several factors such as cutting parameters, cutting material, shape of cutting tool or cutting insert, micro-structure of machined material and other known as technological parameters. By improving of these parameters, we can improve machined surface. In the machining, there is important to identify the characteristics of main product of these processes - workpiece, but also the byproduct - the chip. Size and shape of chip has impact on lifetime of cutting tools and its inappropriate form can influence the machine functionality and lifetime, too. This article deals with elimination of long chip created when machining of shaft in automotive industry and with impact of shape of chip-breaker on shape of chip in various cutting conditions based on production requirements.

Machine learning for the structure-energy-property landscapes of molecular crystals.

PubMed

Musil, Félix; De, Sandip; Yang, Jack; Campbell, Joshua E; Day, Graeme M; Ceriotti, Michele

2018-02-07

Molecular crystals play an important role in several fields of science and technology. They frequently crystallize in different polymorphs with substantially different physical properties. To help guide the synthesis of candidate materials, atomic-scale modelling can be used to enumerate the stable polymorphs and to predict their properties, as well as to propose heuristic rules to rationalize the correlations between crystal structure and materials properties. Here we show how a recently-developed machine-learning (ML) framework can be used to achieve inexpensive and accurate predictions of the stability and properties of polymorphs, and a data-driven classification that is less biased and more flexible than typical heuristic rules. We discuss, as examples, the lattice energy and property landscapes of pentacene and two azapentacene isomers that are of interest as organic semiconductor materials. We show that we can estimate force field or DFT lattice energies with sub-kJ mol -1 accuracy, using only a few hundred reference configurations, and reduce by a factor of ten the computational effort needed to predict charge mobility in the crystal structures. The automatic structural classification of the polymorphs reveals a more detailed picture of molecular packing than that provided by conventional heuristics, and helps disentangle the role of hydrogen bonded and π-stacking interactions in determining molecular self-assembly. This observation demonstrates that ML is not just a black-box scheme to interpolate between reference calculations, but can also be used as a tool to gain intuitive insights into structure-property relations in molecular crystal engineering.

modlAMP: Python for antimicrobial peptides.

PubMed

Müller, Alex T; Gabernet, Gisela; Hiss, Jan A; Schneider, Gisbert

2017-09-01

We have implemented the lecular esign aboratory's nti icrobial eptides package ( ), a Python-based software package for the design, classification and visual representation of peptide data. modlAMP offers functions for molecular descriptor calculation and the retrieval of amino acid sequences from public or local sequence databases, and provides instant access to precompiled datasets for machine learning. The package also contains methods for the analysis and representation of circular dichroism spectra. The modlAMP Python package is available under the BSD license from URL http://doi.org/10.5905/ethz-1007-72 or via pip from the Python Package Index (PyPI). gisbert.schneider@pharma.ethz.ch. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine.

PubMed

Ping, Peipei; Hermjakob, Henning; Polson, Jennifer S; Benos, Panagiotis V; Wang, Wei

2018-04-27

In the digital age of cardiovascular medicine, the rate of biomedical discovery can be greatly accelerated by the guidance and resources required to unearth potential collections of knowledge. A unified computational platform leverages metadata to not only provide direction but also empower researchers to mine a wealth of biomedical information and forge novel mechanistic insights. This review takes the opportunity to present an overview of the cloud-based computational environment, including the functional roles of metadata, the architecture schema of indexing and search, and the practical scenarios of machine learning-supported molecular signature extraction. By introducing several established resources and state-of-the-art workflows, we share with our readers a broadly defined informatics framework to phenotype cardiovascular health and disease. © 2018 American Heart Association, Inc.

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

NASA Astrophysics Data System (ADS)

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

2017-11-01

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Cryptic out-of-frame translational initiation of TBCE rescues tubulin formation in compound heterozygous HRD.

PubMed

Tian, Guoling; Huang, Melissa C; Parvari, Ruti; Diaz, George A; Cowan, Nicholas J

2006-09-05

Microtubules are indispensable dynamic structures that contribute to many essential biological functions. Assembly of the native alpha/beta tubulin heterodimer, the subunit that polymerizes to form microtubules, requires the participation of several molecular chaperones, namely prefoldin, the cytosolic chaperonin CCT, and a series of five tubulin-specific chaperones termed cofactors A-E (TBCA-E). Among these, TBCC, TBCD, and TBCE are essential in higher eukaryotes; they function together as a multimolecular machine that assembles quasinative CCT-generated alpha- and beta-tubulin polypeptides into new heterodimers. Deletion and truncation mutations in the gene encoding TBCE have been shown to cause the rare autosomal recessive syndrome known as HRD, a devastating disorder characterized by congenital hypoparathyroidism, mental retardation, facial dysmorphism, and extreme growth failure. Here we identify cryptic translational initiation at each of three out-of-frame AUG codons upstream of the genetic lesion as a unique mechanism that rescues a mutant HRD allele by producing a functional TBCE protein. Our data explain how afflicted individuals, who would otherwise lack the capacity to make functional TBCE, can survive and point to a limiting capacity to fold tubulin heterodimers de novo as a contributing factor to disease pathogenesis.

Cryptic out-of-frame translational initiation of TBCE rescues tubulin formation in compound heterozygous HRD

PubMed Central

Tian, Guoling; Huang, Melissa C.; Parvari, Ruti; Diaz, George A.; Cowan, Nicholas J.

2006-01-01

Microtubules are indispensable dynamic structures that contribute to many essential biological functions. Assembly of the native α/β tubulin heterodimer, the subunit that polymerizes to form microtubules, requires the participation of several molecular chaperones, namely prefoldin, the cytosolic chaperonin CCT, and a series of five tubulin-specific chaperones termed cofactors A–E (TBCA–E). Among these, TBCC, TBCD, and TBCE are essential in higher eukaryotes; they function together as a multimolecular machine that assembles quasinative CCT-generated α- and β-tubulin polypeptides into new heterodimers. Deletion and truncation mutations in the gene encoding TBCE have been shown to cause the rare autosomal recessive syndrome known as HRD, a devastating disorder characterized by congenital hypoparathyroidism, mental retardation, facial dysmorphism, and extreme growth failure. Here we identify cryptic translational initiation at each of three out-of-frame AUG codons upstream of the genetic lesion as a unique mechanism that rescues a mutant HRD allele by producing a functional TBCE protein. Our data explain how afflicted individuals, who would otherwise lack the capacity to make functional TBCE, can survive and point to a limiting capacity to fold tubulin heterodimers de novo as a contributing factor to disease pathogenesis. PMID:16938882

Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins.

PubMed

Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

2017-09-05

In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

New Insights Into “Plant Memories”

ScienceCinema

Sanbonmatsu, Karissa

2018-01-16

A special stretch of ribonucleic acid (RNA) called COOLAIR is revealing its inner structure and function to scientists, displaying a striking resemblance to an RNA molecular machine, territory previously understood to be limited to the cells’ protein factory (the ‘ribosome’) and not a skill set given to mere strings of RNA. “We are uncovering the nuts and bolts of plant memories,” said Karissa Sanbonmatsu of Los Alamos National Laboratory, lead author on a new article this week in the journal Cell Reports. In the past 5 years or so, material in the cell known as “junk DNA” had actually turned out not to be junk at all. Instead, it was shown to produce RNA molecules that play key roles in the development of organs in the embryo, as well as affecting cancer, brain function and plant biology.

Molecular transformers in the cell: lessons learned from the DegP protease-chaperone.

PubMed

Sawa, Justyna; Heuck, Alexander; Ehrmann, Michael; Clausen, Tim

2010-04-01

Structure-function analysis of DegP revealed a novel mechanism for protease and chaperone regulation. Binding of unfolded proteins induces the oligomer reassembly from the resting hexamer (DegP6) into the functional protease-chaperone DegP12/24. The newly formed cage exhibits the characteristics of a proteolytic folding chamber, shredding those proteins that are severely misfolded while stabilizing and protecting proteins present in their native state. Isolation of native DegP complexes with folded outer membrane proteins (OMPs) highlights the importance of DegP in OMP biogenesis. The encapsulated OMP beta-barrel is significantly stabilized in the hydrophobic chamber of DegP12/24 and thus DegP seems to employ a reciprocal mechanism to those chaperones assisting the folding of water soluble proteins via polar interactions. In addition, we discuss in this review similarities to other complex proteolytic machines that, like DegP, are under control of a substrate-induced or stress-induced oligomer conversion.

The pilus usher controls protein interactions via domain masking and is functional as an oligomer

DOE PAGES

Werneburg, Glenn T.; Li, Huilin; Henderson, Nadine S.; ...

2015-06-08

The chaperone/usher (CU) pathway is responsible for biogenesis of organelles termed pili or fimbriae in Gram-negative bacteria. Type 1 pili expressed by uropathogenic Escherichia coli are prototypical structures assembled by the CU pathway. Assembly and secretion of pili by the CU pathway requires a dedicated periplasmic chaperone and a multidomain outer membrane protein termed the usher (FimD). We show that the FimD C-terminal domains provide the high-affinity substrate binding site, but that these domains are masked in the resting usher. Domain masking requires the FimD plug domain, which served as a central switch controlling usher activation. In addition, we demonstratemore » that usher molecules can act in trans for pilus biogenesis, providing conclusive evidence for a functional usher oligomer. These results reveal mechanisms by which molecular machines such as the usher regulate and harness protein-protein interactions, and suggest that ushers may interact in a cooperative manner during pilus assembly in bacteria.« less

Modelling of internal architecture of kinesin nanomotor as a machine language.

PubMed

Khataee, H R; Ibrahim, M Y

2012-09-01

Kinesin is a protein-based natural nanomotor that transports molecular cargoes within cells by walking along microtubules. Kinesin nanomotor is considered as a bio-nanoagent which is able to sense the cell through its sensors (i.e. its heads and tail), make the decision internally and perform actions on the cell through its actuator (i.e. its motor domain). The study maps the agent-based architectural model of internal decision-making process of kinesin nanomotor to a machine language using an automata algorithm. The applied automata algorithm receives the internal agent-based architectural model of kinesin nanomotor as a deterministic finite automaton (DFA) model and generates a regular machine language. The generated regular machine language was acceptable by the architectural DFA model of the nanomotor and also in good agreement with its natural behaviour. The internal agent-based architectural model of kinesin nanomotor indicates the degree of autonomy and intelligence of the nanomotor interactions with its cell. Thus, our developed regular machine language can model the degree of autonomy and intelligence of kinesin nanomotor interactions with its cell as a language. Modelling of internal architectures of autonomous and intelligent bio-nanosystems as machine languages can lay the foundation towards the concept of bio-nanoswarms and next phases of the bio-nanorobotic systems development.

Synthetic biology and its alternatives. Descartes, Kant and the idea of engineering biological machines.

PubMed

Kogge, Werner; Richter, Michael

2013-06-01

The engineering-based approach of synthetic biology is characterized by an assumption that 'engineering by design' enables the construction of 'living machines'. These 'machines', as biological machines, are expected to display certain properties of life, such as adapting to changing environments and acting in a situated way. This paper proposes that a tension exists between the expectations placed on biological artefacts and the notion of producing such systems by means of engineering; this tension makes it seem implausible that biological systems, especially those with properties characteristic of living beings, can in fact be produced using the specific methods of engineering. We do not claim that engineering techniques have nothing to contribute to the biotechnological construction of biological artefacts. However, drawing on Descartes's and Kant's thinking on the relationship between the organism and the machine, we show that it is considerably more plausible to assume that distinctively biological artefacts emerge within a paradigm different from the paradigm of the Cartesian machine that underlies the engineering approach. We close by calling for increased attention to be paid to approaches within molecular biology and chemistry that rest on conceptions different from those of synthetic biology's engineering paradigm. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population.

PubMed

Mateen, Bilal Akhter; Bussas, Matthias; Doogan, Catherine; Waller, Denise; Saverino, Alessia; Király, Franz J; Playford, E Diane

2018-05-01

To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. Prospective cohort study. Tertiary neurological and neurosurgical center. In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). The principal outcome was a fall during the in-patient stay ( n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test.

Engineered Surface Properties of Porous Tungsten from Cryogenic Machining

NASA Astrophysics Data System (ADS)

Schoop, Julius Malte

Porous tungsten is used to manufacture dispenser cathodes due to it refractory properties. Surface porosity is critical to functional performance of dispenser cathodes because it allows for an impregnated ceramic compound to migrate to the emitting surface, lowering its work function. Likewise, surface roughness is important because it is necessary to ensure uniform wetting of the molten impregnate during high temperature service. Current industry practice to achieve surface roughness and surface porosity requirements involves the use of a plastic infiltrant during machining. After machining, the infiltrant is baked and the cathode pellet is impregnated. In this context, cryogenic machining is investigated as a substitutionary process for the current plastic infiltration process. Along with significant reductions in cycle time and resource use, surface quality of cryogenically machined un-infiltrated (as-sintered) porous tungsten has been shown to significantly outperform dry machining. The present study is focused on examining the relationship between machining parameters and cooling condition on the as-machined surface integrity of porous tungsten. The effects of cryogenic pre-cooling, rake angle, cutting speed, depth of cut and feed are all taken into consideration with respect to machining-induced surface morphology. Cermet and Polycrystalline diamond (PCD) cutting tools are used to develop high performance cryogenic machining of porous tungsten. Dry and pre-heated machining were investigated as a means to allow for ductile mode machining, yet severe tool-wear and undesirable smearing limited the feasibility of these approaches. By using modified PCD cutting tools, high speed machining of porous tungsten at cutting speeds up to 400 m/min is achieved for the first time. Beyond a critical speed, brittle fracture and built-up edge are eliminated as the result of a brittle to ductile transition. A model of critical chip thickness ( hc ) effects based on cutting force, temperature and surface roughness data is developed and used to study the deformation mechanisms of porous tungsten under different machining conditions. It is found that when hmax = hc, ductile mode machining of otherwise highly brittle porous tungsten is possible. The value of hc is approximately the same as the average ligament size of the 80% density porous tungsten workpiece.

Development of techniques to enhance man/machine communication

NASA Technical Reports Server (NTRS)

Targ, R.; Cole, P.; Puthoff, H.

1974-01-01

A four-state random stimulus generator, considered to function as an ESP teaching machine was used to investigate an approach to facilitating interactions between man and machines. A subject tries to guess in which of four states the machine is. The machine offers the user feedback and reinforcement as to the correctness of his choice. Using this machine, 148 volunteer subjects were screened under various protocols. Several whose learning slope and/or mean score departed significantly from chance expectation were identified. Direct physiological evidence of perception of remote stimuli not presented to any known sense of the percipient using electroencephalographic (EEG) output when a light was flashed in a distant room was also studied.

Future of Mechatronics and Human

NASA Astrophysics Data System (ADS)

Harashima, Fumio; Suzuki, Satoshi

This paper mentions circumstance of mechatronics that sustain our human society, and introduces HAM(Human Adaptive Mechatronics)-project as one of research projects to create new human-machine system. The key point of HAM is skill, and analysis of skill and establishment of assist method to enhance total performance of human-machine system are main research concerns. As study of skill is an elucidation of human itself, analyses of human higher function are significant. In this paper, after surveying researches of human brain functions, an experimental analysis of human characteristic in machine operation is shown as one example of our research activities. We used hovercraft simulator as verification system including observation, voluntary motion control and machine operation that are needed to general machine operation. Process and factors to become skilled were investigated by identification of human control characteristics with measurement of the operator's line-of sight. It was confirmed that early switching of sub-controllers / reference signals in human and enhancement of space perception are significant.

Modelling of human-machine interaction in equipment design of manufacturing cells

NASA Astrophysics Data System (ADS)

Cochran, David S.; Arinez, Jorge F.; Collins, Micah T.; Bi, Zhuming

2017-08-01

This paper proposes a systematic approach to model human-machine interactions (HMIs) in supervisory control of machining operations; it characterises the coexistence of machines and humans for an enterprise to balance the goals of automation/productivity and flexibility/agility. In the proposed HMI model, an operator is associated with a set of behavioural roles as a supervisor for multiple, semi-automated manufacturing processes. The model is innovative in the sense that (1) it represents an HMI based on its functions for process control but provides the flexibility for ongoing improvements in the execution of manufacturing processes; (2) it provides a computational tool to define functional requirements for an operator in HMIs. The proposed model can be used to design production systems at different levels of an enterprise architecture, particularly at the machine level in a production system where operators interact with semi-automation to accomplish the goal of 'autonomation' - automation that augments the capabilities of human beings.

Space Station man-machine automation trade-off analysis

NASA Technical Reports Server (NTRS)

Zimmerman, W. F.; Bard, J.; Feinberg, A.

1985-01-01

The man machine automation tradeoff methodology presented is of four research tasks comprising the autonomous spacecraft system technology (ASST) project. ASST was established to identify and study system level design problems for autonomous spacecraft. Using the Space Station as an example spacecraft system requiring a certain level of autonomous control, a system level, man machine automation tradeoff methodology is presented that: (1) optimizes man machine mixes for different ground and on orbit crew functions subject to cost, safety, weight, power, and reliability constraints, and (2) plots the best incorporation plan for new, emerging technologies by weighing cost, relative availability, reliability, safety, importance to out year missions, and ease of retrofit. A fairly straightforward approach is taken by the methodology to valuing human productivity, it is still sensitive to the important subtleties associated with designing a well integrated, man machine system. These subtleties include considerations such as crew preference to retain certain spacecraft control functions; or valuing human integration/decision capabilities over equivalent hardware/software where appropriate.

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

PubMed

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Designing Anticancer Peptides by Constructive Machine Learning.

PubMed

Grisoni, Francesca; Neuhaus, Claudia S; Gabernet, Gisela; Müller, Alex T; Hiss, Jan A; Schneider, Gisbert

2018-04-21

Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on α-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs by transfer learning. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Machine Learning Based Dimensionality Reduction Facilitates Ligand Diffusion Paths Assessment: A Case of Cytochrome P450cam.

PubMed

Rydzewski, J; Nowak, W

2016-04-12

In this work we propose an application of a nonlinear dimensionality reduction method to represent the high-dimensional configuration space of the ligand-protein dissociation process in a manner facilitating interpretation. Rugged ligand expulsion paths are mapped into 2-dimensional space. The mapping retains the main structural changes occurring during the dissociation. The topological similarity of the reduced paths may be easily studied using the Fréchet distances, and we show that this measure facilitates machine learning classification of the diffusion pathways. Further, low-dimensional configuration space allows for identification of residues active in transport during the ligand diffusion from a protein. The utility of this approach is illustrated by examination of the configuration space of cytochrome P450cam involved in expulsing camphor by means of enhanced all-atom molecular dynamics simulations. The expulsion trajectories are sampled and constructed on-the-fly during molecular dynamics simulations using the recently developed memetic algorithms [ Rydzewski, J.; Nowak, W. J. Chem. Phys. 2015 , 143 ( 12 ), 124101 ]. We show that the memetic algorithms are effective for enforcing the ligand diffusion and cavity exploration in the P450cam-camphor complex. Furthermore, we demonstrate that machine learning techniques are helpful in inspecting ligand diffusion landscapes and provide useful tools to examine structural changes accompanying rare events.

Adaptive Training of Manual Control: 1. Comparison of Three Adaptive Variables and Two Logic Schemes.

ERIC Educational Resources Information Center

Norman, D. A.; And Others

"Machine controlled adaptive training is a promising concept. In adaptive training the task presented to the trainee varies as a function of how well he performs. In machine controlled training, adaptive logic performs a function analogous to that performed by a skilled operator." This study looks at the ways in which gain-effective time…

Multiple machine learning based descriptive and predictive workflow for the identification of potential PTP1B inhibitors.

PubMed

Chandra, Sharat; Pandey, Jyotsana; Tamrakar, Akhilesh Kumar; Siddiqi, Mohammad Imran

2017-01-01

In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors. Several models from each algorithm have been constructed and optimized, with the different combination of molecular descriptors and structural fingerprints. For the training and test sets, most of the predictive models showed more than 90% of overall prediction accuracies. The best model was obtained with support vector machine approach and has Matthews Correlation Coefficient of 0.82 for the external test set, which was further employed for the virtual screening of Maybridge small compound database. Five compounds were subsequently selected for experimental assay. Out of these two compounds were found to inhibit PTP1B with significant inhibitory activity in in-vitro inhibition assay. The structural fragments which are important for PTP1B inhibition were identified by naïve Bayesian method and can be further exploited to design new molecules around the identified scaffolds. The descriptive and predictive modeling strategy applied in this study is capable of identifying PTP1B inhibitors from the large compound libraries. Copyright © 2016 Elsevier Inc. All rights reserved.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

The effect of the use of a TNF-alpha inhibitor in hypothermic machine perfusion on kidney function after transplantation.

PubMed

Diuwe, Piotr; Domagala, Piotr; Durlik, Magdalena; Trzebicki, Janusz; Chmura, Andrzej; Kwiatkowski, Artur

2017-08-01

One of the most important problems in transplantation medicine is the ischemia/reperfusion injury of the organs to be transplanted. The aim of the present study was to assess the effect of tumor necrosis factor-alpha (TNF-alpha) inhibitor etanercept on the machine perfusion hypothermia of renal allograft kidney function and organ perfusion. No statistically significant differences were found in the impact of the applied intervention on kidney machine perfusion during which the average flow and vascular resistance were evaluated. There were no statistically significant differences in the occurrence of delayed graft function (DGF). Fewer events in patients who received a kidney from the etanercept treated Group A compared to the patients who received a kidney from the control Group B were observed when comparing the functional DGF and occurrence of acute rejection episodes, however, there was no statistically significant difference. In summary, no effect of treatment with etanercept an inhibitor of TNF-alpha in a hypothermic machine perfusion on renal allograft renal survival and its perfusion were detected in this study. However, treatment of the isolated organ may be important for the future of transplantation medicine. Copyright © 2017 Elsevier Inc. All rights reserved.

Multiprocessor Z-Buffer Architecture for High-Speed, High Complexity Computer Image Generation.

DTIC Science & Technology

1983-12-01

Oversampling 50 17. "Poking Through" Effects 51 18. Sampling Paths 52 19. Triangle Variables 54 20. Intelligent Tiling Algorithm 61 21. Tiler Functional Blocks...64 * 22. HSD Interface 65 23. Tiling Machine Setup 67 24. Tiling Machine 68 25. Tile Accumulate 69 26. A lx$ Sorting Machine 77 27. A 2x8 Sorting...Delay 227 87. Effect of Triangle Size on Tiler Throughput Rates 229 88. Tiling Machine Setup Stage Performance for Oversample Mode 234 89. Tiling

Embedded control system for computerized franking machine

NASA Astrophysics Data System (ADS)

Shi, W. M.; Zhang, L. B.; Xu, F.; Zhan, H. W.

2007-12-01

This paper presents a novel control system for franking machine. A methodology for operating a franking machine using the functional controls consisting of connection, configuration and franking electromechanical drive is studied. A set of enabling technologies to synthesize postage management software architectures driven microprocessor-based embedded systems is proposed. The cryptographic algorithm that calculates mail items is analyzed to enhance the postal indicia accountability and security. The study indicated that the franking machine is reliability, performance and flexibility in printing mail items.

Prediction and control of the service-related properties of parts at the technological preparation stage and during the manufacture process

NASA Astrophysics Data System (ADS)

Bez'iazychnyi, V. F.

The paper is concerned with the problem of optimizing the machining of aircraft engine parts in order to satisfy certain requirements for tool wear, machining precision and surface layer characteristics, and hardening depth. A generalized multiple-objective function and its computer implementation are developed which make it possible to optimize the machining process without the use of experimental data. Alternative methods of controlling the machining process are discussed.

Elastic Molecular Machines and a New Motive Force in Protein Mechanisms,

DTIC Science & Technology

1987-01-01

coupling of the second kind. It is proposed that these new considerations are relevant to mechanisms for the turning on and off of elastic forces in protein mechanisms as varied as those of enzymes and muscle contraction .

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

Quantitative structure-retention relationship models for the prediction of the reversed-phase HPLC gradient retention based on the heuristic method and support vector machine.

PubMed

Du, Hongying; Wang, Jie; Yao, Xiaojun; Hu, Zhide

2009-01-01

The heuristic method (HM) and support vector machine (SVM) were used to construct quantitative structure-retention relationship models by a series of compounds to predict the gradient retention times of reversed-phase high-performance liquid chromatography (HPLC) in three different columns. The aims of this investigation were to predict the retention times of multifarious compounds, to find the main properties of the three columns, and to indicate the theory of separation procedures. In our method, we correlated the retention times of many diverse structural analytes in three columns (Symmetry C18, Chromolith, and SG-MIX) with their representative molecular descriptors, calculated from the molecular structures alone. HM was used to select the most important molecular descriptors and build linear regression models. Furthermore, non-linear regression models were built using the SVM method; the performance of the SVM models were better than that of the HM models, and the prediction results were in good agreement with the experimental values. This paper could give some insights into the factors that were likely to govern the gradient retention process of the three investigated HPLC columns, which could theoretically supervise the practical experiment.

Mesoscale Simulation and Machine Learning of Asphaltene Aggregation Phase Behavior and Molecular Assembly Landscapes.

PubMed

Wang, Jiang; Gayatri, Mohit A; Ferguson, Andrew L

2017-05-11

Asphaltenes constitute the heaviest fraction of the aromatic group in crude oil. Aggregation and precipitation of asphaltenes during petroleum processing costs the petroleum industry billions of dollars each year due to downtime and production inefficiencies. Asphaltene aggregation proceeds via a hierarchical self-assembly process that is well-described by the Yen-Mullins model. Nevertheless, the microscopic details of the emergent cluster morphologies and their relative stability under different processing conditions remain poorly understood. We perform coarse-grained molecular dynamics simulations of a prototypical asphaltene molecule to establish a phase diagram mapping the self-assembled morphologies as a function of temperature, pressure, and n-heptane:toluene solvent ratio informing how to control asphaltene aggregation by regulating external processing conditions. We then combine our simulations with graph matching and nonlinear manifold learning to determine low-dimensional free energy surfaces governing asphaltene self-assembly. In doing so, we introduce a variant of diffusion maps designed to handle data sets with large local density variations, and report the first application of many-body diffusion maps to molecular self-assembly to recover a pseudo-1D free energy landscape. Increasing pressure only weakly affects the landscape, serving only to destabilize the largest aggregates. Increasing temperature and toluene solvent fraction stabilizes small cluster sizes and loose bonding arrangements. Although the underlying molecular mechanisms differ, the strikingly similar effect of these variables on the free energy landscape suggests that toluene acts upon asphaltene self-assembly as an effective temperature.

Application of kernel method in fluorescence molecular tomography

NASA Astrophysics Data System (ADS)

Zhao, Yue; Baikejiang, Reheman; Li, Changqing

2017-02-01

Reconstruction of fluorescence molecular tomography (FMT) is an ill-posed inverse problem. Anatomical guidance in the FMT reconstruction can improve FMT reconstruction efficiently. We have developed a kernel method to introduce the anatomical guidance into FMT robustly and easily. The kernel method is from machine learning for pattern analysis and is an efficient way to represent anatomical features. For the finite element method based FMT reconstruction, we calculate a kernel function for each finite element node from an anatomical image, such as a micro-CT image. Then the fluorophore concentration at each node is represented by a kernel coefficient vector and the corresponding kernel function. In the FMT forward model, we have a new system matrix by multiplying the sensitivity matrix with the kernel matrix. Thus, the kernel coefficient vector is the unknown to be reconstructed following a standard iterative reconstruction process. We convert the FMT reconstruction problem into the kernel coefficient reconstruction problem. The desired fluorophore concentration at each node can be calculated accordingly. Numerical simulation studies have demonstrated that the proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images. In the proposed kernel method, the anatomical guidance can be obtained directly from the anatomical image and is included in the forward modeling. One of the advantages is that we do not need to segment the anatomical image for the targets and background.

The helical domain of the EcoR124I motor subunit participates in ATPase activity and dsDNA translocation

PubMed Central

Shamayeva, Katsiaryna; Guzanova, Alena; Řeha, David; Csefalvay, Eva; Carey, Jannette; Weiserova, Marie

2017-01-01

Type I restriction-modification enzymes are multisubunit, multifunctional molecular machines that recognize specific DNA target sequences, and their multisubunit organization underlies their multifunctionality. EcoR124I is the archetype of Type I restriction-modification family IC and is composed of three subunit types: HsdS, HsdM, and HsdR. DNA cleavage and ATP-dependent DNA translocation activities are housed in the distinct domains of the endonuclease/motor subunit HsdR. Because the multiple functions are integrated in this large subunit of 1,038 residues, a large number of interdomain contacts might be expected. The crystal structure of EcoR124I HsdR reveals a surprisingly sparse number of contacts between helicase domain 2 and the C-terminal helical domain that is thought to be involved in assembly with HsdM. Only two potential hydrogen-bonding contacts are found in a very small contact region. In the present work, the relevance of these two potential hydrogen-bonding interactions for the multiple activities of EcoR124I is evaluated by analysing mutant enzymes using in vivo and in vitro experiments. Molecular dynamics simulations are employed to provide structural interpretation of the functional data. The results indicate that the helical C-terminal domain is involved in the DNA translocation, cleavage, and ATPase activities of HsdR, and a role in controlling those activities is suggested. PMID:28133570

Hypothermic machine perfusion in kidney transplantation.

PubMed

De Deken, Julie; Kocabayoglu, Peri; Moers, Cyril

2016-06-01

This article summarizes novel developments in hypothermic machine perfusion (HMP) as an organ preservation modality for kidneys recovered from deceased donors. HMP has undergone a renaissance in recent years. This renewed interest has arisen parallel to a shift in paradigms; not only optimal preservation of an often marginal quality graft is required, but also improved graft function and tools to predict the latter are expected from HMP. The focus of attention in this field is currently drawn to the protection of endothelial integrity by means of additives to the perfusion solution, improvement of the HMP solution, choice of temperature, duration of perfusion, and machine settings. HMP may offer the opportunity to assess aspects of graft viability before transplantation, which can potentially aid preselection of grafts based on characteristics such as perfusate biomarkers, as well as measurement of machine perfusion dynamics parameters. HMP has proven to be beneficial as a kidney preservation method for all types of renal grafts, most notably those retrieved from extended criteria donors. Large numbers of variables during HMP, such as duration, machine settings and additives to the perfusion solution are currently being investigated to improve renal function and graft survival. In addition, the search for biomarkers has become a focus of attention to predict graft function posttransplant.

Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

PubMed Central

Xue, Yong; Chen, Shihui; Liu, Yong

2017-01-01

Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical features with powerful representation capability. Research on cancer molecular images using deep learning techniques is also increasing dynamically. Hence, in this paper, we review the applications of deep learning in molecular imaging in terms of tumor lesion segmentation, tumor classification, and survival prediction. We also outline some future directions in which researchers may develop more powerful deep learning models for better performance in the applications in cancer molecular imaging. PMID:29114182

Enhanced Learning through Design Problems--Teaching a Components-Based Course through Design

ERIC Educational Resources Information Center

Jensen, Bogi Bech; Hogberg, Stig; Jensen, Frida av Flotum; Mijatovic, Nenad

2012-01-01

This paper describes a teaching method used in an electrical machines course, where the students learn about electrical machines by designing them. The aim of the course is not to teach design, albeit this is a side product, but rather to teach the fundamentals and the function of electrical machines through design. The teaching method is…

Using Multiple FPGA Architectures for Real-time Processing of Low-level Machine Vision Functions

Treesearch

Thomas H. Drayer; William E. King; Philip A. Araman; Joseph G. Tront; Richard W. Conners

1995-01-01

In this paper, we investigate the use of multiple Field Programmable Gate Array (FPGA) architectures for real-time machine vision processing. The use of FPGAs for low-level processing represents an excellent tradeoff between software and special purpose hardware implementations. A library of modules that implement common low-level machine vision operations is presented...

Draft Genome Sequence of thermoalkaliphilic Caldalkalibacillus thermarum strain TA2.A1 Reveals Molecular Adaptations to Extreme pH and Temperature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kalamorz, Falk; Keis, Stefanie; Stanton, Jo-Ann

The genes and molecular machines that allow for a thermoalkaliphilic lifestyle have not been defined. To address this goal, we report on the improved high-quality draft genome sequence of Caldalkalibacillus thermarum strain TA2.A1, an obligately aerobic bacterium that grows optimally at pH 9.5 and 65 to 70 C on a wide variety of carbon and energy sources.

Determination of the Microscopic Structure of Surface and Overlayers, Adsorbate-Adsorbate Interaction Energies, and Rates of Surface Processes.

DTIC Science & Technology

1982-12-28

molecular beam-surface scattering, high pressure microreactor , heterogeneous catalysis. :116. AmTRAC? ’CAuI1ae 4111, 8ee 1 111 It oesey -1lP d ify by...Crystallography.. . ..... ....................... 4 11. Design and Construction of a High Pressure Catalvtic Microreactor ... microreactor has been designed and constructed. This micro- reactor will be a useful adjunct to the molecular beam machine since in the former overall

A bio-inspired approach for the design of a multifunctional robotic end-effector customized for automated maintenance of a reconfigurable vibrating screen.

PubMed

Makinde, O A; Mpofu, K; Vrabic, R; Ramatsetse, B I

2017-01-01

The development of a robotic-driven maintenance solution capable of automatically maintaining reconfigurable vibrating screen (RVS) machine when utilized in dangerous and hazardous underground mining environment has called for the design of a multifunctional robotic end-effector capable of carrying out all the maintenance tasks on the RVS machine. In view of this, the paper presents a bio-inspired approach which unfolds the design of a novel multifunctional robotic end-effector embedded with mechanical and control mechanisms capable of automatically maintaining the RVS machine. To achieve this, therblig and morphological methodologies (which classifies the motions as well as the actions required by the robotic end-effector in carrying out RVS machine maintenance tasks), obtained from a detailed analogy of how human being (i.e. a machine maintenance manager) will carry out different maintenance tasks on the RVS machine, were used to obtain the maintenance objective functions or goals of the multifunctional robotic end-effector as well as the maintenance activity constraints of the RVS machine that must be adhered to by the multifunctional robotic end-effector during the machine maintenance. The results of the therblig and morphological analyses of five (5) different maintenance tasks capture and classify one hundred and thirty-four (134) repetitive motions and fifty-four (54) functions required in automating the maintenance tasks of the RVS machine. Based on these findings, a worm-gear mechanism embedded with fingers extruded with a hexagonal shaped heads capable of carrying out the "gripping and ungrasping" and "loosening and bolting" functions of the robotic end-effector and an electric cylinder actuator module capable of carrying out "unpinning and hammering" functions of the robotic end-effector were integrated together to produce the customized multifunctional robotic end-effector capable of automatically maintaining the RVS machine. The axial forces ([Formula: see text] and [Formula: see text]), normal forces ([Formula: see text]) and total load [Formula: see text] acting on the teeth of the worm-gear module of the multifunctional robotic end-effector during the gripping of worn-out or new RVS machine subsystems, which are 978.547, 1245.06 and 1016.406 N, respectively, were satisfactory. The nominal bending and torsional stresses acting on the shoulder of the socket module of the multifunctional robotic end-effector during the loosing and tightening of bolts, which are 1450.72 and 179.523 MPa, respectively, were satisfactory. The hammering and unpinning forces utilized by the electric cylinder actuator module of the multifunctional robotic end-effector during the unpinning and hammering of screen panel pins out of and into the screen panels were satisfactory.

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.

PubMed

Sartori, Juliana M; Reckziegel, Ramiro; Passos, Ives Cavalcante; Czepielewski, Leticia S; Fijtman, Adam; Sodré, Leonardo A; Massuda, Raffael; Goi, Pedro D; Vianna-Sulzbach, Miréia; Cardoso, Taiane de Azevedo; Kapczinski, Flávio; Mwangi, Benson; Gama, Clarissa S

2018-08-01

Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well established, daily-life functioning impairments bulge among the consequences of the proposed progression. The objective of this study was to analyze MRI volumetric modifications in BD and healthy controls (HC) as possible predictors of daily-life functioning through a machine learning approach. Ninety-four participants (35 DSM-IV BD type I and 59 HC) underwent clinical and functioning assessments, and structural MRI. Functioning was assessed using the Functioning Assessment Short Test (FAST). The machine learning analysis was used to identify possible candidates of regional brain volumes that could predict functioning status, through a support vector regression algorithm. Patients with BD and HC did not differ in age, education and marital status. There were significant differences between groups in gender, BMI, FAST score, and employment status. There was significant correlation between observed and predicted FAST score for patients with BD, but not for controls. According to the model, the brain structures volumes that could predict FAST scores were: left superior frontal cortex, left rostral medial frontal cortex, right white matter total volume and right lateral ventricle volume. The machine learning approach demonstrated that brain volume changes in MRI were predictors of FAST score in patients with BD and could identify specific brain areas related to functioning impairment. Copyright © 2018 Elsevier Ltd. All rights reserved.

A review of supervised machine learning applied to ageing research.

PubMed

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Hypothermic machine perfusion permits extended cold ischemia times with improved early graft function.

PubMed

Guy, Alison; McGrogan, Damian; Inston, Nicholas; Ready, Andrew

2015-04-01

The logistics of deceased-donor renal transplants are largely affected by cold ischemia time. However, to attain successful outcomes, other issues must be considered. Extending cold ischemia time to accommodate these issues would be valuable. We investigated the role of hypothermic machine perfusion to extend cold ischaemia time. Deceased-donor kidneys were allocated to a storage method, depending on predicted time to operation. Kidneys to be transplanted from 8:00 AM to 8:00 PM in the transplant room remained in static cold storage. If predicted operating time was out of hours, the kidney was transferred to hypothermic machine perfusion and transplanted at the earliest opportunity on the dedicated transplant list. There were 74 kidneys transplanted from hypothermic machine perfusion and 101 kidneys from static cold storage. Median cold ischemia time was 23.85 hours in the hypothermic machine perfusion group, compared with 13 hours in the static cold storage group (P ≤ .0001). There were 20 kidneys (27%) from hypothermic machine perfusion that had delayed graft function, compared with 47 kidneys (47%) in the static cold storage group (P = .012). There were no other significant differences in graft or postoperative complications. This study demonstrated that improved early graft outcomes can be achieved following longer cold ischemia time by using hypothermic machine perfusion rather than static cold storage. This effect is likely multifactorial including the inherent effects of hypothermic machine perfusion, improved recipient preparation, and possibly better perioperative conditions.

Chatter active control in a lathe machine using magnetostrictive actuator

NASA Astrophysics Data System (ADS)

Nosouhi, R.; Behbahani, S.

2011-01-01

This paper analyzes the chatter phenomena in lathe machines. Chatter is one of the main causes of inaccuracy, reduction of life cycle of the machine and tool wear in machine tools. This phenomenon limits the depth of cut as a function of the cutting speed, which consequently reduces the material removal rate and machining efficiency. Chatter control is therefore important since it increases the stability region in machining and increases the critical depth of cut in machining case. To control the chatter in lathe machines, a magnetostrictive actuator is used. The materials with magnetostriction properties are kind of smart materials of which their length changes as a result of applying an exterior magnetic field, which make them suitable for control applications. It is assumed that the actuator applies the proper force exactly at the point where the machining force is applied on the tool. In this paper the chatter stability lobes is excelled as a result of applying a PID controller on the magnetostrictive actuator equipped-tool in turning.

Variant Interpretation: Functional Assays to the Rescue.

PubMed

Starita, Lea M; Ahituv, Nadav; Dunham, Maitreya J; Kitzman, Jacob O; Roth, Frederick P; Seelig, Georg; Shendure, Jay; Fowler, Douglas M

2017-09-07

Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of "lookup tables" of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Prediction of p38 map kinase inhibitory activity of 3, 4-dihydropyrido [3, 2-d] pyrimidone derivatives using an expert system based on principal component analysis and least square support vector machine

PubMed Central

Shahlaei, M.; Saghaie, L.

2014-01-01

A quantitative structure–activity relationship (QSAR) study is suggested for the prediction of biological activity (pIC50) of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors. Modeling of the biological activities of compounds of interest as a function of molecular structures was established by means of principal component analysis (PCA) and least square support vector machine (LS-SVM) methods. The results showed that the pIC50 values calculated by LS-SVM are in good agreement with the experimental data, and the performance of the LS-SVM regression model is superior to the PCA-based model. The developed LS-SVM model was applied for the prediction of the biological activities of pyrimidone derivatives, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.460 for LS-SVM. The study provided a novel and effective approach for predicting biological activities of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors and disclosed that LS-SVM can be used as a powerful chemometrics tool for QSAR studies. PMID:26339262

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

MANUFACTURING PROCESS FUNCTIONS--I. AN ALTERNATIVE MODEL AND ITS COMPARISON WITH EXISTING FUNCTIONS (AND)II. SELECTION OF TRAINEES AND CONTROL OF THEIR PROGRESS.

ERIC Educational Resources Information Center

GLOVER, J.H.

THE CHIEF OBJECTIVE OF THIS STUDY OF SPEED-SKILL ACQUISITION WAS TO FIND A MATHEMATICAL MODEL CAPABLE OF SIMPLE GRAPHIC INTERPRETATION FOR INDUSTRIAL TRAINING AND PRODUCTION SCHEDULING AT THE SHOP FLOOR LEVEL. STUDIES OF MIDDLE SKILL DEVELOPMENT IN MACHINE AND VEHICLE ASSEMBLY, AIRCRAFT PRODUCTION, SPOOLMAKING AND THE MACHINING OF PARTS CONFIRMED…

Bio-Inspired Human-Level Machine Learning

DTIC Science & Technology

2015-10-25

extensions to high-level cognitive functions such as anagram solving problem. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...extensions to high-level cognitive functions such as anagram solving problem. We expect that the bio-inspired human-level machine learning combined with...numbers of 1011 neurons and 1014 synaptic connections in the human brain. In previous work, we experimentally demonstrated the feasibility of cognitive

Low-Dimensional Materials for Optoelectronic and Bioelectronic Applications

NASA Astrophysics Data System (ADS)

Hong, Tu

In this thesis, we first discuss the fundamentals of ab initio electronic structure theory and density functional theory (DFT). We also discuss statistics related to computing thermodynamic averages of molecular dynamics (MD). We then use this theory to analyze and compare the structural, dynamical, and electronic properties of liquid water next to prototypical metals including platinum, graphite, and graphene. Our results are built on Born-Oppenheimer molecular dynamics (BOMD) generated using density functional theory (DFT) which explicitly include van der Waals (vdW) interactions within a first principles approach. All calculations reported use large simulation cells, allowing for an accurate treatment of the water-electrode interfaces. We have included vdW interactions through the use of the optB86b-vdW exchange correlation functional. Comparisons with the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional are also shown. We find an initial peak, due to chemisorption, in the density profile of the liquid water-Pt interface not seen in the liquid water-graphite interface, liquid watergraphene interface, nor interfaces studied previously. To further investigate this chemisorption peak, we also report differences in the electronic structure of single water molecules on both Pt and graphite surfaces. We find that a covalent bond forms between the single water molecule and the platinum surface, but not between the single water molecule and the graphite surface. We also discuss the effects that defects and dopants in the graphite and graphene surfaces have on the structure and dynamics of liquid water. Lastly, we introduce artificial neural networks (ANNs), and demonstrate how they can be used to machine learn electronic structure calculations. As a proof of principle, we show the success of an ANN potential energy surfaces for a dimer molecule with a Lennard-Jones potential.

Soft electroactive actuators and hard ratchet-wheels enable unidirectional locomotion of hybrid machine

NASA Astrophysics Data System (ADS)

Sun, Wenjie; Liu, Fan; Ma, Ziqi; Li, Chenghai; Zhou, Jinxiong

2017-01-01

Combining synergistically the muscle-like actuation of soft materials and load-carrying and locomotive capability of hard mechanical components results in hybrid soft machines that can exhibit specific functions. Here, we describe the design, fabrication, modeling and experiment of a hybrid soft machine enabled by marrying unidirectionally actuated dielectric elastomer (DE) membrane-spring system and ratchet wheels. Subjected to an applied voltage 8.2 kV at ramping velocity 820 V/s, the hybrid machine prototype exhibits monotonic uniaxial locomotion with an averaged velocity 0.5mm/s. The underlying physics and working mechanisms of the soft machine are verified and elucidated by finite element simulation.

An information-carrying and knowledge-producing molecular machine. A Monte-Carlo simulation.

PubMed

Kuhn, Christoph

2012-02-01

The concept called Knowledge is a measure of the quality of genetically transferred information. Its usefulness is demonstrated quantitatively in a Monte-Carlo simulation on critical steps in a origin of life model. The model describes the origin of a bio-like genetic apparatus by a long sequence of physical-chemical steps: it starts with the presence of a self-replicating oligomer and a specifically structured environment in time and space that allow for the formation of aggregates such as assembler-hairpins-devices and, at a later stage, an assembler-hairpins-enzyme device-a first translation machine.

A machine for the preliminary investigation of design features influencing the wear behaviour of knee prostheses.

PubMed

McGloughlin, T M; Murphy, D M; Kavanagh, A G

2004-01-01

Degradation of tibial inserts in vivo has been found to be multifactorial in nature, resulting in a complex interaction of many variables. A range of kinematic conditions occurs at the tibio-femoral interface, giving rise to various degrees of rolling and sliding at this interface. The movement of the tibio-femoral contact point may be an influential factor in the overall wear of ultra-high molecular weight polyethylene (UHMWPE) tibial components. As part of this study a three-station wear-test machine was designed and built to investigate the influence of rolling and sliding on the wear behaviour of specific design aspects of contemporary knee prostheses. Using the machine, it is possible to monitor the effect of various slide roll ratios on the performance of contemporary bearing designs from a geometrical and materials perspective.

The wear of cross-linked polyethylene against itself.

PubMed

Joyce, T J; Ash, H E; Unsworth, A

1996-01-01

Cross-linked polyethylene (XLPE) may have an application as a material for an all-plastic surface replacement finger joint. It is inexpensive, biocompatible and can be injection-moulded into the complex shapes that are found on the ends of the finger bones. Further, the cross-linking of polyethylene has significantly improved its mechanical properties. Therefore, the opportunity exists for an all-XLPE joint, and so the wear characteristics of XLPE sliding against itself have been investigated. Wear tests were carried out on both reciprocating pin-on-plate machines and a finger function simulator. The reciprocating pin-on-plate machines had pins loaded at 10 N and 40 N. All pin-on-plate tests show wear factors from the plates very much greater than those of the pins. After 349 km of sliding, a mean wear factor of 0.46 x 10(-6) mm3/N m was found for the plates compared with 0.021 x 10(-6) mm3/N m for the pins. A fatigue mechanism may be causing this phenomenon of greater plate wear. Tests using the finger function simulator give an average wear rate of 0.22 x 10(-6) mm3/N m after 368 km. This sliding distance is equivalent to 12.5 years of use in vivo. The wear factors found were comparable with those of ultra-high molecular weight polyethylene (UHMWPE) against a metallic counterface and, therefore, as the loads across the finger joint are much less than those across the knee or the hip, it is probable that an all-XLPE finger joint will be viable from a wear point of view.

Piece-wise quadratic approximations of arbitrary error functions for fast and robust machine learning.

PubMed

Gorban, A N; Mirkes, E M; Zinovyev, A

2016-12-01

Most of machine learning approaches have stemmed from the application of minimizing the mean squared distance principle, based on the computationally efficient quadratic optimization methods. However, when faced with high-dimensional and noisy data, the quadratic error functionals demonstrated many weaknesses including high sensitivity to contaminating factors and dimensionality curse. Therefore, a lot of recent applications in machine learning exploited properties of non-quadratic error functionals based on L 1 norm or even sub-linear potentials corresponding to quasinorms L p (0

Radiation tolerant combinational logic cell

NASA Technical Reports Server (NTRS)

Maki, Gary R. (Inventor); Whitaker, Sterling (Inventor); Gambles, Jody W. (Inventor)

2009-01-01

A system has a reduced sensitivity to Single Event Upset and/or Single Event Transient(s) compared to traditional logic devices. In a particular embodiment, the system includes an input, a logic block, a bias stage, a state machine, and an output. The logic block is coupled to the input. The logic block is for implementing a logic function, receiving a data set via the input, and generating a result f by applying the data set to the logic function. The bias stage is coupled to the logic block. The bias stage is for receiving the result from the logic block and presenting it to the state machine. The state machine is coupled to the bias stage. The state machine is for receiving, via the bias stage, the result generated by the logic block. The state machine is configured to retain a state value for the system. The state value is typically based on the result generated by the logic block. The output is coupled to the state machine. The output is for providing the value stored by the state machine. Some embodiments of the invention produce dual rail outputs Q and Q'. The logic block typically contains combinational logic and is similar, in size and transistor configuration, to a conventional CMOS combinational logic design. However, only a very small portion of the circuits of these embodiments, is sensitive to Single Event Upset and/or Single Event Transients.

Department of Defense Logistics Roadmap 2008. Volume 1

DTIC Science & Technology

2008-07-01

machine readable identification mark on the Department’s tangible qualifying assets, and establishes the data management protocols needed to...uniquely identify items with a Unique Item Identifier (UII) via machine - readable information (MRI) marking represented by a two-dimensional data...property items with a machine -readable Unique Item Identifier (UII), which is a set of globally unique data elements. The UII is used in functional

Coupling for joining a ball nut to a machine tool carriage

DOEpatents

Gerth, Howard L.

1979-01-01

The present invention relates to an improved coupling for joining a lead screw ball nut to a machine tool carriage. The ball nut is coupled to the machine tool carriage by a plurality of laterally flexible bolts which function as hinges during the rotation of the lead screw for substantially reducing lateral carriage movement due to wobble in the lead screw.

Machine learning for medical images analysis.

PubMed

Criminisi, A

2016-10-01

This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorithms, and (ii) We discuss how the issue of collecting large labelled datasets applies to both conventional algorithms as well as machine learning techniques. The size of the training database is a function of model complexity rather than a characteristic of machine learning methods. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Nature versus design: synthetic biology or how to build a biological non-machine.

PubMed

Porcar, M; Peretó, J

2016-04-18

The engineering ideal of synthetic biology presupposes that organisms are composed of standard, interchangeable parts with a predictive behaviour. In one word, organisms are literally recognized as machines. Yet living objects are the result of evolutionary processes without any purposiveness, not of a design by external agents. Biological components show massive overlapping and functional degeneracy, standard-free complexity, intrinsic variation and context dependent performances. However, although organisms are not full-fledged machines, synthetic biologists may still be eager for machine-like behaviours from artificially modified biosystems.

Prospects of application of additive technologies for increasing the efficiency of impeller machines

NASA Astrophysics Data System (ADS)

Belova, O. V.; Borisov, Yu. A.

2017-08-01

Impeller machine is a device in which the flow path carries out the supply (or retraction) of mechanical energy to the flow of a working fluid passing through the machine. To increase the efficiency of impeller machines, it is necessary to use design modern technologies, namely the use of numerical methods for conducting research in the field of gas dynamics, as well as additive manufacturing (AM) for the of both prototypes and production model. AM technologies are deservedly rightly called revolutionary because they give unique possibility for manufacturing products, creating perfect forms, both light and durable. The designers face the challenge of developing a new design methodology, since AM allows the use of the concept of "Complexity For Free". The "Complexity For Free" conception is based on: complexity of the form; hierarchical complexity; complexity of the material; functional complexity. The new technical items design method according to a functional principle is also investigated.