Surface similarity-based molecular query-retrieval

PubMed Central

Singh, Rahul

2007-01-01

Background Discerning the similarity between molecules is a challenging problem in drug discovery as well as in molecular biology. The importance of this problem is due to the fact that the biochemical characteristics of a molecule are closely related to its structure. Therefore molecular similarity is a key notion in investigations targeting exploration of molecular structural space, query-retrieval in molecular databases, and structure-activity modelling. Determining molecular similarity is related to the choice of molecular representation. Currently, representations with high descriptive power and physical relevance like 3D surface-based descriptors are available. Information from such representations is both surface-based and volumetric. However, most techniques for determining molecular similarity tend to focus on idealized 2D graph-based descriptors due to the complexity that accompanies reasoning with more elaborate representations. Results This paper addresses the problem of determining similarity when molecules are described using complex surface-based representations. It proposes an intrinsic, spherical representation that systematically maps points on a molecular surface to points on a standard coordinate system (a sphere). Molecular surface properties such as shape, field strengths, and effects due to field super-positioningcan then be captured as distributions on the surface of the sphere. Surface-based molecular similarity is subsequently determined by computing the similarity of the surface-property distributions using a novel formulation of histogram-intersection. The similarity formulation is not only sensitive to the 3D distribution of the surface properties, but is also highly efficient to compute. Conclusion The proposed method obviates the computationally expensive step of molecular pose-optimisation, can incorporate conformational variations, and facilitates highly efficient determination of similarity by directly comparing molecular surfaces and surface-based properties. Retrieval performance, applications in structure-activity modeling of complex biological properties, and comparisons with existing research and commercial methods demonstrate the validity and effectiveness of the approach. PMID:17634096

Computational modeling in melanoma for novel drug discovery.

PubMed

Pennisi, Marzio; Russo, Giulia; Di Salvatore, Valentina; Candido, Saverio; Libra, Massimo; Pappalardo, Francesco

2016-06-01

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model.

PubMed

Wang, Zhaocai; Ji, Zuwen; Wang, Xiaoming; Wu, Tunhua; Huang, Wei

2017-12-01

As a promising approach to solve the computationally intractable problem, the method based on DNA computing is an emerging research area including mathematics, computer science and molecular biology. The task scheduling problem, as a well-known NP-complete problem, arranges n jobs to m individuals and finds the minimum execution time of last finished individual. In this paper, we use a biologically inspired computational model and describe a new parallel algorithm to solve the task scheduling problem by basic DNA molecular operations. In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n 2 ) time complexity. Copyright © 2017. Published by Elsevier B.V.

Computational Modeling of Airway and Pulmonary Vascular Structure and Function: Development of a “Lung Physiome”

PubMed Central

Tawhai, M. H.; Clark, A. R.; Donovan, G. M.; Burrowes, K. S.

2011-01-01

Computational models of lung structure and function necessarily span multiple spatial and temporal scales, i.e., dynamic molecular interactions give rise to whole organ function, and the link between these scales cannot be fully understood if only molecular or organ-level function is considered. Here, we review progress in constructing multiscale finite element models of lung structure and function that are aimed at providing a computational framework for bridging the spatial scales from molecular to whole organ. These include structural models of the intact lung, embedded models of the pulmonary airways that couple to model lung tissue, and models of the pulmonary vasculature that account for distinct structural differences at the extra- and intra-acinar levels. Biophysically based functional models for tissue deformation, pulmonary blood flow, and airway bronchoconstriction are also described. The development of these advanced multiscale models has led to a better understanding of complex physiological mechanisms that govern regional lung perfusion and emergent heterogeneity during bronchoconstriction. PMID:22011236

Bounding the electrostatic free energies associated with linear continuum models of molecular solvation.

PubMed

Bardhan, Jaydeep P; Knepley, Matthew G; Anitescu, Mihai

2009-03-14

The importance of electrostatic interactions in molecular biology has driven extensive research toward the development of accurate and efficient theoretical and computational models. Linear continuum electrostatic theory has been surprisingly successful, but the computational costs associated with solving the associated partial differential equations (PDEs) preclude the theory's use in most dynamical simulations. Modern generalized-Born models for electrostatics can reproduce PDE-based calculations to within a few percent and are extremely computationally efficient but do not always faithfully reproduce interactions between chemical groups. Recent work has shown that a boundary-integral-equation formulation of the PDE problem leads naturally to a new approach called boundary-integral-based electrostatics estimation (BIBEE) to approximate electrostatic interactions. In the present paper, we prove that the BIBEE method can be used to rigorously bound the actual continuum-theory electrostatic free energy. The bounds are validated using a set of more than 600 proteins. Detailed numerical results are presented for structures of the peptide met-enkephalin taken from a molecular-dynamics simulation. These bounds, in combination with our demonstration that the BIBEE methods accurately reproduce pairwise interactions, suggest a new approach toward building a highly accurate yet computationally tractable electrostatic model.

Bounding the electrostatic free energies associated with linear continuum models of molecular solvation

NASA Astrophysics Data System (ADS)

Bardhan, Jaydeep P.; Knepley, Matthew G.; Anitescu, Mihai

2009-03-01

The importance of electrostatic interactions in molecular biology has driven extensive research toward the development of accurate and efficient theoretical and computational models. Linear continuum electrostatic theory has been surprisingly successful, but the computational costs associated with solving the associated partial differential equations (PDEs) preclude the theory's use in most dynamical simulations. Modern generalized-Born models for electrostatics can reproduce PDE-based calculations to within a few percent and are extremely computationally efficient but do not always faithfully reproduce interactions between chemical groups. Recent work has shown that a boundary-integral-equation formulation of the PDE problem leads naturally to a new approach called boundary-integral-based electrostatics estimation (BIBEE) to approximate electrostatic interactions. In the present paper, we prove that the BIBEE method can be used to rigorously bound the actual continuum-theory electrostatic free energy. The bounds are validated using a set of more than 600 proteins. Detailed numerical results are presented for structures of the peptide met-enkephalin taken from a molecular-dynamics simulation. These bounds, in combination with our demonstration that the BIBEE methods accurately reproduce pairwise interactions, suggest a new approach toward building a highly accurate yet computationally tractable electrostatic model.

wFReDoW: A Cloud-Based Web Environment to Handle Molecular Docking Simulations of a Fully Flexible Receptor Model

PubMed Central

De Paris, Renata; Frantz, Fábio A.; Norberto de Souza, Osmar; Ruiz, Duncan D. A.

2013-01-01

Molecular docking simulations of fully flexible protein receptor (FFR) models are coming of age. In our studies, an FFR model is represented by a series of different conformations derived from a molecular dynamic simulation trajectory of the receptor. For each conformation in the FFR model, a docking simulation is executed and analyzed. An important challenge is to perform virtual screening of millions of ligands using an FFR model in a sequential mode since it can become computationally very demanding. In this paper, we propose a cloud-based web environment, called web Flexible Receptor Docking Workflow (wFReDoW), which reduces the CPU time in the molecular docking simulations of FFR models to small molecules. It is based on the new workflow data pattern called self-adaptive multiple instances (P-SaMIs) and on a middleware built on Amazon EC2 instances. P-SaMI reduces the number of molecular docking simulations while the middleware speeds up the docking experiments using a High Performance Computing (HPC) environment on the cloud. The experimental results show a reduction in the total elapsed time of docking experiments and the quality of the new reduced receptor models produced by discarding the nonpromising conformations from an FFR model ruled by the P-SaMI data pattern. PMID:23691504

Inquiry-Based Learning of Molecular Phylogenetics

ERIC Educational Resources Information Center

Campo, Daniel; Garcia-Vazquez, Eva

2008-01-01

Reconstructing phylogenies from nucleotide sequences is a challenge for students because it strongly depends on evolutionary models and computer tools that are frequently updated. We present here an inquiry-based course aimed at learning how to trace a phylogeny based on sequences existing in public databases. Computer tools are freely available…

Rotational relaxation of molecular hydrogen at moderate temperatures

NASA Technical Reports Server (NTRS)

Sharma, S. P.

1994-01-01

Using a coupled rotation-vibration-dissociation model the rotational relaxation times for molecular hydrogen as a function of final temperature (500-5000 K), in a hypothetical scenario of sudden compression, are computed. The theoretical model is based on a master equation solver. The bound-bound and bound-free transition rates have been computed using a quasiclassical trajectory method. A review of the available experimental data on the rotational relaxation of hydrogen is presented, with a critical overview of the method of measurements and data reduction, including the sources of errors. These experimental data are then compared with the computed results.

Computer-aided molecular modeling techniques for predicting the stability of drug cyclodextrin inclusion complexes in aqueous solutions

NASA Astrophysics Data System (ADS)

Faucci, Maria Teresa; Melani, Fabrizio; Mura, Paola

2002-06-01

Molecular modeling was used to investigate factors influencing complex formation between cyclodextrins and guest molecules and predict their stability through a theoretical model based on the search for a correlation between experimental stability constants ( Ks) and some theoretical parameters describing complexation (docking energy, host-guest contact surfaces, intermolecular interaction fields) calculated from complex structures at a minimum conformational energy, obtained through stochastic methods based on molecular dynamic simulations. Naproxen, ibuprofen, ketoprofen and ibuproxam were used as model drug molecules. Multiple Regression Analysis allowed identification of the significant factors for the complex stability. A mathematical model ( r=0.897) related log Ks with complex docking energy and lipophilic molecular fields of cyclodextrin and drug.

Virtual Transgenics: Using a Molecular Biology Simulation to Impact Student Academic Achievement and Attitudes

NASA Astrophysics Data System (ADS)

Shegog, Ross; Lazarus, Melanie M.; Murray, Nancy G.; Diamond, Pamela M.; Sessions, Nathalie; Zsigmond, Eva

2012-10-01

The transgenic mouse model is useful for studying the causes and potential cures for human genetic diseases. Exposing high school biology students to laboratory experience in developing transgenic animal models is logistically prohibitive. Computer-based simulation, however, offers this potential in addition to advantages of fidelity and reach. This study describes and evaluates a computer-based simulation to train advanced placement high school science students in laboratory protocols, a transgenic mouse model was produced. A simulation module on preparing a gene construct in the molecular biology lab was evaluated using a randomized clinical control design with advanced placement high school biology students in Mercedes, Texas ( n = 44). Pre-post tests assessed procedural and declarative knowledge, time on task, attitudes toward computers for learning and towards science careers. Students who used the simulation increased their procedural and declarative knowledge regarding molecular biology compared to those in the control condition (both p < 0.005). Significant increases continued to occur with additional use of the simulation ( p < 0.001). Students in the treatment group became more positive toward using computers for learning ( p < 0.001). The simulation did not significantly affect attitudes toward science in general. Computer simulation of complex transgenic protocols have potential to provide a "virtual" laboratory experience as an adjunct to conventional educational approaches.

Molecular robots with sensors and intelligence.

PubMed

Hagiya, Masami; Konagaya, Akihiko; Kobayashi, Satoshi; Saito, Hirohide; Murata, Satoshi

2014-06-17

CONSPECTUS: What we can call a molecular robot is a set of molecular devices such as sensors, logic gates, and actuators integrated into a consistent system. The molecular robot is supposed to react autonomously to its environment by receiving molecular signals and making decisions by molecular computation. Building such a system has long been a dream of scientists; however, despite extensive efforts, systems having all three functions (sensing, computation, and actuation) have not been realized yet. This Account introduces an ongoing research project that focuses on the development of molecular robotics funded by MEXT (Ministry of Education, Culture, Sports, Science and Technology, Japan). This 5 year project started in July 2012 and is titled "Development of Molecular Robots Equipped with Sensors and Intelligence". The major issues in the field of molecular robotics all correspond to a feedback (i.e., plan-do-see) cycle of a robotic system. More specifically, these issues are (1) developing molecular sensors capable of handling a wide array of signals, (2) developing amplification methods of signals to drive molecular computing devices, (3) accelerating molecular computing, (4) developing actuators that are controllable by molecular computers, and (5) providing bodies of molecular robots encapsulating the above molecular devices, which implement the conformational changes and locomotion of the robots. In this Account, the latest contributions to the project are reported. There are four research teams in the project that specialize on sensing, intelligence, amoeba-like actuation, and slime-like actuation, respectively. The molecular sensor team is focusing on the development of molecular sensors that can handle a variety of signals. This team is also investigating methods to amplify signals from the molecular sensors. The molecular intelligence team is developing molecular computers and is currently focusing on a new photochemical technology for accelerating DNA-based computations. They also introduce novel computational models behind various kinds of molecular computers necessary for designing such computers. The amoeba robot team aims at constructing amoeba-like robots. The team is trying to incorporate motor proteins, including kinesin and microtubules (MTs), for use as actuators implemented in a liposomal compartment as a robot body. They are also developing a methodology to link DNA-based computation and molecular motor control. The slime robot team focuses on the development of slime-like robots. The team is evaluating various gels, including DNA gel and BZ gel, for use as actuators, as well as the body material to disperse various molecular devices in it. They also try to control the gel actuators by DNA signals coming from molecular computers.

Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships.

PubMed

Ivanciuc, Ovidiu

2013-06-01

Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.

Molecular Sticker Model Stimulation on Silicon for a Maximum Clique Problem

PubMed Central

Ning, Jianguo; Li, Yanmei; Yu, Wen

2015-01-01

Molecular computers (also called DNA computers), as an alternative to traditional electronic computers, are smaller in size but more energy efficient, and have massive parallel processing capacity. However, DNA computers may not outperform electronic computers owing to their higher error rates and some limitations of the biological laboratory. The stickers model, as a typical DNA-based computer, is computationally complete and universal, and can be viewed as a bit-vertically operating machine. This makes it attractive for silicon implementation. Inspired by the information processing method on the stickers computer, we propose a novel parallel computing model called DEM (DNA Electronic Computing Model) on System-on-a-Programmable-Chip (SOPC) architecture. Except for the significant difference in the computing medium—transistor chips rather than bio-molecules—the DEM works similarly to DNA computers in immense parallel information processing. Additionally, a plasma display panel (PDP) is used to show the change of solutions, and helps us directly see the distribution of assignments. The feasibility of the DEM is tested by applying it to compute a maximum clique problem (MCP) with eight vertices. Owing to the limited computing sources on SOPC architecture, the DEM could solve moderate-size problems in polynomial time. PMID:26075867

Incorporation of the TIP4P water model into a continuum solvent for computing solvation free energy

NASA Astrophysics Data System (ADS)

Yang, Pei-Kun

2014-10-01

The continuum solvent model is one of the commonly used strategies to compute solvation free energy especially for large-scale conformational transitions such as protein folding or to calculate the binding affinity of protein-protein/ligand interactions. However, the dielectric polarization for computing solvation free energy from the continuum solvent is different than that obtained from molecular dynamic simulations. To mimic the dielectric polarization surrounding a solute in molecular dynamic simulations, the first-shell water molecules was modeled using a charge distribution of TIP4P in a hard sphere; the time-averaged charge distribution from the first-shell water molecules were estimated based on the coordination number of the solute, and the orientation distribution of the first-shell waters and the intermediate water molecules were treated as that of a bulk solvent. Based on this strategy, an equation describing the solvation free energy of ions was derived.

A Series of Molecular Dynamics and Homology Modeling Computer Labs for an Undergraduate Molecular Modeling Course

ERIC Educational Resources Information Center

Elmore, Donald E.; Guayasamin, Ryann C.; Kieffer, Madeleine E.

2010-01-01

As computational modeling plays an increasingly central role in biochemical research, it is important to provide students with exposure to common modeling methods in their undergraduate curriculum. This article describes a series of computer labs designed to introduce undergraduate students to energy minimization, molecular dynamics simulations,…

Ndarts

NASA Technical Reports Server (NTRS)

Jain, Abhinandan

2011-01-01

Ndarts software provides algorithms for computing quantities associated with the dynamics of articulated, rigid-link, multibody systems. It is designed as a general-purpose dynamics library that can be used for the modeling of robotic platforms, space vehicles, molecular dynamics, and other such applications. The architecture and algorithms in Ndarts are based on the Spatial Operator Algebra (SOA) theory for computational multibody and robot dynamics developed at JPL. It uses minimal, internal coordinate models. The algorithms are low-order, recursive scatter/ gather algorithms. In comparison with the earlier Darts++ software, this version has a more general and cleaner design needed to support a larger class of computational dynamics needs. It includes a frames infrastructure, allows algorithms to operate on subgraphs of the system, and implements lazy and deferred computation for better efficiency. Dynamics modeling modules such as Ndarts are core building blocks of control and simulation software for space, robotic, mechanism, bio-molecular, and material systems modeling.

Molecular factor computing for predictive spectroscopy.

PubMed

Dai, Bin; Urbas, Aaron; Douglas, Craig C; Lodder, Robert A

2007-08-01

The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0-14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. In engineering simulations, four MFC filters gave an adequate calibration model (r2 = 0.995, RMSEC = 0.229%, RMSECV = 0.339%, p = 0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r2 = 0.993, RMSEC = 0.359%, RMSECV = 0.551%, p = 0.05 by f test). The first actual MFC prototype gave an RMSECV = 0.735%. MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.

Introducing Molecular Life Science Students to Model Building Using Computer Simulations

ERIC Educational Resources Information Center

Aegerter-Wilmsen, Tinri; Kettenis, Dik; Sessink, Olivier; Hartog, Rob; Bisseling, Ton; Janssen, Fred

2006-01-01

Computer simulations can facilitate the building of models of natural phenomena in research, such as in the molecular life sciences. In order to introduce molecular life science students to the use of computer simulations for model building, a digital case was developed in which students build a model of a pattern formation process in…

Computational Model of a Positive BDNF Feedback Loop in Hippocampal Neurons Following Inhibitory Avoidance Training

ERIC Educational Resources Information Center

Zhang, Yili; Smolen, Paul; Alberini, Cristina M.; Baxter, Douglas A.; Byrne, John H.

2016-01-01

Inhibitory avoidance (IA) training in rodents initiates a molecular cascade within hippocampal neurons. This cascade contributes to the transition of short- to long-term memory (i.e., consolidation). Here, a differential equation-based model was developed to describe a positive feedback loop within this molecular cascade. The feedback loop begins…

Molecular-Level Computational Investigation of Mechanical Transverse Behavior of p-Phenylene Terephthalamide (PPTA) Fibers

DTIC Science & Technology

2013-01-01

fabricated today are based on polymer matrix composites containing Kevlarw KM2 reinforcements , the present work will deal with generic PPTA fibers . In...Multi-length scale enriched continuum-level material model for Kevlarw- fiber reinforced polymer-matrix composites”, Journal of Materials...mechanical transverse behavior of p-phenylene terephthalamide (PPTA) fibers Purpose – A series of all-atom molecular-level computational analyses is

Novel dimer based descriptors with solvational computation for QSAR study of oxadiazoylbenzoyl-ureas as novel insect-growth regulators.

PubMed

Fan, Feng; Cheng, Jiagao; Li, Zhong; Xu, Xiaoyong; Qian, Xuhong

2010-02-01

Molecular aggregation state of bioactive compounds plays a key role in its bio-interactive procedure. In this article, based on the structure information of dimers, the simplest model of molecular aggregation state, and combined with solvational computation, total four descriptors (DeltaV, MR2, DeltaE(1), and DeltaE(2)) were calculated for QSAR study of a novel insect-growth regulator, N-(5-phenyl-1,3,4-oxadiazol-2-yl)-N'-benzoyl urea. Two QSAR models were constructed with r(2) = 0.671, q(2) = 0.516 and r(2) = 0.816, q(2) = 0.695, respectively. It implicates that the bioactivity may strongly depend on the characters of molecular aggregation state, especially on the dimeric transport ability from oil phase to water phase. Copyright 2009 Wiley Periodicals, Inc.

Computer-Aided Drug Discovery: Molecular Docking of Diminazene Ligands to DNA Minor Groove

ERIC Educational Resources Information Center

Kholod, Yana; Hoag, Erin; Muratore, Katlynn; Kosenkov, Dmytro

2018-01-01

The reported project-based laboratory unit introduces upper-division undergraduate students to the basics of computer-aided drug discovery as a part of a computational chemistry laboratory course. The students learn to perform model binding of organic molecules (ligands) to the DNA minor groove with computer-aided drug discovery (CADD) tools. The…

A Self-Instructional Approach To the Teaching of Enzymology Involving Computer-Based Sequence Analysis and Molecular Modelling.

ERIC Educational Resources Information Center

Attwood, Paul V.

1997-01-01

Describes a self-instructional assignment approach to the teaching of advanced enzymology. Presents an assignment that offers a means of teaching enzymology to students that exposes them to modern computer-based techniques of analyzing protein structure and relates structure to enzyme function. (JRH)

The impact of computer science in molecular medicine: enabling high-throughput research.

PubMed

de la Iglesia, Diana; García-Remesal, Miguel; de la Calle, Guillermo; Kulikowski, Casimir; Sanz, Ferran; Maojo, Víctor

2013-01-01

The Human Genome Project and the explosion of high-throughput data have transformed the areas of molecular and personalized medicine, which are producing a wide range of studies and experimental results and providing new insights for developing medical applications. Research in many interdisciplinary fields is resulting in data repositories and computational tools that support a wide diversity of tasks: genome sequencing, genome-wide association studies, analysis of genotype-phenotype interactions, drug toxicity and side effects assessment, prediction of protein interactions and diseases, development of computational models, biomarker discovery, and many others. The authors of the present paper have developed several inventories covering tools, initiatives and studies in different computational fields related to molecular medicine: medical informatics, bioinformatics, clinical informatics and nanoinformatics. With these inventories, created by mining the scientific literature, we have carried out several reviews of these fields, providing researchers with a useful framework to locate, discover, search and integrate resources. In this paper we present an analysis of the state-of-the-art as it relates to computational resources for molecular medicine, based on results compiled in our inventories, as well as results extracted from a systematic review of the literature and other scientific media. The present review is based on the impact of their related publications and the available data and software resources for molecular medicine. It aims to provide information that can be useful to support ongoing research and work to improve diagnostics and therapeutics based on molecular-level insights.

From transistor to trapped-ion computers for quantum chemistry.

PubMed

Yung, M-H; Casanova, J; Mezzacapo, A; McClean, J; Lamata, L; Aspuru-Guzik, A; Solano, E

2014-01-07

Over the last few decades, quantum chemistry has progressed through the development of computational methods based on modern digital computers. However, these methods can hardly fulfill the exponentially-growing resource requirements when applied to large quantum systems. As pointed out by Feynman, this restriction is intrinsic to all computational models based on classical physics. Recently, the rapid advancement of trapped-ion technologies has opened new possibilities for quantum control and quantum simulations. Here, we present an efficient toolkit that exploits both the internal and motional degrees of freedom of trapped ions for solving problems in quantum chemistry, including molecular electronic structure, molecular dynamics, and vibronic coupling. We focus on applications that go beyond the capacity of classical computers, but may be realizable on state-of-the-art trapped-ion systems. These results allow us to envision a new paradigm of quantum chemistry that shifts from the current transistor to a near-future trapped-ion-based technology.

From transistor to trapped-ion computers for quantum chemistry

PubMed Central

Yung, M.-H.; Casanova, J.; Mezzacapo, A.; McClean, J.; Lamata, L.; Aspuru-Guzik, A.; Solano, E.

2014-01-01

Over the last few decades, quantum chemistry has progressed through the development of computational methods based on modern digital computers. However, these methods can hardly fulfill the exponentially-growing resource requirements when applied to large quantum systems. As pointed out by Feynman, this restriction is intrinsic to all computational models based on classical physics. Recently, the rapid advancement of trapped-ion technologies has opened new possibilities for quantum control and quantum simulations. Here, we present an efficient toolkit that exploits both the internal and motional degrees of freedom of trapped ions for solving problems in quantum chemistry, including molecular electronic structure, molecular dynamics, and vibronic coupling. We focus on applications that go beyond the capacity of classical computers, but may be realizable on state-of-the-art trapped-ion systems. These results allow us to envision a new paradigm of quantum chemistry that shifts from the current transistor to a near-future trapped-ion-based technology. PMID:24395054

Computational prediction of formulation strategies for beyond-rule-of-5 compounds.

PubMed

Bergström, Christel A S; Charman, William N; Porter, Christopher J H

2016-06-01

The physicochemical properties of some contemporary drug candidates are moving towards higher molecular weight, and coincidentally also higher lipophilicity in the quest for biological selectivity and specificity. These physicochemical properties move the compounds towards beyond rule-of-5 (B-r-o-5) chemical space and often result in lower water solubility. For such B-r-o-5 compounds non-traditional delivery strategies (i.e. those other than conventional tablet and capsule formulations) typically are required to achieve adequate exposure after oral administration. In this review, we present the current status of computational tools for prediction of intestinal drug absorption, models for prediction of the most suitable formulation strategies for B-r-o-5 compounds and models to obtain an enhanced understanding of the interplay between drug, formulation and physiological environment. In silico models are able to identify the likely molecular basis for low solubility in physiologically relevant fluids such as gastric and intestinal fluids. With this baseline information, a formulation scientist can, at an early stage, evaluate different orally administered, enabling formulation strategies. Recent computational models have emerged that predict glass-forming ability and crystallisation tendency and therefore the potential utility of amorphous solid dispersion formulations. Further, computational models of loading capacity in lipids, and therefore the potential for formulation as a lipid-based formulation, are now available. Whilst such tools are useful for rapid identification of suitable formulation strategies, they do not reveal drug localisation and molecular interaction patterns between drug and excipients. For the latter, Molecular Dynamics simulations provide an insight into the interplay between drug, formulation and intestinal fluid. These different computational approaches are reviewed. Additionally, we analyse the molecular requirements of different targets, since these can provide an early signal that enabling formulation strategies will be required. Based on the analysis we conclude that computational biopharmaceutical profiling can be used to identify where non-conventional gateways, such as prediction of 'formulate-ability' during lead optimisation and early development stages, are important and may ultimately increase the number of orally tractable contemporary targets. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Computational Design of Molecularly Imprinted Polymers

NASA Astrophysics Data System (ADS)

Subrahmanyam, Sreenath; Piletsky, Sergey A.

Artificial receptors have been in use for several decades as sensor elements, in affinity separation, and as models for investigation of molecular recognition. Although there have been numerous publications on the use of molecular modeling in characterization of their affinity and selectivity, very few attempts have been made on the application of molecular modeling in computational design of synthetic receptors. This chapter discusses recent successes in the use of computational design for the development of one particular branch of synthetic receptors - molecularly imprinted polymers.

Teaching 1H NMR Spectrometry Using Computer Modeling.

ERIC Educational Resources Information Center

Habata, Yoichi; Akabori, Sadatoshi

2001-01-01

Molecular modeling by computer is used to display stereochemistry, molecular orbitals, structure of transition states, and progress of reactions. Describes new ideas for teaching 1H NMR spectroscopy using computer modeling. (Contains 12 references.) (ASK)

SELDI PROTEINCHIP-BASED LIVER BIOMARKERS IN FUNGICIDE EXPOSED ZEBRAFISH

EPA Science Inventory

The research presented here is part of a three-phased small fish computational toxicology project using a combination of 1) whole organism endpoints, 2) genomic, proteomic, and metabolomic approaches, and 3) computational modeling to (a) identify new molecular biomarkers of expos...

Assessment of Molecular Modeling & Simulation

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

None

2002-01-03

This report reviews the development and applications of molecular and materials modeling in Europe and Japan in comparison to those in the United States. Topics covered include computational quantum chemistry, molecular simulations by molecular dynamics and Monte Carlo methods, mesoscale modeling of material domains, molecular-structure/macroscale property correlations like QSARs and QSPRs, and related information technologies like informatics and special-purpose molecular-modeling computers. The panel's findings include the following: The United States leads this field in many scientific areas. However, Canada has particular strengths in DFT methods and homogeneous catalysis; Europe in heterogeneous catalysis, mesoscale, and materials modeling; and Japan in materialsmore » modeling and special-purpose computing. Major government-industry initiatives are underway in Europe and Japan, notably in multi-scale materials modeling and in development of chemistry-capable ab-initio molecular dynamics codes.« less

The emerging role of cloud computing in molecular modelling.

PubMed

Ebejer, Jean-Paul; Fulle, Simone; Morris, Garrett M; Finn, Paul W

2013-07-01

There is a growing recognition of the importance of cloud computing for large-scale and data-intensive applications. The distinguishing features of cloud computing and their relationship to other distributed computing paradigms are described, as are the strengths and weaknesses of the approach. We review the use made to date of cloud computing for molecular modelling projects and the availability of front ends for molecular modelling applications. Although the use of cloud computing technologies for molecular modelling is still in its infancy, we demonstrate its potential by presenting several case studies. Rapid growth can be expected as more applications become available and costs continue to fall; cloud computing can make a major contribution not just in terms of the availability of on-demand computing power, but could also spur innovation in the development of novel approaches that utilize that capacity in more effective ways. Copyright © 2013 Elsevier Inc. All rights reserved.

An Evaluation of the Scattering Law for Light and Heavy Water in ENDF-6 Format, Based on Experimental Data and Molecular Dynamics

NASA Astrophysics Data System (ADS)

Márquez Damián, J. I.; Granada, J. R.; Malaspina, D. C.

2014-04-01

In this work we present an evaluation in ENDF-6 format of the scattering law for light and heavy water computed using the LEAPR module of NJOY99. The models used in this evaluation are based on experimental data on light water dynamics measured by Novikov, partial structure factors obtained by Soper, and molecular dynamics calculations performed with GROMACS using a reparameterized version of the flexible SPC model by Toukan and Rahman. The models use the Egelstaff-Schofield diffusion equation for translational motion, and a continuous spectrum calculated from the velocity autocorrelation function computed with GROMACS. The scattering law for H in H2O is computed using the incoherent approximation, and the scattering law D and O in D2O are computed using the Sköld approximation for coherent scattering. The calculations show significant improvement over ENDF/B-VI and ENDF/B-VII when compared with measurements of the total cross section, differential scattering experiments and quasi-elastic neutron scattering experiments (QENS).

Clustering molecular dynamics trajectories for optimizing docking experiments.

PubMed

De Paris, Renata; Quevedo, Christian V; Ruiz, Duncan D; Norberto de Souza, Osmar; Barros, Rodrigo C

2015-01-01

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

Computational drug discovery

PubMed Central

Ou-Yang, Si-sheng; Lu, Jun-yan; Kong, Xiang-qian; Liang, Zhong-jie; Luo, Cheng; Jiang, Hualiang

2012-01-01

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field. PMID:22922346

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.

Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation.

PubMed

Lopes, J S; Arenas, M; Posada, D; Beaumont, M A

2014-03-01

The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets.

Modelling and enhanced molecular dynamics to steer structure-based drug discovery.

PubMed

Kalyaanamoorthy, Subha; Chen, Yi-Ping Phoebe

2014-05-01

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

From Occasional Choices to Inevitable Musts: A Computational Model of Nicotine Addiction

PubMed Central

Metin, Selin; Sengor, N. Serap

2012-01-01

Although, there are considerable works on the neural mechanisms of reward-based learning and decision making, and most of them mention that addiction can be explained by malfunctioning in these cognitive processes, there are very few computational models. This paper focuses on nicotine addiction, and a computational model for nicotine addiction is proposed based on the neurophysiological basis of addiction. The model compromises different levels ranging from molecular basis to systems level, and it demonstrates three different possible behavioral patterns which are addict, nonaddict, and indecisive. The dynamical behavior of the proposed model is investigated with tools used in analyzing nonlinear dynamical systems, and the relation between the behavioral patterns and the dynamics of the system is discussed. PMID:23251144

Equivalent Discrete-Time Channel Modeling for Molecular Communication With Emphasize on an Absorbing Receiver.

PubMed

Damrath, Martin; Korte, Sebastian; Hoeher, Peter Adam

2017-01-01

This paper introduces the equivalent discrete-time channel model (EDTCM) to the area of diffusion-based molecular communication (DBMC). Emphasis is on an absorbing receiver, which is based on the so-called first passage time concept. In the wireless communications community the EDTCM is well known. Therefore, it is anticipated that the EDTCM improves the accessibility of DBMC and supports the adaptation of classical wireless communication algorithms to the area of DBMC. Furthermore, the EDTCM has the capability to provide a remarkable reduction of computational complexity compared to random walk based DBMC simulators. Besides the exact EDTCM, three approximations thereof based on binomial, Gaussian, and Poisson approximation are proposed and analyzed in order to further reduce computational complexity. In addition, the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is adapted to all four channel models. Numerical results show the performance of the exact EDTCM, illustrate the performance of the adapted BCJR algorithm, and demonstrate the accuracy of the approximations.

Modelling of Cosmic Molecular Masers: Introduction to a Computation Cookbook

NASA Astrophysics Data System (ADS)

Sobolev, Andrej M.; Gray, Malcolm D.

2012-07-01

Numerical modeling of molecular masers is necessary in order to understand their nature and diagnostic capabilities. Model construction requires elaboration of a basic description which allows computation, that is a definition of the parameter space and basic physical relations. Usually, this requires additional thorough studies that can consist of the following stages/parts: relevant molecular spectroscopy and collisional rate coefficients; conditions in and around the masing region (that part of space where population inversion is realized); geometry and size of the masing region (including the question of whether maser spots are discrete clumps or line-of-sight correlations in a much bigger region) and propagation of maser radiation. Output of the maser computer modeling can have the following forms: exploration of parameter space (where do inversions appear in particular maser transitions and their combinations, which parameter values describe a `typical' source, and so on); modeling of individual sources (line flux ratios, spectra, images and their variability); analysis of the pumping mechanism; predictions (new maser transitions, correlations in variability of different maser transitions, and the like). Described schemes (constituents and hierarchy) of the model input and output are based mainly on the experience of the authors and make no claim to be dogmatic.

Mathematical modeling and computational prediction of cancer drug resistance.

PubMed

Sun, Xiaoqiang; Hu, Bin

2017-06-23

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

GPU-Accelerated Molecular Modeling Coming Of Age

PubMed Central

Stone, John E.; Hardy, David J.; Ufimtsev, Ivan S.

2010-01-01

Graphics processing units (GPUs) have traditionally been used in molecular modeling solely for visualization of molecular structures and animation of trajectories resulting from molecular dynamics simulations. Modern GPUs have evolved into fully programmable, massively parallel co-processors that can now be exploited to accelerate many scientific computations, typically providing about one order of magnitude speedup over CPU code and in special cases providing speedups of two orders of magnitude. This paper surveys the development of molecular modeling algorithms that leverage GPU computing, the advances already made and remaining issues to be resolved, and the continuing evolution of GPU technology that promises to become even more useful to molecular modeling. Hardware acceleration with commodity GPUs is expected to benefit the overall computational biology community by bringing teraflops performance to desktop workstations and in some cases potentially changing what were formerly batch-mode computational jobs into interactive tasks. PMID:20675161

GPU-accelerated molecular modeling coming of age.

PubMed

Stone, John E; Hardy, David J; Ufimtsev, Ivan S; Schulten, Klaus

2010-09-01

Graphics processing units (GPUs) have traditionally been used in molecular modeling solely for visualization of molecular structures and animation of trajectories resulting from molecular dynamics simulations. Modern GPUs have evolved into fully programmable, massively parallel co-processors that can now be exploited to accelerate many scientific computations, typically providing about one order of magnitude speedup over CPU code and in special cases providing speedups of two orders of magnitude. This paper surveys the development of molecular modeling algorithms that leverage GPU computing, the advances already made and remaining issues to be resolved, and the continuing evolution of GPU technology that promises to become even more useful to molecular modeling. Hardware acceleration with commodity GPUs is expected to benefit the overall computational biology community by bringing teraflops performance to desktop workstations and in some cases potentially changing what were formerly batch-mode computational jobs into interactive tasks. (c) 2010 Elsevier Inc. All rights reserved.

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.

Coarse-grained molecular dynamics simulations for giant protein-DNA complexes

NASA Astrophysics Data System (ADS)

Takada, Shoji

Biomolecules are highly hierarchic and intrinsically flexible. Thus, computational modeling calls for multi-scale methodologies. We have been developing a coarse-grained biomolecular model where on-average 10-20 atoms are grouped into one coarse-grained (CG) particle. Interactions among CG particles are tuned based on atomistic interactions and the fluctuation matching algorithm. CG molecular dynamics methods enable us to simulate much longer time scale motions of much larger molecular systems than fully atomistic models. After broad sampling of structures with CG models, we can easily reconstruct atomistic models, from which one can continue conventional molecular dynamics simulations if desired. Here, we describe our CG modeling methodology for protein-DNA complexes, together with various biological applications, such as the DNA duplication initiation complex, model chromatins, and transcription factor dynamics on chromatin-like environment.

Quantum Chemically Estimated Abraham Solute Parameters Using Multiple Solvent-Water Partition Coefficients and Molecular Polarizability.

PubMed

Liang, Yuzhen; Xiong, Ruichang; Sandler, Stanley I; Di Toro, Dominic M

2017-09-05

Polyparameter Linear Free Energy Relationships (pp-LFERs), also called Linear Solvation Energy Relationships (LSERs), are used to predict many environmentally significant properties of chemicals. A method is presented for computing the necessary chemical parameters, the Abraham parameters (AP), used by many pp-LFERs. It employs quantum chemical calculations and uses only the chemical's molecular structure. The method computes the Abraham E parameter using density functional theory computed molecular polarizability and the Clausius-Mossotti equation relating the index refraction to the molecular polarizability, estimates the Abraham V as the COSMO calculated molecular volume, and computes the remaining AP S, A, and B jointly with a multiple linear regression using sixty-five solvent-water partition coefficients computed using the quantum mechanical COSMO-SAC solvation model. These solute parameters, referred to as Quantum Chemically estimated Abraham Parameters (QCAP), are further adjusted by fitting to experimentally based APs using QCAP parameters as the independent variables so that they are compatible with existing Abraham pp-LFERs. QCAP and adjusted QCAP for 1827 neutral chemicals are included. For 24 solvent-water systems including octanol-water, predicted log solvent-water partition coefficients using adjusted QCAP have the smallest root-mean-square errors (RMSEs, 0.314-0.602) compared to predictions made using APs estimated using the molecular fragment based method ABSOLV (0.45-0.716). For munition and munition-like compounds, adjusted QCAP has much lower RMSE (0.860) than does ABSOLV (4.45) which essentially fails for these compounds.

Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

NASA Astrophysics Data System (ADS)

Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.

2015-03-01

We present Π4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

Analogs of methyllycaconitine as novel noncompetitive inhibitors of nicotinic receptors: pharmacological characterization, computational modeling, and pharmacophore development.

PubMed

McKay, Dennis B; Chang, Cheng; González-Cestari, Tatiana F; McKay, Susan B; El-Hajj, Raed A; Bryant, Darrell L; Zhu, Michael X; Swaan, Peter W; Arason, Kristjan M; Pulipaka, Aravinda B; Orac, Crina M; Bergmeier, Stephen C

2007-05-01

As a novel approach to drug discovery involving neuronal nicotinic acetylcholine receptors (nAChRs), our laboratory targeted nonagonist binding sites (i.e., noncompetitive binding sites, negative allosteric binding sites) located on nAChRs. Cultured bovine adrenal cells were used as neuronal models to investigate interactions of 67 analogs of methyllycaconitine (MLA) on native alpha3beta4* nAChRs. The availability of large numbers of structurally related molecules presents a unique opportunity for the development of pharmacophore models for noncompetitive binding sites. Our MLA analogs inhibited nicotine-mediated functional activation of both native and recombinant alpha3beta4* nAChRs with a wide range of IC(50) values (0.9-115 microM). These analogs had little or no inhibitory effects on agonist binding to native or recombinant nAChRs, supporting noncompetitive inhibitory activity. Based on these data, two highly predictive 3D quantitative structure-activity relationship (comparative molecular field analysis and comparative molecular similarity index analysis) models were generated. These computational models were successfully validated and provided insights into the molecular interactions of MLA analogs with nAChRs. In addition, a pharmacophore model was constructed to analyze and visualize the binding requirements to the analog binding site. The pharmacophore model was subsequently applied to search structurally diverse molecular databases to prospectively identify novel inhibitors. The rapid identification of eight molecules from database mining and our successful demonstration of in vitro inhibitory activity support the utility of these computational models as novel tools for the efficient retrieval of inhibitors. These results demonstrate the effectiveness of computational modeling and pharmacophore development, which may lead to the identification of new therapeutic drugs that target novel sites on nAChRs.

Informing Mechanistic Toxicology with Computational Molecular Models

EPA Science Inventory

Computational molecular models of chemicals interacting with biomolecular targets provides toxicologists a valuable, affordable, and sustainable source of in silico molecular level information that augments, enriches, and complements in vitro and in vivo effo...

Computer-Assisted Drug Formulation Design: Novel Approach in Drug Delivery.

PubMed

Metwally, Abdelkader A; Hathout, Rania M

2015-08-03

We hypothesize that, by using several chemo/bio informatics tools and statistical computational methods, we can study and then predict the behavior of several drugs in model nanoparticulate lipid and polymeric systems. Accordingly, two different matrices comprising tripalmitin, a core component of solid lipid nanoparticles (SLN), and PLGA were first modeled using molecular dynamics simulation, and then the interaction of drugs with these systems was studied by means of computing the free energy of binding using the molecular docking technique. These binding energies were hence correlated with the loadings of these drugs in the nanoparticles obtained experimentally from the available literature. The obtained relations were verified experimentally in our laboratory using curcumin as a model drug. Artificial neural networks were then used to establish the effect of the drugs' molecular descriptors on the binding energies and hence on the drug loading. The results showed that the used soft computing methods can provide an accurate method for in silico prediction of drug loading in tripalmitin-based and PLGA nanoparticulate systems. These results have the prospective of being applied to other nano drug-carrier systems, and this integrated statistical and chemo/bio informatics approach offers a new toolbox to the formulation science by proposing what we present as computer-assisted drug formulation design (CADFD).

Biological intuition in alignment-free methods: response to Posada.

PubMed

Ragan, Mark A; Chan, Cheong Xin

2013-08-01

A recent editorial in Journal of Molecular Evolution highlights opportunities and challenges facing molecular evolution in the era of next-generation sequencing. Abundant sequence data should allow more-complex models to be fit at higher confidence, making phylogenetic inference more reliable and improving our understanding of evolution at the molecular level. However, concern that approaches based on multiple sequence alignment may be computationally infeasible for large datasets is driving the development of so-called alignment-free methods for sequence comparison and phylogenetic inference. The recent editorial characterized these approaches as model-free, not based on the concept of homology, and lacking in biological intuition. We argue here that alignment-free methods have not abandoned models or homology, and can be biologically intuitive.

Evaluation of synthetic linear motor-molecule actuation energetics

PubMed Central

Brough, Branden; Northrop, Brian H.; Schmidt, Jacob J.; Tseng, Hsian-Rong; Houk, Kendall N.; Stoddart, J. Fraser; Ho, Chih-Ming

2006-01-01

By applying atomic force microscope (AFM)-based force spectroscopy together with computational modeling in the form of molecular force-field simulations, we have determined quantitatively the actuation energetics of a synthetic motor-molecule. This multidisciplinary approach was performed on specifically designed, bistable, redox-controllable [2]rotaxanes to probe the steric and electrostatic interactions that dictate their mechanical switching at the single-molecule level. The fusion of experimental force spectroscopy and theoretical computational modeling has revealed that the repulsive electrostatic interaction, which is responsible for the molecular actuation, is as high as 65 kcal·mol−1, a result that is supported by ab initio calculations. PMID:16735470

Charting molecular free-energy landscapes with an atlas of collective variables

NASA Astrophysics Data System (ADS)

Hashemian, Behrooz; Millán, Daniel; Arroyo, Marino

2016-11-01

Collective variables (CVs) are a fundamental tool to understand molecular flexibility, to compute free energy landscapes, and to enhance sampling in molecular dynamics simulations. However, identifying suitable CVs is challenging, and is increasingly addressed with systematic data-driven manifold learning techniques. Here, we provide a flexible framework to model molecular systems in terms of a collection of locally valid and partially overlapping CVs: an atlas of CVs. The specific motivation for such a framework is to enhance the applicability and robustness of CVs based on manifold learning methods, which fail in the presence of periodicities in the underlying conformational manifold. More generally, using an atlas of CVs rather than a single chart may help us better describe different regions of conformational space. We develop the statistical mechanics foundation for our multi-chart description and propose an algorithmic implementation. The resulting atlas of data-based CVs are then used to enhance sampling and compute free energy surfaces in two model systems, alanine dipeptide and β-D-glucopyranose, whose conformational manifolds have toroidal and spherical topologies.

Leveraging Modeling Approaches: Reaction Networks and Rules

PubMed Central

Blinov, Michael L.; Moraru, Ion I.

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high resolution and/or high throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatio-temporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks – the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks. PMID:22161349

Leveraging modeling approaches: reaction networks and rules.

PubMed

Blinov, Michael L; Moraru, Ion I

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high-resolution and/or high-throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatiotemporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks - the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks.

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system

PubMed Central

2010-01-01

Background The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the “in silico” stochastic event based modeling approach to find the molecular dynamics of the system. Results In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Conclusions Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics. PMID:21143785

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system.

PubMed

Ghosh, Preetam; Ghosh, Samik; Basu, Kalyan; Das, Sajal K; Zhang, Chaoyang

2010-12-01

The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the "in silico" stochastic event based modeling approach to find the molecular dynamics of the system. In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics.

Mesoscale energy deposition footprint model for kiloelectronvolt cluster bombardment of solids.

PubMed

Russo, Michael F; Garrison, Barbara J

2006-10-15

Molecular dynamics simulations have been performed to model 5-keV C60 and Au3 projectile bombardment of an amorphous water substrate. The goal is to obtain detailed insights into the dynamics of motion in order to develop a straightforward and less computationally demanding model of the process of ejection. The molecular dynamics results provide the basis for the mesoscale energy deposition footprint model. This model provides a method for predicting relative yields based on information from less than 1 ps of simulation time.

Collision-induced Absorption in the Infrared: A Data Base for Modelling Planetary and Stellar Atmospheres

NASA Technical Reports Server (NTRS)

Borysow, Aleksandra

1998-01-01

Accurate knowledge of certain collision-induced absorption continua of molecular pairs such as H2-H2, H2-He, H2-CH4, CO2-CO2, etc., is a prerequisite for most spectral analyses and modelling attempts of atmospheres of planets and cold stars. We collect and regularly update simple, state of the art computer programs for the calculation of the absorption coefficient of such molecular pairs over a broad range of temperatures and frequencies, for the various rotovibrational bands. The computational results are in agreement with the existing laboratory measurements of such absorption continua, recorded with a spectral resolution of a few wavenumbers, but reliable computational results may be expected even in the far wings, and at temperatures for which laboratory measurements do not exist. Detailed information is given concerning the systems thus studied, the temperature and frequency ranges considered, the rotovibrational bands thus modelled, and how one may obtain copies of the FORTRAN77 computer programs by e-mail.

A theoretical-electron-density databank using a model of real and virtual spherical atoms.

PubMed

Nassour, Ayoub; Domagala, Slawomir; Guillot, Benoit; Leduc, Theo; Lecomte, Claude; Jelsch, Christian

2017-08-01

A database describing the electron density of common chemical groups using combinations of real and virtual spherical atoms is proposed, as an alternative to the multipolar atom modelling of the molecular charge density. Theoretical structure factors were computed from periodic density functional theory calculations on 38 crystal structures of small molecules and the charge density was subsequently refined using a density model based on real spherical atoms and additional dummy charges on the covalent bonds and on electron lone-pair sites. The electron-density parameters of real and dummy atoms present in a similar chemical environment were averaged on all the molecules studied to build a database of transferable spherical atoms. Compared with the now-popular databases of transferable multipolar parameters, the spherical charge modelling needs fewer parameters to describe the molecular electron density and can be more easily incorporated in molecular modelling software for the computation of electrostatic properties. The construction method of the database is described. In order to analyse to what extent this modelling method can be used to derive meaningful molecular properties, it has been applied to the urea molecule and to biotin/streptavidin, a protein/ligand complex.

Homology Modeling of Dopamine D2 and D3 Receptors: Molecular Dynamics Refinement and Docking Evaluation

PubMed Central

Platania, Chiara Bianca Maria; Salomone, Salvatore; Leggio, Gian Marco; Drago, Filippo; Bucolo, Claudio

2012-01-01

Dopamine (DA) receptors, a class of G-protein coupled receptors (GPCRs), have been targeted for drug development for the treatment of neurological, psychiatric and ocular disorders. The lack of structural information about GPCRs and their ligand complexes has prompted the development of homology models of these proteins aimed at structure-based drug design. Crystal structure of human dopamine D3 (hD3) receptor has been recently solved. Based on the hD3 receptor crystal structure we generated dopamine D2 and D3 receptor models and refined them with molecular dynamics (MD) protocol. Refined structures, obtained from the MD simulations in membrane environment, were subsequently used in molecular docking studies in order to investigate potential sites of interaction. The structure of hD3 and hD2L receptors was differentiated by means of MD simulations and D3 selective ligands were discriminated, in terms of binding energy, by docking calculation. Robust correlation of computed and experimental Ki was obtained for hD3 and hD2L receptor ligands. In conclusion, the present computational approach seems suitable to build and refine structure models of homologous dopamine receptors that may be of value for structure-based drug discovery of selective dopaminergic ligands. PMID:22970199

Toward integration of in vivo molecular computing devices: successes and challenges

PubMed Central

Hayat, Sikander; Hinze, Thomas

2008-01-01

The computing power unleashed by biomolecule based massively parallel computational units has been the focus of many interdisciplinary studies that couple state of the art ideas from mathematical logic, theoretical computer science, bioengineering, and nanotechnology to fulfill some computational task. The output can influence, for instance, release of a drug at a specific target, gene expression, cell population, or be a purely mathematical entity. Analysis of the results of several studies has led to the emergence of a general set of rules concerning the implementation and optimization of in vivo computational units. Taking two recent studies on in vivo computing as examples, we discuss the impact of mathematical modeling and simulation in the field of synthetic biology and on in vivo computing. The impact of the emergence of gene regulatory networks and the potential of proteins acting as “circuit wires” on the problem of interconnecting molecular computing device subunits is also highlighted. PMID:19404433

Open Source Molecular Modeling

PubMed Central

Pirhadi, Somayeh; Sunseri, Jocelyn; Koes, David Ryan

2016-01-01

The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. PMID:27631126

Evaluation of bending modulus of lipid bilayers using undulation and orientation analysis

NASA Astrophysics Data System (ADS)

Chaurasia, Adarsh K.; Rukangu, Andrew M.; Philen, Michael K.; Seidel, Gary D.; Freeman, Eric C.

2018-03-01

In the current paper, phospholipid bilayers are modeled using coarse-grained molecular dynamics simulations with the MARTINI force field. The extracted molecular trajectories are analyzed using Fourier analysis of the undulations and orientation vectors to establish the differences between the two approaches for evaluating the bending modulus. The current work evaluates and extends the implementation of the Fourier analysis for molecular trajectories using a weighted horizon-based averaging approach. The effect of numerical parameters in the analysis of these trajectories is explored by conducting parametric studies. Computational modeling results are validated against experimentally characterized bending modulus of lipid membranes using a shape fluctuation analysis. The computational framework is then used to estimate the bending moduli for different types of lipids (phosphocholine, phosphoethanolamine, and phosphoglycerol). This work provides greater insight into the numerical aspects of evaluating the bilayer bending modulus, provides validation for the orientation analysis technique, and explores differences in bending moduli based on differences in the lipid nanostructures.

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

PubMed Central

De Paris, Renata; Quevedo, Christian V.; Ruiz, Duncan D.; Norberto de Souza, Osmar; Barros, Rodrigo C.

2015-01-01

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. PMID:25873944

Novel 3-D Computer Model Can Help Predict Pathogens’ Roles in Cancer | Poster

Cancer.gov

To understand how bacterial and viral infections contribute to human cancers, four NCI at Frederick scientists turned not to the lab bench, but to a computer. The team has created the world’s first—and currently, only—3-D computational approach for studying interactions between pathogen proteins and human proteins based on a molecular adaptation known as interface mimicry.

Physics Computing '92: Proceedings of the 4th International Conference

NASA Astrophysics Data System (ADS)

de Groot, Robert A.; Nadrchal, Jaroslav

1993-04-01

The Table of Contents for the book is as follows: * Preface * INVITED PAPERS * Ab Initio Theoretical Approaches to the Structural, Electronic and Vibrational Properties of Small Clusters and Fullerenes: The State of the Art * Neural Multigrid Methods for Gauge Theories and Other Disordered Systems * Multicanonical Monte Carlo Simulations * On the Use of the Symbolic Language Maple in Physics and Chemistry: Several Examples * Nonequilibrium Phase Transitions in Catalysis and Population Models * Computer Algebra, Symmetry Analysis and Integrability of Nonlinear Evolution Equations * The Path-Integral Quantum Simulation of Hydrogen in Metals * Digital Optical Computing: A New Approach of Systolic Arrays Based on Coherence Modulation of Light and Integrated Optics Technology * Molecular Dynamics Simulations of Granular Materials * Numerical Implementation of a K.A.M. Algorithm * Quasi-Monte Carlo, Quasi-Random Numbers and Quasi-Error Estimates * What Can We Learn from QMC Simulations * Physics of Fluctuating Membranes * Plato, Apollonius, and Klein: Playing with Spheres * Steady States in Nonequilibrium Lattice Systems * CONVODE: A REDUCE Package for Differential Equations * Chaos in Coupled Rotators * Symplectic Numerical Methods for Hamiltonian Problems * Computer Simulations of Surfactant Self Assembly * High-dimensional and Very Large Cellular Automata for Immunological Shape Space * A Review of the Lattice Boltzmann Method * Electronic Structure of Solids in the Self-interaction Corrected Local-spin-density Approximation * Dedicated Computers for Lattice Gauge Theory Simulations * Physics Education: A Survey of Problems and Possible Solutions * Parallel Computing and Electronic-Structure Theory * High Precision Simulation Techniques for Lattice Field Theory * CONTRIBUTED PAPERS * Case Study of Microscale Hydrodynamics Using Molecular Dynamics and Lattice Gas Methods * Computer Modelling of the Structural and Electronic Properties of the Supported Metal Catalysis * Ordered Particle Simulations for Serial and MIMD Parallel Computers * "NOLP" -- Program Package for Laser Plasma Nonlinear Optics * Algorithms to Solve Nonlinear Least Square Problems * Distribution of Hydrogen Atoms in Pd-H Computed by Molecular Dynamics * A Ray Tracing of Optical System for Protein Crystallography Beamline at Storage Ring-SIBERIA-2 * Vibrational Properties of a Pseudobinary Linear Chain with Correlated Substitutional Disorder * Application of the Software Package Mathematica in Generalized Master Equation Method * Linelist: An Interactive Program for Analysing Beam-foil Spectra * GROMACS: A Parallel Computer for Molecular Dynamics Simulations * GROMACS Method of Virial Calculation Using a Single Sum * The Interactive Program for the Solution of the Laplace Equation with the Elimination of Singularities for Boundary Functions * Random-Number Generators: Testing Procedures and Comparison of RNG Algorithms * Micro-TOPIC: A Tokamak Plasma Impurities Code * Rotational Molecular Scattering Calculations * Orthonormal Polynomial Method for Calibrating of Cryogenic Temperature Sensors * Frame-based System Representing Basis of Physics * The Role of Massively Data-parallel Computers in Large Scale Molecular Dynamics Simulations * Short-range Molecular Dynamics on a Network of Processors and Workstations * An Algorithm for Higher-order Perturbation Theory in Radiative Transfer Computations * Hydrostochastics: The Master Equation Formulation of Fluid Dynamics * HPP Lattice Gas on Transputers and Networked Workstations * Study on the Hysteresis Cycle Simulation Using Modeling with Different Functions on Intervals * Refined Pruning Techniques for Feed-forward Neural Networks * Random Walk Simulation of the Motion of Transient Charges in Photoconductors * The Optical Hysteresis in Hydrogenated Amorphous Silicon * Diffusion Monte Carlo Analysis of Modern Interatomic Potentials for He * A Parallel Strategy for Molecular Dynamics Simulations of Polar Liquids on Transputer Arrays * Distribution of Ions Reflected on Rough Surfaces * The Study of Step Density Distribution During Molecular Beam Epitaxy Growth: Monte Carlo Computer Simulation * Towards a Formal Approach to the Construction of Large-scale Scientific Applications Software * Correlated Random Walk and Discrete Modelling of Propagation through Inhomogeneous Media * Teaching Plasma Physics Simulation * A Theoretical Determination of the Au-Ni Phase Diagram * Boson and Fermion Kinetics in One-dimensional Lattices * Computational Physics Course on the Technical University * Symbolic Computations in Simulation Code Development and Femtosecond-pulse Laser-plasma Interaction Studies * Computer Algebra and Integrated Computing Systems in Education of Physical Sciences * Coordinated System of Programs for Undergraduate Physics Instruction * Program Package MIRIAM and Atomic Physics of Extreme Systems * High Energy Physics Simulation on the T_Node * The Chapman-Kolmogorov Equation as Representation of Huygens' Principle and the Monolithic Self-consistent Numerical Modelling of Lasers * Authoring System for Simulation Developments * Molecular Dynamics Study of Ion Charge Effects in the Structure of Ionic Crystals * A Computational Physics Introductory Course * Computer Calculation of Substrate Temperature Field in MBE System * Multimagnetical Simulation of the Ising Model in Two and Three Dimensions * Failure of the CTRW Treatment of the Quasicoherent Excitation Transfer * Implementation of a Parallel Conjugate Gradient Method for Simulation of Elastic Light Scattering * Algorithms for Study of Thin Film Growth * Algorithms and Programs for Physics Teaching in Romanian Technical Universities * Multicanonical Simulation of 1st order Transitions: Interface Tension of the 2D 7-State Potts Model * Two Numerical Methods for the Calculation of Periodic Orbits in Hamiltonian Systems * Chaotic Behavior in a Probabilistic Cellular Automata? * Wave Optics Computing by a Networked-based Vector Wave Automaton * Tensor Manipulation Package in REDUCE * Propagation of Electromagnetic Pulses in Stratified Media * The Simple Molecular Dynamics Model for the Study of Thermalization of the Hot Nucleon Gas * Electron Spin Polarization in PdCo Alloys Calculated by KKR-CPA-LSD Method * Simulation Studies of Microscopic Droplet Spreading * A Vectorizable Algorithm for the Multicolor Successive Overrelaxation Method * Tetragonality of the CuAu I Lattice and Its Relation to Electronic Specific Heat and Spin Susceptibility * Computer Simulation of the Formation of Metallic Aggregates Produced by Chemical Reactions in Aqueous Solution * Scaling in Growth Models with Diffusion: A Monte Carlo Study * The Nucleus as the Mesoscopic System * Neural Network Computation as Dynamic System Simulation * First-principles Theory of Surface Segregation in Binary Alloys * Data Smooth Approximation Algorithm for Estimating the Temperature Dependence of the Ice Nucleation Rate * Genetic Algorithms in Optical Design * Application of 2D-FFT in the Study of Molecular Exchange Processes by NMR * Advanced Mobility Model for Electron Transport in P-Si Inversion Layers * Computer Simulation for Film Surfaces and its Fractal Dimension * Parallel Computation Techniques and the Structure of Catalyst Surfaces * Educational SW to Teach Digital Electronics and the Corresponding Text Book * Primitive Trinomials (Mod 2) Whose Degree is a Mersenne Exponent * Stochastic Modelisation and Parallel Computing * Remarks on the Hybrid Monte Carlo Algorithm for the ∫4 Model * An Experimental Computer Assisted Workbench for Physics Teaching * A Fully Implicit Code to Model Tokamak Plasma Edge Transport * EXPFIT: An Interactive Program for Automatic Beam-foil Decay Curve Analysis * Mapping Technique for Solving General, 1-D Hamiltonian Systems * Freeway Traffic, Cellular Automata, and Some (Self-Organizing) Criticality * Photonuclear Yield Analysis by Dynamic Programming * Incremental Representation of the Simply Connected Planar Curves * Self-convergence in Monte Carlo Methods * Adaptive Mesh Technique for Shock Wave Propagation * Simulation of Supersonic Coronal Streams and Their Interaction with the Solar Wind * The Nature of Chaos in Two Systems of Ordinary Nonlinear Differential Equations * Considerations of a Window-shopper * Interpretation of Data Obtained by RTP 4-Channel Pulsed Radar Reflectometer Using a Multi Layer Perceptron * Statistics of Lattice Bosons for Finite Systems * Fractal Based Image Compression with Affine Transformations * Algorithmic Studies on Simulation Codes for Heavy-ion Reactions * An Energy-Wise Computer Simulation of DNA-Ion-Water Interactions Explains the Abnormal Structure of Poly[d(A)]:Poly[d(T)] * Computer Simulation Study of Kosterlitz-Thouless-Like Transitions * Problem-oriented Software Package GUN-EBT for Computer Simulation of Beam Formation and Transport in Technological Electron-Optical Systems * Parallelization of a Boundary Value Solver and its Application in Nonlinear Dynamics * The Symbolic Classification of Real Four-dimensional Lie Algebras * Short, Singular Pulses Generation by a Dye Laser at Two Wavelengths Simultaneously * Quantum Monte Carlo Simulations of the Apex-Oxygen-Model * Approximation Procedures for the Axial Symmetric Static Einstein-Maxwell-Higgs Theory * Crystallization on a Sphere: Parallel Simulation on a Transputer Network * FAMULUS: A Software Product (also) for Physics Education * MathCAD vs. FAMULUS -- A Brief Comparison * First-principles Dynamics Used to Study Dissociative Chemisorption * A Computer Controlled System for Crystal Growth from Melt * A Time Resolved Spectroscopic Method for Short Pulsed Particle Emission * Green's Function Computation in Radiative Transfer Theory * Random Search Optimization Technique for One-criteria and Multi-criteria Problems * Hartley Transform Applications to Thermal Drift Elimination in Scanning Tunneling Microscopy * Algorithms of Measuring, Processing and Interpretation of Experimental Data Obtained with Scanning Tunneling Microscope * Time-dependent Atom-surface Interactions * Local and Global Minima on Molecular Potential Energy Surfaces: An Example of N3 Radical * Computation of Bifurcation Surfaces * Symbolic Computations in Quantum Mechanics: Energies in Next-to-solvable Systems * A Tool for RTP Reactor and Lamp Field Design * Modelling of Particle Spectra for the Analysis of Solid State Surface * List of Participants

Computer-Based Exercises To Supplement the Teaching of Stereochemical Aspects of Drug Action.

ERIC Educational Resources Information Center

Harrold, Marc W.

1995-01-01

At the Duquesne University (PA) school of pharmacy, five self-paced computer exercises using a molecular modeling program have been implemented to teach stereochemical concepts. The approach, designed for small-group learning, has been well received and found effective in enhancing students' understanding of the concepts. (Author/MSE)

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

Generalized theoretical method for the interaction between arbitrary nonuniform electric field and molecular vibrations: Toward near-field infrared spectroscopy and microscopy

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

Iwasa, Takeshi, E-mail: tiwasa@mail.sci.hokudai.ac.jp; Takenaka, Masato; Taketsugu, Tetsuya

A theoretical method to compute infrared absorption spectra when a molecule is interacting with an arbitrary nonuniform electric field such as near-fields is developed and numerically applied to simple model systems. The method is based on the multipolar Hamiltonian where the light-matter interaction is described by a spatial integral of the inner product of the molecular polarization and applied electric field. The computation scheme is developed under the harmonic approximation for the molecular vibrations and the framework of modern electronic structure calculations such as the density functional theory. Infrared reflection absorption and near-field infrared absorption are considered as model systems.more » The obtained IR spectra successfully reflect the spatial structure of the applied electric field and corresponding vibrational modes, demonstrating applicability of the present method to analyze modern nanovibrational spectroscopy using near-fields. The present method can use arbitral electric fields and thus can integrate two fields such as computational chemistry and electromagnetics.« less

Generalized theoretical method for the interaction between arbitrary nonuniform electric field and molecular vibrations: Toward near-field infrared spectroscopy and microscopy.

PubMed

Iwasa, Takeshi; Takenaka, Masato; Taketsugu, Tetsuya

2016-03-28

A theoretical method to compute infrared absorption spectra when a molecule is interacting with an arbitrary nonuniform electric field such as near-fields is developed and numerically applied to simple model systems. The method is based on the multipolar Hamiltonian where the light-matter interaction is described by a spatial integral of the inner product of the molecular polarization and applied electric field. The computation scheme is developed under the harmonic approximation for the molecular vibrations and the framework of modern electronic structure calculations such as the density functional theory. Infrared reflection absorption and near-field infrared absorption are considered as model systems. The obtained IR spectra successfully reflect the spatial structure of the applied electric field and corresponding vibrational modes, demonstrating applicability of the present method to analyze modern nanovibrational spectroscopy using near-fields. The present method can use arbitral electric fields and thus can integrate two fields such as computational chemistry and electromagnetics.

Diffusion-Based Model for Synaptic Molecular Communication Channel.

PubMed

Khan, Tooba; Bilgin, Bilgesu A; Akan, Ozgur B

2017-06-01

Computational methods have been extensively used to understand the underlying dynamics of molecular communication methods employed by nature. One very effective and popular approach is to utilize a Monte Carlo simulation. Although it is very reliable, this method can have a very high computational cost, which in some cases renders the simulation impractical. Therefore, in this paper, for the special case of an excitatory synaptic molecular communication channel, we present a novel mathematical model for the diffusion and binding of neurotransmitters that takes into account the effects of synaptic geometry in 3-D space and re-absorption of neurotransmitters by the transmitting neuron. Based on this model we develop a fast deterministic algorithm, which calculates expected value of the output of this channel, namely, the amplitude of excitatory postsynaptic potential (EPSP), for given synaptic parameters. We validate our algorithm by a Monte Carlo simulation, which shows total agreement between the results of the two methods. Finally, we utilize our model to quantify the effects of variation in synaptic parameters, such as position of release site, receptor density, size of postsynaptic density, diffusion coefficient, uptake probability, and number of neurotransmitters in a vesicle, on maximum number of bound receptors that directly affect the peak amplitude of EPSP.

A novel integrated framework and improved methodology of computer-aided drug design.

PubMed

Chen, Calvin Yu-Chian

2013-01-01

Computer-aided drug design (CADD) is a critical initiating step of drug development, but a single model capable of covering all designing aspects remains to be elucidated. Hence, we developed a drug design modeling framework that integrates multiple approaches, including machine learning based quantitative structure-activity relationship (QSAR) analysis, 3D-QSAR, Bayesian network, pharmacophore modeling, and structure-based docking algorithm. Restrictions for each model were defined for improved individual and overall accuracy. An integration method was applied to join the results from each model to minimize bias and errors. In addition, the integrated model adopts both static and dynamic analysis to validate the intermolecular stabilities of the receptor-ligand conformation. The proposed protocol was applied to identifying HER2 inhibitors from traditional Chinese medicine (TCM) as an example for validating our new protocol. Eight potent leads were identified from six TCM sources. A joint validation system comprised of comparative molecular field analysis, comparative molecular similarity indices analysis, and molecular dynamics simulation further characterized the candidates into three potential binding conformations and validated the binding stability of each protein-ligand complex. The ligand pathway was also performed to predict the ligand "in" and "exit" from the binding site. In summary, we propose a novel systematic CADD methodology for the identification, analysis, and characterization of drug-like candidates.

Geometric and electrostatic modeling using molecular rigidity functions

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

Mu, Lin; Xia, Kelin; Wei, Guowei

Geometric and electrostatic modeling is an essential component in computational biophysics and molecular biology. Commonly used geometric representations admit geometric singularities such as cusps, tips and self-intersecting facets that lead to computational instabilities in the molecular modeling. Our present work explores the use of flexibility and rigidity index (FRI), which has a proved superiority in protein B-factor prediction, for biomolecular geometric representation and associated electrostatic analysis. FRI rigidity surfaces are free of geometric singularities. We propose a rigidity based Poisson–Boltzmann equation for biomolecular electrostatic analysis. These approaches to surface and electrostatic modeling are validated by a set of 21 proteins.more » Our results are compared with those of established methods. Finally, being smooth and analytically differentiable, FRI rigidity functions offer excellent curvature analysis, which characterizes concave and convex regions on protein surfaces. Polarized curvatures constructed by using the product of minimum curvature and electrostatic potential is shown to predict potential protein–ligand binding sites.« less

Computer Modeling of the Structure and Spectra of Fluorescent Proteins

PubMed Central

Grigorenko, B.L.; Savitsky, A.P.

2009-01-01

Fluorescent proteins from the family of green fluorescent proteins are intensively used as biomarkers in living systems. The chromophore group based on the hydroxybenzylidene-imidazoline molecule, which is formed in nature from three amino-acid residues inside the protein globule and well shielded from external media, is responsible for light absorption and fluorescence. Along with the intense experimental studies of the properties of fluorescent proteins and their chromophores by biochemical, X-ray, and spectroscopic tools, in recent years, computer modeling has been used to characterize their properties and spectra. We present in this review the most interesting results of the molecular modeling of the structural parameters and optical and vibrational spectra of the chromophorecontaining domains of fluorescent proteins by methods of quantum chemistry, molecular dynamics, and combined quantum-mechanical-molecular-mechanical approaches. The main emphasis is on the correlation of theoretical and experimental data and on the predictive power of modeling, which may be useful for creating new, efficient biomarkers. PMID:22649601

Geometric and electrostatic modeling using molecular rigidity functions

DOE PAGES

Mu, Lin; Xia, Kelin; Wei, Guowei

2017-03-01

Geometric and electrostatic modeling is an essential component in computational biophysics and molecular biology. Commonly used geometric representations admit geometric singularities such as cusps, tips and self-intersecting facets that lead to computational instabilities in the molecular modeling. Our present work explores the use of flexibility and rigidity index (FRI), which has a proved superiority in protein B-factor prediction, for biomolecular geometric representation and associated electrostatic analysis. FRI rigidity surfaces are free of geometric singularities. We propose a rigidity based Poisson–Boltzmann equation for biomolecular electrostatic analysis. These approaches to surface and electrostatic modeling are validated by a set of 21 proteins.more » Our results are compared with those of established methods. Finally, being smooth and analytically differentiable, FRI rigidity functions offer excellent curvature analysis, which characterizes concave and convex regions on protein surfaces. Polarized curvatures constructed by using the product of minimum curvature and electrostatic potential is shown to predict potential protein–ligand binding sites.« less

Using Molecular Visualization to Explore Protein Structure and Function and Enhance Student Facility with Computational Tools

ERIC Educational Resources Information Center

Terrell, Cassidy R.; Listenberger, Laura L.

2017-01-01

Recognizing that undergraduate students can benefit from analysis of 3D protein structure and function, we have developed a multiweek, inquiry-based molecular visualization project for Biochemistry I students. This project uses a virtual model of cyclooxygenase-1 (COX-1) to guide students through multiple levels of protein structure analysis. The…

High Performance Parallel Computational Nanotechnology

NASA Technical Reports Server (NTRS)

Saini, Subhash; Craw, James M. (Technical Monitor)

1995-01-01

At a recent press conference, NASA Administrator Dan Goldin encouraged NASA Ames Research Center to take a lead role in promoting research and development of advanced, high-performance computer technology, including nanotechnology. Manufacturers of leading-edge microprocessors currently perform large-scale simulations in the design and verification of semiconductor devices and microprocessors. Recently, the need for this intensive simulation and modeling analysis has greatly increased, due in part to the ever-increasing complexity of these devices, as well as the lessons of experiences such as the Pentium fiasco. Simulation, modeling, testing, and validation will be even more important for designing molecular computers because of the complex specification of millions of atoms, thousands of assembly steps, as well as the simulation and modeling needed to ensure reliable, robust and efficient fabrication of the molecular devices. The software for this capacity does not exist today, but it can be extrapolated from the software currently used in molecular modeling for other applications: semi-empirical methods, ab initio methods, self-consistent field methods, Hartree-Fock methods, molecular mechanics; and simulation methods for diamondoid structures. In as much as it seems clear that the application of such methods in nanotechnology will require powerful, highly powerful systems, this talk will discuss techniques and issues for performing these types of computations on parallel systems. We will describe system design issues (memory, I/O, mass storage, operating system requirements, special user interface issues, interconnects, bandwidths, and programming languages) involved in parallel methods for scalable classical, semiclassical, quantum, molecular mechanics, and continuum models; molecular nanotechnology computer-aided designs (NanoCAD) techniques; visualization using virtual reality techniques of structural models and assembly sequences; software required to control mini robotic manipulators for positional control; scalable numerical algorithms for reliability, verifications and testability. There appears no fundamental obstacle to simulating molecular compilers and molecular computers on high performance parallel computers, just as the Boeing 777 was simulated on a computer before manufacturing it.

Modeling of diatomic molecule using the Morse potential and the Verlet algorithm

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

Fidiani, Elok

Performing molecular modeling usually uses special software for Molecular Dynamics (MD) such as: GROMACS, NAMD, JMOL etc. Molecular dynamics is a computational method to calculate the time dependent behavior of a molecular system. In this work, MATLAB was used as numerical method for a simple modeling of some diatomic molecules: HCl, H{sub 2} and O{sub 2}. MATLAB is a matrix based numerical software, in order to do numerical analysis, all the functions and equations describing properties of atoms and molecules must be developed manually in MATLAB. In this work, a Morse potential was generated to describe the bond interaction betweenmore » the two atoms. In order to analyze the simultaneous motion of molecules, the Verlet Algorithm derived from Newton’s Equations of Motion (classical mechanics) was operated. Both the Morse potential and the Verlet algorithm were integrated using MATLAB to derive physical properties and the trajectory of the molecules. The data computed by MATLAB is always in the form of a matrix. To visualize it, Visualized Molecular Dynamics (VMD) was performed. Such method is useful for development and testing some types of interaction on a molecular scale. Besides, this can be very helpful for describing some basic principles of molecular interaction for educational purposes.« less

Computational neuropharmacology: dynamical approaches in drug discovery.

PubMed

Aradi, Ildiko; Erdi, Péter

2006-05-01

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

QSPR modeling: graph connectivity indices versus line graph connectivity indices

PubMed

Basak; Nikolic; Trinajstic; Amic; Beslo

2000-07-01

Five QSPR models of alkanes were reinvestigated. Properties considered were molecular surface-dependent properties (boiling points and gas chromatographic retention indices) and molecular volume-dependent properties (molar volumes and molar refractions). The vertex- and edge-connectivity indices were used as structural parameters. In each studied case we computed connectivity indices of alkane trees and alkane line graphs and searched for the optimum exponent. Models based on indices with an optimum exponent and on the standard value of the exponent were compared. Thus, for each property we generated six QSPR models (four for alkane trees and two for the corresponding line graphs). In all studied cases QSPR models based on connectivity indices with optimum exponents have better statistical characteristics than the models based on connectivity indices with the standard value of the exponent. The comparison between models based on vertex- and edge-connectivity indices gave in two cases (molar volumes and molar refractions) better models based on edge-connectivity indices and in three cases (boiling points for octanes and nonanes and gas chromatographic retention indices) better models based on vertex-connectivity indices. Thus, it appears that the edge-connectivity index is more appropriate to be used in the structure-molecular volume properties modeling and the vertex-connectivity index in the structure-molecular surface properties modeling. The use of line graphs did not improve the predictive power of the connectivity indices. Only in one case (boiling points of nonanes) a better model was obtained with the use of line graphs.

Students' use of atomic and molecular models in learning chemistry

NASA Astrophysics Data System (ADS)

O'Connor, Eileen Ann

1997-09-01

The objective of this study was to investigate the development of introductory college chemistry students' use of atomic and molecular models to explain physical and chemical phenomena. The study was conducted during the first semester of the course at a University and College II. Public institution (Carnegie Commission of Higher Education, 1973). Students' use of models was observed during one-on-one interviews conducted over the course of the semester. The approach to introductory chemistry emphasized models. Students were exposed to over two-hundred and fifty atomic and molecular models during lectures, were assigned text readings that used over a thousand models, and worked interactively with dozens of models on the computer. These models illustrated various features of the spatial organization of valence electrons and nuclei in atoms and molecules. Despite extensive exposure to models in lectures, in textbook, and in computer-based activities, the students in the study based their explanation in large part on a simple Bohr model (electrons arranged in concentric circles around the nuclei)--a model that had not been introduced in the course. Students used visual information from their models to construct their explanation, while overlooking inter-atomic and intra-molecular forces which are not represented explicitly in the models. In addition, students often explained phenomena by adding separate information about the topic without either integrating or logically relating this information into a cohesive explanation. The results of the study demonstrate that despite the extensive use of models in chemistry instruction, students do not necessarily apply them appropriately in explaining chemical and physical phenomena. The results of this study suggest that for the power of models as aids to learning to be more fully realized, chemistry professors must give more attention to the selection, use, integration, and limitations of models in their instruction.

Fragment-Based Docking: Development of the CHARMMing Web User Interface as a Platform for Computer-Aided Drug Design

PubMed Central

2015-01-01

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser.1 One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing’s capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of “re-dockings” with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing’s docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening. PMID:25151852

Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design.

PubMed

Pevzner, Yuri; Frugier, Emilie; Schalk, Vinushka; Caflisch, Amedeo; Woodcock, H Lee

2014-09-22

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser. One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing's capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of "re-dockings" with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing's docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening.

CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions.

PubMed

Kostal, Jakub; Voutchkova-Kostal, Adelina

2016-01-19

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

PubMed Central

Simões, Tiago; Lopes, Daniel; Dias, Sérgio; Fernandes, Francisco; Pereira, João; Jorge, Joaquim; Bajaj, Chandrajit; Gomes, Abel

2017-01-01

Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing. PMID:29520122

Three-dimensional quantitative structure-activity relationship analysis for human pregnane X receptor for the prediction of CYP3A4 induction in human hepatocytes: structure-based comparative molecular field analysis.

PubMed

Handa, Koichi; Nakagome, Izumi; Yamaotsu, Noriyuki; Gouda, Hiroaki; Hirono, Shuichi

2015-01-01

The pregnane X receptor [PXR (NR1I2)] induces the expression of xenobiotic metabolic genes and transporter genes. In this study, we aimed to establish a computational method for quantifying the enzyme-inducing potencies of different compounds via their ability to activate PXR, for the application in drug discovery and development. To achieve this purpose, we developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) for predicting enzyme-inducing potencies, based on computer-ligand docking to multiple PXR protein structures sampled from the trajectory of a molecular dynamics simulation. Molecular mechanics-generalized born/surface area scores representing the ligand-protein-binding free energies were calculated for each ligand. As a result, the predicted enzyme-inducing potencies for compounds generated by the CoMFA model were in good agreement with the experimental values. Finally, we concluded that this 3D-QSAR model has the potential to predict the enzyme-inducing potencies of novel compounds with high precision and therefore has valuable applications in the early stages of the drug discovery process. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Molecular Modeling and Computational Chemistry at Humboldt State University.

ERIC Educational Resources Information Center

Paselk, Richard A.; Zoellner, Robert W.

2002-01-01

Describes a molecular modeling and computational chemistry (MM&CC) facility for undergraduate instruction and research at Humboldt State University. This facility complex allows the introduction of MM&CC throughout the chemistry curriculum with tailored experiments in general, organic, and inorganic courses as well as a new molecular modeling…

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.

Magnetic polyoxometalates: from molecular magnetism to molecular spintronics and quantum computing.

PubMed

Clemente-Juan, Juan M; Coronado, Eugenio; Gaita-Ariño, Alejandro

2012-11-21

In this review we discuss the relevance of polyoxometalate (POM) chemistry to provide model objects in molecular magnetism. We present several potential applications in nanomagnetism, in particular, in molecular spintronics and quantum computing.

Materials-by-design: computation, synthesis, and characterization from atoms to structures

NASA Astrophysics Data System (ADS)

Yeo, Jingjie; Jung, Gang Seob; Martín-Martínez, Francisco J.; Ling, Shengjie; Gu, Grace X.; Qin, Zhao; Buehler, Markus J.

2018-05-01

In the 50 years that succeeded Richard Feynman’s exposition of the idea that there is ‘plenty of room at the bottom’ for manipulating individual atoms for the synthesis and manufacturing processing of materials, the materials-by-design paradigm is being developed gradually through synergistic integration of experimental material synthesis and characterization with predictive computational modeling and optimization. This paper reviews how this paradigm creates the possibility to develop materials according to specific, rational designs from the molecular to the macroscopic scale. We discuss promising techniques in experimental small-scale material synthesis and large-scale fabrication methods to manipulate atomistic or macroscale structures, which can be designed by computational modeling. These include recombinant protein technology to produce peptides and proteins with tailored sequences encoded by recombinant DNA, self-assembly processes induced by conformational transition of proteins, additive manufacturing for designing complex structures, and qualitative and quantitative characterization of materials at different length scales. We describe important material characterization techniques using numerous methods of spectroscopy and microscopy. We detail numerous multi-scale computational modeling techniques that complements these experimental techniques: DFT at the atomistic scale; fully atomistic and coarse-grain molecular dynamics at the molecular to mesoscale; continuum modeling at the macroscale. Additionally, we present case studies that utilize experimental and computational approaches in an integrated manner to broaden our understanding of the properties of two-dimensional materials and materials based on silk and silk-elastin-like proteins.

Molecular Dynamics based on a Generalized Born solvation model: application to protein folding

NASA Astrophysics Data System (ADS)

Onufriev, Alexey

2004-03-01

An accurate description of the aqueous environment is essential for realistic biomolecular simulations, but may become very expensive computationally. We have developed a version of the Generalized Born model suitable for describing large conformational changes in macromolecules. The model represents the solvent implicitly as continuum with the dielectric properties of water, and include charge screening effects of salt. The computational cost associated with the use of this model in Molecular Dynamics simulations is generally considerably smaller than the cost of representing water explicitly. Also, compared to traditional Molecular Dynamics simulations based on explicit water representation, conformational changes occur much faster in implicit solvation environment due to the absence of viscosity. The combined speed-up allow one to probe conformational changes that occur on much longer effective time-scales. We apply the model to folding of a 46-residue three helix bundle protein (residues 10-55 of protein A, PDB ID 1BDD). Starting from an unfolded structure at 450 K, the protein folds to the lowest energy state in 6 ns of simulation time, which takes about a day on a 16 processor SGI machine. The predicted structure differs from the native one by 2.4 A (backbone RMSD). Analysis of the structures seen on the folding pathway reveals details of the folding process unavailable form experiment.

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

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

Ibrahim, Khaled Z.; Epifanovsky, Evgeny; Williams, Samuel

Coupled-cluster methods provide highly accurate models of molecular structure through explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix–matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts tomore » extend the Libtensor framework to work in the distributed memory environment in a scalable and energy-efficient manner. We achieve up to 240× speedup compared with the optimized shared memory implementation of Libtensor. We attain scalability to hundreds of thousands of compute cores on three distributed-memory architectures (Cray XC30 and XC40, and IBM Blue Gene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance, tasking and bulk synchronous models. Nevertheless, we preserve a unified interface to both programming models to maintain the productivity of computational quantum chemists.« less

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

DOE PAGES

Ibrahim, Khaled Z.; Epifanovsky, Evgeny; Williams, Samuel; ...

2017-03-08

Coupled-cluster methods provide highly accurate models of molecular structure through explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix–matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts tomore » extend the Libtensor framework to work in the distributed memory environment in a scalable and energy-efficient manner. We achieve up to 240× speedup compared with the optimized shared memory implementation of Libtensor. We attain scalability to hundreds of thousands of compute cores on three distributed-memory architectures (Cray XC30 and XC40, and IBM Blue Gene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance, tasking and bulk synchronous models. Nevertheless, we preserve a unified interface to both programming models to maintain the productivity of computational quantum chemists.« less

Integrated software environment based on COMKAT for analyzing tracer pharmacokinetics with molecular imaging.

PubMed

Fang, Yu-Hua Dean; Asthana, Pravesh; Salinas, Cristian; Huang, Hsuan-Ming; Muzic, Raymond F

2010-01-01

An integrated software package, Compartment Model Kinetic Analysis Tool (COMKAT), is presented in this report. COMKAT is an open-source software package with many functions for incorporating pharmacokinetic analysis in molecular imaging research and has both command-line and graphical user interfaces. With COMKAT, users may load and display images, draw regions of interest, load input functions, select kinetic models from a predefined list, or create a novel model and perform parameter estimation, all without having to write any computer code. For image analysis, COMKAT image tool supports multiple image file formats, including the Digital Imaging and Communications in Medicine (DICOM) standard. Image contrast, zoom, reslicing, display color table, and frame summation can be adjusted in COMKAT image tool. It also displays and automatically registers images from 2 modalities. Parametric imaging capability is provided and can be combined with the distributed computing support to enhance computation speeds. For users without MATLAB licenses, a compiled, executable version of COMKAT is available, although it currently has only a subset of the full COMKAT capability. Both the compiled and the noncompiled versions of COMKAT are free for academic research use. Extensive documentation, examples, and COMKAT itself are available on its wiki-based Web site, http://comkat.case.edu. Users are encouraged to contribute, sharing their experience, examples, and extensions of COMKAT. With integrated functionality specifically designed for imaging and kinetic modeling analysis, COMKAT can be used as a software environment for molecular imaging and pharmacokinetic analysis.

Antibody-controlled actuation of DNA-based molecular circuits.

PubMed

Engelen, Wouter; Meijer, Lenny H H; Somers, Bram; de Greef, Tom F A; Merkx, Maarten

2017-02-17

DNA-based molecular circuits allow autonomous signal processing, but their actuation has relied mostly on RNA/DNA-based inputs, limiting their application in synthetic biology, biomedicine and molecular diagnostics. Here we introduce a generic method to translate the presence of an antibody into a unique DNA strand, enabling the use of antibodies as specific inputs for DNA-based molecular computing. Our approach, antibody-templated strand exchange (ATSE), uses the characteristic bivalent architecture of antibodies to promote DNA-strand exchange reactions both thermodynamically and kinetically. Detailed characterization of the ATSE reaction allowed the establishment of a comprehensive model that describes the kinetics and thermodynamics of ATSE as a function of toehold length, antibody-epitope affinity and concentration. ATSE enables the introduction of complex signal processing in antibody-based diagnostics, as demonstrated here by constructing molecular circuits for multiplex antibody detection, integration of multiple antibody inputs using logic gates and actuation of enzymes and DNAzymes for signal amplification.

Antibody-controlled actuation of DNA-based molecular circuits

NASA Astrophysics Data System (ADS)

Engelen, Wouter; Meijer, Lenny H. H.; Somers, Bram; de Greef, Tom F. A.; Merkx, Maarten

2017-02-01

DNA-based molecular circuits allow autonomous signal processing, but their actuation has relied mostly on RNA/DNA-based inputs, limiting their application in synthetic biology, biomedicine and molecular diagnostics. Here we introduce a generic method to translate the presence of an antibody into a unique DNA strand, enabling the use of antibodies as specific inputs for DNA-based molecular computing. Our approach, antibody-templated strand exchange (ATSE), uses the characteristic bivalent architecture of antibodies to promote DNA-strand exchange reactions both thermodynamically and kinetically. Detailed characterization of the ATSE reaction allowed the establishment of a comprehensive model that describes the kinetics and thermodynamics of ATSE as a function of toehold length, antibody-epitope affinity and concentration. ATSE enables the introduction of complex signal processing in antibody-based diagnostics, as demonstrated here by constructing molecular circuits for multiplex antibody detection, integration of multiple antibody inputs using logic gates and actuation of enzymes and DNAzymes for signal amplification.

Computational Modeling and Treatment Identification in the Myelodysplastic Syndromes.

PubMed

Drusbosky, Leylah M; Cogle, Christopher R

2017-10-01

This review discusses the need for computational modeling in myelodysplastic syndromes (MDS) and early test results. As our evolving understanding of MDS reveals a molecularly complicated disease, the need for sophisticated computer analytics is required to keep track of the number and complex interplay among the molecular abnormalities. Computational modeling and digital drug simulations using whole exome sequencing data input have produced early results showing high accuracy in predicting treatment response to standard of care drugs. Furthermore, the computational MDS models serve as clinically relevant MDS cell lines for pre-clinical assays of investigational agents. MDS is an ideal disease for computational modeling and digital drug simulations. Current research is focused on establishing the prediction value of computational modeling. Future research will test the clinical advantage of computer-informed therapy in MDS.

Computationally Guided Design of Polymer Electrolytes for Battery Applications

NASA Astrophysics Data System (ADS)

Wang, Zhen-Gang; Webb, Michael; Savoie, Brett; Miller, Thomas

We develop an efficient computational framework for guiding the design of polymer electrolytes for Li battery applications. Short-times molecular dynamics (MD) simulations are employed to identify key structural and dynamic features in the solvation and motion of Li ions, such as the structure of the solvation shells, the spatial distribution of solvation sites, and the polymer segmental mobility. Comparative studies on six polyester-based polymers and polyethylene oxide (PEO) yield good agreement with experimental data on the ion conductivities, and reveal significant differences in the ion diffusion mechanism between PEO and the polyesters. The molecular insights from the MD simulations are used to build a chemically specific coarse-grained model in the spirit of the dynamic bond percolation model of Druger, Ratner and Nitzan. We apply this coarse-grained model to characterize Li ion diffusion in several existing and yet-to-be synthesized polyethers that differ by oxygen content and backbone stiffness. Good agreement is obtained between the predictions of the coarse-grained model and long-timescale atomistic MD simulations, thus providing validation of the model. Our study predicts higher Li ion diffusivity in poly(trimethylene oxide-alt-ethylene oxide) than in PEO. These results demonstrate the potential of this computational framework for rapid screening of new polymer electrolytes based on ion diffusivity.

Simplified Modeling of Oxidation of Hydrocarbons

NASA Technical Reports Server (NTRS)

Bellan, Josette; Harstad, Kenneth

2008-01-01

A method of simplified computational modeling of oxidation of hydrocarbons is undergoing development. This is one of several developments needed to enable accurate computational simulation of turbulent, chemically reacting flows. At present, accurate computational simulation of such flows is difficult or impossible in most cases because (1) the numbers of grid points needed for adequate spatial resolution of turbulent flows in realistically complex geometries are beyond the capabilities of typical supercomputers now in use and (2) the combustion of typical hydrocarbons proceeds through decomposition into hundreds of molecular species interacting through thousands of reactions. Hence, the combination of detailed reaction- rate models with the fundamental flow equations yields flow models that are computationally prohibitive. Hence, further, a reduction of at least an order of magnitude in the dimension of reaction kinetics is one of the prerequisites for feasibility of computational simulation of turbulent, chemically reacting flows. In the present method of simplified modeling, all molecular species involved in the oxidation of hydrocarbons are classified as either light or heavy; heavy molecules are those having 3 or more carbon atoms. The light molecules are not subject to meaningful decomposition, and the heavy molecules are considered to decompose into only 13 specified constituent radicals, a few of which are listed in the table. One constructs a reduced-order model, suitable for use in estimating the release of heat and the evolution of temperature in combustion, from a base comprising the 13 constituent radicals plus a total of 26 other species that include the light molecules and related light free radicals. Then rather than following all possible species through their reaction coordinates, one follows only the reduced set of reaction coordinates of the base. The behavior of the base was examined in test computational simulations of the combustion of heptane in a stirred reactor at various initial pressures ranging from 0.1 to 6 MPa. Most of the simulations were performed for stoichiometric mixtures; some were performed for fuel/oxygen mole ratios of 1/2 and 2.

Errors in the Calculation of 27Al Nuclear Magnetic Resonance Chemical Shifts

PubMed Central

Wang, Xianlong; Wang, Chengfei; Zhao, Hui

2012-01-01

Computational chemistry is an important tool for signal assignment of 27Al nuclear magnetic resonance spectra in order to elucidate the species of aluminum(III) in aqueous solutions. The accuracy of the popular theoretical models for computing the 27Al chemical shifts was evaluated by comparing the calculated and experimental chemical shifts in more than one hundred aluminum(III) complexes. In order to differentiate the error due to the chemical shielding tensor calculation from that due to the inadequacy of the molecular geometry prediction, single-crystal X-ray diffraction determined structures were used to build the isolated molecule models for calculating the chemical shifts. The results were compared with those obtained using the calculated geometries at the B3LYP/6-31G(d) level. The isotropic chemical shielding constants computed at different levels have strong linear correlations even though the absolute values differ in tens of ppm. The root-mean-square difference between the experimental chemical shifts and the calculated values is approximately 5 ppm for the calculations based on the X-ray structures, but more than 10 ppm for the calculations based on the computed geometries. The result indicates that the popular theoretical models are adequate in calculating the chemical shifts while an accurate molecular geometry is more critical. PMID:23203134

On the computation of molecular surface correlations for protein docking using fourier techniques.

PubMed

Sakk, Eric

2007-08-01

The computation of surface correlations using a variety of molecular models has been applied to the unbound protein docking problem. Because of the computational complexity involved in examining all possible molecular orientations, the fast Fourier transform (FFT) (a fast numerical implementation of the discrete Fourier transform (DFT)) is generally applied to minimize the number of calculations. This approach is rooted in the convolution theorem which allows one to inverse transform the product of two DFTs in order to perform the correlation calculation. However, such a DFT calculation results in a cyclic or "circular" correlation which, in general, does not lead to the same result as the linear correlation desired for the docking problem. In this work, we provide computational bounds for constructing molecular models used in the molecular surface correlation problem. The derived bounds are then shown to be consistent with various intuitive guidelines previously reported in the protein docking literature. Finally, these bounds are applied to different molecular models in order to investigate their effect on the correlation calculation.

The Virtual Liver Project: Modeling Tissue Response To Chemicals Through Multiscale Simulation

EPA Science Inventory

The US EPA Virtual Liver Project is aimed at simulating the risk of toxic effects from environmental chemicals in silico. The computational systems model of organ injury due to chronic chemical exposure is based on: (i) the dynamics of perturbed molecular pathways, (ii) their lin...

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

Computational Study Exploring the Interaction Mechanism of Benzimidazole Derivatives as Potent Cattle Bovine Viral Diarrhea Virus Inhibitors.

PubMed

Wang, Jinghui; Yang, Yinfeng; Li, Yan; Wang, Yonghua

2016-07-27

Bovine viral diarrhea virus (BVDV) infections are prevailing in cattle populations on a worldwide scale. The BVDV RNA-dependent RNA polymerase (RdRp), as a promising target for new anti-BVDV drug development, has attracted increasing attention. To explore the interaction mechanism of 65 benzimidazole scaffold-based derivatives as BVDV inhibitors, presently, a computational study was performed based on a combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) simulations. The resultant optimum CoMFA and CoMSIA models present proper reliabilities and strong predictive abilities (with Q(2) = 0. 64, R(2)ncv = 0.93, R(2)pred = 0.80 and Q(2) = 0. 65, R(2)ncv = 0.98, R(2)pred = 0.86, respectively). In addition, there was good concordance between these models, molecular docking, and MD results. Moreover, the MM-PBSA energy analysis reveals that the major driving force for ligand binding is the polar solvation contribution term. Hopefully, these models and the obtained findings could offer better understanding of the interaction mechanism of BVDV inhibitors as well as benefit the new discovery of more potent BVDV inhibitors.

Computer simulation of surface and film processes

NASA Technical Reports Server (NTRS)

Tiller, W. A.; Halicioglu, M. T.

1984-01-01

All the investigations which were performed employed in one way or another a computer simulation technique based on atomistic level considerations. In general, three types of simulation methods were used for modeling systems with discrete particles that interact via well defined potential functions: molecular dynamics (a general method for solving the classical equations of motion of a model system); Monte Carlo (the use of Markov chain ensemble averaging technique to model equilibrium properties of a system); and molecular statics (provides properties of a system at T = 0 K). The effects of three-body forces on the vibrational frequencies of triatomic cluster were investigated. The multilayer relaxation phenomena for low index planes of an fcc crystal was analyzed also as a function of the three-body interactions. Various surface properties for Si and SiC system were calculated. Results obtained from static simulation calculations for slip formation were presented. The more elaborate molecular dynamics calculations on the propagation of cracks in two-dimensional systems were outlined.

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.

Programmable energy landscapes for kinetic control of DNA strand displacement.

PubMed

Machinek, Robert R F; Ouldridge, Thomas E; Haley, Natalie E C; Bath, Jonathan; Turberfield, Andrew J

2014-11-10

DNA is used to construct synthetic systems that sense, actuate, move and compute. The operation of many dynamic DNA devices depends on toehold-mediated strand displacement, by which one DNA strand displaces another from a duplex. Kinetic control of strand displacement is particularly important in autonomous molecular machinery and molecular computation, in which non-equilibrium systems are controlled through rates of competing processes. Here, we introduce a new method based on the creation of mismatched base pairs as kinetic barriers to strand displacement. Reaction rate constants can be tuned across three orders of magnitude by altering the position of such a defect without significantly changing the stabilities of reactants or products. By modelling reaction free-energy landscapes, we explore the mechanistic basis of this control mechanism. We also demonstrate that oxDNA, a coarse-grained model of DNA, is capable of accurately predicting and explaining the impact of mismatches on displacement kinetics.

The R.E.D. tools: advances in RESP and ESP charge derivation and force field library building.

PubMed

Dupradeau, François-Yves; Pigache, Adrien; Zaffran, Thomas; Savineau, Corentin; Lelong, Rodolphe; Grivel, Nicolas; Lelong, Dimitri; Rosanski, Wilfried; Cieplak, Piotr

2010-07-28

Deriving atomic charges and building a force field library for a new molecule are key steps when developing a force field required for conducting structural and energy-based analysis using molecular mechanics. Derivation of popular RESP charges for a set of residues is a complex and error prone procedure because it depends on numerous input parameters. To overcome these problems, the R.E.D. Tools (RESP and ESP charge Derive, ) have been developed to perform charge derivation in an automatic and straightforward way. The R.E.D. program handles chemical elements up to bromine in the periodic table. It interfaces different quantum mechanical programs employed for geometry optimization and computing molecular electrostatic potential(s), and performs charge fitting using the RESP program. By defining tight optimization criteria and by controlling the molecular orientation of each optimized geometry, charge values are reproduced at any computer platform with an accuracy of 0.0001 e. The charges can be fitted using multiple conformations, making them suitable for molecular dynamics simulations. R.E.D. allows also for defining charge constraints during multiple molecule charge fitting, which are used to derive charges for molecular fragments. Finally, R.E.D. incorporates charges into a force field library, readily usable in molecular dynamics computer packages. For complex cases, such as a set of homologous molecules belonging to a common family, an entire force field topology database is generated. Currently, the atomic charges and force field libraries have been developed for more than fifty model systems and stored in the RESP ESP charge DDataBase. Selected results related to non-polarizable charge models are presented and discussed.

QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations.

PubMed

Valdés-Martiní, José R; Marrero-Ponce, Yovani; García-Jacas, César R; Martinez-Mayorga, Karina; Barigye, Stephen J; Vaz d'Almeida, Yasser Silveira; Pham-The, Hai; Pérez-Giménez, Facundo; Morell, Carlos A

2017-06-07

In previous reports, Marrero-Ponce et al. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topological Molecular Computational Design-Computer Aided Rational Drug Design) molecular descriptors. These MDs codify molecular information based on the bilinear, quadratic and linear algebraic forms and the graph-theoretical electronic-density and edge-adjacency matrices in order to consider atom- and bond-based relations, respectively. These MDs have been successfully applied in the screening of chemical compounds of different therapeutic applications ranging from antimalarials, antibacterials, tyrosinase inhibitors and so on. To compute these MDs, a computational program with the same name was initially developed. However, this in house software barely offered the functionalities required in contemporary molecular modeling tasks, in addition to the inherent limitations that made its usability impractical. Therefore, the present manuscript introduces the QuBiLS-MAS (acronym for Quadratic, Bilinear and N-Linear mapS based on graph-theoretic electronic-density Matrices and Atomic weightingS) software designed to compute topological (0-2.5D) molecular descriptors based on bilinear, quadratic and linear algebraic forms for atom- and bond-based relations. The QuBiLS-MAS module was designed as standalone software, in which extensions and generalizations of the former ToMoCoMD-CARDD 2D-algebraic indices are implemented, considering the following aspects: (a) two new matrix normalization approaches based on double-stochastic and mutual probability formalisms; (b) topological constraints (cut-offs) to take into account particular inter-atomic relations; (c) six additional atomic properties to be used as weighting schemes in the calculation of the molecular vectors; (d) four new local-fragments to consider molecular regions of interest; (e) number of lone-pair electrons in chemical structure defined by diagonal coefficients in matrix representations; and (f) several aggregation operators (invariants) applied over atom/bond-level descriptors in order to compute global indices. This software permits the parallel computation of the indices, contains a batch processing module and data curation functionalities. This program was developed in Java v1.7 using the Chemistry Development Kit library (version 1.4.19). The QuBiLS-MAS software consists of two components: a desktop interface (GUI) and an API library allowing for the easy integration of the latter in chemoinformatics applications. The relevance of the novel extensions and generalizations implemented in this software is demonstrated through three studies. Firstly, a comparative Shannon's entropy based variability study for the proposed QuBiLS-MAS and the DRAGON indices demonstrates superior performance for the former. A principal component analysis reveals that the QuBiLS-MAS approach captures chemical information orthogonal to that codified by the DRAGON descriptors. Lastly, a QSAR study for the binding affinity to the corticosteroid-binding globulin using Cramer's steroid dataset is carried out. From these analyses, it is revealed that the QuBiLS-MAS approach for atom-pair relations yields similar-to-superior performance with regard to other QSAR methodologies reported in the literature. Therefore, the QuBiLS-MAS approach constitutes a useful tool for the diversity analysis of chemical compound datasets and high-throughput screening of structure-activity data.

A Case Study of the Introduction of RISC-based Computing and a Telecommunications Link to a Suburban High School.

ERIC Educational Resources Information Center

Hakerem, Gita; And Others

This study reports the efforts of the Water and Molecular Networks Project (WAMNet), a program in which high school chemistry students use computer simulations developed at Boston University (Massachusetts) to model the three-dimensional structure of molecules and the hydrogen bond network that holds water molecules together. This case study…

Boolean logic tree of graphene-based chemical system for molecular computation and intelligent molecular search query.

PubMed

Huang, Wei Tao; Luo, Hong Qun; Li, Nian Bing

2014-05-06

The most serious, and yet unsolved, problem of constructing molecular computing devices consists in connecting all of these molecular events into a usable device. This report demonstrates the use of Boolean logic tree for analyzing the chemical event network based on graphene, organic dye, thrombin aptamer, and Fenton reaction, organizing and connecting these basic chemical events. And this chemical event network can be utilized to implement fluorescent combinatorial logic (including basic logic gates and complex integrated logic circuits) and fuzzy logic computing. On the basis of the Boolean logic tree analysis and logic computing, these basic chemical events can be considered as programmable "words" and chemical interactions as "syntax" logic rules to construct molecular search engine for performing intelligent molecular search query. Our approach is helpful in developing the advanced logic program based on molecules for application in biosensing, nanotechnology, and drug delivery.

Rapid communication: Computational simulation and analysis of a candidate for the design of a novel silk-based biopolymer.

PubMed

Golas, Ewa I; Czaplewski, Cezary

2014-09-01

This work theoretically investigates the mechanical properties of a novel silk-derived biopolymer as polymerized in silico from sericin and elastin-like monomers. Molecular Dynamics simulations and Steered Molecular Dynamics were the principal computational methods used, the latter of which applies an external force onto the system and thereby enables an observation of its response to stress. The models explored herein are single-molecule approximations, and primarily serve as tools in a rational design process for the preliminary assessment of properties in a new material candidate. © 2014 Wiley Periodicals, Inc.

Kinetic barriers in the isomerization of substituted ureas: implications for computer-aided drug design.

PubMed

Loeffler, Johannes R; Ehmki, Emanuel S R; Fuchs, Julian E; Liedl, Klaus R

2016-05-01

Urea derivatives are ubiquitously found in many chemical disciplines. N,N'-substituted ureas may show different conformational preferences depending on their substitution pattern. The high energetic barrier for isomerization of the cis and trans state poses additional challenges on computational simulation techniques aiming at a reproduction of the biological properties of urea derivatives. Herein, we investigate energetics of urea conformations and their interconversion using a broad spectrum of methodologies ranging from data mining, via quantum chemistry to molecular dynamics simulation and free energy calculations. We find that the inversion of urea conformations is inherently slow and beyond the time scale of typical simulation protocols. Therefore, extra care needs to be taken by computational chemists to work with appropriate model systems. We find that both knowledge-driven approaches as well as physics-based methods may guide molecular modelers towards accurate starting structures for expensive calculations to ensure that conformations of urea derivatives are modeled as adequately as possible.

Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction

DTIC Science & Technology

2013-05-21

minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished... molecular weight, was non-toxic, and abolished bacterial growth at 13 mM, with putative activity against pantetheine-phosphate adenylyltransferase, an...time period. Metabolic genome-scale models of bacteria have provided a computational framework for in silico simulations to evaluate how metabolic

A multiscale computational approach to dissect early events in the Erb family receptor mediated activation, differential signaling, and relevance to oncogenic transformations.

PubMed

Liu, Yingting; Purvis, Jeremy; Shih, Andrew; Weinstein, Joshua; Agrawal, Neeraj; Radhakrishnan, Ravi

2007-06-01

We describe a hierarchical multiscale computational approach based on molecular dynamics simulations, free energy-based molecular docking simulations, deterministic network-based kinetic modeling, and hybrid discrete/continuum stochastic dynamics protocols to study the dimer-mediated receptor activation characteristics of the Erb family receptors, specifically the epidermal growth factor receptor (EGFR). Through these modeling approaches, we are able to extend the prior modeling of EGF-mediated signal transduction by considering specific EGFR tyrosine kinase (EGFRTK) docking interactions mediated by differential binding and phosphorylation of different C-terminal peptide tyrosines on the RTK tail. By modeling signal flows through branching pathways of the EGFRTK resolved on a molecular basis, we are able to transcribe the effects of molecular alterations in the receptor (e.g., mutant forms of the receptor) to differing kinetic behavior and downstream signaling response. Our molecular dynamics simulations show that the drug sensitizing mutation (L834R) of EGFR stabilizes the active conformation to make the system constitutively active. Docking simulations show preferential characteristics (for wildtype vs. mutant receptors) in inhibitor binding as well as preferential enhancement of phosphorylation of particular substrate tyrosines over others. We find that in comparison to the wildtype system, the L834R mutant RTK preferentially binds the inhibitor erlotinib, as well as preferentially phosphorylates the substrate tyrosine Y1068 but not Y1173. We predict that these molecular level changes result in preferential activation of the Akt signaling pathway in comparison to the Erk signaling pathway for cells with normal EGFR expression. For cells with EGFR over expression, the mutant over activates both Erk and Akt pathways, in comparison to wildtype. These results are consistent with qualitative experimental measurements reported in the literature. We discuss these consequences in light of how the network topology and signaling characteristics of altered (mutant) cell lines are shaped differently in relationship to native cell lines.

Exploring Biomolecular Recognition by Modeling and Simulation

NASA Astrophysics Data System (ADS)

Wade, Rebecca

2007-12-01

Biomolecular recognition is complex. The balance between the different molecular properties that contribute to molecular recognition, such as shape, electrostatics, dynamics and entropy, varies from case to case. This, along with the extent of experimental characterization, influences the choice of appropriate computational approaches to study biomolecular interactions. I will present computational studies in which we aim to make concerted use of bioinformatics, biochemical network modeling and molecular simulation techniques to study protein-protein and protein-small molecule interactions and to facilitate computer-aided drug design.

Probe molecules (PrM) approach in adverse outcome pathway (AOP) based high throughput screening (HTS): in vivo discovery for developing in vitro target methods

EPA Science Inventory

Efficient and accurate adverse outcome pathway (AOP) based high-throughput screening (HTS) methods use a systems biology based approach to computationally model in vitro cellular and molecular data for rapid chemical prioritization; however, not all HTS assays are grounded by rel...

Incorporating modeling and simulations in undergraduate biophysical chemistry course to promote understanding of structure-dynamics-function relationships in proteins.

PubMed

Hati, Sanchita; Bhattacharyya, Sudeep

2016-01-01

A project-based biophysical chemistry laboratory course, which is offered to the biochemistry and molecular biology majors in their senior year, is described. In this course, the classroom study of the structure-function of biomolecules is integrated with the discovery-guided laboratory study of these molecules using computer modeling and simulations. In particular, modern computational tools are employed to elucidate the relationship between structure, dynamics, and function in proteins. Computer-based laboratory protocols that we introduced in three modules allow students to visualize the secondary, super-secondary, and tertiary structures of proteins, analyze non-covalent interactions in protein-ligand complexes, develop three-dimensional structural models (homology model) for new protein sequences and evaluate their structural qualities, and study proteins' intrinsic dynamics to understand their functions. In the fourth module, students are assigned to an authentic research problem, where they apply their laboratory skills (acquired in modules 1-3) to answer conceptual biophysical questions. Through this process, students gain in-depth understanding of protein dynamics-the missing link between structure and function. Additionally, the requirement of term papers sharpens students' writing and communication skills. Finally, these projects result in new findings that are communicated in peer-reviewed journals. © 2016 The International Union of Biochemistry and Molecular Biology.

Computing biological functions using BioΨ, a formal description of biological processes based on elementary bricks of actions

PubMed Central

Pérès, Sabine; Felicori, Liza; Rialle, Stéphanie; Jobard, Elodie; Molina, Franck

2010-01-01

Motivation: In the available databases, biological processes are described from molecular and cellular points of view, but these descriptions are represented with text annotations that make it difficult to handle them for computation. Consequently, there is an obvious need for formal descriptions of biological processes. Results: We present a formalism that uses the BioΨ concepts to model biological processes from molecular details to networks. This computational approach, based on elementary bricks of actions, allows us to calculate on biological functions (e.g. process comparison, mapping structure–function relationships, etc.). We illustrate its application with two examples: the functional comparison of proteases and the functional description of the glycolysis network. This computational approach is compatible with detailed biological knowledge and can be applied to different kinds of systems of simulation. Availability: www.sysdiag.cnrs.fr/publications/supplementary-materials/BioPsi_Manager/ Contact: sabine.peres@sysdiag.cnrs.fr; franck.molina@sysdiag.cnrs.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20448138

Open source molecular modeling.

PubMed

Pirhadi, Somayeh; Sunseri, Jocelyn; Koes, David Ryan

2016-09-01

The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. An updated online version of this catalog can be found at https://opensourcemolecularmodeling.github.io. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Mixed QM/MM molecular electrostatic potentials.

PubMed

Hernández, B; Luque, F J; Orozco, M

2000-05-01

A new method is presented for the calculation of the Molecular Electrostatic Potential (MEP) in large systems. Based on the mixed Quantum Mechanics/Molecular Mechanics (QM/MM) approach, the method assumes both a quantum and classical description for the molecule, and the calculation of the MEP in the space surrounding the molecule is made using this dual treatment. The MEP at points close to the molecule is computed using a full QM formalism, while a pure classical evaluation of the MEP is used for points located at large distances from the molecule. The algorithm allows the user to select the desired level of accuracy in the MEP, so that the definition of the regions where the MEP is computed at the classical or QM levels is adjusted automatically. The potential use of this QM/MM MEP in molecular modeling studies is discussed.

Virtual Transgenics: Using a Molecular Biology Simulation to Impact Student Academic Achievement and Attitudes

ERIC Educational Resources Information Center

Shegog, Ross; Lazarus, Melanie M.; Murray, Nancy G.; Diamond, Pamela M.; Sessions, Nathalie; Zsigmond, Eva

2012-01-01

The transgenic mouse model is useful for studying the causes and potential cures for human genetic diseases. Exposing high school biology students to laboratory experience in developing transgenic animal models is logistically prohibitive. Computer-based simulation, however, offers this potential in addition to advantages of fidelity and reach.…

Radiative Heat Transfer modelling in a Heavy-Duty Diesel Engine

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

Paul, Chandan; Sircar, Arpan; Ferreyro-Fernandez, Sebastian

Detailed radiation modelling in piston engines has received relatively little attention to date. Recently, it is being revisited in light of current trends towards higher operating pressures and higher levels of exhaust-gas recirculation, both of which enhance molecular gas radiation. Advanced high-efficiency engines also are expected to function closer to the limits of stable operation, where even small perturbations to the energy balance can have a large influence on system behavior. Here several different spectral radiation property models and radiative transfer equation (RTE) solvers have been implemented in an OpenFOAM-based engine CFD code, and simulations have been performed for amore » heavy-duty diesel engine. Differences in computed temperature fields, NO and soot levels, and wall heat transfer rates are shown for different combinations of spectral models and RTE solvers. The relative importance of molecular gas radiation versus soot radiation is examined. And the influence of turbulence-radiation interactions is determined by comparing results obtained using local mean values of composition and temperature to compute radiative emission and absorption with those obtained using a particle-based transported probability density function method.« less

Computational Materials Research

NASA Technical Reports Server (NTRS)

Hinkley, Jeffrey A. (Editor); Gates, Thomas S. (Editor)

1996-01-01

Computational Materials aims to model and predict thermodynamic, mechanical, and transport properties of polymer matrix composites. This workshop, the second coordinated by NASA Langley, reports progress in measurements and modeling at a number of length scales: atomic, molecular, nano, and continuum. Assembled here are presentations on quantum calculations for force field development, molecular mechanics of interfaces, molecular weight effects on mechanical properties, molecular dynamics applied to poling of polymers for electrets, Monte Carlo simulation of aromatic thermoplastics, thermal pressure coefficients of liquids, ultrasonic elastic constants, group additivity predictions, bulk constitutive models, and viscoplasticity characterization.

Training a molecular automaton to play a game

NASA Astrophysics Data System (ADS)

Pei, Renjun; Matamoros, Elizabeth; Liu, Manhong; Stefanovic, Darko; Stojanovic, Milan N.

2010-11-01

Research at the interface between chemistry and cybernetics has led to reports of `programmable molecules', but what does it mean to say `we programmed a set of solution-phase molecules to do X'? A survey of recently implemented solution-phase circuitry indicates that this statement could be replaced with `we pre-mixed a set of molecules to do X and functional subsets of X'. These hard-wired mixtures are then exposed to a set of molecular inputs, which can be interpreted as being keyed to human moves in a game, or as assertions of logical propositions. In nucleic acids-based systems, stemming from DNA computation, these inputs can be seen as generic oligonucleotides. Here, we report using reconfigurable nucleic acid catalyst-based units to build a multipurpose reprogrammable molecular automaton that goes beyond single-purpose `hard-wired' molecular automata. The automaton covers all possible responses to two consecutive sets of four inputs (such as four first and four second moves for a generic set of trivial two-player two-move games). This is a model system for more general molecular field programmable gate array (FPGA)-like devices that can be programmed by example, which means that the operator need not have any knowledge of molecular computing methods.

Training a molecular automaton to play a game.

PubMed

Pei, Renjun; Matamoros, Elizabeth; Liu, Manhong; Stefanovic, Darko; Stojanovic, Milan N

2010-11-01

Research at the interface between chemistry and cybernetics has led to reports of 'programmable molecules', but what does it mean to say 'we programmed a set of solution-phase molecules to do X'? A survey of recently implemented solution-phase circuitry indicates that this statement could be replaced with 'we pre-mixed a set of molecules to do X and functional subsets of X'. These hard-wired mixtures are then exposed to a set of molecular inputs, which can be interpreted as being keyed to human moves in a game, or as assertions of logical propositions. In nucleic acids-based systems, stemming from DNA computation, these inputs can be seen as generic oligonucleotides. Here, we report using reconfigurable nucleic acid catalyst-based units to build a multipurpose reprogrammable molecular automaton that goes beyond single-purpose 'hard-wired' molecular automata. The automaton covers all possible responses to two consecutive sets of four inputs (such as four first and four second moves for a generic set of trivial two-player two-move games). This is a model system for more general molecular field programmable gate array (FPGA)-like devices that can be programmed by example, which means that the operator need not have any knowledge of molecular computing methods.

Relative complexation energies for Li(+) ion in solution: molecular level solvation versus polarizable continuum model study.

PubMed

Eilmes, Andrzej; Kubisiak, Piotr

2010-01-21

Relative complexation energies for the lithium cation in acetonitrile and diethyl ether have been studied. Quantum-chemical calculations explicitly describing the solvation of Li(+) have been performed based on structures obtained from molecular dynamics simulations. The effect of an increasing number of solvent molecules beyond the first solvation shell has been found to consist in reduction of the differences in complexation energies for different coordination numbers. Explicit-solvation data have served as a benchmark to the results of polarizable continuum model (PCM) calculations. It has been demonstrated that the PCM approach can yield relative complexation energies comparable to the predictions based on molecular-level solvation, but at significantly lower computational cost. The best agreement between the explicit-solvation and the PCM results has been obtained when the van der Waals surface was adopted to build the molecular cavity.

SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

PubMed

Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

2013-03-01

Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Carrier mobility in double-helix DNA and RNA: A quantum chemistry study with Marcus-Hush theory.

PubMed

Wu, Tao; Sun, Lei; Shi, Qi; Deng, Kaiming; Deng, Weiqiao; Lu, Ruifeng

2016-12-21

Charge mobilities of six DNAs and RNAs have been computed using quantum chemistry calculation combined with the Marcus-Hush theory. Based on this simulation model, we obtained quite reasonable results when compared with the experiment, and the obtained charge mobility strongly depends on the molecular reorganization and electronic coupling. Besides, we find that hole mobilities are larger than electron mobilities no matter in DNAs or in RNAs, and the hole mobility of 2L8I can reach 1.09 × 10 -1 cm 2 V -1 s -1 which can be applied in the molecular wire. The findings also show that our theoretical model can be regarded as a promising candidate for screening DNA- and RNA-based molecular electronic devices.

Carrier mobility in double-helix DNA and RNA: A quantum chemistry study with Marcus-Hush theory

NASA Astrophysics Data System (ADS)

Wu, Tao; Sun, Lei; Shi, Qi; Deng, Kaiming; Deng, Weiqiao; Lu, Ruifeng

2016-12-01

Charge mobilities of six DNAs and RNAs have been computed using quantum chemistry calculation combined with the Marcus-Hush theory. Based on this simulation model, we obtained quite reasonable results when compared with the experiment, and the obtained charge mobility strongly depends on the molecular reorganization and electronic coupling. Besides, we find that hole mobilities are larger than electron mobilities no matter in DNAs or in RNAs, and the hole mobility of 2L8I can reach 1.09 × 10-1 cm2 V-1 s-1 which can be applied in the molecular wire. The findings also show that our theoretical model can be regarded as a promising candidate for screening DNA- and RNA-based molecular electronic devices.

DMG-α--a computational geometry library for multimolecular systems.

PubMed

Szczelina, Robert; Murzyn, Krzysztof

2014-11-24

The DMG-α library grants researchers in the field of computational biology, chemistry, and biophysics access to an open-sourced, easy to use, and intuitive software for performing fine-grained geometric analysis of molecular systems. The library is capable of computing power diagrams (weighted Voronoi diagrams) in three dimensions with 3D periodic boundary conditions, computing approximate projective 2D Voronoi diagrams on arbitrarily defined surfaces, performing shape properties recognition using α-shape theory and can do exact Solvent Accessible Surface Area (SASA) computation. The software is written mainly as a template-based C++ library for greater performance, but a rich Python interface (pydmga) is provided as a convenient way to manipulate the DMG-α routines. To illustrate possible applications of the DMG-α library, we present results of sample analyses which allowed to determine nontrivial geometric properties of two Escherichia coli-specific lipids as emerging from molecular dynamics simulations of relevant model bilayers.

Testing the Use of Implicit Solvent in the Molecular Dynamics Modelling of DNA Flexibility

NASA Astrophysics Data System (ADS)

Mitchell, J.; Harris, S.

DNA flexibility controls packaging, looping and in some cases sequence specific protein binding. Molecular dynamics simulations carried out with a computationally efficient implicit solvent model are potentially a powerful tool for studying larger DNA molecules than can be currently simulated when water and counterions are represented explicitly. In this work we compare DNA flexibility at the base pair step level modelled using an implicit solvent model to that previously determined from explicit solvent simulations and database analysis. Although much of the sequence dependent behaviour is preserved in implicit solvent, the DNA is considerably more flexible when the approximate model is used. In addition we test the ability of the implicit solvent to model stress induced DNA disruptions by simulating a series of DNA minicircle topoisomers which vary in size and superhelical density. When compared with previously run explicit solvent simulations, we find that while the levels of DNA denaturation are similar using both computational methodologies, the specific structural form of the disruptions is different.

A continuous stochastic model for non-equilibrium dense gases

NASA Astrophysics Data System (ADS)

Sadr, M.; Gorji, M. H.

2017-12-01

While accurate simulations of dense gas flows far from the equilibrium can be achieved by direct simulation adapted to the Enskog equation, the significant computational demand required for collisions appears as a major constraint. In order to cope with that, an efficient yet accurate solution algorithm based on the Fokker-Planck approximation of the Enskog equation is devised in this paper; the approximation is very much associated with the Fokker-Planck model derived from the Boltzmann equation by Jenny et al. ["A solution algorithm for the fluid dynamic equations based on a stochastic model for molecular motion," J. Comput. Phys. 229, 1077-1098 (2010)] and Gorji et al. ["Fokker-Planck model for computational studies of monatomic rarefied gas flows," J. Fluid Mech. 680, 574-601 (2011)]. The idea behind these Fokker-Planck descriptions is to project the dynamics of discrete collisions implied by the molecular encounters into a set of continuous Markovian processes subject to the drift and diffusion. Thereby, the evolution of particles representing the governing stochastic process becomes independent from each other and thus very efficient numerical schemes can be constructed. By close inspection of the Enskog operator, it is observed that the dense gas effects contribute further to the advection of molecular quantities. That motivates a modelling approach where the dense gas corrections can be cast in the extra advection of particles. Therefore, the corresponding Fokker-Planck approximation is derived such that the evolution in the physical space accounts for the dense effects present in the pressure, stress tensor, and heat fluxes. Hence the consistency between the devised Fokker-Planck approximation and the Enskog operator is shown for the velocity moments up to the heat fluxes. For validation studies, a homogeneous gas inside a box besides Fourier, Couette, and lid-driven cavity flow setups is considered. The results based on the Fokker-Planck model are compared with respect to benchmark simulations, where good agreement is found for the flow field along with the transport properties.

A Computational Model Predicting Disruption of Blood Vessel Development

PubMed Central

Kleinstreuer, Nicole; Dix, David; Rountree, Michael; Baker, Nancy; Sipes, Nisha; Reif, David; Spencer, Richard; Knudsen, Thomas

2013-01-01

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a variety of biological pathways linked to endothelial cell (EC) behavior, extracellular matrix (ECM) remodeling and the local generation of chemokines and growth factors. Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors, and tractable computational models that offset mathematical and biological complexity. Here, we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D (http://www.compucell3d.org/) modeling environment supplemented with semi-automatic knowledgebase creation. The model incorporates vascular endothelial growth factor signals, pro- and anti-angiogenic inflammatory chemokine signals, and the plasminogen activating system of enzymes and proteases linked to ECM interactions, to simulate nascent EC organization, growth and remodeling. The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors, such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords. Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPA's ToxCast high-throughput screening (HTS) dataset. Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog, 5HPP-33, on in vitro angiogenesis with respect to both concentration-response and morphological consequences. These findings support the utility of cell agent-based models for simulating a morphogenetic series of events and for the first time demonstrate the applicability of these models for predictive toxicology. PMID:23592958

Computational 3D structures of drug-targeting proteins in the 2009-H1N1 influenza A virus

NASA Astrophysics Data System (ADS)

Du, Qi-Shi; Wang, Shu-Qing; Huang, Ri-Bo; Chou, Kuo-Chen

2010-01-01

The neuraminidase (NA) and M2 proton channel of influenza virus are the drug-targeting proteins, based on which several drugs were developed. However these once powerful drugs encountered drug-resistant problem to the H5N1 and H1N1 flu. To address this problem, the computational 3D structures of NA and M2 proteins of 2009-H1N1 influenza virus were built using the molecular modeling technique and computational chemistry method. Based on the models the structure features of NA and M2 proteins were analyzed, the docking structures of drug-protein complexes were computed, and the residue mutations were annotated. The results may help to solve the drug-resistant problem and stimulate designing more effective drugs against 2009-H1N1 influenza pandemic.

Recent advances in mathematical modeling of developmental abnormalities using mechanistic information.

PubMed

Kavlock, R J

1997-01-01

During the last several years, significant changes in the risk assessment process for developmental toxicity of environmental contaminants have begun to emerge. The first of these changes is the development and beginning use of statistically based dose-response models [the benchmark dose (BMD) approach] that better utilize data derived from existing testing approaches. Accompanying this change is the greater emphasis placed on understanding and using mechanistic information to yield more accurate, reliable, and less uncertain risk assessments. The next stage in the evolution of risk assessment will be the use of biologically based dose-response (BBDR) models that begin to build into the statistically based models factors related to the underlying kinetic, biochemical, and/or physiologic processes perturbed by a toxicant. Such models are now emerging from several research laboratories. The introduction of quantitative models and the incorporation of biologic information into them has pointed to the need for even more sophisticated modifications for which we offer the term embryologically based dose-response (EBDR) models. Because these models would be based upon the understanding of normal morphogenesis, they represent a quantum leap in our thinking, but their complexity presents daunting challenges both to the developmental biologist and the developmental toxicologist. Implementation of these models will require extensive communication between developmental toxicologists, molecular embryologists, and biomathematicians. The remarkable progress in the understanding of mammalian embryonic development at the molecular level that has occurred over the last decade combined with advances in computing power and computational models should eventually enable these as yet hypothetical models to be brought into use.

Optimizing legacy molecular dynamics software with directive-based offload

NASA Astrophysics Data System (ADS)

Michael Brown, W.; Carrillo, Jan-Michael Y.; Gavhane, Nitin; Thakkar, Foram M.; Plimpton, Steven J.

2015-10-01

Directive-based programming models are one solution for exploiting many-core coprocessors to increase simulation rates in molecular dynamics. They offer the potential to reduce code complexity with offload models that can selectively target computations to run on the CPU, the coprocessor, or both. In this paper, we describe modifications to the LAMMPS molecular dynamics code to enable concurrent calculations on a CPU and coprocessor. We demonstrate that standard molecular dynamics algorithms can run efficiently on both the CPU and an x86-based coprocessor using the same subroutines. As a consequence, we demonstrate that code optimizations for the coprocessor also result in speedups on the CPU; in extreme cases up to 4.7X. We provide results for LAMMPS benchmarks and for production molecular dynamics simulations using the Stampede hybrid supercomputer with both Intel® Xeon Phi™ coprocessors and NVIDIA GPUs. The optimizations presented have increased simulation rates by over 2X for organic molecules and over 7X for liquid crystals on Stampede. The optimizations are available as part of the "Intel package" supplied with LAMMPS.

Computing with motile bio-agents

NASA Astrophysics Data System (ADS)

Nicolau, Dan V., Jr.; Burrage, Kevin; Nicolau, Dan V.

2007-12-01

We describe a model of computation of the parallel type, which we call 'computing with bio-agents', based on the concept that motions of biological objects such as bacteria or protein molecular motors in confined spaces can be regarded as computations. We begin with the observation that the geometric nature of the physical structures in which model biological objects move modulates the motions of the latter. Consequently, by changing the geometry, one can control the characteristic trajectories of the objects; on the basis of this, we argue that such systems are computing devices. We investigate the computing power of mobile bio-agent systems and show that they are computationally universal in the sense that they are capable of computing any Boolean function in parallel. We argue also that using appropriate conditions, bio-agent systems can solve NP-complete problems in probabilistic polynomial time.

Tertiary structure-based analysis of microRNA–target interactions

PubMed Central

Gan, Hin Hark; Gunsalus, Kristin C.

2013-01-01

Current computational analysis of microRNA interactions is based largely on primary and secondary structure analysis. Computationally efficient tertiary structure-based methods are needed to enable more realistic modeling of the molecular interactions underlying miRNA-mediated translational repression. We incorporate algorithms for predicting duplex RNA structures, ionic strength effects, duplex entropy and free energy, and docking of duplex–Argonaute protein complexes into a pipeline to model and predict miRNA–target duplex binding energies. To ensure modeling accuracy and computational efficiency, we use an all-atom description of RNA and a continuum description of ionic interactions using the Poisson–Boltzmann equation. Our method predicts the conformations of two constructs of Caenorhabditis elegans let-7 miRNA–target duplexes to an accuracy of ∼3.8 Å root mean square distance of their NMR structures. We also show that the computed duplex formation enthalpies, entropies, and free energies for eight miRNA–target duplexes agree with titration calorimetry data. Analysis of duplex–Argonaute docking shows that structural distortions arising from single-base-pair mismatches in the seed region influence the activity of the complex by destabilizing both duplex hybridization and its association with Argonaute. Collectively, these results demonstrate that tertiary structure-based modeling of miRNA interactions can reveal structural mechanisms not accessible with current secondary structure-based methods. PMID:23417009

Assessing the accuracy of improved force-matched water models derived from Ab initio molecular dynamics simulations.

PubMed

Köster, Andreas; Spura, Thomas; Rutkai, Gábor; Kessler, Jan; Wiebeler, Hendrik; Vrabec, Jadran; Kühne, Thomas D

2016-07-15

The accuracy of water models derived from ab initio molecular dynamics simulations by means on an improved force-matching scheme is assessed for various thermodynamic, transport, and structural properties. It is found that although the resulting force-matched water models are typically less accurate than fully empirical force fields in predicting thermodynamic properties, they are nevertheless much more accurate than generally appreciated in reproducing the structure of liquid water and in fact superseding most of the commonly used empirical water models. This development demonstrates the feasibility to routinely parametrize computationally efficient yet predictive potential energy functions based on accurate ab initio molecular dynamics simulations for a large variety of different systems. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

CABS-flex: Server for fast simulation of protein structure fluctuations.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kmiecik, Sebastian

2013-07-01

The CABS-flex server (http://biocomp.chem.uw.edu.pl/CABSflex) implements CABS-model-based protocol for the fast simulations of near-native dynamics of globular proteins. In this application, the CABS model was shown to be a computationally efficient alternative to all-atom molecular dynamics--a classical simulation approach. The simulation method has been validated on a large set of molecular dynamics simulation data. Using a single input (user-provided file in PDB format), the CABS-flex server outputs an ensemble of protein models (in all-atom PDB format) reflecting the flexibility of the input structure, together with the accompanying analysis (residue mean-square-fluctuation profile and others). The ensemble of predicted models can be used in structure-based studies of protein functions and interactions.

Efficient grid-based techniques for density functional theory

NASA Astrophysics Data System (ADS)

Rodriguez-Hernandez, Juan Ignacio

Understanding the chemical and physical properties of molecules and materials at a fundamental level often requires quantum-mechanical models for these substance's electronic structure. This type of many body quantum mechanics calculation is computationally demanding, hindering its application to substances with more than a few hundreds atoms. The supreme goal of many researches in quantum chemistry---and the topic of this dissertation---is to develop more efficient computational algorithms for electronic structure calculations. In particular, this dissertation develops two new numerical integration techniques for computing molecular and atomic properties within conventional Kohn-Sham-Density Functional Theory (KS-DFT) of molecular electronic structure. The first of these grid-based techniques is based on the transformed sparse grid construction. In this construction, a sparse grid is generated in the unit cube and then mapped to real space according to the pro-molecular density using the conditional distribution transformation. The transformed sparse grid was implemented in program deMon2k, where it is used as the numerical integrator for the exchange-correlation energy and potential in the KS-DFT procedure. We tested our grid by computing ground state energies, equilibrium geometries, and atomization energies. The accuracy on these test calculations shows that our grid is more efficient than some previous integration methods: our grids use fewer points to obtain the same accuracy. The transformed sparse grids were also tested for integrating, interpolating and differentiating in different dimensions (n = 1,2,3,6). The second technique is a grid-based method for computing atomic properties within QTAIM. It was also implemented in deMon2k. The performance of the method was tested by computing QTAIM atomic energies, charges, dipole moments, and quadrupole moments. For medium accuracy, our method is the fastest one we know of.

Grid-Based Surface Generalized Born Model for Calculation of Electrostatic Binding Free Energies.

PubMed

Forouzesh, Negin; Izadi, Saeed; Onufriev, Alexey V

2017-10-23

Fast and accurate calculation of solvation free energies is central to many applications, such as rational drug design. In this study, we present a grid-based molecular surface implementation of "R6" flavor of the generalized Born (GB) implicit solvent model, named GBNSR6. The speed, accuracy relative to numerical Poisson-Boltzmann treatment, and sensitivity to grid surface parameters are tested on a set of 15 small protein-ligand complexes and a set of biomolecules in the range of 268 to 25099 atoms. Our results demonstrate that the proposed model provides a relatively successful compromise between the speed and accuracy of computing polar components of the solvation free energies (ΔG pol ) and binding free energies (ΔΔG pol ). The model tolerates a relatively coarse grid size h = 0.5 Å, where the grid artifact error in computing ΔΔG pol remains in the range of k B T ∼ 0.6 kcal/mol. The estimated ΔΔG pol s are well correlated (r 2 = 0.97) with the numerical Poisson-Boltzmann reference, while showing virtually no systematic bias and RMSE = 1.43 kcal/mol. The grid-based GBNSR6 model is available in Amber (AmberTools) package of molecular simulation programs.

Molecular dynamics-based model of VEGF-A and its heparin interactions.

PubMed

Uciechowska-Kaczmarzyk, Urszula; Babik, Sándor; Zsila, Ferenc; Bojarski, Krzysztof Kamil; Beke-Somfai, Tamás; Samsonov, Sergey A

2018-06-01

We present a computational model of the Vascular Endothelial Growth Factor (VEGF), an important regulator of blood vessels formation, which function is affected by its heparin interactions. Although structures of a receptor binding (RBD) and a heparin binding domain (HBD) of VEGF are known, there are structural data neither on the 12 amino acids interdomain linker nor on its complexes with heparin. We apply molecular docking and molecular dynamics techniques combined with circular dichroism spectroscopy to model the full structure of the dimeric VEGF and to propose putative molecular mechanisms underlying the function of VEGF/VEGF receptors/heparin system. We show that both the conformational flexibility of the linker and the formation of HBD-heparin-HBD sandwich-like structures regulate the mutual disposition of HBDs and so affect the VEGF-mediated signalling. Copyright © 2018 Elsevier Inc. All rights reserved.

Effect of Material Ion Exchanges on the Mechanical Stiffness Properties and Shear Deformation of Hydrated Cement Material Chemistry Structure C-S-H Jennite -- A Computational Modeling Study

NASA Astrophysics Data System (ADS)

Adebiyi, Babatunde Mattew

Material properties and performance are governed by material molecular chemistry structures and molecular level interactions. Methods to understand relationships between the material properties and performance and their correlation to the molecular level chemistry and morphology, and thus find ways of manipulating and adjusting matters at the atomistic level in order to improve material performance, are required. A computational material modeling methodology is investigated and demonstrated for a key cement hydrated component material chemistry structure of Calcium-Silicate-Hydrate (C-S-H) Jennite in this work. The effect of material ion exchanges on the mechanical stiffness properties and shear deformation behavior of hydrated cement material chemistry structure of Calcium Silicate Hydrate (C-S-H) Jennite was studied. Calcium ions were replaced with Magnesium ions in Jennite structure of the C-S-H gel. Different level of substitution of the ions was used. The traditional Jennite structure was obtained from the American Mineralogist Crystal Structure Database and super cells of the structures were created using a Molecular Dynamics Analyzer and Visualizer Material Studio. Molecular dynamics parameters used in the modeling analysis were determined by carrying out initial dynamic studies. 64 unit cell of C-S-H Jennite was used in material modeling analysis studies based on convergence results obtained from the elastic modulus and total energies. NVT forcite dynamics using COMPASS force field based on 200 ps dynamics time was used to determine mechanical modulus of the traditional C-S-H gel and the Magnesium ion modified structures. NVT Discover dynamics using COMPASS forcefield was used in the material modeling studies to investigate the influence of ionic exchange on the shear deformation of the associated material chemistry structures. A prior established quasi-static deformation method to emulate shear deformation of C-S-H material chemistry structure that is based on a triclinic crystal structure was used, by deforming the triclinic crystal structure at 0.2 degree per time step for 75 steps of deformation. It was observed that there is a decrease in the total energies of the systems as the percentage of magnesium ion increases in the C-S-H Jennite molecular structure systems. Investigation of effect of ion exchange on the elastic modulus shows that the elastic stiffness modulus tends to decrease as the amount of Mg in the systems increases, using either COMPASS or universal force field. On the other hand, shear moduli obtained after deforming the structures computed from the stress-strain curve obtained from material modeling increases as the amount of Mg increases in the system. The present investigations also showed that ultimate shear stress obtained from predicted shear stress---strain also increases with amount of Mg in the chemistry structure. Present study clearly demonstrates that computational material modeling following molecular dynamics analysis methodology is an effective way to predict and understand the effective material chemistry and additive changes on the stiffness and deformation characteristics in cementitious materials, and the results suggest that this method can be extended to other materials.

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network.

PubMed

Di Paola, Vieri; Marijuán, Pedro C; Lahoz-Beltra, Rafael

2004-01-01

Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.

An approach to the origin of self-replicating system. I - Intermolecular interactions

NASA Technical Reports Server (NTRS)

Macelroy, R. D.; Coeckelenbergh, Y.; Rein, R.

1978-01-01

The present paper deals with the characteristics and potentialities of a recently developed computer-based molecular modeling system. Some characteristics of current coding systems are examined and are extrapolated to the apparent requirements of primitive prebiological coding systems.

Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology

EPA Science Inventory

A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose–response, and time-course p...

The Hartree-Fock calculation of the magnetic properties of molecular solutes

NASA Astrophysics Data System (ADS)

Cammi, R.

1998-08-01

In this paper we set the formal bases for the calculation of the magnetic susceptibility and of the nuclear magnetic shielding tensors for molecular solutes described within the framework of the polarizable continuum model (PCM). The theory has been developed at self-consistent field (SCF) level and adapted to be used within the framework of some of the computational procedures of larger use, i.e., the gauge invariant atomic orbital method (GIAO) and the continuous set gauge transformation method (CSGT). The numerical results relative to the magnetizabilities and chemical shielding of acetonitrile and nitrometane in various solvents computed with the PCM-CSGT method are also presented.

Acid/base equilibria in clusters and their role in proton exchange membranes: Computational insight

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

Glezakou, Vanda A; Dupuis, Michel; Mundy, Christopher J

2007-10-24

We describe molecular orbital theory and ab initio molecular dynamics studies of acid/base equilibria of clusters AH:(H 2O) n↔A -:H +(H 2O) n in low hydration regime (n = 1-4), where AH is a model of perfluorinated sulfonic acids, RSO 3H (R = CF 3CF 2), encountered in polymeric electrolyte membranes of fuel cells. Free energy calculations on the neutral and ion pair structures for n = 3 indicate that the two configurations are close in energy and are accessible in the fluctuation dynamics of proton transport. For n = 1,2 the only relevant configuration is the neutral form. Thismore » was verified through ab initio metadynamics simulations. These findings suggest that bases are directly involved in the proton transport at low hydration levels. In addition, the gas phase proton affinity of the model sulfonic acid RSO 3H was found to be comparable to the proton affinity of water. Thus, protonated acids can also play a role in proton transport under low hydration conditions and under high concentration of protons. This work was supported by the Division of Chemical Science, Office of Basic Energy Sciences, US Department of Energy (DOE under Contract DE-AC05-76RL)1830. Computations were performed on computers of the Molecular Interactions and Transformations (MI&T) group and MSCF facility of EMSL, sponsored by US DOE and OBER located at PNNL. This work was benefited from resource of the National Energy Research Scientific Computing Centre, supported by the Office of Science of the US DOE, under Contract No. DE-AC03-76SF00098.« less

Computer-Based Semantic Network in Molecular Biology: A Demonstration.

ERIC Educational Resources Information Center

Callman, Joshua L.; And Others

This paper analyzes the hardware and software features that would be desirable in a computer-based semantic network system for representing biology knowledge. It then describes in detail a prototype network of molecular biology knowledge that has been developed using Filevision software and a Macintosh computer. The prototype contains about 100…

Computational Systems Biology in Cancer: Modeling Methods and Applications

PubMed Central

Materi, Wayne; Wishart, David S.

2007-01-01

In recent years it has become clear that carcinogenesis is a complex process, both at the molecular and cellular levels. Understanding the origins, growth and spread of cancer, therefore requires an integrated or system-wide approach. Computational systems biology is an emerging sub-discipline in systems biology that utilizes the wealth of data from genomic, proteomic and metabolomic studies to build computer simulations of intra and intercellular processes. Several useful descriptive and predictive models of the origin, growth and spread of cancers have been developed in an effort to better understand the disease and potential therapeutic approaches. In this review we describe and assess the practical and theoretical underpinnings of commonly-used modeling approaches, including ordinary and partial differential equations, petri nets, cellular automata, agent based models and hybrid systems. A number of computer-based formalisms have been implemented to improve the accessibility of the various approaches to researchers whose primary interest lies outside of model development. We discuss several of these and describe how they have led to novel insights into tumor genesis, growth, apoptosis, vascularization and therapy. PMID:19936081

Predicting neutron damage using TEM with in situ ion irradiation and computer modeling

NASA Astrophysics Data System (ADS)

Kirk, Marquis A.; Li, Meimei; Xu, Donghua; Wirth, Brian D.

2018-01-01

We have constructed a computer model of irradiation defect production closely coordinated with TEM and in situ ion irradiation of Molybdenum at 80 °C over a range of dose, dose rate and foil thickness. We have reexamined our previous ion irradiation data to assign appropriate error and uncertainty based on more recent work. The spatially dependent cascade cluster dynamics model is updated with recent Molecular Dynamics results for cascades in Mo. After a careful assignment of both ion and neutron irradiation dose values in dpa, TEM data are compared for both ion and neutron irradiated Mo from the same source material. Using the computer model of defect formation and evolution based on the in situ ion irradiation of thin foils, the defect microstructure, consisting of densities and sizes of dislocation loops, is predicted for neutron irradiation of bulk material at 80 °C and compared with experiment. Reasonable agreement between model prediction and experimental data demonstrates a promising direction in understanding and predicting neutron damage using a closely coordinated program of in situ ion irradiation experiment and computer simulation.

Comparative Investigation of Normal Modes and Molecular Dynamics of Hepatitis C NS5B Protein

NASA Astrophysics Data System (ADS)

Asafi, M. S.; Yildirim, A.; Tekpinar, M.

2016-04-01

Understanding dynamics of proteins has many practical implications in terms of finding a cure for many protein related diseases. Normal mode analysis and molecular dynamics methods are widely used physics-based computational methods for investigating dynamics of proteins. In this work, we studied dynamics of Hepatitis C NS5B protein with molecular dynamics and normal mode analysis. Principal components obtained from a 100 nanoseconds molecular dynamics simulation show good overlaps with normal modes calculated with a coarse-grained elastic network model. Coarse-grained normal mode analysis takes at least an order of magnitude shorter time. Encouraged by this good overlaps and short computation times, we analyzed further low frequency normal modes of Hepatitis C NS5B. Motion directions and average spatial fluctuations have been analyzed in detail. Finally, biological implications of these motions in drug design efforts against Hepatitis C infections have been elaborated.

Gradient Models in Molecular Biophysics: Progress, Challenges, Opportunities

PubMed Central

Bardhan, Jaydeep P.

2014-01-01

In the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g. molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding features such as nonlocal dielectric response, and nonlinearities resulting from dielectric saturation. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost forty years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The paper concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics. PMID:25505358

Gradient Models in Molecular Biophysics: Progress, Challenges, Opportunities.

PubMed

Bardhan, Jaydeep P

2013-12-01

In the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g. molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding features such as nonlocal dielectric response, and nonlinearities resulting from dielectric saturation. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost forty years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The paper concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics.

Gradient models in molecular biophysics: progress, challenges, opportunities

NASA Astrophysics Data System (ADS)

Bardhan, Jaydeep P.

2013-12-01

In the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g., molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding nonlocal dielectric response. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain, and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost 40 years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The review concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics.

A pharmacophore model specific to active site of CYP1A2 with a novel molecular modeling explorer and CoMFA.

PubMed

Zhang, Tao; Wei, Dong-Qing; Chou, Kuo-Chen

2012-03-01

Comparative molecular field analysis (CoMFA) is a widely used 3D-QSAR method by which we can investigate the potential relation between biological activity of compounds and their structural features. In this study, a new application of this approach is presented by combining the molecular modeling with a new developed pharmacophore model specific to CYP1A2 active site. During constructing the model, we used the molecular dynamics simulation and molecular docking method to select the sensible binding conformations for 17 CYP1A2 substrates based on the experimental data. Subsequently, the results obtained via the alignment of binding conformations of substrates were projected onto the active- site residues, upon which a simple blueprint of active site was produced. It was validated by the experimental and computational results that the model did exhibit the high degree of rationality and provide useful insights into the substrate binding. It is anticipated that our approach can be extended to investigate the protein-ligand interactions for many other enzyme-catalyzed systems as well.

Coarse-Grained Lattice Model Simulations of Sequence-Structure Fitness of a Ribosome-Inactivating Protein

DTIC Science & Technology

2007-11-05

limits of what is considered practical when applying all-atom molecular - dynamics simulation methods. Lattice models provide computationally robust...of expectation values from the density of states. All-atom molecular - dynamics simulations provide the most rigorous sampling method to generate con... molecular - dynamics simulations of protein folding,6–9 reported studies of computing a heat capacity or other calorimetric observables have been limited to

3D Pharmacophore-Based Virtual Screening and Docking Approaches toward the Discovery of Novel HPPD Inhibitors.

PubMed

Fu, Ying; Sun, Yi-Na; Yi, Ke-Han; Li, Ming-Qiang; Cao, Hai-Feng; Li, Jia-Zhong; Ye, Fei

2017-06-09

p -Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking-based virtual screening were performed to identify novel potential HPPD inhibitors. The complex-based pharmacophore model (CBP) with 0.721 of ROC used for screening compounds showed remarkable ability to retrieve known active ligands from among decoy molecules. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5 (DS 2.5) to discern interactions with key residues at the active site of HPPD. Four compounds with top rankings in the HipHop model and well-known binding model were finally chosen as lead compounds with potential inhibitory effects on the active site of target. The results provided powerful insight into the development of novel HPPD inhibitors herbicides using computational techniques.

Multilevel functional genomics data integration as a tool for understanding physiology: a network biology perspective.

PubMed

Davidsen, Peter K; Turan, Nil; Egginton, Stuart; Falciani, Francesco

2016-02-01

The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of "omics" technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to "learn" biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed. Copyright © 2016 the American Physiological Society.

Getting the Right Answers for the Right Reasons: Toward Predictive Molecular Simulations of Water with Many-Body Potential Energy Functions.

PubMed

Paesani, Francesco

2016-09-20

The central role played by water in fundamental processes relevant to different disciplines, including chemistry, physics, biology, materials science, geology, and climate research, cannot be overemphasized. It is thus not surprising that, since the pioneering work by Stillinger and Rahman, many theoretical and computational studies have attempted to develop a microscopic description of the unique properties of water under different thermodynamic conditions. Consequently, numerous molecular models based on either molecular mechanics or ab initio approaches have been proposed over the years. However, despite continued progress, the correct prediction of the properties of water from small gas-phase clusters to the liquid phase and ice through a single molecular model remains challenging. To large extent, this is due to the difficulties encountered in the accurate modeling of the underlying hydrogen-bond network in which both number and strength of the hydrogen bonds vary continuously as a result of a subtle interplay between energetic, entropic, and nuclear quantum effects. In the past decade, the development of efficient algorithms for correlated electronic structure calculations of small molecular complexes, accompanied by tremendous progress in the analytical representation of multidimensional potential energy surfaces, opened the doors to the design of highly accurate potential energy functions built upon rigorous representations of the many-body expansion (MBE) of the interaction energies. This Account provides a critical overview of the performance of the MB-pol many-body potential energy function through a systematic analysis of energetic, structural, thermodynamic, and dynamical properties as well as of vibrational spectra of water from the gas to the condensed phase. It is shown that MB-pol achieves unprecedented accuracy across all phases of water through a quantitative description of each individual term of the MBE, with a physically correct representation of both short- and long-range many-body contributions. Comparisons with experimental data probing different regions of the water potential energy surface from clusters to bulk demonstrate that MB-pol represents a major step toward the long-sought-after "universal model" capable of accurately describing the molecular properties of water under different conditions and in different environments. Along this path, future challenges include the extension of the many-body scheme adopted by MB-pol to the description of generic solutes as well as the integration of MB-pol in an efficient theoretical and computational framework to model acid-base reactions in aqueous environments. In this context, given the nontraditional form of the MB-pol energy and force expressions, synergistic efforts by theoretical/computational chemists/physicists and computer scientists will be critical for the development of high-performance software for many-body molecular dynamics simulations.

Models@Home: distributed computing in bioinformatics using a screensaver based approach.

PubMed

Krieger, Elmar; Vriend, Gert

2002-02-01

Due to the steadily growing computational demands in bioinformatics and related scientific disciplines, one is forced to make optimal use of the available resources. A straightforward solution is to build a network of idle computers and let each of them work on a small piece of a scientific challenge, as done by Seti@Home (http://setiathome.berkeley.edu), the world's largest distributed computing project. We developed a generally applicable distributed computing solution that uses a screensaver system similar to Seti@Home. The software exploits the coarse-grained nature of typical bioinformatics projects. Three major considerations for the design were: (1) often, many different programs are needed, while the time is lacking to parallelize them. Models@Home can run any program in parallel without modifications to the source code; (2) in contrast to the Seti project, bioinformatics applications are normally more sensitive to lost jobs. Models@Home therefore includes stringent control over job scheduling; (3) to allow use in heterogeneous environments, Linux and Windows based workstations can be combined with dedicated PCs to build a homogeneous cluster. We present three practical applications of Models@Home, running the modeling programs WHAT IF and YASARA on 30 PCs: force field parameterization, molecular dynamics docking, and database maintenance.

A Scalable O(N) Algorithm for Large-Scale Parallel First-Principles Molecular Dynamics Simulations

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

Osei-Kuffuor, Daniel; Fattebert, Jean-Luc

2014-01-01

Traditional algorithms for first-principles molecular dynamics (FPMD) simulations only gain a modest capability increase from current petascale computers, due to their O(N 3) complexity and their heavy use of global communications. To address this issue, we are developing a truly scalable O(N) complexity FPMD algorithm, based on density functional theory (DFT), which avoids global communications. The computational model uses a general nonorthogonal orbital formulation for the DFT energy functional, which requires knowledge of selected elements of the inverse of the associated overlap matrix. We present a scalable algorithm for approximately computing selected entries of the inverse of the overlap matrix,more » based on an approximate inverse technique, by inverting local blocks corresponding to principal submatrices of the global overlap matrix. The new FPMD algorithm exploits sparsity and uses nearest neighbor communication to provide a computational scheme capable of extreme scalability. Accuracy is controlled by the mesh spacing of the finite difference discretization, the size of the localization regions in which the electronic orbitals are confined, and a cutoff beyond which the entries of the overlap matrix can be omitted when computing selected entries of its inverse. We demonstrate the algorithm's excellent parallel scaling for up to O(100K) atoms on O(100K) processors, with a wall-clock time of O(1) minute per molecular dynamics time step.« less

Computer display and manipulation of biological molecules

NASA Technical Reports Server (NTRS)

Coeckelenbergh, Y.; Macelroy, R. D.; Hart, J.; Rein, R.

1978-01-01

This paper describes a computer model that was designed to investigate the conformation of molecules, macromolecules and subsequent complexes. Utilizing an advanced 3-D dynamic computer display system, the model is sufficiently versatile to accommodate a large variety of molecular input and to generate data for multiple purposes such as visual representation of conformational changes, and calculation of conformation and interaction energy. Molecules can be built on the basis of several levels of information. These include the specification of atomic coordinates and connectivities and the grouping of building blocks and duplicated substructures using symmetry rules found in crystals and polymers such as proteins and nucleic acids. Called AIMS (Ames Interactive Molecular modeling System), the model is now being used to study pre-biotic molecular evolution toward life.

Computation of Dielectric Response in Molecular Solids for High Capacitance Organic Dielectrics.

PubMed

Heitzer, Henry M; Marks, Tobin J; Ratner, Mark A

2016-09-20

The dielectric response of a material is central to numerous processes spanning the fields of chemistry, materials science, biology, and physics. Despite this broad importance across these disciplines, describing the dielectric environment of a molecular system at the level of first-principles theory and computation remains a great challenge and is of importance to understand the behavior of existing systems as well as to guide the design and synthetic realization of new ones. Furthermore, with recent advances in molecular electronics, nanotechnology, and molecular biology, it has become necessary to predict the dielectric properties of molecular systems that are often difficult or impossible to measure experimentally. In these scenarios, it is would be highly desirable to be able to determine dielectric response through efficient, accurate, and chemically informative calculations. A good example of where theoretical modeling of dielectric response would be valuable is in the development of high-capacitance organic gate dielectrics for unconventional electronics such as those that could be fabricated by high-throughput printing techniques. Gate dielectrics are fundamental components of all transistor-based logic circuitry, and the combination high dielectric constant and nanoscopic thickness (i.e., high capacitance) is essential to achieving high switching speeds and low power consumption. Molecule-based dielectrics offer the promise of cheap, flexible, and mass producible electronics when used in conjunction with unconventional organic or inorganic semiconducting materials to fabricate organic field effect transistors (OFETs). The molecular dielectrics developed to date typically have limited dielectric response, which results in low capacitances, translating into poor performance of the resulting OFETs. Furthermore, the development of better performing dielectric materials has been hindered by the current highly empirical and labor-intensive pace of synthetic progress. An accurate and efficient theoretical computational approach could drastically decrease this time by screening potential dielectric materials and providing reliable design rules for future molecular dielectrics. Until recently, accurate calculation of dielectric responses in molecular materials was difficult and highly approximate. Most previous modeling efforts relied on classical formalisms to relate molecular polarizability to macroscopic dielectric properties. These efforts often vastly overestimated polarizability in the subject materials and ignored crucial material properties that can affect dielectric response. Recent advances in first-principles calculations via density functional theory (DFT) with periodic boundary conditions have allowed accurate computation of dielectric properties in molecular materials. In this Account, we outline the methodology used to calculate dielectric properties of molecular materials. We demonstrate the validity of this approach on model systems, capturing the frequency dependence of the dielectric response and achieving quantitative accuracy compared with experiment. This method is then used as a guide to new high-capacitance molecular dielectrics by determining what materials and chemical properties are important in maximizing dielectric response in self-assembled monolayers (SAMs). It will be seen that this technique is a powerful tool for understanding and designing new molecular dielectric systems, the properties of which are fundamental to many scientific areas.

From laptop to benchtop to bedside: Structure-based Drug Design on Protein Targets

PubMed Central

Chen, Lu; Morrow, John K.; Tran, Hoang T.; Phatak, Sharangdhar S.; Du-Cuny, Lei; Zhang, Shuxing

2013-01-01

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. PMID:22316152

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.

Agent based simulations in disease modeling Comment on "Towards a unified approach in the modeling of fibrosis: A review with research perspectives" by Martine Ben Amar and Carlo Bianca

NASA Astrophysics Data System (ADS)

Pappalardo, Francesco; Pennisi, Marzio

2016-07-01

Fibrosis represents a process where an excessive tissue formation in an organ follows the failure of a physiological reparative or reactive process. Mathematical and computational techniques may be used to improve the understanding of the mechanisms that lead to the disease and to test potential new treatments that may directly or indirectly have positive effects against fibrosis [1]. In this scenario, Ben Amar and Bianca [2] give us a broad picture of the existing mathematical and computational tools that have been used to model fibrotic processes at the molecular, cellular, and tissue levels. Among such techniques, agent based models (ABM) can give a valuable contribution in the understanding and better management of fibrotic diseases.

A Review of Computational Methods in Materials Science: Examples from Shock-Wave and Polymer Physics

PubMed Central

Steinhauser, Martin O.; Hiermaier, Stefan

2009-01-01

This review discusses several computational methods used on different length and time scales for the simulation of material behavior. First, the importance of physical modeling and its relation to computer simulation on multiscales is discussed. Then, computational methods used on different scales are shortly reviewed, before we focus on the molecular dynamics (MD) method. Here we survey in a tutorial-like fashion some key issues including several MD optimization techniques. Thereafter, computational examples for the capabilities of numerical simulations in materials research are discussed. We focus on recent results of shock wave simulations of a solid which are based on two different modeling approaches and we discuss their respective assets and drawbacks with a view to their application on multiscales. Then, the prospects of computer simulations on the molecular length scale using coarse-grained MD methods are covered by means of examples pertaining to complex topological polymer structures including star-polymers, biomacromolecules such as polyelectrolytes and polymers with intrinsic stiffness. This review ends by highlighting new emerging interdisciplinary applications of computational methods in the field of medical engineering where the application of concepts of polymer physics and of shock waves to biological systems holds a lot of promise for improving medical applications such as extracorporeal shock wave lithotripsy or tumor treatment. PMID:20054467

Molecular Modeling Studies of 4,5-Dihydro-1H-pyrazolo[4,3-h] quinazoline Derivatives as Potent CDK2/Cyclin A Inhibitors Using 3D-QSAR and Docking

PubMed Central

Ai, Yong; Wang, Shao-Teng; Sun, Ping-Hua; Song, Fa-Jun

2010-01-01

CDK2/cyclin A has appeared as an attractive drug targets over the years with diverse therapeutic potentials. A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) followed by molecular docking studies were performed on a series of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as potent CDK2/cyclin A inhibitors. The CoMFA and CoMSIA models, using 38 molecules in the training set, gave r2cv values of 0.747 and 0.518 and r2 values of 0.970 and 0.934, respectively. 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained from molecular modeling studies may be helpful to design novel inhibitors of CDK2/cyclin A with desired activity. PMID:21152296

Molecular modeling studies of 4,5-dihydro-1H-pyrazolo[4,3-h] quinazoline derivatives as potent CDK2/Cyclin a inhibitors using 3D-QSAR and docking.

PubMed

Ai, Yong; Wang, Shao-Teng; Sun, Ping-Hua; Song, Fa-Jun

2010-09-28

CDK2/cyclin A has appeared as an attractive drug targets over the years with diverse therapeutic potentials. A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) followed by molecular docking studies were performed on a series of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as potent CDK2/cyclin A inhibitors. The CoMFA and CoMSIA models, using 38 molecules in the training set, gave r(2) (cv) values of 0.747 and 0.518 and r(2) values of 0.970 and 0.934, respectively. 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained from molecular modeling studies may be helpful to design novel inhibitors of CDK2/cyclin A with desired activity.

DNA-programmed dynamic assembly of quantum dots for molecular computation.

PubMed

He, Xuewen; Li, Zhi; Chen, Muzi; Ma, Nan

2014-12-22

Despite the widespread use of quantum dots (QDs) for biosensing and bioimaging, QD-based bio-interfaceable and reconfigurable molecular computing systems have not yet been realized. DNA-programmed dynamic assembly of multi-color QDs is presented for the construction of a new class of fluorescence resonance energy transfer (FRET)-based QD computing systems. A complete set of seven elementary logic gates (OR, AND, NOR, NAND, INH, XOR, XNOR) are realized using a series of binary and ternary QD complexes operated by strand displacement reactions. The integration of different logic gates into a half-adder circuit for molecular computation is also demonstrated. This strategy is quite versatile and straightforward for logical operations and would pave the way for QD-biocomputing-based intelligent molecular diagnostics. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Binding stability of peptides on major histocompatibility complex class I proteins: role of entropy and dynamics.

PubMed

Gul, Ahmet; Erman, Burak

2018-01-16

Prediction of peptide binding on specific human leukocyte antigens (HLA) has long been studied with successful results. We herein describe the effects of entropy and dynamics by investigating the binding stabilities of 10 nanopeptides on various HLA Class I alleles using a theoretical model based on molecular dynamics simulations. The fluctuational entropies of the peptides are estimated over a temperature range of 310-460 K. The estimated entropies correlate well with experimental binding affinities of the peptides: peptides that have higher binding affinities have lower entropies compared to non-binders, which have significantly larger entropies. The computation of the entropies is based on a simple model that requires short molecular dynamics trajectories and allows for approximate but rapid determination. The paper draws attention to the long neglected dynamic aspects of peptide binding, and provides a fast computation scheme that allows for rapid scanning of large numbers of peptides on selected HLA antigens, which may be useful in defining the right peptides for personal immunotherapy.

Binding stability of peptides on major histocompatibility complex class I proteins: role of entropy and dynamics

NASA Astrophysics Data System (ADS)

Gul, Ahmet; Erman, Burak

2018-03-01

Prediction of peptide binding on specific human leukocyte antigens (HLA) has long been studied with successful results. We herein describe the effects of entropy and dynamics by investigating the binding stabilities of 10 nanopeptides on various HLA Class I alleles using a theoretical model based on molecular dynamics simulations. The fluctuational entropies of the peptides are estimated over a temperature range of 310-460 K. The estimated entropies correlate well with experimental binding affinities of the peptides: peptides that have higher binding affinities have lower entropies compared to non-binders, which have significantly larger entropies. The computation of the entropies is based on a simple model that requires short molecular dynamics trajectories and allows for approximate but rapid determination. The paper draws attention to the long neglected dynamic aspects of peptide binding, and provides a fast computation scheme that allows for rapid scanning of large numbers of peptides on selected HLA antigens, which may be useful in defining the right peptides for personal immunotherapy.

Coarse-Grained Models Reveal Functional Dynamics – II. Molecular Dynamics Simulation at the Coarse-Grained Level – Theories and Biological Applications

PubMed Central

Chng, Choon-Peng; Yang, Lee-Wei

2008-01-01

Molecular dynamics (MD) simulation has remained the most indispensable tool in studying equilibrium/non-equilibrium conformational dynamics since its advent 30 years ago. With advances in spectroscopy accompanying solved biocomplexes in growing sizes, sampling their dynamics that occur at biologically interesting spatial/temporal scales becomes computationally intractable; this motivated the use of coarse-grained (CG) approaches. CG-MD models are used to study folding and conformational transitions in reduced resolution and can employ enlarged time steps due to the absence of some of the fastest motions in the system. The Boltzmann-Inversion technique, heavily used in parameterizing these models, provides a smoothed-out effective potential on which molecular conformation evolves at a faster pace thus stretching simulations into tens of microseconds. As a result, a complete catalytic cycle of HIV-1 protease or the assembly of lipid-protein mixtures could be investigated by CG-MD to gain biological insights. In this review, we survey the theories developed in recent years, which are categorized into Folding-based and Molecular-Mechanics-based. In addition, physical bases in the selection of CG beads/time-step, the choice of effective potentials, representation of solvent, and restoration of molecular representations back to their atomic details are systematically discussed. PMID:19812774

Long Range Debye-Hückel Correction for Computation of Grid-based Electrostatic Forces Between Biomacromolecules

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

Mereghetti, Paolo; Martinez, M.; Wade, Rebecca C.

Brownian dynamics (BD) simulations can be used to study very large molecular systems, such as models of the intracellular environment, using atomic-detail structures. Such simulations require strategies to contain the computational costs, especially for the computation of interaction forces and energies. A common approach is to compute interaction forces between macromolecules by precomputing their interaction potentials on three-dimensional discretized grids. For long-range interactions, such as electrostatics, grid-based methods are subject to finite size errors. We describe here the implementation of a Debye-Hückel correction to the grid-based electrostatic potential used in the SDA BD simulation software that was applied to simulatemore » solutions of bovine serum albumin and of hen egg white lysozyme.« less

Module-based multiscale simulation of angiogenesis in skeletal muscle

PubMed Central

2011-01-01

Background Mathematical modeling of angiogenesis has been gaining momentum as a means to shed new light on the biological complexity underlying blood vessel growth. A variety of computational models have been developed, each focusing on different aspects of the angiogenesis process and occurring at different biological scales, ranging from the molecular to the tissue levels. Integration of models at different scales is a challenging and currently unsolved problem. Results We present an object-oriented module-based computational integration strategy to build a multiscale model of angiogenesis that links currently available models. As an example case, we use this approach to integrate modules representing microvascular blood flow, oxygen transport, vascular endothelial growth factor transport and endothelial cell behavior (sensing, migration and proliferation). Modeling methodologies in these modules include algebraic equations, partial differential equations and agent-based models with complex logical rules. We apply this integrated model to simulate exercise-induced angiogenesis in skeletal muscle. The simulation results compare capillary growth patterns between different exercise conditions for a single bout of exercise. Results demonstrate how the computational infrastructure can effectively integrate multiple modules by coordinating their connectivity and data exchange. Model parameterization offers simulation flexibility and a platform for performing sensitivity analysis. Conclusions This systems biology strategy can be applied to larger scale integration of computational models of angiogenesis in skeletal muscle, or other complex processes in other tissues under physiological and pathological conditions. PMID:21463529

`Inter-Arrival Time' Inspired Algorithm and its Application in Clustering and Molecular Phylogeny

NASA Astrophysics Data System (ADS)

Kolekar, Pandurang S.; Kale, Mohan M.; Kulkarni-Kale, Urmila

2010-10-01

Bioinformatics, being multidisciplinary field, involves applications of various methods from allied areas of Science for data mining using computational approaches. Clustering and molecular phylogeny is one of the key areas in Bioinformatics, which help in study of classification and evolution of organisms. Molecular phylogeny algorithms can be divided into distance based and character based methods. But most of these methods are dependent on pre-alignment of sequences and become computationally intensive with increase in size of data and hence demand alternative efficient approaches. `Inter arrival time distribution' (IATD) is a popular concept in the theory of stochastic system modeling but its potential in molecular data analysis has not been fully explored. The present study reports application of IATD in Bioinformatics for clustering and molecular phylogeny. The proposed method provides IATDs of nucleotides in genomic sequences. The distance function based on statistical parameters of IATDs is proposed and distance matrix thus obtained is used for the purpose of clustering and molecular phylogeny. The method is applied on a dataset of 3' non-coding region sequences (NCR) of Dengue virus type 3 (DENV-3), subtype III, reported in 2008. The phylogram thus obtained revealed the geographical distribution of DENV-3 isolates. Sri Lankan DENV-3 isolates were further observed to be clustered in two sub-clades corresponding to pre and post Dengue hemorrhagic fever emergence groups. These results are consistent with those reported earlier, which are obtained using pre-aligned sequence data as an input. These findings encourage applications of the IATD based method in molecular phylogenetic analysis in particular and data mining in general.

AN EFFICIENT HIGHER-ORDER FAST MULTIPOLE BOUNDARY ELEMENT SOLUTION FOR POISSON-BOLTZMANN BASED MOLECULAR ELECTROSTATICS

PubMed Central

Bajaj, Chandrajit; Chen, Shun-Chuan; Rand, Alexander

2011-01-01

In order to compute polarization energy of biomolecules, we describe a boundary element approach to solving the linearized Poisson-Boltzmann equation. Our approach combines several important features including the derivative boundary formulation of the problem and a smooth approximation of the molecular surface based on the algebraic spline molecular surface. State of the art software for numerical linear algebra and the kernel independent fast multipole method is used for both simplicity and efficiency of our implementation. We perform a variety of computational experiments, testing our method on a number of actual proteins involved in molecular docking and demonstrating the effectiveness of our solver for computing molecular polarization energy. PMID:21660123

Grid computing in large pharmaceutical molecular modeling.

PubMed

Claus, Brian L; Johnson, Stephen R

2008-07-01

Most major pharmaceutical companies have employed grid computing to expand their compute resources with the intention of minimizing additional financial expenditure. Historically, one of the issues restricting widespread utilization of the grid resources in molecular modeling is the limited set of suitable applications amenable to coarse-grained parallelization. Recent advances in grid infrastructure technology coupled with advances in application research and redesign will enable fine-grained parallel problems, such as quantum mechanics and molecular dynamics, which were previously inaccessible to the grid environment. This will enable new science as well as increase resource flexibility to load balance and schedule existing workloads.

Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things

PubMed Central

Akan, Ozgur B.

2018-01-01

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics. PMID:29415019

Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things.

PubMed

Kuscu, Murat; Akan, Ozgur B

2018-01-01

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics.

Coupling Molecular Modeling to the Traditional "IR-ID" Exercise in the Introductory Organic Chemistry Laboratory

ERIC Educational Resources Information Center

Stokes-Huby, Heather; Vitale, Dale E.

2007-01-01

This exercise integrates the infrared unknown identification ("IR-ID") experiment common to most organic laboratory syllabi with computer molecular modeling. In this modification students are still required to identify unknown compounds from their IR spectra, but must additionally match some of the absorptions with computed frequencies they…

Microarray-based cancer prediction using soft computing approach.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2009-05-26

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

Long range Debye-Hückel correction for computation of grid-based electrostatic forces between biomacromolecules

PubMed Central

2014-01-01

Background Brownian dynamics (BD) simulations can be used to study very large molecular systems, such as models of the intracellular environment, using atomic-detail structures. Such simulations require strategies to contain the computational costs, especially for the computation of interaction forces and energies. A common approach is to compute interaction forces between macromolecules by precomputing their interaction potentials on three-dimensional discretized grids. For long-range interactions, such as electrostatics, grid-based methods are subject to finite size errors. We describe here the implementation of a Debye-Hückel correction to the grid-based electrostatic potential used in the SDA BD simulation software that was applied to simulate solutions of bovine serum albumin and of hen egg white lysozyme. Results We found that the inclusion of the long-range electrostatic correction increased the accuracy of both the protein-protein interaction profiles and the protein diffusion coefficients at low ionic strength. Conclusions An advantage of this method is the low additional computational cost required to treat long-range electrostatic interactions in large biomacromolecular systems. Moreover, the implementation described here for BD simulations of protein solutions can also be applied in implicit solvent molecular dynamics simulations that make use of gridded interaction potentials. PMID:25045516

CABS-flex: server for fast simulation of protein structure fluctuations

PubMed Central

Jamroz, Michal; Kolinski, Andrzej; Kmiecik, Sebastian

2013-01-01

The CABS-flex server (http://biocomp.chem.uw.edu.pl/CABSflex) implements CABS-model–based protocol for the fast simulations of near-native dynamics of globular proteins. In this application, the CABS model was shown to be a computationally efficient alternative to all-atom molecular dynamics—a classical simulation approach. The simulation method has been validated on a large set of molecular dynamics simulation data. Using a single input (user-provided file in PDB format), the CABS-flex server outputs an ensemble of protein models (in all-atom PDB format) reflecting the flexibility of the input structure, together with the accompanying analysis (residue mean-square-fluctuation profile and others). The ensemble of predicted models can be used in structure-based studies of protein functions and interactions. PMID:23658222

Computer-assisted determination of minimum energy conformations. 7: A pharmacophore model of the active region of the alpha2-adrenoceptor

NASA Astrophysics Data System (ADS)

Ashman, William P.; Mickiewicz, A. P.; Nelson, Todd M.

1992-09-01

Molecular modeling and computational chemistry techniques are used to analyze compounds in developing pharmacophores of biological receptors to use as templates in structure activity relationship studies and to design new chemicals having physiological activity of interest. In this study, the results of x-ray crystal analyses and PM3 semi-empirical molecular orbital conformational analyses are used to determine the three-dimensional representations of selected adrenergic compounds known to be agonists with the alpha2-adrenoceptor in achieving optimized geometries and electrostatic parameters. The alpha2-adrenergic agonists interact with the adrenergic system receptors to produce various increases or decreases in hemodynamic responses (i.e., hypertension, hypotension, and bradycardia) and sedation. A pharmacophore model of the active region of the alpha2-adrenoceptor is described based on the superimposition of common structural, electrostatic, and physicochemical features of the compounds. Using the model to predict compound adrenergic activity and to design alpha2-adrenergic compounds is discussed.

Three-dimensional time-dependent computer modeling of the electrothermal atomizers for analytical spectrometry

NASA Astrophysics Data System (ADS)

Tsivilskiy, I. V.; Nagulin, K. Yu.; Gilmutdinov, A. Kh.

2016-02-01

A full three-dimensional nonstationary numerical model of graphite electrothermal atomizers of various types is developed. The model is based on solution of a heat equation within solid walls of the atomizer with a radiative heat transfer and numerical solution of a full set of Navier-Stokes equations with an energy equation for a gas. Governing equations for the behavior of a discrete phase, i.e., atomic particles suspended in a gas (including gas-phase processes of evaporation and condensation), are derived from the formal equations molecular kinetics by numerical solution of the Hertz-Langmuir equation. The following atomizers test the model: a Varian standard heated electrothermal vaporizer (ETV), a Perkin Elmer standard THGA transversely heated graphite tube with integrated platform (THGA), and the original double-stage tube-helix atomizer (DSTHA). The experimental verification of computer calculations is carried out by a method of shadow spectral visualization of the spatial distributions of atomic and molecular vapors in an analytical space of an atomizer.

Incorporating Modeling and Simulations in Undergraduate Biophysical Chemistry Course to Promote Understanding of Structure-Dynamics-Function Relationships in Proteins

ERIC Educational Resources Information Center

Hati, Sanchita; Bhattacharyya, Sudeep

2016-01-01

A project-based biophysical chemistry laboratory course, which is offered to the biochemistry and molecular biology majors in their senior year, is described. In this course, the classroom study of the structure-function of biomolecules is integrated with the discovery-guided laboratory study of these molecules using computer modeling and…

Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discovery

NASA Astrophysics Data System (ADS)

Cleves, Ann E.; Jain, Ajay N.

2008-03-01

Inductive bias is the set of assumptions that a person or procedure makes in making a prediction based on data. Different methods for ligand-based predictive modeling have different inductive biases, with a particularly sharp contrast between 2D and 3D similarity methods. A unique aspect of ligand design is that the data that exist to test methodology have been largely man-made, and that this process of design involves prediction. By analyzing the molecular similarities of known drugs, we show that the inductive bias of the historic drug discovery process has a very strong 2D bias. In studying the performance of ligand-based modeling methods, it is critical to account for this issue in dataset preparation, use of computational controls, and in the interpretation of results. We propose specific strategies to explicitly address the problems posed by inductive bias considerations.

Predictive Finite Rate Model for Oxygen-Carbon Interactions at High Temperature

NASA Astrophysics Data System (ADS)

Poovathingal, Savio

An oxidation model for carbon surfaces is developed to predict ablation rates for carbon heat shields used in hypersonic vehicles. Unlike existing empirical models, the approach used here was to probe gas-surface interactions individually and then based on an understanding of the relevant fundamental processes, build a predictive model that would be accurate over a wide range of pressures and temperatures, and even microstructures. Initially, molecular dynamics was used to understand the oxidation processes on the surface. The molecular dynamics simulations were compared to molecular beam experiments and good qualitative agreement was observed. The simulations reproduced cylindrical pitting observed in the experiments where oxidation was rapid and primarily occurred around a defect. However, the studies were limited to small systems at low temperatures and could simulate time scales only of the order of nanoseconds. Molecular beam experiments at high surface temperature indicated that a majority of surface reaction products were produced through thermal mechanisms. Since the reactions were thermal, they occurred over long time scales which were computationally prohibitive for molecular dynamics to simulate. The experiments provided detailed dynamical data on the scattering of O, O2, CO, and CO2 and it was found that the data from molecular beam experiments could be used directly to build a model. The data was initially used to deduce surface reaction probabilities at 800 K. The reaction probabilities were then incorporated into the direct simulation Monte Carlo (DSMC) method. Simulations were performed where the microstructure was resolved and dissociated oxygen convected and diffused towards it. For a gas-surface temperature of 800 K, it was found that despite CO being the dominant surface reaction product, a gas-phase reaction forms significant CO2 within the microstructure region. It was also found that surface area did not play any role in concentration of reaction products because the reaction probabilities were in the diffusion dominant regime. The molecular beam data at different surface temperatures was then used to build a finite rate model. Each reaction mechanism and all rate parameters of the new model were determined individually based on the molecular beam data. Despite the experiments being performed at near vacuum conditions, the finite rate model developed using the data could be used at pressures and temperatures relevant to hypersonic conditions. The new model was implemented in a computational fluid dynamics (CFD) solver and flow over a hypersonic vehicle was simulated. The new model predicted similar overall mass loss rates compared to existing models, however, the individual species production rates were completely different. The most notable difference was that the new model (based on molecular beam data) predicts CO as the oxidation reaction product with virtually no CO2 production, whereas existing models predict the exact opposite trend. CO being the dominant oxidation product is consistent with recent high enthalpy wind tunnel experiments. The discovery that measurements taken in molecular beam facilities are able to determine individual reaction mechanisms, including dependence on surface coverage, opens up an entirely new way of constructing ablation models.

Modeling Radiative Heat Transfer and Turbulence-Radiation Interactions in Engines

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

Paul, Chandan; Sircar, Arpan; Ferreyro-Fernandez, Sebastian

Detailed radiation modelling in piston engines has received relatively little attention to date. Recently, it is being revisited in light of current trends towards higher operating pressures and higher levels of exhaust-gas recirculation, both of which enhance molecular gas radiation. Advanced high-efficiency engines also are expected to function closer to the limits of stable operation, where even small perturbations to the energy balance can have a large influence on system behavior. Here several different spectral radiation property models and radiative transfer equation (RTE) solvers have been implemented in an OpenFOAM-based engine CFD code, and simulations have been performed for amore » full-load (peak pressure ~200 bar) heavy-duty diesel engine. Differences in computed temperature fields, NO and soot levels, and wall heat transfer rates are shown for different combinations of spectral models and RTE solvers. The relative importance of molecular gas radiation versus soot radiation is examined. And the influence of turbulence-radiation interactions is determined by comparing results obtained using local mean values of composition and temperature to compute radiative emission and absorption with those obtained using a particle-based transported probability density function method.« less

Molecular dynamics simulations in hybrid particle-continuum schemes: Pitfalls and caveats

NASA Astrophysics Data System (ADS)

Stalter, S.; Yelash, L.; Emamy, N.; Statt, A.; Hanke, M.; Lukáčová-Medvid'ová, M.; Virnau, P.

2018-03-01

Heterogeneous multiscale methods (HMM) combine molecular accuracy of particle-based simulations with the computational efficiency of continuum descriptions to model flow in soft matter liquids. In these schemes, molecular simulations typically pose a computational bottleneck, which we investigate in detail in this study. We find that it is preferable to simulate many small systems as opposed to a few large systems, and that a choice of a simple isokinetic thermostat is typically sufficient while thermostats such as Lowe-Andersen allow for simulations at elevated viscosity. We discuss suitable choices for time steps and finite-size effects which arise in the limit of very small simulation boxes. We also argue that if colloidal systems are considered as opposed to atomistic systems, the gap between microscopic and macroscopic simulations regarding time and length scales is significantly smaller. We propose a novel reduced-order technique for the coupling to the macroscopic solver, which allows us to approximate a non-linear stress-strain relation efficiently and thus further reduce computational effort of microscopic simulations.

An implicit divalent counterion force field for RNA molecular dynamics

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

Henke, Paul S.; Mak, Chi H., E-mail: cmak@usc.edu; Center of Applied Mathematical Sciences, University of Southern California, Los Angeles, California 90089

How to properly account for polyvalent counterions in a molecular dynamics simulation of polyelectrolytes such as nucleic acids remains an open question. Not only do counterions such as Mg{sup 2+} screen electrostatic interactions, they also produce attractive intrachain interactions that stabilize secondary and tertiary structures. Here, we show how a simple force field derived from a recently reported implicit counterion model can be integrated into a molecular dynamics simulation for RNAs to realistically reproduce key structural details of both single-stranded and base-paired RNA constructs. This divalent counterion model is computationally efficient. It works with existing atomistic force fields, or coarse-grainedmore » models may be tuned to work with it. We provide optimized parameters for a coarse-grained RNA model that takes advantage of this new counterion force field. Using the new model, we illustrate how the structural flexibility of RNA two-way junctions is modified under different salt conditions.« less

Structural insights into human microsomal epoxide hydrolase by combined homology modeling, molecular dynamics simulations, and molecular docking calculations.

PubMed

Saenz-Méndez, Patricia; Katz, Aline; Pérez-Kempner, María Lucía; Ventura, Oscar N; Vázquez, Marta

2017-04-01

A new homology model of human microsomal epoxide hydrolase was derived based on multiple templates. The model obtained was fully evaluated, including MD simulations and ensemble-based docking, showing that the quality of the structure is better than that of only previously known model. Particularly, a catalytic triad was clearly identified, in agreement with the experimental information available. Analysis of intermediates in the enzymatic mechanism led to the identification of key residues for substrate binding, stereoselectivity, and intermediate stabilization during the reaction. In particular, we have confirmed the role of the oxyanion hole and the conserved motif (HGXP) in epoxide hydrolases, in excellent agreement with known experimental and computational data on similar systems. The model obtained is the first one that fully agrees with all the experimental observations on the system. Proteins 2017; 85:720-730. © 2016 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A self-consistent transport model for molecular conduction based on extended Hückel theory with full three-dimensional electrostatics

NASA Astrophysics Data System (ADS)

Zahid, F.; Paulsson, M.; Polizzi, E.; Ghosh, A. W.; Siddiqui, L.; Datta, S.

2005-08-01

We present a transport model for molecular conduction involving an extended Hückel theoretical treatment of the molecular chemistry combined with a nonequilibrium Green's function treatment of quantum transport. The self-consistent potential is approximated by CNDO (complete neglect of differential overlap) method and the electrostatic effects of metallic leads (bias and image charges) are included through a three-dimensional finite element method. This allows us to capture spatial details of the electrostatic potential profile, including effects of charging, screening, and complicated electrode configurations employing only a single adjustable parameter to locate the Fermi energy. As this model is based on semiempirical methods it is computationally inexpensive and flexible compared to ab initio models, yet at the same time it is able to capture salient qualitative features as well as several relevant quantitative details of transport. We apply our model to investigate recent experimental data on alkane dithiol molecules obtained in a nanopore setup. We also present a comparison study of single molecule transistors and identify electronic properties that control their performance.

Optimizing legacy molecular dynamics software with directive-based offload

DOE PAGES

Michael Brown, W.; Carrillo, Jan-Michael Y.; Gavhane, Nitin; ...

2015-05-14

The directive-based programming models are one solution for exploiting many-core coprocessors to increase simulation rates in molecular dynamics. They offer the potential to reduce code complexity with offload models that can selectively target computations to run on the CPU, the coprocessor, or both. In our paper, we describe modifications to the LAMMPS molecular dynamics code to enable concurrent calculations on a CPU and coprocessor. We also demonstrate that standard molecular dynamics algorithms can run efficiently on both the CPU and an x86-based coprocessor using the same subroutines. As a consequence, we demonstrate that code optimizations for the coprocessor also resultmore » in speedups on the CPU; in extreme cases up to 4.7X. We provide results for LAMMAS benchmarks and for production molecular dynamics simulations using the Stampede hybrid supercomputer with both Intel (R) Xeon Phi (TM) coprocessors and NVIDIA GPUs: The optimizations presented have increased simulation rates by over 2X for organic molecules and over 7X for liquid crystals on Stampede. The optimizations are available as part of the "Intel package" supplied with LAMMPS. (C) 2015 Elsevier B.V. All rights reserved.« less

An autonomous molecular computer for logical control of gene expression.

PubMed

Benenson, Yaakov; Gil, Binyamin; Ben-Dor, Uri; Adar, Rivka; Shapiro, Ehud

2004-05-27

Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems. Recently, simple molecular-scale autonomous programmable computers were demonstrated allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for 'logical' control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.

Electronic Structure at Electrode/Electrolyte Interfaces in Magnesium based Batteries

NASA Astrophysics Data System (ADS)

Balachandran, Janakiraman; Siegel, Donald

2015-03-01

Magnesium is a promising multivalent element for use in next generation electrochemical energy storage systems. However, a wide range of challenges such as low coulombic efficiency, low/varying capacity and cyclability need to be resolved in order to realize Mg based batteries. Many of these issues can be related to interfacial phenomena between the Mg anode and common electrolytes. Ab-initio based computational models of these interfaces can provide insights on the interfacial interactions that can be difficult to probe experimentally. In this work we present ab-initio computations of common electrolyte solvents (THF, DME) in contact with two model electrode surfaces namely -- (i) an ``SEI-free'' electrode based on Mg metal and, (ii) a ``passivated'' electrode consisting of MgO. We perform GW calculations to predict the reorganization of the molecular orbitals (HOMO/LUMO) upon contact with the these surfaces and their alignment with respect to the Fermi energy of the electrodes. These computations are in turn compared with more efficient GGA (PBE) & Hybrid (HSE) functional calculations. The results obtained from these computations enable us to qualitatively describe the stability of these solvent molecules at electrode-electrolyte interfaces

Algorithms of GPU-enabled reactive force field (ReaxFF) molecular dynamics.

PubMed

Zheng, Mo; Li, Xiaoxia; Guo, Li

2013-04-01

Reactive force field (ReaxFF), a recent and novel bond order potential, allows for reactive molecular dynamics (ReaxFF MD) simulations for modeling larger and more complex molecular systems involving chemical reactions when compared with computation intensive quantum mechanical methods. However, ReaxFF MD can be approximately 10-50 times slower than classical MD due to its explicit modeling of bond forming and breaking, the dynamic charge equilibration at each time-step, and its one order smaller time-step than the classical MD, all of which pose significant computational challenges in simulation capability to reach spatio-temporal scales of nanometers and nanoseconds. The very recent advances of graphics processing unit (GPU) provide not only highly favorable performance for GPU enabled MD programs compared with CPU implementations but also an opportunity to manage with the computing power and memory demanding nature imposed on computer hardware by ReaxFF MD. In this paper, we present the algorithms of GMD-Reax, the first GPU enabled ReaxFF MD program with significantly improved performance surpassing CPU implementations on desktop workstations. The performance of GMD-Reax has been benchmarked on a PC equipped with a NVIDIA C2050 GPU for coal pyrolysis simulation systems with atoms ranging from 1378 to 27,283. GMD-Reax achieved speedups as high as 12 times faster than Duin et al.'s FORTRAN codes in Lammps on 8 CPU cores and 6 times faster than the Lammps' C codes based on PuReMD in terms of the simulation time per time-step averaged over 100 steps. GMD-Reax could be used as a new and efficient computational tool for exploiting very complex molecular reactions via ReaxFF MD simulation on desktop workstations. Copyright © 2013 Elsevier Inc. All rights reserved.

Computational Methods in Drug Discovery

PubMed Central

Sliwoski, Gregory; Kothiwale, Sandeepkumar; Meiler, Jens

2014-01-01

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature. PMID:24381236

Asynchronous Replica Exchange Software for Grid and Heterogeneous Computing.

PubMed

Gallicchio, Emilio; Xia, Junchao; Flynn, William F; Zhang, Baofeng; Samlalsingh, Sade; Mentes, Ahmet; Levy, Ronald M

2015-11-01

Parallel replica exchange sampling is an extended ensemble technique often used to accelerate the exploration of the conformational ensemble of atomistic molecular simulations of chemical systems. Inter-process communication and coordination requirements have historically discouraged the deployment of replica exchange on distributed and heterogeneous resources. Here we describe the architecture of a software (named ASyncRE) for performing asynchronous replica exchange molecular simulations on volunteered computing grids and heterogeneous high performance clusters. The asynchronous replica exchange algorithm on which the software is based avoids centralized synchronization steps and the need for direct communication between remote processes. It allows molecular dynamics threads to progress at different rates and enables parameter exchanges among arbitrary sets of replicas independently from other replicas. ASyncRE is written in Python following a modular design conducive to extensions to various replica exchange schemes and molecular dynamics engines. Applications of the software for the modeling of association equilibria of supramolecular and macromolecular complexes on BOINC campus computational grids and on the CPU/MIC heterogeneous hardware of the XSEDE Stampede supercomputer are illustrated. They show the ability of ASyncRE to utilize large grids of desktop computers running the Windows, MacOS, and/or Linux operating systems as well as collections of high performance heterogeneous hardware devices.

Mark R. Nimlos | NREL

Science.gov Websites

, reaction kinetics, computational modeling, photochemistry, and molecular spectroscopy. Nimlos has served as Chemical reaction energetics and kinetics Biomass pyrolysis and gasification Heterogeneous catalysis in zeolites Quantum modeling and kinetic modeling of reaction Molecular dynamics modeling

Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

NASA Astrophysics Data System (ADS)

Nu’aim, M. N.; Bustam, M. A.

2018-04-01

By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.

A computational chemistry perspective on the current status and future direction of hepatitis B antiviral drug discovery.

PubMed

Morgnanesi, Dante; Heinrichs, Eric J; Mele, Anthony R; Wilkinson, Sean; Zhou, Suzanne; Kulp, John L

2015-11-01

Computational chemical biology, applied to research on hepatitis B virus (HBV), has two major branches: bioinformatics (statistical models) and first-principle methods (molecular physics). While bioinformatics focuses on statistical tools and biological databases, molecular physics uses mathematics and chemical theory to study the interactions of biomolecules. Three computational techniques most commonly used in HBV research are homology modeling, molecular docking, and molecular dynamics. Homology modeling is a computational simulation to predict protein structure and has been used to construct conformers of the viral polymerase (reverse transcriptase domain and RNase H domain) and the HBV X protein. Molecular docking is used to predict the most likely orientation of a ligand when it is bound to a protein, as well as determining an energy score of the docked conformation. Molecular dynamics is a simulation that analyzes biomolecule motions and determines conformation and stability patterns. All of these modeling techniques have aided in the understanding of resistance mutations on HBV non-nucleos(t)ide reverse-transcriptase inhibitor binding. Finally, bioinformatics can be used to study the DNA and RNA protein sequences of viruses to both analyze drug resistance and to genotype the viral genomes. Overall, with these techniques, and others, computational chemical biology is becoming more and more necessary in hepatitis B research. This article forms part of a symposium in Antiviral Research on "An unfinished story: from the discovery of the Australia antigen to the development of new curative therapies for hepatitis B." Copyright © 2015 Elsevier B.V. All rights reserved.

Adversarial Threshold Neural Computer for Molecular de Novo Design.

PubMed

Putin, Evgeny; Asadulaev, Arip; Vanhaelen, Quentin; Ivanenkov, Yan; Aladinskaya, Anastasia V; Aliper, Alex; Zhavoronkov, Alex

2018-03-30

In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp 3 -rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed in vitro validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds.

Atomistic modelling of scattering data in the Collaborative Computational Project for Small Angle Scattering (CCP-SAS)

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

Perkins, Stephen J.; Wright, David W.; Zhang, Hailiang

2016-10-14

The capabilities of current computer simulations provide a unique opportunity to model small-angle scattering (SAS) data at the atomistic level, and to include other structural constraints ranging from molecular and atomistic energetics to crystallography, electron microscopy and NMR. This extends the capabilities of solution scattering and provides deeper insights into the physics and chemistry of the systems studied. Realizing this potential, however, requires integrating the experimental data with a new generation of modelling software. To achieve this, the CCP-SAS collaboration (http://www.ccpsas.org/) is developing open-source, high-throughput and user-friendly software for the atomistic and coarse-grained molecular modelling of scattering data. Robust state-of-the-artmore » molecular simulation engines and molecular dynamics and Monte Carlo force fields provide constraints to the solution structure inferred from the small-angle scattering data, which incorporates the known physical chemistry of the system. The implementation of this software suite involves a tiered approach in whichGenAppprovides the deployment infrastructure for running applications on both standard and high-performance computing hardware, andSASSIEprovides a workflow framework into which modules can be plugged to prepare structures, carry out simulations, calculate theoretical scattering data and compare results with experimental data.GenAppproduces the accessible web-based front end termedSASSIE-web, andGenAppandSASSIEalso make community SAS codes available. Applications are illustrated by case studies: (i) inter-domain flexibility in two- to six-domain proteins as exemplified by HIV-1 Gag, MASP and ubiquitin; (ii) the hinge conformation in human IgG2 and IgA1 antibodies; (iii) the complex formed between a hexameric protein Hfq and mRNA; and (iv) synthetic `bottlebrush' polymers.« less

Atomistic modelling of scattering data in the Collaborative Computational Project for Small Angle Scattering (CCP-SAS).

PubMed

Perkins, Stephen J; Wright, David W; Zhang, Hailiang; Brookes, Emre H; Chen, Jianhan; Irving, Thomas C; Krueger, Susan; Barlow, David J; Edler, Karen J; Scott, David J; Terrill, Nicholas J; King, Stephen M; Butler, Paul D; Curtis, Joseph E

2016-12-01

The capabilities of current computer simulations provide a unique opportunity to model small-angle scattering (SAS) data at the atomistic level, and to include other structural constraints ranging from molecular and atomistic energetics to crystallography, electron microscopy and NMR. This extends the capabilities of solution scattering and provides deeper insights into the physics and chemistry of the systems studied. Realizing this potential, however, requires integrating the experimental data with a new generation of modelling software. To achieve this, the CCP-SAS collaboration (http://www.ccpsas.org/) is developing open-source, high-throughput and user-friendly software for the atomistic and coarse-grained molecular modelling of scattering data. Robust state-of-the-art molecular simulation engines and molecular dynamics and Monte Carlo force fields provide constraints to the solution structure inferred from the small-angle scattering data, which incorporates the known physical chemistry of the system. The implementation of this software suite involves a tiered approach in which GenApp provides the deployment infrastructure for running applications on both standard and high-performance computing hardware, and SASSIE provides a workflow framework into which modules can be plugged to prepare structures, carry out simulations, calculate theoretical scattering data and compare results with experimental data. GenApp produces the accessible web-based front end termed SASSIE-web , and GenApp and SASSIE also make community SAS codes available. Applications are illustrated by case studies: (i) inter-domain flexibility in two- to six-domain proteins as exemplified by HIV-1 Gag, MASP and ubiquitin; (ii) the hinge conformation in human IgG2 and IgA1 antibodies; (iii) the complex formed between a hexameric protein Hfq and mRNA; and (iv) synthetic 'bottlebrush' polymers.

From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

PubMed Central

Zhu, Hao

2017-01-01

Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

Computing by physical interaction in neurons.

PubMed

Aur, Dorian; Jog, Mandar; Poznanski, Roman R

2011-12-01

The electrodynamics of action potentials represents the fundamental level where information is integrated and processed in neurons. The Hodgkin-Huxley model cannot explain the non-stereotyped spatial charge density dynamics that occur during action potential propagation. Revealed in experiments as spike directivity, the non-uniform charge density dynamics within neurons carry meaningful information and suggest that fragments of information regarding our memories are endogenously stored in structural patterns at a molecular level and are revealed only during spiking activity. The main conceptual idea is that under the influence of electric fields, efficient computation by interaction occurs between charge densities embedded within molecular structures and the transient developed flow of electrical charges. This process of computation underlying electrical interactions and molecular mechanisms at the subcellular level is dissimilar from spiking neuron models that are completely devoid of physical interactions. Computation by interaction describes a more powerful continuous model of computation than the one that consists of discrete steps as represented in Turing machines.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics.

PubMed

Wu, Xiao-Lin; Sun, Chuanyu; Beissinger, Timothy M; Rosa, Guilherme Jm; Weigel, Kent A; Gatti, Natalia de Leon; Gianola, Daniel

2012-09-25

Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

PubMed Central

2012-01-01

Background Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Results Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Conclusions Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs. PMID:23009363

A stochastic thermostat algorithm for coarse-grained thermomechanical modeling of large-scale soft matters: Theory and application to microfilaments

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

Li, Tong; Gu, YuanTong, E-mail: yuantong.gu@qut.edu.au

As all-atom molecular dynamics method is limited by its enormous computational cost, various coarse-grained strategies have been developed to extend the length scale of soft matters in the modeling of mechanical behaviors. However, the classical thermostat algorithm in highly coarse-grained molecular dynamics method would underestimate the thermodynamic behaviors of soft matters (e.g. microfilaments in cells), which can weaken the ability of materials to overcome local energy traps in granular modeling. Based on all-atom molecular dynamics modeling of microfilament fragments (G-actin clusters), a new stochastic thermostat algorithm is developed to retain the representation of thermodynamic properties of microfilaments at extra coarse-grainedmore » level. The accuracy of this stochastic thermostat algorithm is validated by all-atom MD simulation. This new stochastic thermostat algorithm provides an efficient way to investigate the thermomechanical properties of large-scale soft matters.« less

A computational model of selection by consequences: log survivor plots.

PubMed

Kulubekova, Saule; McDowell, J J

2008-06-01

[McDowell, J.J, 2004. A computational model of selection by consequences. J. Exp. Anal. Behav. 81, 297-317] instantiated the principle of selection by consequences in a virtual organism with an evolving repertoire of possible behaviors undergoing selection, reproduction, and mutation over many generations. The process is based on the computational approach, which is non-deterministic and rules-based. The model proposes a causal account for operant behavior. McDowell found that the virtual organism consistently showed a hyperbolic relationship between response and reinforcement rates according to the quantitative law of effect. To continue validation of the computational model, the present study examined its behavior on the molecular level by comparing the virtual organism's IRT distributions in the form of log survivor plots to findings from live organisms. Log survivor plots did not show the "broken-stick" feature indicative of distinct bouts and pauses in responding, although the bend in slope of the plots became more defined at low reinforcement rates. The shape of the virtual organism's log survivor plots was more consistent with the data on reinforced responding in pigeons. These results suggest that log survivor plot patterns of the virtual organism were generally consistent with the findings from live organisms providing further support for the computational model of selection by consequences as a viable account of operant behavior.

Molecular basis of intramolecular electron transfer in proteins during radical-mediated oxidations: Computer simulation studies in model tyrosine-cysteine peptides in solution

PubMed Central

Petruk, Ariel A.; Bartesaghi, Silvina; Trujillo, Madia; Estrin, Darío A.; Murgida, Daniel; Kalyanaraman, Balaraman; Marti, Marcelo A.; Radi, Rafael

2012-01-01

Experimental studies in hemeproteins and model Tyr/Cys-containing peptides exposed to oxidizing and nitrating species suggest that intramolecular electron transfer (IET) between tyrosyl radicals (Tyr-O●) and Cys residues controls oxidative modification yields. The molecular basis of this IET process is not sufficiently understood with structural atomic detail. Herein, we analyzed using molecular dynamics and quantum mechanics-based computational calculations, mechanistic possibilities for the radical transfer reaction in Tyr/Cys-containing peptides in solution and correlated them with existing experimental data. Our results support that Tyr-O● to Cys radical transfer is mediated by an acid/base equilibrium that involves deprotonation of Cys to form the thiolate, followed by a likely rate-limiting transfer process to yield cysteinyl radical and a Tyr phenolate; proton uptake by Tyr completes the reaction. Both, the pKa values of the Tyr phenol and Cys thiol groups and the energetic and kinetics of the reversible IET are revealed as key physico-chemical factors. The proposed mechanism constitutes a case of sequential, acid/base equilibrium-dependent and solvent-mediated, proton-coupled electron transfer and explains the dependency of oxidative yields in Tyr/Cys peptides as a function of the number of alanine spacers. These findings contribute to explain oxidative modifications in proteins that contain sequence and/or spatially close Tyr-Cys residues. PMID:22640642

Highly Efficient Computation of the Basal kon using Direct Simulation of Protein-Protein Association with Flexible Molecular Models.

PubMed

Saglam, Ali S; Chong, Lillian T

2016-01-14

An essential baseline for determining the extent to which electrostatic interactions enhance the kinetics of protein-protein association is the "basal" kon, which is the rate constant for association in the absence of electrostatic interactions. However, since such association events are beyond the milliseconds time scale, it has not been practical to compute the basal kon by directly simulating the association with flexible models. Here, we computed the basal kon for barnase and barstar, two of the most rapidly associating proteins, using highly efficient, flexible molecular simulations. These simulations involved (a) pseudoatomic protein models that reproduce the molecular shapes, electrostatic, and diffusion properties of all-atom models, and (b) application of the weighted ensemble path sampling strategy, which enhanced the efficiency of generating association events by >130-fold. We also examined the extent to which the computed basal kon is affected by inclusion of intermolecular hydrodynamic interactions in the simulations.

Treatment of atomic and molecular line blanketing by opacity sampling. [atmospheric optics - stellar atmospheres

NASA Technical Reports Server (NTRS)

Johnson, H. R.; Krupp, B. M.

1975-01-01

An opacity sampling (OS) technique for treating the radiative opacity of large numbers of atomic and molecular lines in cool stellar atmospheres is presented. Tests were conducted and results show that the structure of atmospheric models is accurately fixed by the use of 1000 frequency points, and 500 frequency points is often adequate. The effects of atomic and molecular lines are separately studied. A test model computed by using the OS method agrees very well with a model having identical atmospheric parameters computed by the giant line (opacity distribution function) method.

Direction-dependent secondary bonds and their stepwise melting in a uracil-based molecular crystal studied by infrared spectroscopy and theoretical modeling

NASA Astrophysics Data System (ADS)

Szekrényes, Zsolt; Nagy, Péter R.; Tarczay, György; Maggini, Laura; Bonifazi, Davide; Kamarás, Katalin

2018-01-01

Three types of supramolecular interactions are identified in the three crystallographic directions in crystals of 1,4-bis[(1-hexylurac-6-yl) ethynyl]benzene, a uracil-based molecule with a linear backbone. These three interactions, characterized by their strongest component, are: intermolecular double H-bonds along the molecular axis, London dispersion interaction of hexyl chains connecting these linear assemblies, and π - π stacking of the aromatic rings perpendicular to the molecular planes. On heating, two transitions happen, disordering of hexyl chains at 473 K, followed by H-bond melting at 534 K. The nature of the bonds and transitions was established by matrix-isolation and temperature-dependent infrared spectroscopy and supported by theoretical computations.

Combined quantum and molecular mechanics (QM/MM).

PubMed

Friesner, Richard A

2004-12-01

We describe the current state of the art of mixed quantum mechanics/molecular mechanics (QM/MM) methodology, with a particular focus on modeling of enzymatic reactions. Over the past decade, the effectiveness of these methods has increased dramatically, based on improved quantum chemical methods, advances in the description of the QM/MM interface, and reductions in the cost/performance of computing hardware. Two examples of pharmaceutically relevant applications, cytochrome P450 and class C β-lactamase, are presented.: © 2004 Elsevier Ltd . All rights reserved.

Introductory Molecular Orbital Theory: An Honors General Chemistry Computational Lab as Implemented Using Three-Dimensional Modeling Software

ERIC Educational Resources Information Center

Ruddick, Kristie R.; Parrill, Abby L.; Petersen, Richard L.

2012-01-01

In this study, a computational molecular orbital theory experiment was implemented in a first-semester honors general chemistry course. Students used the GAMESS (General Atomic and Molecular Electronic Structure System) quantum mechanical software (as implemented in ChemBio3D) to optimize the geometry for various small molecules. Extended Huckel…

Visualizing functional motions of membrane transporters with molecular dynamics simulations.

PubMed

Shaikh, Saher A; Li, Jing; Enkavi, Giray; Wen, Po-Chao; Huang, Zhijian; Tajkhorshid, Emad

2013-01-29

Computational modeling and molecular simulation techniques have become an integral part of modern molecular research. Various areas of molecular sciences continue to benefit from, indeed rely on, the unparalleled spatial and temporal resolutions offered by these technologies, to provide a more complete picture of the molecular problems at hand. Because of the continuous development of more efficient algorithms harvesting ever-expanding computational resources, and the emergence of more advanced and novel theories and methodologies, the scope of computational studies has expanded significantly over the past decade, now including much larger molecular systems and far more complex molecular phenomena. Among the various computer modeling techniques, the application of molecular dynamics (MD) simulation and related techniques has particularly drawn attention in biomolecular research, because of the ability of the method to describe the dynamical nature of the molecular systems and thereby to provide a more realistic representation, which is often needed for understanding fundamental molecular properties. The method has proven to be remarkably successful in capturing molecular events and structural transitions highly relevant to the function and/or physicochemical properties of biomolecular systems. Herein, after a brief introduction to the method of MD, we use a number of membrane transport proteins studied in our laboratory as examples to showcase the scope and applicability of the method and its power in characterizing molecular motions of various magnitudes and time scales that are involved in the function of this important class of membrane proteins.

Visualizing Functional Motions of Membrane Transporters with Molecular Dynamics Simulations

PubMed Central

2013-01-01

Computational modeling and molecular simulation techniques have become an integral part of modern molecular research. Various areas of molecular sciences continue to benefit from, indeed rely on, the unparalleled spatial and temporal resolutions offered by these technologies, to provide a more complete picture of the molecular problems at hand. Because of the continuous development of more efficient algorithms harvesting ever-expanding computational resources, and the emergence of more advanced and novel theories and methodologies, the scope of computational studies has expanded significantly over the past decade, now including much larger molecular systems and far more complex molecular phenomena. Among the various computer modeling techniques, the application of molecular dynamics (MD) simulation and related techniques has particularly drawn attention in biomolecular research, because of the ability of the method to describe the dynamical nature of the molecular systems and thereby to provide a more realistic representation, which is often needed for understanding fundamental molecular properties. The method has proven to be remarkably successful in capturing molecular events and structural transitions highly relevant to the function and/or physicochemical properties of biomolecular systems. Herein, after a brief introduction to the method of MD, we use a number of membrane transport proteins studied in our laboratory as examples to showcase the scope and applicability of the method and its power in characterizing molecular motions of various magnitudes and time scales that are involved in the function of this important class of membrane proteins. PMID:23298176

FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-X-ray computed tomography.

PubMed

Ale, Angelique; Ermolayev, Vladimir; Herzog, Eva; Cohrs, Christian; de Angelis, Martin Hrabé; Ntziachristos, Vasilis

2012-06-01

The development of hybrid optical tomography methods to improve imaging performance has been suggested over a decade ago and has been experimentally demonstrated in animals and humans. Here we examined in vivo performance of a camera-based hybrid fluorescence molecular tomography (FMT) system for 360° imaging combined with X-ray computed tomography (XCT). Offering an accurately co-registered, information-rich hybrid data set, FMT-XCT has new imaging possibilities compared to stand-alone FMT and XCT. We applied FMT-XCT to a subcutaneous 4T1 tumor mouse model, an Aga2 osteogenesis imperfecta model and a Kras lung cancer mouse model, using XCT information during FMT inversion. We validated in vivo imaging results against post-mortem planar fluorescence images of cryoslices and histology data. Besides offering concurrent anatomical and functional information, FMT-XCT resulted in the most accurate FMT performance to date. These findings indicate that addition of FMT optics into the XCT gantry may be a potent upgrade for small-animal XCT systems.

Logic integration of mRNA signals by an RNAi-based molecular computer.

PubMed

Xie, Zhen; Liu, Siyuan John; Bleris, Leonidas; Benenson, Yaakov

2010-05-01

Synthetic in vivo molecular 'computers' could rewire biological processes by establishing programmable, non-native pathways between molecular signals and biological responses. Multiple molecular computer prototypes have been shown to work in simple buffered solutions. Many of those prototypes were made of DNA strands and performed computations using cycles of annealing-digestion or strand displacement. We have previously introduced RNA interference (RNAi)-based computing as a way of implementing complex molecular logic in vivo. Because it also relies on nucleic acids for its operation, RNAi computing could benefit from the tools developed for DNA systems. However, these tools must be harnessed to produce bioactive components and be adapted for harsh operating environments that reflect in vivo conditions. In a step toward this goal, we report the construction and implementation of biosensors that 'transduce' mRNA levels into bioactive, small interfering RNA molecules via RNA strand exchange in a cell-free Drosophila embryo lysate, a step beyond simple buffered environments. We further integrate the sensors with our RNAi 'computational' module to evaluate two-input logic functions on mRNA concentrations. Our results show how RNA strand exchange can expand the utility of RNAi computing and point toward the possibility of using strand exchange in a native biological setting.

An autonomous molecular computer for logical control of gene expression

PubMed Central

Benenson, Yaakov; Gil, Binyamin; Ben-Dor, Uri; Adar, Rivka; Shapiro, Ehud

2013-01-01

Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems1–7. Recently, simple molecular-scale autonomous programmable computers were demonstrated8–15 allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for ‘logical’ control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton12–17; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes18–22 associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug. PMID:15116117

Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays

NASA Astrophysics Data System (ADS)

Guha, Rajarshi; Schürer, Stephan C.

2008-06-01

Computational toxicology is emerging as an encouraging alternative to experimental testing. The Molecular Libraries Screening Center Network (MLSCN) as part of the NIH Molecular Libraries Roadmap has recently started generating large and diverse screening datasets, which are publicly available in PubChem. In this report, we investigate various aspects of developing computational models to predict cell toxicity based on cell proliferation screening data generated in the MLSCN. By capturing feature-based information in those datasets, such predictive models would be useful in evaluating cell-based screening results in general (for example from reporter assays) and could be used as an aid to identify and eliminate potentially undesired compounds. Specifically we present the results of random forest ensemble models developed using different cell proliferation datasets and highlight protocols to take into account their extremely imbalanced nature. Depending on the nature of the datasets and the descriptors employed we were able to achieve percentage correct classification rates between 70% and 85% on the prediction set, though the accuracy rate dropped significantly when the models were applied to in vivo data. In this context we also compare the MLSCN cell proliferation results with animal acute toxicity data to investigate to what extent animal toxicity can be correlated and potentially predicted by proliferation results. Finally, we present a visualization technique that allows one to compare a new dataset to the training set of the models to decide whether the new dataset may be reliably predicted.

Computational Workbench for Multibody Dynamics

NASA Technical Reports Server (NTRS)

Edmonds, Karina

2007-01-01

PyCraft is a computer program that provides an interactive, workbenchlike computing environment for developing and testing algorithms for multibody dynamics. Examples of multibody dynamic systems amenable to analysis with the help of PyCraft include land vehicles, spacecraft, robots, and molecular models. PyCraft is based on the Spatial-Operator- Algebra (SOA) formulation for multibody dynamics. The SOA operators enable construction of simple and compact representations of complex multibody dynamical equations. Within the Py-Craft computational workbench, users can, essentially, use the high-level SOA operator notation to represent the variety of dynamical quantities and algorithms and to perform computations interactively. PyCraft provides a Python-language interface to underlying C++ code. Working with SOA concepts, a user can create and manipulate Python-level operator classes in order to implement and evaluate new dynamical quantities and algorithms. During use of PyCraft, virtually all SOA-based algorithms are available for computational experiments.

ReaDDy - A Software for Particle-Based Reaction-Diffusion Dynamics in Crowded Cellular Environments

PubMed Central

Schöneberg, Johannes; Noé, Frank

2013-01-01

We introduce the software package ReaDDy for simulation of detailed spatiotemporal mechanisms of dynamical processes in the cell, based on reaction-diffusion dynamics with particle resolution. In contrast to other particle-based reaction kinetics programs, ReaDDy supports particle interaction potentials. This permits effects such as space exclusion, molecular crowding and aggregation to be modeled. The biomolecules simulated can be represented as a sphere, or as a more complex geometry such as a domain structure or polymer chain. ReaDDy bridges the gap between small-scale but highly detailed molecular dynamics or Brownian dynamics simulations and large-scale but little-detailed reaction kinetics simulations. ReaDDy has a modular design that enables the exchange of the computing core by efficient platform-specific implementations or dynamical models that are different from Brownian dynamics. PMID:24040218

Theory of wavelet-based coarse-graining hierarchies for molecular dynamics.

PubMed

Rinderspacher, Berend Christopher; Bardhan, Jaydeep P; Ismail, Ahmed E

2017-07-01

We present a multiresolution approach to compressing the degrees of freedom and potentials associated with molecular dynamics, such as the bond potentials. The approach suggests a systematic way to accelerate large-scale molecular simulations with more than two levels of coarse graining, particularly applications of polymeric materials. In particular, we derive explicit models for (arbitrarily large) linear (homo)polymers and iterative methods to compute large-scale wavelet decompositions from fragment solutions. This approach does not require explicit preparation of atomistic-to-coarse-grained mappings, but instead uses the theory of diffusion wavelets for graph Laplacians to develop system-specific mappings. Our methodology leads to a hierarchy of system-specific coarse-grained degrees of freedom that provides a conceptually clear and mathematically rigorous framework for modeling chemical systems at relevant model scales. The approach is capable of automatically generating as many coarse-grained model scales as necessary, that is, to go beyond the two scales in conventional coarse-grained strategies; furthermore, the wavelet-based coarse-grained models explicitly link time and length scales. Furthermore, a straightforward method for the reintroduction of omitted degrees of freedom is presented, which plays a major role in maintaining model fidelity in long-time simulations and in capturing emergent behaviors.

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

Ibrahim, Khaled Z.; Epifanovsky, Evgeny; Williams, Samuel W.

Coupled-cluster methods provide highly accurate models of molecular structure by explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix-matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts tomore » extend the Libtensor framework to work in the distributed memory environment in a scalable and energy efficient manner. We achieve up to 240 speedup compared with the best optimized shared memory implementation. We attain scalability to hundreds of thousands of compute cores on three distributed-memory architectures, (Cray XC30&XC40, BlueGene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance. Nevertheless, we preserve a uni ed interface to both programming models to maintain the productivity of computational quantum chemists.« less

Robust mechanobiological behavior emerges in heterogeneous myosin systems.

PubMed

Egan, Paul F; Moore, Jeffrey R; Ehrlicher, Allen J; Weitz, David A; Schunn, Christian; Cagan, Jonathan; LeDuc, Philip

2017-09-26

Biological complexity presents challenges for understanding natural phenomenon and engineering new technologies, particularly in systems with molecular heterogeneity. Such complexity is present in myosin motor protein systems, and computational modeling is essential for determining how collective myosin interactions produce emergent system behavior. We develop a computational approach for altering myosin isoform parameters and their collective organization, and support predictions with in vitro experiments of motility assays with α-actinins as molecular force sensors. The computational approach models variations in single myosin molecular structure, system organization, and force stimuli to predict system behavior for filament velocity, energy consumption, and robustness. Robustness is the range of forces where a filament is expected to have continuous velocity and depends on used myosin system energy. Myosin systems are shown to have highly nonlinear behavior across force conditions that may be exploited at a systems level by combining slow and fast myosin isoforms heterogeneously. Results suggest some heterogeneous systems have lower energy use near stall conditions and greater energy consumption when unloaded, therefore promoting robustness. These heterogeneous system capabilities are unique in comparison with homogenous systems and potentially advantageous for high performance bionanotechnologies. Findings open doors at the intersections of mechanics and biology, particularly for understanding and treating myosin-related diseases and developing approaches for motor molecule-based technologies.

Robust mechanobiological behavior emerges in heterogeneous myosin systems

NASA Astrophysics Data System (ADS)

Egan, Paul F.; Moore, Jeffrey R.; Ehrlicher, Allen J.; Weitz, David A.; Schunn, Christian; Cagan, Jonathan; LeDuc, Philip

2017-09-01

Biological complexity presents challenges for understanding natural phenomenon and engineering new technologies, particularly in systems with molecular heterogeneity. Such complexity is present in myosin motor protein systems, and computational modeling is essential for determining how collective myosin interactions produce emergent system behavior. We develop a computational approach for altering myosin isoform parameters and their collective organization, and support predictions with in vitro experiments of motility assays with α-actinins as molecular force sensors. The computational approach models variations in single myosin molecular structure, system organization, and force stimuli to predict system behavior for filament velocity, energy consumption, and robustness. Robustness is the range of forces where a filament is expected to have continuous velocity and depends on used myosin system energy. Myosin systems are shown to have highly nonlinear behavior across force conditions that may be exploited at a systems level by combining slow and fast myosin isoforms heterogeneously. Results suggest some heterogeneous systems have lower energy use near stall conditions and greater energy consumption when unloaded, therefore promoting robustness. These heterogeneous system capabilities are unique in comparison with homogenous systems and potentially advantageous for high performance bionanotechnologies. Findings open doors at the intersections of mechanics and biology, particularly for understanding and treating myosin-related diseases and developing approaches for motor molecule-based technologies.

Natural Phenolic Inhibitors of Trichothecene Biosynthesis by the Wheat Fungal Pathogen Fusarium culmorum: A Computational Insight into the Structure-Activity Relationship

PubMed Central

Pani, Giovanna; Dessì, Alessandro; Dallocchio, Roberto; Scherm, Barbara; Azara, Emanuela; Delogu, Giovanna

2016-01-01

A model of the trichodiene synthase (TRI5) of the wheat fungal pathogen and type-B trichothecene producer Fusarium culmorum was developed based on homology modelling with the crystallized protein of F. sporotrichioides. Eight phenolic molecules, namely ferulic acid 1, apocynin 2, propyl gallate 3, eugenol 4, Me-dehydrozingerone 5, eugenol dimer 6, magnolol 7, and ellagic acid 8, were selected for their ability to inhibit trichothecene production and/or fungal vegetative growth in F. culmorum. The chemical structures of phenols were constructed and partially optimised based on Molecular Mechanics (MM) studies and energy minimisation by Density Functional Theory (DFT). Docking analysis of the phenolic molecules was run on the 3D model of F. culmorum TRI5. Experimental biological activity, molecular descriptors and interacting-structures obtained from computational analysis were compared. Besides the catalytic domain, three privileged sites in the interaction with the inhibitory molecules were identified on the protein surface. The TRI5-ligand interactions highlighted in this study represent a powerful tool to the identification of new Fusarium-targeted molecules with potential as trichothecene inhibitors. PMID:27294666

Molecular modeling in structural nano-toxicology: interactions of nano-particles with nano-machinery of cells.

PubMed

Yanamala, Naveena; Kagan, Valerian E; Shvedova, Anna A

2013-12-01

Over the past two decades, nanotechnology has emerged as a key player in various disciplines of science and technology. Some of the most exciting applications are in the field of biomedicine - for theranostics (for combined diagnostic and therapeutic purposes) as well as for exploration of biological systems. A detailed understanding of the molecular interactions between nanoparticles and biological nano-machinery - macromolecules, membranes, and intracellular organelles - is crucial for obtaining adequate information on mechanisms of action of nanomaterials as well as a perspective on the long term effects of these materials and their possible toxicological outcomes. This review focuses on the use of structure-based computational molecular modeling as a tool to understand and to predict the interactions between nanomaterials and nano-biosystems. We review major approaches and provide examples of computational analysis of the structural principles behind such interactions. A rationale on how nanoparticles of different sizes, shape, structure and chemical properties can affect the organization and functions of nano-machinery of cells is also presented. Published by Elsevier B.V.

Computationally Efficient Multiconfigurational Reactive Molecular Dynamics

PubMed Central

Yamashita, Takefumi; Peng, Yuxing; Knight, Chris; Voth, Gregory A.

2012-01-01

It is a computationally demanding task to explicitly simulate the electronic degrees of freedom in a system to observe the chemical transformations of interest, while at the same time sampling the time and length scales required to converge statistical properties and thus reduce artifacts due to initial conditions, finite-size effects, and limited sampling. One solution that significantly reduces the computational expense consists of molecular models in which effective interactions between particles govern the dynamics of the system. If the interaction potentials in these models are developed to reproduce calculated properties from electronic structure calculations and/or ab initio molecular dynamics simulations, then one can calculate accurate properties at a fraction of the computational cost. Multiconfigurational algorithms model the system as a linear combination of several chemical bonding topologies to simulate chemical reactions, also sometimes referred to as “multistate”. These algorithms typically utilize energy and force calculations already found in popular molecular dynamics software packages, thus facilitating their implementation without significant changes to the structure of the code. However, the evaluation of energies and forces for several bonding topologies per simulation step can lead to poor computational efficiency if redundancy is not efficiently removed, particularly with respect to the calculation of long-ranged Coulombic interactions. This paper presents accurate approximations (effective long-range interaction and resulting hybrid methods) and multiple-program parallelization strategies for the efficient calculation of electrostatic interactions in reactive molecular simulations. PMID:25100924

Heuristic lipophilicity potential for computer-aided rational drug design

NASA Astrophysics Data System (ADS)

Du, Qishi; Arteca, Gustavo A.; Mezey, Paul G.

1997-09-01

In this contribution we suggest a heuristic molecular lipophilicitypotential (HMLP), which is a structure-based technique requiring noempirical indices of atomic lipophilicity. The input data used in thisapproach are molecular geometries and molecular surfaces. The HMLP is amodified electrostatic potential, combined with the averaged influences fromthe molecular environment. Quantum mechanics is used to calculate theelectron density function ρ(r) and the electrostatic potential V(r), andfrom this information a lipophilicity potential L(r) is generated. The HMLPis a unified lipophilicity and hydrophilicity potential. The interactions ofdipole and multipole moments, hydrogen bonds, and charged atoms in amolecule are included in the hydrophilic interactions in this model. TheHMLP is used to study hydrogen bonds and water-octanol partitioncoefficients in several examples. The calculated results show that the HMLPgives qualitatively and quantitatively correct, as well as chemicallyreasonable, results in cases where comparisons are available. Thesecomparisons indicate that the HMLP has advantages over the empiricallipophilicity potential in many aspects. The HMLP is a three-dimensional andeasily visualizable representation of molecular lipophilicity, suggested asa potential tool in computer-aided three-dimensional drug design.

COMPUTATIONAL MODELING OF SIGNALING PATHWAYS MEDIATING CELL CYCLE AND APOPTOTIC RESPONSES TO IONIZING RADIATION MEDIATED DNA DAMAGE

EPA Science Inventory

Demonstrated of the use of a computational systems biology approach to model dose response relationships. Also discussed how the biologically motivated dose response models have only limited reference to the underlying molecular level. Discussed the integration of Computational S...

Fast parallel molecular algorithms for DNA-based computation: factoring integers.

PubMed

Chang, Weng-Long; Guo, Minyi; Ho, Michael Shan-Hui

2005-06-01

The RSA public-key cryptosystem is an algorithm that converts input data to an unrecognizable encryption and converts the unrecognizable data back into its original decryption form. The security of the RSA public-key cryptosystem is based on the difficulty of factoring the product of two large prime numbers. This paper demonstrates to factor the product of two large prime numbers, and is a breakthrough in basic biological operations using a molecular computer. In order to achieve this, we propose three DNA-based algorithms for parallel subtractor, parallel comparator, and parallel modular arithmetic that formally verify our designed molecular solutions for factoring the product of two large prime numbers. Furthermore, this work indicates that the cryptosystems using public-key are perhaps insecure and also presents clear evidence of the ability of molecular computing to perform complicated mathematical operations.

Consistent View of Protein Fluctuations from All-Atom Molecular Dynamics and Coarse-Grained Dynamics with Knowledge-Based Force-Field.

PubMed

Jamroz, Michal; Orozco, Modesto; Kolinski, Andrzej; Kmiecik, Sebastian

2013-01-08

It is widely recognized that atomistic Molecular Dynamics (MD), a classical simulation method, captures the essential physics of protein dynamics. That idea is supported by a theoretical study showing that various MD force-fields provide a consensus picture of protein fluctuations in aqueous solution [Rueda, M. et al. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 796-801]. However, atomistic MD cannot be applied to most biologically relevant processes due to its limitation to relatively short time scales. Much longer time scales can be accessed by properly designed coarse-grained models. We demonstrate that the aforementioned consensus view of protein dynamics from short (nanosecond) time scale MD simulations is fairly consistent with the dynamics of the coarse-grained protein model - the CABS model. The CABS model employs stochastic dynamics (a Monte Carlo method) and a knowledge-based force-field, which is not biased toward the native structure of a simulated protein. Since CABS-based dynamics allows for the simulation of entire folding (or multiple folding events) in a single run, integration of the CABS approach with all-atom MD promises a convenient (and computationally feasible) means for the long-time multiscale molecular modeling of protein systems with atomistic resolution.

Targeted Therapy Database (TTD): A Model to Match Patient's Molecular Profile with Current Knowledge on Cancer Biology

PubMed Central

Mocellin, Simone; Shrager, Jeff; Scolyer, Richard; Pasquali, Sandro; Verdi, Daunia; Marincola, Francesco M.; Briarava, Marta; Gobbel, Randy; Rossi, Carlo; Nitti, Donato

2010-01-01

Background The efficacy of current anticancer treatments is far from satisfactory and many patients still die of their disease. A general agreement exists on the urgency of developing molecularly targeted therapies, although their implementation in the clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty of testing each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle. As a consequence, we are witnessing a paradoxical situation where most hypotheses about the molecular and cellular biology of cancer remain clinically untested and therefore do not translate into a therapeutic benefit for patients. Objective To present a computational method aimed to comprehensively exploit the scientific knowledge in order to foster the development of personalized cancer treatment by matching the patient's molecular profile with the available evidence on targeted therapy. Methods To this aim we focused on melanoma, an increasingly diagnosed malignancy for which the need for novel therapeutic approaches is paradigmatic since no effective treatment is available in the advanced setting. Relevant data were manually extracted from peer-reviewed full-text original articles describing any type of anti-melanoma targeted therapy tested in any type of experimental or clinical model. To this purpose, Medline, Embase, Cancerlit and the Cochrane databases were searched. Results and Conclusions We created a manually annotated database (Targeted Therapy Database, TTD) where the relevant data are gathered in a formal representation that can be computationally analyzed. Dedicated algorithms were set up for the identification of the prevalent therapeutic hypotheses based on the available evidence and for ranking treatments based on the molecular profile of individual patients. In this essay we describe the principles and computational algorithms of an original method developed to fully exploit the available knowledge on cancer biology with the ultimate goal of fruitfully driving both preclinical and clinical research on anticancer targeted therapy. In the light of its theoretical nature, the prediction performance of this model must be validated before it can be implemented in the clinical setting. PMID:20706624

Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.

PubMed

Mocellin, Simone; Shrager, Jeff; Scolyer, Richard; Pasquali, Sandro; Verdi, Daunia; Marincola, Francesco M; Briarava, Marta; Gobbel, Randy; Rossi, Carlo; Nitti, Donato

2010-08-10

The efficacy of current anticancer treatments is far from satisfactory and many patients still die of their disease. A general agreement exists on the urgency of developing molecularly targeted therapies, although their implementation in the clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty of testing each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle. As a consequence, we are witnessing a paradoxical situation where most hypotheses about the molecular and cellular biology of cancer remain clinically untested and therefore do not translate into a therapeutic benefit for patients. To present a computational method aimed to comprehensively exploit the scientific knowledge in order to foster the development of personalized cancer treatment by matching the patient's molecular profile with the available evidence on targeted therapy. To this aim we focused on melanoma, an increasingly diagnosed malignancy for which the need for novel therapeutic approaches is paradigmatic since no effective treatment is available in the advanced setting. Relevant data were manually extracted from peer-reviewed full-text original articles describing any type of anti-melanoma targeted therapy tested in any type of experimental or clinical model. To this purpose, Medline, Embase, Cancerlit and the Cochrane databases were searched. We created a manually annotated database (Targeted Therapy Database, TTD) where the relevant data are gathered in a formal representation that can be computationally analyzed. Dedicated algorithms were set up for the identification of the prevalent therapeutic hypotheses based on the available evidence and for ranking treatments based on the molecular profile of individual patients. In this essay we describe the principles and computational algorithms of an original method developed to fully exploit the available knowledge on cancer biology with the ultimate goal of fruitfully driving both preclinical and clinical research on anticancer targeted therapy. In the light of its theoretical nature, the prediction performance of this model must be validated before it can be implemented in the clinical setting.

Radiative Heat Transfer and Turbulence-Radiation Interactions in a Heavy-Duty Diesel Engine

NASA Astrophysics Data System (ADS)

Paul, C.; Sircar, A.; Ferreyro, S.; Imren, A.; Haworth, D. C.; Roy, S.; Ge, W.; Modest, M. F.

2016-11-01

Radiation in piston engines has received relatively little attention to date. Recently, it is being revisited in light of current trends towards higher operating pressures and higher levels of exhaust-gas recirculation, both of which enhance molecular gas radiation. Advanced high-efficiency engines also are expected to function closer to the limits of stable operation, where even small perturbations to the energy balance can have a large influence on system behavior. Here several different spectral radiation property models and radiative transfer equation (RTE) solvers have been implemented in an OpenFOAM-based engine CFD code, and simulations have been performed for a heavy-duty diesel engine. Differences in computed temperature fields, NO and soot levels, and wall heat transfer rates are shown for different combinations of spectral models and RTE solvers. The relative importance of molecular gas radiation versus soot radiation is examined. And the influence of turbulence-radiation interactions is determined by comparing results obtained using local mean values of composition and temperature to compute radiative emission and absorption with those obtained using a particle-based transported probability density function method. DOE, NSF.

Designing Ionic Liquids for CO2 Capture: What’s the role for computation?

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

Brennecke, Joan F.

Presentation on the computational aspects of ionic liquid selection for carbon dioxide capture to the conference attendees at the New Vistas in Molecular Thermodynamics: Experimentation, Molecular Modeling, and Inverse Design, Berkeley, CA, January 7 through 9, 2018

Computational and experimental characterization of a pyrrolidinium-based ionic liquid for electrolyte applications

NASA Astrophysics Data System (ADS)

Torabifard, Hedieh; Reed, Luke; Berry, Matthew T.; Hein, Jason E.; Menke, Erik; Cisneros, G. Andrés

2017-10-01

The development of Li-ion batteries for energy storage has received significant attention. The synthesis and characterization of electrolytes in these batteries are an important component of this development. Ionic liquids (ILs) have been proposed as possible electrolytes in these devices. Thus, the accurate determination of thermophysical properties for these solvents becomes important for determining their applicability as electrolytes. In this contribution, we present the synthesis and experimental/computational characterization of thermodynamic and transport properties of a pyrrolidinium based ionic liquid as a first step to investigate the possible applicability of this class of ILs for Li-ion batteries. A quantum mechanical-based force field with many-body polarizable interactions has been developed for the simulation of spirocyclic pyrrolidinium, [sPyr+], with BF4- and Li+. Molecular dynamics calculations employing intra-molecular polarization predicted larger heat of vaporization and self-diffusion coefficients and smaller densities in comparison with the model without intra-molecular polarization, indicating that the inclusion of this term can significantly effect the inter-ionic interactions. The calculated properties are in good agreement with available experimental data for similar IL pairs and isothermal titration calorimetry data for [sPyr+][BF4-].

Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines.

PubMed

Jamal, Salma; Scaria, Vinod

2014-01-01

Background. Traditional Chinese medicine encompasses a well established alternate system of medicine based on a broad range of herbal formulations and is practiced extensively in the region for the treatment of a wide variety of diseases. In recent years, several reports describe in depth studies of the molecular ingredients of traditional Chinese medicines on the biological activities including anti-bacterial activities. The availability of a well-curated dataset of molecular ingredients of traditional Chinese medicines and accurate in-silico cheminformatics models for data mining for antitubercular agents and computational filters to prioritize molecules has prompted us to search for potential hits from these datasets. Results. We used a consensus approach to predict molecules with potential antitubercular activities from a large dataset of molecular ingredients of traditional Chinese medicines available in the public domain. We further prioritized 160 molecules based on five computational filters (SMARTSfilter) so as to avoid potentially undesirable molecules. We further examined the molecules for permeability across Mycobacterial cell wall and for potential activities against non-replicating and drug tolerant Mycobacteria. Additional in-depth literature surveys for the reported antitubercular activities of the molecular ingredients and their sources were considered for drawing support to prioritization. Conclusions. Our analysis suggests that datasets of molecular ingredients of traditional Chinese medicines offer a new opportunity to mine for potential biological activities. In this report, we suggest a proof-of-concept methodology to prioritize molecules for further experimental assays using a variety of computational tools. We also additionally suggest that a subset of prioritized molecules could be used for evaluation for tuberculosis due to their additional effect against non-replicating tuberculosis as well as the additional hepato-protection offered by the source of these ingredients.

Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines

PubMed Central

Jamal, Salma

2014-01-01

Background. Traditional Chinese medicine encompasses a well established alternate system of medicine based on a broad range of herbal formulations and is practiced extensively in the region for the treatment of a wide variety of diseases. In recent years, several reports describe in depth studies of the molecular ingredients of traditional Chinese medicines on the biological activities including anti-bacterial activities. The availability of a well-curated dataset of molecular ingredients of traditional Chinese medicines and accurate in-silico cheminformatics models for data mining for antitubercular agents and computational filters to prioritize molecules has prompted us to search for potential hits from these datasets. Results. We used a consensus approach to predict molecules with potential antitubercular activities from a large dataset of molecular ingredients of traditional Chinese medicines available in the public domain. We further prioritized 160 molecules based on five computational filters (SMARTSfilter) so as to avoid potentially undesirable molecules. We further examined the molecules for permeability across Mycobacterial cell wall and for potential activities against non-replicating and drug tolerant Mycobacteria. Additional in-depth literature surveys for the reported antitubercular activities of the molecular ingredients and their sources were considered for drawing support to prioritization. Conclusions. Our analysis suggests that datasets of molecular ingredients of traditional Chinese medicines offer a new opportunity to mine for potential biological activities. In this report, we suggest a proof-of-concept methodology to prioritize molecules for further experimental assays using a variety of computational tools. We also additionally suggest that a subset of prioritized molecules could be used for evaluation for tuberculosis due to their additional effect against non-replicating tuberculosis as well as the additional hepato-protection offered by the source of these ingredients. PMID:25081126

Molecular modeling of the microstructure evolution during carbon fiber processing

NASA Astrophysics Data System (ADS)

Desai, Saaketh; Li, Chunyu; Shen, Tongtong; Strachan, Alejandro

2017-12-01

The rational design of carbon fibers with desired properties requires quantitative relationships between the processing conditions, microstructure, and resulting properties. We developed a molecular model that combines kinetic Monte Carlo and molecular dynamics techniques to predict the microstructure evolution during the processes of carbonization and graphitization of polyacrylonitrile (PAN)-based carbon fibers. The model accurately predicts the cross-sectional microstructure of the fibers with the molecular structure of the stabilized PAN fibers and physics-based chemical reaction rates as the only inputs. The resulting structures exhibit key features observed in electron microcopy studies such as curved graphitic sheets and hairpin structures. In addition, computed X-ray diffraction patterns are in good agreement with experiments. We predict the transverse moduli of the resulting fibers between 1 GPa and 5 GPa, in good agreement with experimental results for high modulus fibers and slightly lower than those of high-strength fibers. The transverse modulus is governed by sliding between graphitic sheets, and the relatively low value for the predicted microstructures can be attributed to their perfect longitudinal texture. Finally, the simulations provide insight into the relationships between chemical kinetics and the final microstructure; we observe that high reaction rates result in porous structures with lower moduli.

Particle-Based Methods for Multiscale Modeling of Blood Flow in the Circulation and in Devices: Challenges and Future Directions

PubMed Central

Yamaguchi, Takami; Ishikawa, Takuji; Imai, Y.; Matsuki, N.; Xenos, Mikhail; Deng, Yuefan; Bluestein, Danny

2010-01-01

A major computational challenge for a multiscale modeling is the coupling of disparate length and timescales between molecular mechanics and macroscopic transport, spanning the spatial and temporal scales characterizing the complex processes taking place in flow-induced blood clotting. Flow and pressure effects on a cell-like platelet can be well represented by a continuum mechanics model down to the order of the micrometer level. However, the molecular effects of adhesion/aggregation bonds are on the order of nanometer. A successful multiscale model of platelet response to flow stresses in devices and the ensuing clotting responses should be able to characterize the clotting reactions and their interactions with the flow. This paper attempts to describe a few of the computational methods that were developed in recent years and became available to researchers in the field. They differ from traditional approaches that dominate the field by expanding on prevailing continuum-based approaches, or by completely departing from them, yielding an expanding toolkit that may facilitate further elucidation of the underlying mechanisms of blood flow and the cellular response to it. We offer a paradigm shift by adopting a multidisciplinary approach with fluid dynamics simulations coupled to biophysical and biochemical transport. PMID:20336827

Protein engineering and the use of molecular modeling and simulation: the case of heterodimeric Fc engineering.

PubMed

Spreter Von Kreudenstein, Thomas; Lario, Paula I; Dixit, Surjit B

2014-01-01

Computational and structure guided methods can make significant contributions to the development of solutions for difficult protein engineering problems, including the optimization of next generation of engineered antibodies. In this paper, we describe a contemporary industrial antibody engineering program, based on hypothesis-driven in silico protein optimization method. The foundational concepts and methods of computational protein engineering are discussed, and an example of a computational modeling and structure-guided protein engineering workflow is provided for the design of best-in-class heterodimeric Fc with high purity and favorable biophysical properties. We present the engineering rationale as well as structural and functional characterization data on these engineered designs. Copyright © 2013 Elsevier Inc. All rights reserved.

A spatially localized architecture for fast and modular DNA computing

NASA Astrophysics Data System (ADS)

Chatterjee, Gourab; Dalchau, Neil; Muscat, Richard A.; Phillips, Andrew; Seelig, Georg

2017-09-01

Cells use spatial constraints to control and accelerate the flow of information in enzyme cascades and signalling networks. Synthetic silicon-based circuitry similarly relies on spatial constraints to process information. Here, we show that spatial organization can be a similarly powerful design principle for overcoming limitations of speed and modularity in engineered molecular circuits. We create logic gates and signal transmission lines by spatially arranging reactive DNA hairpins on a DNA origami. Signal propagation is demonstrated across transmission lines of different lengths and orientations and logic gates are modularly combined into circuits that establish the universality of our approach. Because reactions preferentially occur between neighbours, identical DNA hairpins can be reused across circuits. Co-localization of circuit elements decreases computation time from hours to minutes compared to circuits with diffusible components. Detailed computational models enable predictive circuit design. We anticipate our approach will motivate using spatial constraints for future molecular control circuit designs.

Estimating Grass-Soil Bioconcentration of Munitions Compounds from Molecular Structure.

PubMed

Torralba Sanchez, Tifany L; Liang, Yuzhen; Di Toro, Dominic M

2017-10-03

A partitioning-based model is presented to estimate the bioconcentration of five munitions compounds and two munition-like compounds in grasses. The model uses polyparameter linear free energy relationships (pp-LFERs) to estimate the partition coefficients between soil organic carbon and interstitial water and between interstitial water and the plant cuticle, a lipid-like plant component. Inputs for the pp-LFERs are a set of numerical descriptors computed from molecular structure only that characterize the molecular properties that determine the interaction with soil organic carbon, interstitial water, and plant cuticle. The model is validated by predicting concentrations measured in the whole plant during independent uptake experiments with a root-mean-square error (log predicted plant concentration-log observed plant concentration) of 0.429. This highlights the dominant role of partitioning between the exposure medium and the plant cuticle in the bioconcentration of these compounds. The pp-LFERs can be used to assess the environmental risk of munitions compounds and munition-like compounds using only their molecular structure as input.

Case Study: Organotypic human in vitro models of embryonic ...

EPA Pesticide Factsheets

Morphogenetic fusion of tissues is a common event in embryonic development and disruption of fusion is associated with birth defects of the eye, heart, neural tube, phallus, palate, and other organ systems. Embryonic tissue fusion requires precise regulation of cell-cell and cell-matrix interactions that drive proliferation, differentiation, and morphogenesis. Chemical low-dose exposures can disrupt morphogenesis across space and time by interfering with key embryonic fusion events. The Morphogenetic Fusion Task uses computer and in vitro models to elucidate consequences of developmental exposures. The Morphogenetic Fusion Task integrates multiple approaches to model responses to chemicals that leaad to birth defects, including integrative mining on ToxCast DB, ToxRefDB, and chemical structures, advanced computer agent-based models, and human cell-based cultures that model disruption of cellular and molecular behaviors including mechanisms predicted from integrative data mining and agent-based models. The purpose of the poster is to indicate progress on the CSS 17.02 Virtual Tissue Models Morphogenesis Task 1 products for the Board of Scientific Counselors meeting on Nov 16-17.

A Method for Combining Experimentation and Molecular Dynamics Simulation to Improve Cohesive Zone Models for Metallic Microstructures

NASA Technical Reports Server (NTRS)

Hochhalter, J. D.; Glaessgen, E. H.; Ingraffea, A. R.; Aquino, W. A.

2009-01-01

Fracture processes within a material begin at the nanometer length scale at which the formation, propagation, and interaction of fundamental damage mechanisms occur. Physics-based modeling of these atomic processes quickly becomes computationally intractable as the system size increases. Thus, a multiscale modeling method, based on the aggregation of fundamental damage processes occurring at the nanoscale within a cohesive zone model, is under development and will enable computationally feasible and physically meaningful microscale fracture simulation in polycrystalline metals. This method employs atomistic simulation to provide an optimization loop with an initial prediction of a cohesive zone model (CZM). This initial CZM is then applied at the crack front region within a finite element model. The optimization procedure iterates upon the CZM until the finite element model acceptably reproduces the near-crack-front displacement fields obtained from experimental observation. With this approach, a comparison can be made between the original CZM predicted by atomistic simulation and the converged CZM that is based on experimental observation. Comparison of the two CZMs gives insight into how atomistic simulation scales.

Computational studies of Ras and PI3K

NASA Technical Reports Server (NTRS)

Ren, Lei; Cucinotta, Francis A.

2004-01-01

Until recently, experimental techniques in molecular cell biology have been the primary means to investigate biological risk upon space radiation. However, computational modeling provides an alternative theoretical approach, which utilizes various computational tools to simulate proteins, nucleotides, and their interactions. In this study, we are focused on using molecular mechanics (MM) and molecular dynamics (MD) to study the mechanism of protein-protein binding and to estimate the binding free energy between proteins. Ras is a key element in a variety of cell processes, and its activation of phosphoinositide 3-kinase (PI3K) is important for survival of transformed cells. Different computational approaches for this particular study are presented to calculate the solvation energies and binding free energies of H-Ras and PI3K. The goal of this study is to establish computational methods to investigate the roles of different proteins played in the cellular responses to space radiation, including modification of protein function through gene mutation, and to support the studies in molecular cell biology and theoretical kinetics models for our risk assessment project.

Molecular beam mass spectrometer development

NASA Technical Reports Server (NTRS)

Brock, F. J.; Hueser, J. E.

1976-01-01

An analytical model, based on the kinetics theory of a drifting Maxwellian gas is used to determine the nonequilibrium molecular density distribution within a hemispherical shell open aft with its axis parallel to its velocity. The concept of a molecular shield in terrestrial orbit above 200 km is also analyzed using the kinetic theory of a drifting Maxwellian gas. Data are presented for the components of the gas density within the shield due to the free stream atmosphere, outgassing from the shield and enclosed experiments, and atmospheric gas scattered off a shield orbiter system. A description is given of a FORTRAN program for computating the three dimensional transition flow regime past the space shuttle orbiter that employs the Monte Carlo simulation method to model real flow by some thousands of simulated molecules.

Development of computational small animal models and their applications in preclinical imaging and therapy research.

PubMed

Xie, Tianwu; Zaidi, Habib

2016-01-01

The development of multimodality preclinical imaging techniques and the rapid growth of realistic computer simulation tools have promoted the construction and application of computational laboratory animal models in preclinical research. Since the early 1990s, over 120 realistic computational animal models have been reported in the literature and used as surrogates to characterize the anatomy of actual animals for the simulation of preclinical studies involving the use of bioluminescence tomography, fluorescence molecular tomography, positron emission tomography, single-photon emission computed tomography, microcomputed tomography, magnetic resonance imaging, and optical imaging. Other applications include electromagnetic field simulation, ionizing and nonionizing radiation dosimetry, and the development and evaluation of new methodologies for multimodality image coregistration, segmentation, and reconstruction of small animal images. This paper provides a comprehensive review of the history and fundamental technologies used for the development of computational small animal models with a particular focus on their application in preclinical imaging as well as nonionizing and ionizing radiation dosimetry calculations. An overview of the overall process involved in the design of these models, including the fundamental elements used for the construction of different types of computational models, the identification of original anatomical data, the simulation tools used for solving various computational problems, and the applications of computational animal models in preclinical research. The authors also analyze the characteristics of categories of computational models (stylized, voxel-based, and boundary representation) and discuss the technical challenges faced at the present time as well as research needs in the future.

In silico design of anti-atherogenic biomaterials.

PubMed

Lewis, Daniel R; Kholodovych, Vladyslav; Tomasini, Michael D; Abdelhamid, Dalia; Petersen, Latrisha K; Welsh, William J; Uhrich, Kathryn E; Moghe, Prabhas V

2013-10-01

Atherogenesis, the uncontrolled deposition of modified lipoproteins in inflamed arteries, serves as a focal trigger of cardiovascular disease (CVD). Polymeric biomaterials have been envisioned to counteract atherogenesis based on their ability to repress scavenger mediated uptake of oxidized lipoprotein (oxLDL) in macrophages. Following the conceptualization in our laboratories of a new library of amphiphilic macromolecules (AMs), assembled from sugar backbones, aliphatic chains and poly(ethylene glycol) tails, a more rational approach is necessary to parse the diverse features such as charge, hydrophobicity, sugar composition and stereochemistry. In this study, we advance a computational biomaterials design approach to screen and elucidate anti-atherogenic biomaterials with high efficacy. AMs were quantified in terms of not only 1D (molecular formula) and 2D (molecular connectivity) descriptors, but also new 3D (molecular geometry) descriptors of AMs modeled by coarse-grained molecular dynamics (MD) followed by all-atom MD simulations. Quantitative structure-activity relationship (QSAR) models for anti-atherogenic activity were then constructed by screening a total of 1164 descriptors against the corresponding, experimentally measured potency of AM inhibition of oxLDL uptake in human monocyte-derived macrophages. Five key descriptors were identified to provide a strong linear correlation between the predicted and observed anti-atherogenic activity values, and were then used to correctly forecast the efficacy of three newly designed AMs. Thus, a new ligand-based drug design framework was successfully adapted to computationally screen and design biomaterials with cardiovascular therapeutic properties. Copyright © 2013 Elsevier Ltd. All rights reserved.

Elucidation of the Chromatographic Enantiomer Elution Order Through Computational Studies.

PubMed

Sardella, Roccaldo; Ianni, Federica; Macchiarulo, Antonio; Pucciarini, Lucia; Carotti, Andrea; Natalini, Benedetto

2018-01-01

During the last twenty years, the interest towards the development of chiral compound has exponentially been increased. Indeed, the set-up of suitable asymmetric enantioselective synthesis protocols is currently one of the focuses of many pharmaceutical research projects. In this scenario, chiral HPLC separations have gained great importance as well, both for analytical- and preparative-scale applications, the latter devoted to the quantitative isolation of enantiopure compounds. Molecular modelling and quantum chemistry methods can be fruitfully applied to solve chirality related problems especially when enantiomerically pure reference standards are missing. In this framework, with the aim to explain the molecular basis of the enantioselective retention, we performed computational studies to rationalize the enantiomer elution order with both low- and high-molecular weight chiral selectors. Semi-empirical and quantum mechanical computational procedures were successfully applied in the domains of chiral ligand-exchange and chiral ion-exchange chromatography, as well as in studies dealing with the use of polysaccharide-based enantioresolving materials. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

The Secondary Organic Aerosol Processor (SOAP v1.0) model: a unified model with different ranges of complexity based on the molecular surrogate approach

NASA Astrophysics Data System (ADS)

Couvidat, F.; Sartelet, K.

2014-01-01

The Secondary Organic Aerosol Processor (SOAP v1.0) model is presented. This model is designed to be modular with different user options depending on the computing time and the complexity required by the user. This model is based on the molecular surrogate approach, in which each surrogate compound is associated with a molecular structure to estimate some properties and parameters (hygroscopicity, absorption on the aqueous phase of particles, activity coefficients, phase separation). Each surrogate can be hydrophilic (condenses only on the aqueous phase of particles), hydrophobic (condenses only on the organic phase of particles) or both (condenses on both the aqueous and the organic phases of particles). Activity coefficients are computed with the UNIFAC thermodynamic model for short-range interactions and with the AIOMFAC parameterization for medium and long-range interactions between electrolytes and organic compounds. Phase separation is determined by Gibbs energy minimization. The user can choose between an equilibrium and a dynamic representation of the organic aerosol. In the equilibrium representation, compounds in the particle phase are assumed to be at equilibrium with the gas phase. However, recent studies show that the organic aerosol (OA) is not at equilibrium with the gas phase because the organic phase could be semi-solid (very viscous liquid phase). The condensation or evaporation of organic compounds could then be limited by the diffusion in the organic phase due to the high viscosity. A dynamic representation of secondary organic aerosols (SOA) is used with OA divided into layers, the first layer at the center of the particle (slowly reaches equilibrium) and the final layer near the interface with the gas phase (quickly reaches equilibrium).

The Computer Simulation of Liquids by Molecular Dynamics.

ERIC Educational Resources Information Center

Smith, W.

1987-01-01

Proposes a mathematical computer model for the behavior of liquids using the classical dynamic principles of Sir Isaac Newton and the molecular dynamics method invented by other scientists. Concludes that other applications will be successful using supercomputers to go beyond simple Newtonian physics. (CW)

Molecular Isotopic Distribution Analysis (MIDAs) with Adjustable Mass Accuracy

NASA Astrophysics Data System (ADS)

Alves, Gelio; Ogurtsov, Aleksey Y.; Yu, Yi-Kuo

2014-01-01

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/midas/index.html.

Molecular Isotopic Distribution Analysis (MIDAs) with adjustable mass accuracy.

PubMed

Alves, Gelio; Ogurtsov, Aleksey Y; Yu, Yi-Kuo

2014-01-01

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/midas/index.html.

A study of some non-equilibrium driven models and their contribution to the understanding of molecular motors

NASA Astrophysics Data System (ADS)

Mazilu, Irina; Gonzalez, Joshua

2008-03-01

From the point of view of a physicist, a bio-molecular motor represents an interesting non-equilibrium system and it is directly amenable to an analysis using standard methods of non-equilibrium statistical physics. We conduct a rigorous Monte Carlo study of three different driven lattice gas models that retain the basic behavior of three types of cytoskeletal molecular motors. Our models incorporate novel features such as realistic dynamics rules and complex motor-motor interactions. We are interested to have a deeper understanding of how various parameters influence the macroscopic behavior of these systems, what is the density profile and if the system undergoes a phase transition. On the analytical front, we computed the steady-state probability distributions exactly for the one of the models using the matrix method that was established in 1993 by B. Derrida et al. We also explored the possibilities offered by the ``Bethe ansatz'' method by mapping some well studied spin models into asymmetric simple exclusion models (already analyzed using computer simulations), and to use the results obtained for the spin models in finding an exact solution for our problem. We have exhaustive computational studies of the kinesin and dynein molecular motor models that prove to be very useful in checking our analytical work.

A Self-Assisting Protein Folding Model for Teaching Structural Molecular Biology.

PubMed

Davenport, Jodi; Pique, Michael; Getzoff, Elizabeth; Huntoon, Jon; Gardner, Adam; Olson, Arthur

2017-04-04

Structural molecular biology is now becoming part of high school science curriculum thus posing a challenge for teachers who need to convey three-dimensional (3D) structures with conventional text and pictures. In many cases even interactive computer graphics does not go far enough to address these challenges. We have developed a flexible model of the polypeptide backbone using 3D printing technology. With this model we have produced a polypeptide assembly kit to create an idealized model of the Triosephosphate isomerase mutase enzyme (TIM), which forms a structure known as TIM barrel. This kit has been used in a laboratory practical where students perform a step-by-step investigation into the nature of protein folding, starting with the handedness of amino acids to the formation of secondary and tertiary structure. Based on the classroom evidence we collected, we conclude that these models are valuable and inexpensive resource for teaching structural molecular biology. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multiscale geometric modeling of macromolecules I: Cartesian representation

NASA Astrophysics Data System (ADS)

Xia, Kelin; Feng, Xin; Chen, Zhan; Tong, Yiying; Wei, Guo-Wei

2014-01-01

This paper focuses on the geometric modeling and computational algorithm development of biomolecular structures from two data sources: Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in the Eulerian (or Cartesian) representation. Molecular surface (MS) contains non-smooth geometric singularities, such as cusps, tips and self-intersecting facets, which often lead to computational instabilities in molecular simulations, and violate the physical principle of surface free energy minimization. Variational multiscale surface definitions are proposed based on geometric flows and solvation analysis of biomolecular systems. Our approach leads to geometric and potential driven Laplace-Beltrami flows for biomolecular surface evolution and formation. The resulting surfaces are free of geometric singularities and minimize the total free energy of the biomolecular system. High order partial differential equation (PDE)-based nonlinear filters are employed for EMDB data processing. We show the efficacy of this approach in feature-preserving noise reduction. After the construction of protein multiresolution surfaces, we explore the analysis and characterization of surface morphology by using a variety of curvature definitions. Apart from the classical Gaussian curvature and mean curvature, maximum curvature, minimum curvature, shape index, and curvedness are also applied to macromolecular surface analysis for the first time. Our curvature analysis is uniquely coupled to the analysis of electrostatic surface potential, which is a by-product of our variational multiscale solvation models. As an expository investigation, we particularly emphasize the numerical algorithms and computational protocols for practical applications of the above multiscale geometric models. Such information may otherwise be scattered over the vast literature on this topic. Based on the curvature and electrostatic analysis from our multiresolution surfaces, we introduce a new concept, the polarized curvature, for the prediction of protein binding sites.

Protein Modelling: What Happened to the “Protein Structure Gap”?

PubMed Central

Schwede, Torsten

2013-01-01

Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing vision in structural biology as it holds the promise to bypass part of the laborious process of experimental structure solution. Over the last two decades, a paradigm shift has occurred: starting from a situation where the “structure knowledge gap” between the huge number of protein sequences and small number of known structures has hampered the widespread use of structure-based approaches in life science research, today some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. With the scientific focus of interest moving towards larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows studying large and complex molecular machines. Computational modeling and prediction techniques are still facing a number of challenges which hamper the more widespread use by the non-expert scientist. For example, it is often difficult to convey the underlying assumptions of a computational technique, as well as the expected accuracy and structural variability of a specific model. However, these aspects are crucial to understand the limitations of a model, and to decide which interpretations and conclusions can be supported. PMID:24010712

Rational design of liposomal drug delivery systems, a review: Combined experimental and computational studies of lipid membranes, liposomes and their PEGylation.

PubMed

Bunker, Alex; Magarkar, Aniket; Viitala, Tapani

2016-10-01

Combined experimental and computational studies of lipid membranes and liposomes, with the aim to attain mechanistic understanding, result in a synergy that makes possible the rational design of liposomal drug delivery system (LDS) based therapies. The LDS is the leading form of nanoscale drug delivery platform, an avenue in drug research, known as "nanomedicine", that holds the promise to transcend the current paradigm of drug development that has led to diminishing returns. Unfortunately this field of research has, so far, been far more successful in generating publications than new drug therapies. This partly results from the trial and error based methodologies used. We discuss experimental techniques capable of obtaining mechanistic insight into LDS structure and behavior. Insight obtained purely experimentally is, however, limited; computational modeling using molecular dynamics simulation can provide insight not otherwise available. We review computational research, that makes use of the multiscale modeling paradigm, simulating the phospholipid membrane with all atom resolution and the entire liposome with coarse grained models. We discuss in greater detail the computational modeling of liposome PEGylation. Overall, we wish to convey the power that lies in the combined use of experimental and computational methodologies; we hope to provide a roadmap for the rational design of LDS based therapies. Computational modeling is able to provide mechanistic insight that explains the context of experimental results and can also take the lead and inspire new directions for experimental research into LDS development. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg. Copyright © 2016 Elsevier B.V. All rights reserved.

Polymerization and Structure of Bio-Based Plastics: A Computer Simulation

NASA Astrophysics Data System (ADS)

Khot, Shrikant N.; Wool, Richard P.

2001-03-01

We recently examined several hundred chemical pathways to convert chemically functionalized plant oil triglycerides, monoglycerides and reactive diluents into high performance plastics with a broad range of properties (US Patent No. 6,121,398). The resulting polymers had linear, branched, light- and highly-crosslinked chain architectures and could be used as pressure sensitive adhesives, elastomers and high performance rigid thermoset composite resins. To optimize the molecular design and minimize the number of chemical trials in this system with excess degrees of freedom, we developed a computer simulation of the free radical polymerization process. The triglyceride structure, degree of chemical substitution, mole fractions, fatty acid distribution function, and reaction kinetic parameters were used as initial inputs on a 3d lattice simulation. The evolution of the network fractal structure was computed and used to measure crosslink density, dangling ends, degree of reaction and defects in the lattice. The molecular connectivity was used to determine strength via a vector percolation model of fracture. The simulation permitted the optimal design of new bio-based materials with respect to monomer selection, cure reaction conditions and desired properties. Supported by the National Science Foundation

Interactive display of molecular models using a microcomputer system

NASA Technical Reports Server (NTRS)

Egan, J. T.; Macelroy, R. D.

1980-01-01

A simple, microcomputer-based, interactive graphics display system has been developed for the presentation of perspective views of wire frame molecular models. The display system is based on a TERAK 8510a graphics computer system with a display unit consisting of microprocessor, television display and keyboard subsystems. The operating system includes a screen editor, file manager, PASCAL and BASIC compilers and command options for linking and executing programs. The graphics program, written in USCD PASCAL, involves the centering of the coordinate system, the transformation of centered model coordinates into homogeneous coordinates, the construction of a viewing transformation matrix to operate on the coordinates, clipping invisible points, perspective transformation and scaling to screen coordinates; commands available include ZOOM, ROTATE, RESET, and CHANGEVIEW. Data file structure was chosen to minimize the amount of disk storage space. Despite the inherent slowness of the system, its low cost and flexibility suggests general applicability.

Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Building better water models using the shape of the charge distribution of a water molecule

NASA Astrophysics Data System (ADS)

Dharmawardhana, Chamila Chathuranga; Ichiye, Toshiko

2017-11-01

The unique properties of liquid water apparently arise from more than just the tetrahedral bond angle between the nuclei of a water molecule since simple three-site models of water are poor at mimicking these properties in computer simulations. Four- and five-site models add partial charges on dummy sites and are better at modeling these properties, which suggests that the shape of charge distribution is important. Since a multipole expansion of the electrostatic potential describes a charge distribution in an orthogonal basis set that is exact in the limit of infinite order, multipoles may be an even better way to model the charge distribution. In particular, molecular multipoles up to the octupole centered on the oxygen appear to describe the electrostatic potential from electronic structure calculations better than four- and five-site models, and molecular multipole models give better agreement with the temperature and pressure dependence of many liquid state properties of water while retaining the computational efficiency of three-site models. Here, the influence of the shape of the molecular charge distribution on liquid state properties is examined by correlating multipoles of non-polarizable water models with their liquid state properties in computer simulations. This will aid in the development of accurate water models for classical simulations as well as in determining the accuracy needed in quantum mechanical/molecular mechanical studies and ab initio molecular dynamics simulations of water. More fundamentally, this will lead to a greater understanding of how the charge distribution of a water molecule leads to the unique properties of liquid water. In particular, these studies indicate that p-orbital charge out of the molecular plane is important.

Nonequilibrium Green's function theory for nonadiabatic effects in quantum electron transport

NASA Astrophysics Data System (ADS)

Kershaw, Vincent F.; Kosov, Daniel S.

2017-12-01

We develop nonequilibrium Green's function-based transport theory, which includes effects of nonadiabatic nuclear motion in the calculation of the electric current in molecular junctions. Our approach is based on the separation of slow and fast time scales in the equations of motion for Green's functions by means of the Wigner representation. Time derivatives with respect to central time serve as a small parameter in the perturbative expansion enabling the computation of nonadiabatic corrections to molecular Green's functions. Consequently, we produce a series of analytic expressions for non-adiabatic electronic Green's functions (up to the second order in the central time derivatives), which depend not solely on the instantaneous molecular geometry but likewise on nuclear velocities and accelerations. An extended formula for electric current is derived which accounts for the non-adiabatic corrections. This theory is concisely illustrated by the calculations on a model molecular junction.

Nonequilibrium Green's function theory for nonadiabatic effects in quantum electron transport.

PubMed

Kershaw, Vincent F; Kosov, Daniel S

2017-12-14

We develop nonequilibrium Green's function-based transport theory, which includes effects of nonadiabatic nuclear motion in the calculation of the electric current in molecular junctions. Our approach is based on the separation of slow and fast time scales in the equations of motion for Green's functions by means of the Wigner representation. Time derivatives with respect to central time serve as a small parameter in the perturbative expansion enabling the computation of nonadiabatic corrections to molecular Green's functions. Consequently, we produce a series of analytic expressions for non-adiabatic electronic Green's functions (up to the second order in the central time derivatives), which depend not solely on the instantaneous molecular geometry but likewise on nuclear velocities and accelerations. An extended formula for electric current is derived which accounts for the non-adiabatic corrections. This theory is concisely illustrated by the calculations on a model molecular junction.

Atomistic model of the spider silk nanostructure

NASA Astrophysics Data System (ADS)

Keten, Sinan; Buehler, Markus J.

2010-04-01

Spider silk is an ultrastrong and extensible self-assembling biopolymer that outperforms the mechanical characteristics of many synthetic materials including steel. Here we report atomic-level structures that represent aggregates of MaSp1 proteins from the N. Clavipes silk sequence based on a bottom-up computational approach using replica exchange molecular dynamics. We discover that poly-alanine regions predominantly form distinct and orderly beta-sheet crystal domains while disorderly structures are formed by poly-glycine repeats, resembling 31-helices. These could be the molecular source of the large semicrystalline fraction observed in silks, and also form the basis of the so-called "prestretched" molecular configuration. Our structures are validated against experimental data based on dihedral angle pair calculations presented in Ramachandran plots, alpha-carbon atomic distances, as well as secondary structure content.

Crystallization of isotactic polypropylene in different shear regimes

NASA Astrophysics Data System (ADS)

Spina, Roberto; Spekowius, Marcel; Hopmann, Christian

2017-10-01

The investigation of the shear-induced crystallization of isotactic polypropylene in isothermal conditions in different shear regimes is the aim of the present research. A multiscale framework is developed and implemented to compute the nucleation and growth of spherulites, based on material parameters needed to connect crystallization kinetics to the molecular material properties. The framework consists of a macro-model based on a Finite Element Method linked to a micro-model based on Cellular Automata. The main results are the evolution of the crystallization degree and spherulite space filling as a function of imposed temperature ash shear rate.

Quantum mechanics/coarse-grained molecular mechanics (QM/CG-MM)

NASA Astrophysics Data System (ADS)

Sinitskiy, Anton V.; Voth, Gregory A.

2018-01-01

Numerous molecular systems, including solutions, proteins, and composite materials, can be modeled using mixed-resolution representations, of which the quantum mechanics/molecular mechanics (QM/MM) approach has become the most widely used. However, the QM/MM approach often faces a number of challenges, including the high cost of repetitive QM computations, the slow sampling even for the MM part in those cases where a system under investigation has a complex dynamics, and a difficulty in providing a simple, qualitative interpretation of numerical results in terms of the influence of the molecular environment upon the active QM region. In this paper, we address these issues by combining QM/MM modeling with the methodology of "bottom-up" coarse-graining (CG) to provide the theoretical basis for a systematic quantum-mechanical/coarse-grained molecular mechanics (QM/CG-MM) mixed resolution approach. A derivation of the method is presented based on a combination of statistical mechanics and quantum mechanics, leading to an equation for the effective Hamiltonian of the QM part, a central concept in the QM/CG-MM theory. A detailed analysis of different contributions to the effective Hamiltonian from electrostatic, induction, dispersion, and exchange interactions between the QM part and the surroundings is provided, serving as a foundation for a potential hierarchy of QM/CG-MM methods varying in their accuracy and computational cost. A relationship of the QM/CG-MM methodology to other mixed resolution approaches is also discussed.

Quantum mechanics/coarse-grained molecular mechanics (QM/CG-MM).

PubMed

Sinitskiy, Anton V; Voth, Gregory A

2018-01-07

Numerous molecular systems, including solutions, proteins, and composite materials, can be modeled using mixed-resolution representations, of which the quantum mechanics/molecular mechanics (QM/MM) approach has become the most widely used. However, the QM/MM approach often faces a number of challenges, including the high cost of repetitive QM computations, the slow sampling even for the MM part in those cases where a system under investigation has a complex dynamics, and a difficulty in providing a simple, qualitative interpretation of numerical results in terms of the influence of the molecular environment upon the active QM region. In this paper, we address these issues by combining QM/MM modeling with the methodology of "bottom-up" coarse-graining (CG) to provide the theoretical basis for a systematic quantum-mechanical/coarse-grained molecular mechanics (QM/CG-MM) mixed resolution approach. A derivation of the method is presented based on a combination of statistical mechanics and quantum mechanics, leading to an equation for the effective Hamiltonian of the QM part, a central concept in the QM/CG-MM theory. A detailed analysis of different contributions to the effective Hamiltonian from electrostatic, induction, dispersion, and exchange interactions between the QM part and the surroundings is provided, serving as a foundation for a potential hierarchy of QM/CG-MM methods varying in their accuracy and computational cost. A relationship of the QM/CG-MM methodology to other mixed resolution approaches is also discussed.

The Renormalization Group and Its Applications to Generating Coarse-Grained Models of Large Biological Molecular Systems.

PubMed

Koehl, Patrice; Poitevin, Frédéric; Navaza, Rafael; Delarue, Marc

2017-03-14

Understanding the dynamics of biomolecules is the key to understanding their biological activities. Computational methods ranging from all-atom molecular dynamics simulations to coarse-grained normal-mode analyses based on simplified elastic networks provide a general framework to studying these dynamics. Despite recent successes in studying very large systems with up to a 100,000,000 atoms, those methods are currently limited to studying small- to medium-sized molecular systems due to computational limitations. One solution to circumvent these limitations is to reduce the size of the system under study. In this paper, we argue that coarse-graining, the standard approach to such size reduction, must define a hierarchy of models of decreasing sizes that are consistent with each other, i.e., that each model contains the information of the dynamics of its predecessor. We propose a new method, Decimate, for generating such a hierarchy within the context of elastic networks for normal-mode analysis. This method is based on the concept of the renormalization group developed in statistical physics. We highlight the details of its implementation, with a special focus on its scalability to large systems of up to millions of atoms. We illustrate its application on two large systems, the capsid of a virus and the ribosome translation complex. We show that highly decimated representations of those systems, containing down to 1% of their original number of atoms, still capture qualitatively and quantitatively their dynamics. Decimate is available as an OpenSource resource.

Quantum probability ranking principle for ligand-based virtual screening.

PubMed

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Quantum probability ranking principle for ligand-based virtual screening

NASA Astrophysics Data System (ADS)

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations.

PubMed

Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

2015-07-01

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310-323. doi: 10.1002/wcms.1220.

jAMVLE, a New Integrated Molecular Visualization Learning Environment

ERIC Educational Resources Information Center

Bottomley, Steven; Chandler, David; Morgan, Eleanor; Helmerhorst, Erik

2006-01-01

A new computer-based molecular visualization tool has been developed for teaching, and learning, molecular structure. This java-based jmol Amalgamated Molecular Visualization Learning Environment (jAMVLE) is platform-independent, integrated, and interactive. It has an overall graphical user interface that is intuitive and easy to use. The…

Solvation Structure and Thermodynamic Mapping (SSTMap): An Open-Source, Flexible Package for the Analysis of Water in Molecular Dynamics Trajectories.

PubMed

Haider, Kamran; Cruz, Anthony; Ramsey, Steven; Gilson, Michael K; Kurtzman, Tom

2018-01-09

We have developed SSTMap, a software package for mapping structural and thermodynamic water properties in molecular dynamics trajectories. The package introduces automated analysis and mapping of local measures of frustration and enhancement of water structure. The thermodynamic calculations are based on Inhomogeneous Fluid Solvation Theory (IST), which is implemented using both site-based and grid-based approaches. The package also extends the applicability of solvation analysis calculations to multiple molecular dynamics (MD) simulation programs by using existing cross-platform tools for parsing MD parameter and trajectory files. SSTMap is implemented in Python and contains both command-line tools and a Python module to facilitate flexibility in setting up calculations and for automated generation of large data sets involving analysis of multiple solutes. Output is generated in formats compatible with popular Python data science packages. This tool will be used by the molecular modeling community for computational analysis of water in problems of biophysical interest such as ligand binding and protein function.

Molecular dynamics-based refinement and validation for sub-5 Å cryo-electron microscopy maps.

PubMed

Singharoy, Abhishek; Teo, Ivan; McGreevy, Ryan; Stone, John E; Zhao, Jianhua; Schulten, Klaus

2016-07-07

Two structure determination methods, based on the molecular dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5 Å cryo-electron microscopy (EM) maps with either single structures or ensembles of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution. Application of sequential re-refinement enables MDFF to achieve a radius of convergence of ~25 Å demonstrated with the accurate modeling of β-galactosidase and TRPV1 proteins at 3.2 Å and 3.4 Å resolution, respectively. The MDFF refinements uniquely offer map-model validation and B-factor determination criteria based on the inherent dynamics of the macromolecules studied, captured by means of local root mean square fluctuations. The MDFF tools described are available to researchers through an easy-to-use and cost-effective cloud computing resource on Amazon Web Services.

General molecular mechanics method for transition metal carboxylates and its application to the multiple coordination modes in mono- and dinuclear Mn(II) complexes.

PubMed

Deeth, Robert J

2008-08-04

A general molecular mechanics method is presented for modeling the symmetric bidentate, asymmetric bidentate, and bridging modes of metal-carboxylates with a single parameter set by using a double-minimum M-O-C angle-bending potential. The method is implemented within the Molecular Operating Environment (MOE) with parameters based on the Merck molecular force field although, with suitable modifications, other MM packages and force fields could easily be used. Parameters for high-spin d (5) manganese(II) bound to carboxylate and water plus amine, pyridyl, imidazolyl, and pyrazolyl donors are developed based on 26 mononuclear and 29 dinuclear crystallographically characterized complexes. The average rmsd for Mn-L distances is 0.08 A, which is comparable to the experimental uncertainty required to cover multiple binding modes, and the average rmsd in heavy atom positions is around 0.5 A. In all cases, whatever binding mode is reported is also computed to be a stable local minimum. In addition, the structure-based parametrization implicitly captures the energetics and gives the same relative energies of symmetric and asymmetric coordination modes as density functional theory calculations in model and "real" complexes. Molecular dynamics simulations show that carboxylate rotation is favored over "flipping" while a stochastic search algorithm is described for randomly searching conformational space. The model reproduces Mn-Mn distances in dinuclear systems especially accurately, and this feature is employed to illustrate how MM calculations on models for the dimanganese active site of methionine aminopeptidase can help determine some of the details which may be missing from the experimental structure.

Elucidating Hyperconjugation from Electronegativity to Predict Drug Conformational Energy in a High Throughput Manner.

PubMed

Liu, Zhaomin; Pottel, Joshua; Shahamat, Moeed; Tomberg, Anna; Labute, Paul; Moitessier, Nicolas

2016-04-25

Computational chemists use structure-based drug design and molecular dynamics of drug/protein complexes which require an accurate description of the conformational space of drugs. Organic chemists use qualitative chemical principles such as the effect of electronegativity on hyperconjugation, the impact of steric clashes on stereochemical outcome of reactions, and the consequence of resonance on the shape of molecules to rationalize experimental observations. While computational chemists speak about electron densities and molecular orbitals, organic chemists speak about partial charges and localized molecular orbitals. Attempts to reconcile these two parallel approaches such as programs for natural bond orbitals and intrinsic atomic orbitals computing Lewis structures-like orbitals and reaction mechanism have appeared. In the past, we have shown that encoding and quantifying chemistry knowledge and qualitative principles can lead to predictive methods. In the same vein, we thought to understand the conformational behaviors of molecules and to encode this knowledge back into a molecular mechanics tool computing conformational potential energy and to develop an alternative to atom types and training of force fields on large sets of molecules. Herein, we describe a conceptually new approach to model torsion energies based on fundamental chemistry principles. To demonstrate our approach, torsional energy parameters were derived on-the-fly from atomic properties. When the torsional energy terms implemented in GAFF, Parm@Frosst, and MMFF94 were substituted by our method, the accuracy of these force fields to reproduce MP2-derived torsional energy profiles and their transferability to a variety of functional groups and drug fragments were overall improved. In addition, our method did not rely on atom types and consequently did not suffer from poor automated atom type assignments.

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.

Mathematical and Computational Modeling in Complex Biological Systems

PubMed Central

Li, Wenyang; Zhu, Xiaoliang

2017-01-01

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology. PMID:28386558

Mathematical and Computational Modeling in Complex Biological Systems.

PubMed

Ji, Zhiwei; Yan, Ke; Li, Wenyang; Hu, Haigen; Zhu, Xiaoliang

2017-01-01

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

A Combination of Hand-Held Models and Computer Imaging Programs Helps Students Answer Oral Questions about Molecular Structure and Function: A Controlled Investigation of Student Learning

ERIC Educational Resources Information Center

Harris, Michelle A.; Peck, Ronald F.; Colton, Shannon; Morris, Jennifer; Neto, Elias Chaibub; Kallio, Julie

2009-01-01

We conducted a controlled investigation to examine whether a combination of computer imagery and tactile tools helps introductory cell biology laboratory undergraduate students better learn about protein structure/function relationships as compared with computer imagery alone. In all five laboratory sections, students used the molecular imaging…

Electronic health records (EHRs): supporting ASCO's vision of cancer care.

PubMed

Yu, Peter; Artz, David; Warner, Jeremy

2014-01-01

ASCO's vision for cancer care in 2030 is built on the expanding importance of panomics and big data, and envisions enabling better health for patients with cancer by the rapid transformation of systems biology knowledge into cancer care advances. This vision will be heavily dependent on the use of health information technology for computational biology and clinical decision support systems (CDSS). Computational biology will allow us to construct models of cancer biology that encompass the complexity of cancer panomics data and provide us with better understanding of the mechanisms governing cancer behavior. The Agency for Healthcare Research and Quality promotes CDSS based on clinical practice guidelines, which are knowledge bases that grow too slowly to match the rate of panomic-derived knowledge. CDSS that are based on systems biology models will be more easily adaptable to rapid advancements and translational medicine. We describe the characteristics of health data representation, a model for representing molecular data that supports data extraction and use for panomic-based clinical research, and argue for CDSS that are based on systems biology and are algorithm-based.

A model to estimate the relative position of sites for ligands in serum albumins

NASA Astrophysics Data System (ADS)

Motta, Art Adriel Emidio de Araújo; Grassini, Maria Carolina Vilela; Cortez, Célia Martins; Silva, Dilson

2017-11-01

In this work, we present a mathematical-computational model developed to estimate the relative position of ligand binding sites in HSA and BSA, based on the theory of fluorescence quenching, considering the molecular and spectrofluorimetric differences and similarities between these two albumins. Albumin is the largest and the most abundant serum protein in vertebrates. The ability to bind xenobiotics makes albumin important to the bioavailability and effectiveness of drugs.

Development of concepts on the interaction of drugs with opioid receptors

NASA Astrophysics Data System (ADS)

Kuzmina, N. E.; Kuzmin, V. S.

2011-02-01

The development of concepts on the molecular mechanisms of the action of medicinal drugs on the opioid receptors is briefly surveyed. The modern point of view on the mechanism of activation of opioid receptors is given based on the data from chimeric and site-directed mutagenesis of the cloned opioid receptors and the computer-aided simulations of the reception zone and ligand-receptor complexes. Three-dimensional models of the opioid pharmacophore derived by both conventional methods and a comparative analysis of molecular fields are described in detail.

Modeling of a carbon nanotube ultracapacitor.

PubMed

Orphanou, Antonis; Yamada, Toshishige; Yang, Cary Y

2012-03-09

The modeling of carbon nanotube ultracapacitor (CNU) performance based on the simulation of electrolyte ion motion between the cathode and the anode is described. Using a molecular dynamics (MD) approach, the equilibrium positions of the electrode charges interacting through the Coulomb potential are determined, which in turn yield the equipotential surface and electric field associated with the capacitor. With an applied ac voltage, the current is computed based on the nanotube and electrolyte particle distribution and interaction, resulting in the frequency-dependent impedance Z(ω). From the current and impedance profiles, the Nyquist and cyclic voltammetry (CV) plots are then extracted. The results of these calculations compare well with existing experimental data. A lumped-element equivalent circuit for the CNU is proposed and the impedance computed from this circuit correlates well with the simulated and measured impedances.

A Multiscale Agent-Based in silico Model of Liver Fibrosis Progression

PubMed Central

Dutta-Moscato, Joyeeta; Solovyev, Alexey; Mi, Qi; Nishikawa, Taichiro; Soto-Gutierrez, Alejandro; Fox, Ira J.; Vodovotz, Yoram

2014-01-01

Chronic hepatic inflammation involves a complex interplay of inflammatory and mechanical influences, ultimately manifesting in a characteristic histopathology of liver fibrosis. We created an agent-based model (ABM) of liver tissue in order to computationally examine the consequence of liver inflammation. Our liver fibrosis ABM (LFABM) is comprised of literature-derived rules describing molecular and histopathological aspects of inflammation and fibrosis in a section of chemically injured liver. Hepatocytes are modeled as agents within hexagonal lobules. Injury triggers an inflammatory reaction, which leads to activation of local Kupffer cells and recruitment of monocytes from circulation. Portal fibroblasts and hepatic stellate cells are activated locally by the products of inflammation. The various agents in the simulation are regulated by above-threshold concentrations of pro- and anti-inflammatory cytokines and damage-associated molecular pattern molecules. The simulation progresses from chronic inflammation to collagen deposition, exhibiting periportal fibrosis followed by bridging fibrosis, and culminating in disruption of the regular lobular structure. The ABM exhibited key histopathological features observed in liver sections from rats treated with carbon tetrachloride (CCl4). An in silico “tension test” for the hepatic lobules predicted an overall increase in tissue stiffness, in line with clinical elastography literature and published studies in CCl4-treated rats. Therapy simulations suggested differential anti-fibrotic effects of neutralizing tumor necrosis factor alpha vs. enhancing M2 Kupffer cells. We conclude that a computational model of liver inflammation on a structural skeleton of physical forces can recapitulate key histopathological and macroscopic properties of CCl4-injured liver. This multiscale approach linking molecular and chemomechanical stimuli enables a model that could be used to gain translationally relevant insights into liver fibrosis. PMID:25152891

Logic integration of mRNA signals by an RNAi-based molecular computer

PubMed Central

Xie, Zhen; Liu, Siyuan John; Bleris, Leonidas; Benenson, Yaakov

2010-01-01

Synthetic in vivo molecular ‘computers’ could rewire biological processes by establishing programmable, non-native pathways between molecular signals and biological responses. Multiple molecular computer prototypes have been shown to work in simple buffered solutions. Many of those prototypes were made of DNA strands and performed computations using cycles of annealing-digestion or strand displacement. We have previously introduced RNA interference (RNAi)-based computing as a way of implementing complex molecular logic in vivo. Because it also relies on nucleic acids for its operation, RNAi computing could benefit from the tools developed for DNA systems. However, these tools must be harnessed to produce bioactive components and be adapted for harsh operating environments that reflect in vivo conditions. In a step toward this goal, we report the construction and implementation of biosensors that ‘transduce’ mRNA levels into bioactive, small interfering RNA molecules via RNA strand exchange in a cell-free Drosophila embryo lysate, a step beyond simple buffered environments. We further integrate the sensors with our RNAi ‘computational’ module to evaluate two-input logic functions on mRNA concentrations. Our results show how RNA strand exchange can expand the utility of RNAi computing and point toward the possibility of using strand exchange in a native biological setting. PMID:20194121

Computational studies of novel chymase inhibitors against cardiovascular and allergic diseases: mechanism and inhibition.

PubMed

Arooj, Mahreen; Thangapandian, Sundarapandian; John, Shalini; Hwang, Swan; Park, Jong K; Lee, Keun W

2012-12-01

To provide a new idea for drug design, a computational investigation is performed on chymase and its novel 1,4-diazepane-2,5-diones inhibitors that explores the crucial molecular features contributing to binding specificity. Molecular docking studies of inhibitors within the active site of chymase were carried out to rationalize the inhibitory properties of these compounds and understand their inhibition mechanism. The density functional theory method was used to optimize molecular structures with the subsequent analysis of highest occupied molecular orbital, lowest unoccupied molecular orbital, and molecular electrostatic potential maps, which revealed that negative potentials near 1,4-diazepane-2,5-diones ring are essential for effective binding of inhibitors at active site of enzyme. The Bayesian model with receiver operating curve statistic of 0.82 also identified arylsulfonyl and aminocarbonyl as the molecular features favoring and not favoring inhibition of chymase, respectively. Moreover, genetic function approximation was applied to construct 3D quantitative structure-activity relationships models. Two models (genetic function approximation model 1 r(2) = 0.812 and genetic function approximation model 2 r(2) = 0.783) performed better in terms of correlation coefficients and cross-validation analysis. In general, this study is used as example to illustrate how combinational use of 2D/3D quantitative structure-activity relationships modeling techniques, molecular docking, frontier molecular orbital density fields (highest occupied molecular orbital and lowest unoccupied molecular orbital), and molecular electrostatic potential analysis may be useful to gain an insight into the binding mechanism between enzyme and its inhibitors. © 2012 John Wiley & Sons A/S.

Improved Electrostatic Embedding for Fragment-Based Chemical Shift Calculations in Molecular Crystals.

PubMed

Hartman, Joshua D; Balaji, Ashwin; Beran, Gregory J O

2017-12-12

Fragment-based methods predict nuclear magnetic resonance (NMR) chemical shielding tensors in molecular crystals with high accuracy and computational efficiency. Such methods typically employ electrostatic embedding to mimic the crystalline environment, and the quality of the results can be sensitive to the embedding treatment. To improve the quality of this embedding environment for fragment-based molecular crystal property calculations, we borrow ideas from the embedded ion method to incorporate self-consistently polarized Madelung field effects. The self-consistent reproduction of the Madelung potential (SCRMP) model developed here constructs an array of point charges that incorporates self-consistent lattice polarization and which reproduces the Madelung potential at all atomic sites involved in the quantum mechanical region of the system. The performance of fragment- and cluster-based 1 H, 13 C, 14 N, and 17 O chemical shift predictions using SCRMP and density functionals like PBE and PBE0 are assessed. The improved embedding model results in substantial improvements in the predicted 17 O chemical shifts and modest improvements in the 15 N ones. Finally, the performance of the model is demonstrated by examining the assignment of the two oxygen chemical shifts in the challenging γ-polymorph of glycine. Overall, the SCRMP-embedded NMR chemical shift predictions are on par with or more accurate than those obtained with the widely used gauge-including projector augmented wave (GIPAW) model.

Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models

NASA Astrophysics Data System (ADS)

Zang, Tianwu

Predicting the 3-dimentional structure of protein has been a major interest in the modern computational biology. While lots of successful methods can generate models with 3˜5A root-mean-square deviation (RMSD) from the solution, the progress of refining these models is quite slow. It is therefore urgently needed to develop effective methods to bring low-quality models to higher-accuracy ranges (e.g., less than 2 A RMSD). In this thesis, I present several novel computational methods to address the high-accuracy refinement problem. First, an enhanced sampling method, named parallel continuous simulated tempering (PCST), is developed to accelerate the molecular dynamics (MD) simulation. Second, two energy biasing methods, Structure-Based Model (SBM) and Ensemble-Based Model (EBM), are introduced to perform targeted sampling around important conformations. Third, a three-step method is developed to blindly select high-quality models along the MD simulation. These methods work together to make significant refinement of low-quality models without any knowledge of the solution. The effectiveness of these methods is examined in different applications. Using the PCST-SBM method, models with higher global distance test scores (GDT_TS) are generated and selected in the MD simulation of 18 targets from the refinement category of the 10th Critical Assessment of Structure Prediction (CASP10). In addition, in the refinement test of two CASP10 targets using the PCST-EBM method, it is indicated that EBM may bring the initial model to even higher-quality levels. Furthermore, a multi-round refinement protocol of PCST-SBM improves the model quality of a protein to the level that is sufficient high for the molecular replacement in X-ray crystallography. Our results justify the crucial position of enhanced sampling in the protein structure prediction and demonstrate that a considerable improvement of low-accuracy structures is still achievable with current force fields.

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.

PubMed

Ngo, Trieu-Du; Tran, Thanh-Dao; Le, Minh-Tri; Thai, Khac-Minh

2016-11-01

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.

BetaCavityWeb: a webserver for molecular voids and channels

PubMed Central

Kim, Jae-Kwan; Cho, Youngsong; Lee, Mokwon; Laskowski, Roman A.; Ryu, Seong Eon; Sugihara, Kokichi; Kim, Deok-Soo

2015-01-01

Molecular cavities, which include voids and channels, are critical for molecular function. We present a webserver, BetaCavityWeb, which computes these cavities for a given molecular structure and a given spherical probe, and reports their geometrical properties: volume, boundary area, buried area, etc. The server's algorithms are based on the Voronoi diagram of atoms and its derivative construct: the beta-complex. The correctness of the computed result and computational efficiency are both mathematically guaranteed. BetaCavityWeb is freely accessible at the Voronoi Diagram Research Center (VDRC) (http://voronoi.hanyang.ac.kr/betacavityweb). PMID:25904629

MrGrid: A Portable Grid Based Molecular Replacement Pipeline

PubMed Central

Reboul, Cyril F.; Androulakis, Steve G.; Phan, Jennifer M. N.; Whisstock, James C.; Goscinski, Wojtek J.; Abramson, David; Buckle, Ashley M.

2010-01-01

Background The crystallographic determination of protein structures can be computationally demanding and for difficult cases can benefit from user-friendly interfaces to high-performance computing resources. Molecular replacement (MR) is a popular protein crystallographic technique that exploits the structural similarity between proteins that share some sequence similarity. But the need to trial permutations of search models, space group symmetries and other parameters makes MR time- and labour-intensive. However, MR calculations are embarrassingly parallel and thus ideally suited to distributed computing. In order to address this problem we have developed MrGrid, web-based software that allows multiple MR calculations to be executed across a grid of networked computers, allowing high-throughput MR. Methodology/Principal Findings MrGrid is a portable web based application written in Java/JSP and Ruby, and taking advantage of Apple Xgrid technology. Designed to interface with a user defined Xgrid resource the package manages the distribution of multiple MR runs to the available nodes on the Xgrid. We evaluated MrGrid using 10 different protein test cases on a network of 13 computers, and achieved an average speed up factor of 5.69. Conclusions MrGrid enables the user to retrieve and manage the results of tens to hundreds of MR calculations quickly and via a single web interface, as well as broadening the range of strategies that can be attempted. This high-throughput approach allows parameter sweeps to be performed in parallel, improving the chances of MR success. PMID:20386612

Active cell-matrix coupling regulates cellular force landscapes of cohesive epithelial monolayers

NASA Astrophysics Data System (ADS)

Zhao, Tiankai; Zhang, Yao; Wei, Qiong; Shi, Xuechen; Zhao, Peng; Chen, Long-Qing; Zhang, Sulin

2018-03-01

Epithelial cells can assemble into cohesive monolayers with rich morphologies on substrates due to competition between elastic, edge, and interfacial effects. Here we present a molecularly based thermodynamic model, integrating monolayer and substrate elasticity, and force-mediated focal adhesion formation, to elucidate the active biochemical regulation over the cellular force landscapes in cohesive epithelial monolayers, corroborated by microscopy and immunofluorescence studies. The predicted extracellular traction and intercellular tension are both monolayer size and substrate stiffness dependent, suggestive of cross-talks between intercellular and extracellular activities. Our model sets a firm ground toward a versatile computational framework to uncover the molecular origins of morphogenesis and disease in multicellular epithelia.

Unraveling the benzocaine-receptor interaction at molecular level using mass-resolved spectroscopy.

PubMed

Aguado, Edurne; León, Iker; Millán, Judith; Cocinero, Emilio J; Jaeqx, Sander; Rijs, Anouk M; Lesarri, Alberto; Fernández, José A

2013-10-31

The benzocaine-toluene cluster has been used as a model system to mimic the interaction between the local anesthetic benzocaine and the phenylalanine residue in Na(+) channels. The cluster was generated in a supersonic expansion of benzocaine and toluene in helium. Using a combination of mass-resolved laser-based experimental techniques and computational methods, the complex was fully characterized, finding four conformational isomers in which the molecules are bound through N-H···π and π···π weak hydrogen bonds. The structures of the detected isomers closely resemble those predicted for benzocaine in the inner pore of the ion channels, giving experimental support to previously reported molecular chemistry models.

Electric Double-Layer Structure in Primitive Model Electrolytes. Comparing Molecular Dynamics with Local-Density Approximations

DOE PAGES

Giera, Brian; Lawrence Livermore National Lab.; Henson, Neil; ...

2015-02-27

We evaluate the accuracy of local-density approximations (LDAs) using explicit molecular dynamics simulations of binary electrolytes comprised of equisized ions in an implicit solvent. The Bikerman LDA, which considers ions to occupy a lattice, poorly captures excluded volume interactions between primitive model ions. Instead, LDAs based on the Carnahan–Starling (CS) hard-sphere equation of state capture simulated values of ideal and excess chemical potential profiles extremely well, as is the relationship between surface charge density and electrostatic potential. Excellent agreement between the EDL capacitances predicted by CS-LDAs and computed in molecular simulations is found even in systems where ion correlations drivemore » strong density and free charge oscillations within the EDL, despite the inability of LDAs to capture the oscillations in the detailed EDL profiles.« less

Temperature specification in atomistic molecular dynamics and its impact on simulation efficacy

NASA Astrophysics Data System (ADS)

Ocaya, R. O.; Terblans, J. J.

2017-10-01

Temperature is a vital thermodynamical function for physical systems. Knowledge of system temperature permits assessment of system ergodicity, entropy, system state and stability. Rapid theoretical and computational developments in the fields of condensed matter physics, chemistry, material science, molecular biology, nanotechnology and others necessitate clarity in the temperature specification. Temperature-based materials simulations, both standalone and distributed computing, are projected to grow in prominence over diverse research fields. In this article we discuss the apparent variability of temperature modeling formalisms used currently in atomistic molecular dynamics simulations, with respect to system energetics,dynamics and structural evolution. Commercial simulation programs, which by nature are heuristic, do not openly discuss this fundamental question. We address temperature specification in the context of atomistic molecular dynamics. We define a thermostat at 400K relative to a heat bath at 300K firstly using a modified ab-initio Newtonian method, and secondly using a Monte-Carlo method. The thermostatic vacancy formation and cohesion energies, equilibrium lattice constant for FCC copper is then calculated. Finally we compare and contrast the results.

Development of computational small animal models and their applications in preclinical imaging and therapy research

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

Xie, Tianwu; Zaidi, Habib, E-mail: habib.zaidi@hcuge.ch; Geneva Neuroscience Center, Geneva University, Geneva CH-1205

The development of multimodality preclinical imaging techniques and the rapid growth of realistic computer simulation tools have promoted the construction and application of computational laboratory animal models in preclinical research. Since the early 1990s, over 120 realistic computational animal models have been reported in the literature and used as surrogates to characterize the anatomy of actual animals for the simulation of preclinical studies involving the use of bioluminescence tomography, fluorescence molecular tomography, positron emission tomography, single-photon emission computed tomography, microcomputed tomography, magnetic resonance imaging, and optical imaging. Other applications include electromagnetic field simulation, ionizing and nonionizing radiation dosimetry, and themore » development and evaluation of new methodologies for multimodality image coregistration, segmentation, and reconstruction of small animal images. This paper provides a comprehensive review of the history and fundamental technologies used for the development of computational small animal models with a particular focus on their application in preclinical imaging as well as nonionizing and ionizing radiation dosimetry calculations. An overview of the overall process involved in the design of these models, including the fundamental elements used for the construction of different types of computational models, the identification of original anatomical data, the simulation tools used for solving various computational problems, and the applications of computational animal models in preclinical research. The authors also analyze the characteristics of categories of computational models (stylized, voxel-based, and boundary representation) and discuss the technical challenges faced at the present time as well as research needs in the future.« less

Biomolecular electrostatics and solvation: a computational perspective

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

Ren, Pengyu; Chun, Jaehun; Thomas, Dennis G.

2012-11-01

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. Thismore » review discusses the solvation of biomolecules with a computational biophysics view towards describing the phenomenon. While our main focus lies on the computational aspect of the models, we summarize the common characteristics of biomolecular solvation (e.g., solvent structure, polarization, ion binding, and nonpolar behavior) in order to provide reasonable backgrounds to understand the solvation models.« less

Biomolecular electrostatics and solvation: a computational perspective

PubMed Central

Ren, Pengyu; Chun, Jaehun; Thomas, Dennis G.; Schnieders, Michael J.; Marucho, Marcelo; Zhang, Jiajing; Baker, Nathan A.

2012-01-01

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view towards describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g., solvent structure, polarization, ion binding, and nonpolar behavior) in order to provide a background to understand the different types of solvation models. PMID:23217364

Biomolecular electrostatics and solvation: a computational perspective.

PubMed

Ren, Pengyu; Chun, Jaehun; Thomas, Dennis G; Schnieders, Michael J; Marucho, Marcelo; Zhang, Jiajing; Baker, Nathan A

2012-11-01

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

Fragment-Based Electronic Structure Approach for Computing Nuclear Magnetic Resonance Chemical Shifts in Molecular Crystals.

PubMed

Hartman, Joshua D; Beran, Gregory J O

2014-11-11

First-principles chemical shielding tensor predictions play a critical role in studying molecular crystal structures using nuclear magnetic resonance. Fragment-based electronic structure methods have dramatically improved the ability to model molecular crystal structures and energetics using high-level electronic structure methods. Here, a many-body expansion fragment approach is applied to the calculation of chemical shielding tensors in molecular crystals. First, the impact of truncating the many-body expansion at different orders and the role of electrostatic embedding are examined on a series of molecular clusters extracted from molecular crystals. Second, the ability of these techniques to assign three polymorphic forms of the drug sulfanilamide to the corresponding experimental (13)C spectra is assessed. This challenging example requires discriminating among spectra whose (13)C chemical shifts differ by only a few parts per million (ppm) across the different polymorphs. Fragment-based PBE0/6-311+G(2d,p) level chemical shielding predictions correctly assign these three polymorphs and reproduce the sulfanilamide experimental (13)C chemical shifts with 1 ppm accuracy. The results demonstrate that fragment approaches are competitive with the widely used gauge-invariant projector augmented wave (GIPAW) periodic density functional theory calculations.

Computational Toxicology (S)

EPA Science Inventory

The emerging field of computational toxicology applies mathematical and computer models and molecular biological and chemical approaches to explore both qualitative and quantitative relationships between sources of environmental pollutant exposure and adverse health outcomes. Th...

Multiscale Modeling of Damage Processes in fcc Aluminum: From Atoms to Grains

NASA Technical Reports Server (NTRS)

Glaessgen, E. H.; Saether, E.; Yamakov, V.

2008-01-01

Molecular dynamics (MD) methods are opening new opportunities for simulating the fundamental processes of material behavior at the atomistic level. However, current analysis is limited to small domains and increasing the size of the MD domain quickly presents intractable computational demands. A preferred approach to surmount this computational limitation has been to combine continuum mechanics-based modeling procedures, such as the finite element method (FEM), with MD analyses thereby reducing the region of atomic scale refinement. Such multiscale modeling strategies can be divided into two broad classifications: concurrent multiscale methods that directly incorporate an atomistic domain within a continuum domain and sequential multiscale methods that extract an averaged response from the atomistic simulation for later use as a constitutive model in a continuum analysis.

Computational Molecular Modeling for Evaluating the Toxicity of Environmental Chemicals: Prioritizing Bioassay Requirements

EPA Science Inventory

This commentary provides an overview of the challenges that arise from applying molecular modeling tools developed and commonly used for pharmaceutical discovery to the problem of predicting the potential toxicities of environmental chemicals.

Implementing Molecular Dynamics on Hybrid High Performance Computers - Particle-Particle Particle-Mesh

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

Brown, W Michael; Kohlmeyer, Axel; Plimpton, Steven J

The use of accelerators such as graphics processing units (GPUs) has 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 nodes containing more than one type of floating-point processor (e.g. CPU and GPU), are now becoming more prevalent due to these advantages. In this paper, we present a continuation of previous work implementing algorithms for using accelerators into the LAMMPS molecular dynamics software for distributed memory parallel hybrid machines. In our previous work, we focused on acceleration for short-range models with anmore » approach intended to harness the processing power of both the accelerator and (multi-core) CPUs. To augment the existing implementations, we present an efficient implementation of long-range electrostatic force calculation for molecular dynamics. Specifically, we present an implementation of the particle-particle particle-mesh method based on the work by Harvey and De Fabritiis. We present benchmark results on the Keeneland InfiniBand GPU cluster. We provide a performance comparison of the same kernels compiled with both CUDA and OpenCL. We discuss limitations to parallel efficiency and future directions for improving performance on hybrid or heterogeneous computers.« less

Of truth and pathways: chasing bits of information through myriads of articles.

PubMed

Krauthammer, Michael; Kra, Pauline; Iossifov, Ivan; Gomez, Shawn M; Hripcsak, George; Hatzivassiloglou, Vasileios; Friedman, Carol; Rzhetsky, Andrey

2002-01-01

Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.

Star formation in evolving molecular clouds

NASA Astrophysics Data System (ADS)

Völschow, M.; Banerjee, R.; Körtgen, B.

2017-09-01

Molecular clouds are the principle stellar nurseries of our universe; they thus remain a focus of both observational and theoretical studies. From observations, some of the key properties of molecular clouds are well known but many questions regarding their evolution and star formation activity remain open. While numerical simulations feature a large number and complexity of involved physical processes, this plethora of effects may hide the fundamentals that determine the evolution of molecular clouds and enable the formation of stars. Purely analytical models, on the other hand, tend to suffer from rough approximations or a lack of completeness, limiting their predictive power. In this paper, we present a model that incorporates central concepts of astrophysics as well as reliable results from recent simulations of molecular clouds and their evolutionary paths. Based on that, we construct a self-consistent semi-analytical framework that describes the formation, evolution, and star formation activity of molecular clouds, including a number of feedback effects to account for the complex processes inside those objects. The final equation system is solved numerically but at much lower computational expense than, for example, hydrodynamical descriptions of comparable systems. The model presented in this paper agrees well with a broad range of observational results, showing that molecular cloud evolution can be understood as an interplay between accretion, global collapse, star formation, and stellar feedback.

SEQUOIA: significance enhanced network querying through context-sensitive random walk and minimization of network conductance.

PubMed

Jeong, Hyundoo; Yoon, Byung-Jun

2017-03-14

Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphism between the query and the target graphs and ensuring the biological significance of the query results. In this paper, we propose SEQUOIA, a novel network querying algorithm that effectively addresses these issues by utilizing a context-sensitive random walk (CSRW) model for network comparison and minimizing the network conductance of potential matches in the target network. The CSRW model, inspired by the pair hidden Markov model (pair-HMM) that has been widely used for sequence comparison and alignment, can accurately assess the node-to-node correspondence between different graphs by accounting for node insertions and deletions. The proposed algorithm identifies high-scoring network regions based on the CSRW scores, which are subsequently extended by maximally reducing the network conductance of the identified subnetworks. Performance assessment based on real PPI networks and known molecular complexes show that SEQUOIA outperforms existing methods and clearly enhances the biological significance of the query results. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/SEQUOIA .

Heuristic lipophilicity potential for computer-aided rational drug design.

PubMed

Du, Q; Arteca, G A; Mezey, P G

1997-09-01

In this contribution we suggest a heuristic molecular lipophilicity potential (HMLP), which is a structure-based technique requiring no empirical indices of atomic lipophilicity. The input data used in this approach are molecular geometries and molecular surfaces. The HMLP is a modified electrostatic potential, combined with the averaged influences from the molecular environment. Quantum mechanics is used to calculate the electron density function rho(r) and the electrostatic potential V(r), and from this information a lipophilicity potential L(r) is generated. The HMLP is a unified lipophilicity and hydrophilicity potential. The interactions of dipole and multipole moments, hydrogen bonds, and charged atoms in a molecule are included in the hydrophilic interactions in this model. The HMLP is used to study hydrogen bonds and water-octanol partition coefficients in several examples. The calculated results show that the HMLP gives qualitatively and quantitatively correct, as well as chemically reasonable, results in cases where comparisons are available. These comparisons indicate that the HMLP has advantages over the empirical lipophilicity potential in many aspects. The HMLP is a three-dimensional and easily visualizable representation of molecular lipophilicity, suggested as a potential tool in computer-aided three-dimensional drug design.

Automated combinatorial method for fast and robust prediction of lattice thermal conductivity

NASA Astrophysics Data System (ADS)

Plata, Jose J.; Nath, Pinku; Usanmaz, Demet; Toher, Cormac; Fornari, Marco; Buongiorno Nardelli, Marco; Curtarolo, Stefano

The lack of computationally inexpensive and accurate ab-initio based methodologies to predict lattice thermal conductivity, κl, without computing the anharmonic force constants or performing time-consuming ab-initio molecular dynamics, is one of the obstacles preventing the accelerated discovery of new high or low thermal conductivity materials. The Slack equation is the best alternative to other more expensive methodologies but is highly dependent on two variables: the acoustic Debye temperature, θa, and the Grüneisen parameter, γ. Furthermore, different definitions can be used for these two quantities depending on the model or approximation. Here, we present a combinatorial approach based on the quasi-harmonic approximation to elucidate which definitions of both variables produce the best predictions of κl. A set of 42 compounds was used to test accuracy and robustness of all possible combinations. This approach is ideal for obtaining more accurate values than fast screening models based on the Debye model, while being significantly less expensive than methodologies that solve the Boltzmann transport equation.

Computational neural networks in chemistry: Model free mapping devices for predicting chemical reactivity from molecular structure

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

Elrod, D.W.

1992-01-01

Computational neural networks (CNNs) are a computational paradigm inspired by the brain's massively parallel network of highly interconnected neurons. The power of computational neural networks derives not so much from their ability to model the brain as from their ability to learn by example and to map highly complex, nonlinear functions, without the need to explicitly specify the functional relationship. Two central questions about CNNs were investigated in the context of predicting chemical reactions: (1) the mapping properties of neural networks and (2) the representation of chemical information for use in CNNs. Chemical reactivity is here considered an example ofmore » a complex, nonlinear function of molecular structure. CNN's were trained using modifications of the back propagation learning rule to map a three dimensional response surface similar to those typically observed in quantitative structure-activity and structure-property relationships. The computational neural network's mapping of the response surface was found to be robust to the effects of training sample size, noisy data and intercorrelated input variables. The investigation of chemical structure representation led to the development of a molecular structure-based connection-table representation suitable for neural network training. An extension of this work led to a BE-matrix structure representation that was found to be general for several classes of reactions. The CNN prediction of chemical reactivity and regiochemistry was investigated for electrophilic aromatic substitution reactions, Markovnikov addition to alkenes, Saytzeff elimination from haloalkanes, Diels-Alder cycloaddition, and retro Diels-Alder ring opening reactions using these connectivity-matrix derived representations. The reaction predictions made by the CNNs were more accurate than those of an expert system and were comparable to predictions made by chemists.« less

An Insilico Design of Nanoclay Based Nanocomposites and Scaffolds in Bone Tissue Engineering

NASA Astrophysics Data System (ADS)

Sharma, Anurag

A multiscale in silico approach to design polymer nanocomposites and scaffolds for bone tissue engineering applications is described in this study. This study focuses on the role of biomaterials design and selection, structural integrity and mechanical properties evolution during degradation and tissue regeneration in the successful design of polymer nanocomposite scaffolds. Polymer nanocomposite scaffolds are synthesized using aminoacid modified montmorillonite nanoclay with biomineralized hydroxyapatite and polycaprolactone (PCL/in situ HAPclay). Representative molecular models of polymer nanocomposite system are systematically developed using molecular dynamics (MD) technique and successfully validated using material characterization techniques. The constant force steered molecular dynamics (fSMD) simulation results indicate a two-phase nanomechanical behavior of the polymer nanocomposite. The MD and fSMD simulations results provide quantitative contributions of molecular interactions between different constituents of representative models and their effect on nanomechanical responses of nanoclay based polymer nanocomposite system. A finite element (FE) model of PCL/in situ HAPclay scaffold is built using micro-computed tomography images and bridging the nanomechanical properties obtained from fSMD simulations into the FE model. A new reduction factor, K is introduced into modeling results to consider the effect of wall porosity of the polymer scaffold. The effect of accelerated degradation under alkaline conditions and human osteoblast cells culture on the evolution of mechanical properties of scaffolds are studied and the damage mechanics based analytical models are developed. Finally, the novel multiscale models are developed that incorporate the complex molecular and microstructural properties, mechanical properties at nanoscale and structural levels and mechanical properties evolution during degradation and tissue formation in the polymer nanocomposite scaffold. Overall, this study provides a leap into methodologies for in silico design of biomaterials for bone tissue engineering applications. Furthermore, as a part of this work, a molecular dynamics study of rice DNA in the presence of single walled carbon nanotube is carried out to understand the role played by molecular interactions in the conformation changes of rice DNA. The simulations results showed wrapping of DNA onto SWCNT, breaking and forming of hydrogen bonds due to unzipping of Watson-Crick (WC) nucleobase pairs and forming of new non-WC nucleobase pairs in DNA.

On the transport coefficients of hydrogen in the inertial confinement fusion regime

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

Lambert, Flavien; Recoules, Vanina; Decoster, Alain

2011-05-15

Ab initio molecular dynamics is used to compute the thermal and electrical conductivities of hydrogen from 10 to 160 g cm{sup -3} and temperatures up to 800 eV, i.e., thermodynamical conditions relevant to inertial confinement fusion (ICF). The ionic structure is obtained using molecular dynamics simulations based on an orbital-free treatment for the electrons. The transport properties were computed using ab initio simulations in the DFT/LDA approximation. The thermal and electrical conductivities are evaluated using Kubo-Greenwood formulation. Particular attention is paid to the convergence of electronic transport properties with respect to the number of bands and atoms. These calculations aremore » then used to check various analytical models (Hubbard's, Lee-More's and Ichimaru's) widely used in hydrodynamics simulations of ICF capsule implosions. The Lorenz number, which is the ratio between thermal and electrical conductivities, is also computed and compared to the well-known Wiedemann-Franz law in different regimes ranging from the highly degenerate to the kinetic one. This allows us to deduce electrical conductivity from thermal conductivity for analytical model. We find that the coupling of Hubbard and Spitzer models gives a correct description of the behavior of electrical and thermal conductivities in the whole thermodynamic regime.« less

Modeling Molecules

NASA Technical Reports Server (NTRS)

2000-01-01

The molecule modeling method known as Multibody Order (N) Dynamics, or MBO(N)D, was developed by Moldyn, Inc. at Goddard Space Flight Center through funding provided by the SBIR program. The software can model the dynamics of molecules through technology which stimulates low-frequency molecular motions and properties, such as movements among a molecule's constituent parts. With MBO(N)D, a molecule is substructured into a set of interconnected rigid and flexible bodies. These bodies replace the computation burden of mapping individual atoms. Moldyn's technology cuts computation time while increasing accuracy. The MBO(N)D technology is available as Insight II 97.0 from Molecular Simulations, Inc. Currently the technology is used to account for forces on spacecraft parts and to perform molecular analyses for pharmaceutical purposes. It permits the solution of molecular dynamics problems on a moderate workstation, as opposed to on a supercomputer.

Treatment of atomic and molecular line blanketing by opacity sampling

NASA Technical Reports Server (NTRS)

Johnson, H. R.; Krupp, B. M.

1976-01-01

A sampling technique for treating the radiative opacity of large numbers of atomic and molecular lines in cool stellar atmospheres is subjected to several tests. In this opacity sampling (OS) technique, the global opacity is sampled at only a selected set of frequencies, and at each of these frequencies the total monochromatic opacity is obtained by summing the contribution of every relevant atomic and molecular line. In accord with previous results, we find that the structure of atmospheric models is accurately fixed by the use of 1000 frequency points, and 100 frequency points are adequate for many purposes. The effects of atomic and molecular lines are separately studied. A test model computed using the OS method agrees very well with a model having identical atmospheric parameters, but computed with the giant line (opacity distribution function) method.

Hybrid stochastic simulations of intracellular reaction-diffusion systems.

PubMed

Kalantzis, Georgios

2009-06-01

With the observation that stochasticity is important in biological systems, chemical kinetics have begun to receive wider interest. While the use of Monte Carlo discrete event simulations most accurately capture the variability of molecular species, they become computationally costly for complex reaction-diffusion systems with large populations of molecules. On the other hand, continuous time models are computationally efficient but they fail to capture any variability in the molecular species. In this study a hybrid stochastic approach is introduced for simulating reaction-diffusion systems. We developed an adaptive partitioning strategy in which processes with high frequency are simulated with deterministic rate-based equations, and those with low frequency using the exact stochastic algorithm of Gillespie. Therefore the stochastic behavior of cellular pathways is preserved while being able to apply it to large populations of molecules. We describe our method and demonstrate its accuracy and efficiency compared with the Gillespie algorithm for two different systems. First, a model of intracellular viral kinetics with two steady states and second, a compartmental model of the postsynaptic spine head for studying the dynamics of Ca+2 and NMDA receptors.

PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta.

PubMed

Chaudhury, Sidhartha; Lyskov, Sergey; Gray, Jeffrey J

2010-03-01

PyRosetta is a stand-alone Python-based implementation of the Rosetta molecular modeling package that allows users to write custom structure prediction and design algorithms using the major Rosetta sampling and scoring functions. PyRosetta contains Python bindings to libraries that define Rosetta functions including those for accessing and manipulating protein structure, calculating energies and running Monte Carlo-based simulations. PyRosetta can be used in two ways: (i) interactively, using iPython and (ii) script-based, using Python scripting. Interactive mode contains a number of help features and is ideal for beginners while script-mode is best suited for algorithm development. PyRosetta has similar computational performance to Rosetta, can be easily scaled up for cluster applications and has been implemented for algorithms demonstrating protein docking, protein folding, loop modeling and design. PyRosetta is a stand-alone package available at http://www.pyrosetta.org under the Rosetta license which is free for academic and non-profit users. A tutorial, user's manual and sample scripts demonstrating usage are also available on the web site.

PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta

PubMed Central

Chaudhury, Sidhartha; Lyskov, Sergey; Gray, Jeffrey J.

2010-01-01

Summary: PyRosetta is a stand-alone Python-based implementation of the Rosetta molecular modeling package that allows users to write custom structure prediction and design algorithms using the major Rosetta sampling and scoring functions. PyRosetta contains Python bindings to libraries that define Rosetta functions including those for accessing and manipulating protein structure, calculating energies and running Monte Carlo-based simulations. PyRosetta can be used in two ways: (i) interactively, using iPython and (ii) script-based, using Python scripting. Interactive mode contains a number of help features and is ideal for beginners while script-mode is best suited for algorithm development. PyRosetta has similar computational performance to Rosetta, can be easily scaled up for cluster applications and has been implemented for algorithms demonstrating protein docking, protein folding, loop modeling and design. Availability: PyRosetta is a stand-alone package available at http://www.pyrosetta.org under the Rosetta license which is free for academic and non-profit users. A tutorial, user's manual and sample scripts demonstrating usage are also available on the web site. Contact: pyrosetta@graylab.jhu.edu PMID:20061306

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches.

PubMed

Sharma, Ashok K; Srivastava, Gopal N; Roy, Ankita; Sharma, Vineet K

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better ( R 2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better ( R 2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

PubMed Central

Sharma, Ashok K.; Srivastava, Gopal N.; Roy, Ankita; Sharma, Vineet K.

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules. PMID:29249969

First-Principles Molecular Dynamics Studies of Organometallic Complexes and Homogeneous Catalytic Processes.

PubMed

Vidossich, Pietro; Lledós, Agustí; Ujaque, Gregori

2016-06-21

Computational chemistry is a valuable aid to complement experimental studies of organometallic systems and their reactivity. It allows probing mechanistic hypotheses and investigating molecular structures, shedding light on the behavior and properties of molecular assemblies at the atomic scale. When approaching a chemical problem, the computational chemist has to decide on the theoretical approach needed to describe electron/nuclear interactions and the composition of the model used to approximate the actual system. Both factors determine the reliability of the modeling study. The community dedicated much effort to developing and improving the performance and accuracy of theoretical approaches for electronic structure calculations, on which the description of (inter)atomic interactions rely. Here, the importance of the model system used in computational studies is highlighted through examples from our recent research focused on organometallic systems and homogeneous catalytic processes. We show how the inclusion of explicit solvent allows the characterization of molecular events that would otherwise not be accessible in reduced model systems (clusters). These include the stabilization of nascent charged fragments via microscopic solvation (notably, hydrogen bonding), transfer of charge (protons) between distant fragments mediated by solvent molecules, and solvent coordination to unsaturated metal centers. Furthermore, when weak interactions are involved, we show how conformational and solvation properties of organometallic complexes are also affected by the explicit inclusion of solvent molecules. Such extended model systems may be treated under periodic boundary conditions, thus removing the cluster/continuum (or vacuum) boundary, and require a statistical mechanics simulation technique to sample the accessible configurational space. First-principles molecular dynamics, in which atomic forces are computed from electronic structure calculations (namely, density functional theory), is certainly the technique of choice to investigate chemical events in solution. This methodology is well established and thanks to advances in both algorithms and computational resources simulation times required for the modeling of chemical events are nowadays accessible, though the computational requirements use to be high. Specific applications reviewed here include mechanistic studies of the Shilov and Wacker processes, speciation in Pd chemistry, hydrogen bonding to metal centers, and the dynamics of agostic interactions.

Computing molecular fluctuations in biochemical reaction systems based on a mechanistic, statistical theory of irreversible processes.

PubMed

Kulasiri, Don

2011-01-01

We discuss the quantification of molecular fluctuations in the biochemical reaction systems within the context of intracellular processes associated with gene expression. We take the molecular reactions pertaining to circadian rhythms to develop models of molecular fluctuations in this chapter. There are a significant number of studies on stochastic fluctuations in intracellular genetic regulatory networks based on single cell-level experiments. In order to understand the fluctuations associated with the gene expression in circadian rhythm networks, it is important to model the interactions of transcriptional factors with the E-boxes in the promoter regions of some of the genes. The pertinent aspects of a near-equilibrium theory that would integrate the thermodynamical and particle dynamic characteristics of intracellular molecular fluctuations would be discussed, and the theory is extended by using the theory of stochastic differential equations. We then model the fluctuations associated with the promoter regions using general mathematical settings. We implemented ubiquitous Gillespie's algorithms, which are used to simulate stochasticity in biochemical networks, for each of the motifs. Both the theory and the Gillespie's algorithms gave the same results in terms of the time evolution of means and variances of molecular numbers. As biochemical reactions occur far away from equilibrium-hence the use of the Gillespie algorithm-these results suggest that the near-equilibrium theory should be a good approximation for some of the biochemical reactions. © 2011 Elsevier Inc. All rights reserved.

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.

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

PubMed Central

Donovan, Rory M.; Tapia, Jose-Juan; Sullivan, Devin P.; Faeder, James R.; Murphy, Robert F.; Dittrich, Markus; Zuckerman, Daniel M.

2016-01-01

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables. PMID:26845334

Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

DOE PAGES

Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta; ...

2016-08-29

In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure–Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure–Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide themore » recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.« less

Approximations, idealizations and 'experiments' at the physics-biology interface.

PubMed

Rowbottom, Darrell P

2011-06-01

This paper, which is based on recent empirical research at the University of Leeds, the University of Edinburgh, and the University of Bristol, presents two difficulties which arise when condensed matter physicists interact with molecular biologists: (1) the former use models which appear to be too coarse-grained, approximate and/or idealized to serve a useful scientific purpose to the latter; and (2) the latter have a rather narrower view of what counts as an experiment, particularly when it comes to computer simulations, than the former. It argues that these findings are related; that computer simulations are considered to be undeserving of experimental status, by molecular biologists, precisely because of the idealizations and approximations that they involve. The complexity of biological systems is a key factor. The paper concludes by critically examining whether the new research programme of 'systems biology' offers a genuine alternative to the modelling strategies used by physicists. It argues that it does not. Copyright © 2010 Elsevier Ltd. All rights reserved.

Computational Prediction and Validation of an Expert's Evaluation of Chemical Probes

PubMed Central

Litterman, Nadia K.; Lipinski, Christopher A.; Bunin, Barry A.; Ekins, Sean

2016-01-01

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date. PMID:25244007

Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

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

Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta

In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure–Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure–Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide themore » recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.« less

Molecular Modeling of Environmentally Important Processes: Reduction Potentials

ERIC Educational Resources Information Center

Lewis, Anne; Bumpus, John A.; Truhlar, Donald G.; Cramer, Christopher J.

2004-01-01

The increasing use of computational quantum chemistry in the modeling of environmentally important processes is described. The employment of computational quantum mechanics for the prediction of oxidation-reduction potential for solutes in an aqueous medium is discussed.

The effect of the Mihalas, Hummer, and Daeppen equation of state and the molecular opacity on the standard solar model

NASA Technical Reports Server (NTRS)

Kim, Y.-C.; Demarque, P.; Guenther, D. B.

1991-01-01

Improvements to the Yale Rotating Stellar Evolution Code (YREC) by incorporating the Mihalas-Hummer-Daeppen equation of state, an improved opacity interpolation routine, and the effects of molecular opacities, calculated at Los Alamos, have been made. the effect of each of the improvements on the standard solar model has been tested independently by computing the corresponding solar nonradial oscillation frequencies. According to these tests, the Mihalas-Hummer-Daeppen equation of state has very little effect on the model's low l p-mode oscillation spectrum compared to the model using the existing analytical equation of state implemented in YREC. On the other hand, the molecular opacity does improve the model's oscillation spectrum. The effect of molecular opacity on the computed solar oscillation frequencies is much larger than that of the Mihalas-Hummer-Daeppen equation of state. together, the two improvements to the physics reduce the discrepancy with observations by 10 microHz for the low l modes.

Molecular simulation of small Knudsen number flows

NASA Astrophysics Data System (ADS)

Fei, Fei; Fan, Jing

2012-11-01

The direct simulation Monte Carlo (DSMC) method is a powerful particle-based method for modeling gas flows. It works well for relatively large Knudsen (Kn) numbers, typically larger than 0.01, but quickly becomes computationally intensive as Kn decreases due to its time step and cell size limitations. An alternative approach was proposed to relax or remove these limitations, based on replacing pairwise collisions with a stochastic model corresponding to the Fokker-Planck equation [J. Comput. Phys., 229, 1077 (2010); J. Fluid Mech., 680, 574 (2011)]. Similar to the DSMC method, the downside of that approach suffers from computationally statistical noise. To solve the problem, a diffusion-based information preservation (D-IP) method has been developed. The main idea is to track the motion of a simulated molecule from the diffusive standpoint, and obtain the flow velocity and temperature through sampling and averaging the IP quantities. To validate the idea and the corresponding model, several benchmark problems with Kn ˜ 10-3-10-4 have been investigated. It is shown that the IP calculations are not only accurate, but also efficient because they make possible using a time step and cell size over an order of magnitude larger than the mean collision time and mean free path, respectively.

Computational and Experimental Investigations of the Molecular Scale Structure and Dynamics of Gologically Important Fluids and Mineral-Fluid Interfaces

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

Bowers, Geoffrey

United States Department of Energy grant DE-FG02-10ER16128, “Computational and Spectroscopic Investigations of the Molecular Scale Structure and Dynamics of Geologically Important Fluids and Mineral-Fluid Interfaces” (Geoffrey M. Bowers, P.I.) focused on developing a molecular-scale understanding of processes that occur in fluids and at solid-fluid interfaces using the combination of spectroscopic, microscopic, and diffraction studies with molecular dynamics computer modeling. The work is intimately tied to the twin proposal at Michigan State University (DOE DE-FG02-08ER15929; same title: R. James Kirkpatrick, P.I. and A. Ozgur Yazaydin, co-P.I.).

Reasoning with Atomic-Scale Molecular Dynamic Models

ERIC Educational Resources Information Center

Pallant, Amy; Tinker, Robert F.

2004-01-01

The studies reported in this paper are an initial effort to explore the applicability of computational models in introductory science learning. Two instructional interventions are described that use a molecular dynamics model embedded in a set of online learning activities with middle and high school students in 10 classrooms. The studies indicate…

Particle-based methods for multiscale modeling of blood flow in the circulation and in devices: challenges and future directions. Sixth International Bio-Fluid Mechanics Symposium and Workshop March 28-30, 2008 Pasadena, California.

PubMed

Yamaguchi, Takami; Ishikawa, Takuji; Imai, Y; Matsuki, N; Xenos, Mikhail; Deng, Yuefan; Bluestein, Danny

2010-03-01

A major computational challenge for a multiscale modeling is the coupling of disparate length and timescales between molecular mechanics and macroscopic transport, spanning the spatial and temporal scales characterizing the complex processes taking place in flow-induced blood clotting. Flow and pressure effects on a cell-like platelet can be well represented by a continuum mechanics model down to the order of the micrometer level. However, the molecular effects of adhesion/aggregation bonds are on the order of nanometer. A successful multiscale model of platelet response to flow stresses in devices and the ensuing clotting responses should be able to characterize the clotting reactions and their interactions with the flow. This paper attempts to describe a few of the computational methods that were developed in recent years and became available to researchers in the field. They differ from traditional approaches that dominate the field by expanding on prevailing continuum-based approaches, or by completely departing from them, yielding an expanding toolkit that may facilitate further elucidation of the underlying mechanisms of blood flow and the cellular response to it. We offer a paradigm shift by adopting a multidisciplinary approach with fluid dynamics simulations coupled to biophysical and biochemical transport.

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.

Interactive Design Strategy for a Multi-Functional PAMAM Dendrimer-Based Nano-Therapeutic Using Computational Models and Experimental Analysis

PubMed Central

Lee, Inhan; Williams, Christopher R.; Athey, Brian D.; Baker, James R.

2010-01-01

Molecular dynamics simulations of nano-therapeutics as a final product and of all intermediates in the process of generating a multi-functional nano-therapeutic based on a poly(amidoamine) (PAMAM) dendrimer were performed along with chemical analyses of each of them. The actual structures of the dendrimers were predicted, based on potentiometric titration, gel permeation chromatography, and NMR. The chemical analyses determined the numbers of functional molecules, based on the actual structure of the dendrimer. Molecular dynamics simulations calculated the configurations of the intermediates and the radial distributions of functional molecules, based on their numbers. This interactive process between the simulation results and the chemical analyses provided a further strategy to design the next reaction steps and to gain insight into the products at each chemical reaction step. PMID:20700476

Computational and experimental investigation of molecular imprinted polymers for selective extraction of dimethoate and its metabolite omethoate from olive oil.

PubMed

Bakas, Idriss; Oujji, Najwa Ben; Moczko, Ewa; Istamboulie, Georges; Piletsky, Sergey; Piletska, Elena; Ait-Addi, Elhabib; Ait-Ichou, Ihya; Noguer, Thierry; Rouillon, Régis

2013-01-25

This work presents the development of molecularly imprinted polymers (MIPs) for the selective extraction of dimethoate from olive oil. Computational simulations allowed selecting itaconic acid as the monomer showing the highest affinity towards dimethoate. Experimental validation confirmed modelling predictions and showed that the polymer based on IA as functional monomer and omethoate as template molecule displays the highest selectivity for the structurally similar pesticides dimethoate, omethoate and monocrotophos. Molecularly imprinted solid phase extraction (MISPE) method was developed and applied to the clean-up of olive oil extracts. It was found that the most suitable solvents for loading, washing and elution step were respectively hexane, hexane-dichloromethane (85:15%) and methanol. The developed MIPSE was successfully applied to extraction of dimethoate from olive oil, with recovery rates up to 94%. The limits of detection and quantification of the described method were respectively 0.012 and 0.05 μg g(-1). Copyright © 2012 Elsevier B.V. All rights reserved.

Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents.

PubMed

Balakumar, Chandrasekaran; Ramesh, Muthusamy; Tham, Chuin Lean; Khathi, Samukelisiwe Pretty; Kozielski, Frank; Srinivasulu, Cherukupalli; Hampannavar, Girish A; Sayyad, Nisar; Soliman, Mahmoud E; Karpoormath, Rajshekhar

2017-11-29

Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays.

Parallel, stochastic measurement of molecular surface area.

PubMed

Juba, Derek; Varshney, Amitabh

2008-08-01

Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorithms are not well suited. We describe a parallel, stochastic algorithm for molecular surface area computation that maps well to the emerging multi-core architectures. Our algorithm is also progressive, providing a rough estimate of surface area immediately and refining this estimate as time goes on. Furthermore, the algorithm generates points on the molecular surface which can be used for point-based rendering. We demonstrate a GPU implementation of our algorithm and show that it compares favorably with several existing molecular surface computation programs, giving fast estimates of the molecular surface area with good accuracy.

The BioIntelligence Framework: a new computational platform for biomedical knowledge computing.

PubMed

Farley, Toni; Kiefer, Jeff; Lee, Preston; Von Hoff, Daniel; Trent, Jeffrey M; Colbourn, Charles; Mousses, Spyro

2013-01-01

Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.

A polarizable QM/MM approach to the molecular dynamics of amide groups solvated in water

NASA Astrophysics Data System (ADS)

Schwörer, Magnus; Wichmann, Christoph; Tavan, Paul

2016-03-01

The infrared (IR) spectra of polypeptides are dominated by the so-called amide bands. Because they originate from the strongly polar and polarizable amide groups (AGs) making up the backbone, their spectral positions sensitively depend on the local electric fields. Aiming at accurate computations of these IR spectra by molecular dynamics (MD) simulations, which derive atomic forces from a hybrid quantum and molecular mechanics (QM/MM) Hamiltonian, here we consider the effects of solvation in bulk liquid water on the amide bands of the AG model compound N-methyl-acetamide (NMA). As QM approach to NMA we choose grid-based density functional theory (DFT). For the surrounding MM water, we develop, largely based on computations, a polarizable molecular mechanics (PMM) model potential called GP6P, which features six Gaussian electrostatic sources (one induced dipole, five static partial charge distributions) and, therefore, avoids spurious distortions of the DFT electron density in hybrid DFT/PMM simulations. Bulk liquid GP6P is shown to have favorable properties at the thermodynamic conditions of the parameterization and beyond. Lennard-Jones (LJ) parameters of the DFT fragment NMA are optimized by comparing radial distribution functions in the surrounding GP6P liquid with reference data obtained from a "first-principles" DFT-MD simulation. Finally, IR spectra of NMA in GP6P water are calculated from extended DFT/PMM-MD trajectories, in which the NMA is treated by three different DFT functionals (BP, BLYP, B3LYP). Method-specific frequency scaling factors are derived from DFT-MD simulations of isolated NMA. The DFT/PMM-MD simulations with GP6P and with the optimized LJ parameters then excellently predict the effects of aqueous solvation and deuteration observed in the IR spectra of NMA. As a result, the methods required to accurately compute such spectra by DFT/PMM-MD also for larger peptides in aqueous solution are now at hand.

A polarizable QM/MM approach to the molecular dynamics of amide groups solvated in water

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

Schwörer, Magnus; Wichmann, Christoph; Tavan, Paul, E-mail: tavan@physik.uni-muenchen.de

2016-03-21

The infrared (IR) spectra of polypeptides are dominated by the so-called amide bands. Because they originate from the strongly polar and polarizable amide groups (AGs) making up the backbone, their spectral positions sensitively depend on the local electric fields. Aiming at accurate computations of these IR spectra by molecular dynamics (MD) simulations, which derive atomic forces from a hybrid quantum and molecular mechanics (QM/MM) Hamiltonian, here we consider the effects of solvation in bulk liquid water on the amide bands of the AG model compound N-methyl-acetamide (NMA). As QM approach to NMA we choose grid-based density functional theory (DFT). Formore » the surrounding MM water, we develop, largely based on computations, a polarizable molecular mechanics (PMM) model potential called GP6P, which features six Gaussian electrostatic sources (one induced dipole, five static partial charge distributions) and, therefore, avoids spurious distortions of the DFT electron density in hybrid DFT/PMM simulations. Bulk liquid GP6P is shown to have favorable properties at the thermodynamic conditions of the parameterization and beyond. Lennard-Jones (LJ) parameters of the DFT fragment NMA are optimized by comparing radial distribution functions in the surrounding GP6P liquid with reference data obtained from a “first-principles” DFT-MD simulation. Finally, IR spectra of NMA in GP6P water are calculated from extended DFT/PMM-MD trajectories, in which the NMA is treated by three different DFT functionals (BP, BLYP, B3LYP). Method-specific frequency scaling factors are derived from DFT-MD simulations of isolated NMA. The DFT/PMM-MD simulations with GP6P and with the optimized LJ parameters then excellently predict the effects of aqueous solvation and deuteration observed in the IR spectra of NMA. As a result, the methods required to accurately compute such spectra by DFT/PMM-MD also for larger peptides in aqueous solution are now at hand.« less

A third-generation density-functional-theory-based method for calculating canonical molecular orbitals of large molecules.

PubMed

Hirano, Toshiyuki; Sato, Fumitoshi

2014-07-28

We used grid-free modified Cholesky decomposition (CD) to develop a density-functional-theory (DFT)-based method for calculating the canonical molecular orbitals (CMOs) of large molecules. Our method can be used to calculate standard CMOs, analytically compute exchange-correlation terms, and maximise the capacity of next-generation supercomputers. Cholesky vectors were first analytically downscaled using low-rank pivoted CD and CD with adaptive metric (CDAM). The obtained Cholesky vectors were distributed and stored on each computer node in a parallel computer, and the Coulomb, Fock exchange, and pure exchange-correlation terms were calculated by multiplying the Cholesky vectors without evaluating molecular integrals in self-consistent field iterations. Our method enables DFT and massively distributed memory parallel computers to be used in order to very efficiently calculate the CMOs of large molecules.

On the origin of the electrostatic potential difference at a liquid-vacuum interface.

PubMed

Harder, Edward; Roux, Benoît

2008-12-21

The microscopic origin of the interface potential calculated from computer simulations is elucidated by considering a simple model of molecules near an interface. The model posits that molecules are isotropically oriented and their charge density is Gaussian distributed. Molecules that have a charge density that is more negative toward their interior tend to give rise to a negative interface potential relative to the gaseous phase, while charge densities more positive toward their interior give rise to a positive interface potential. The interface potential for the model is compared to the interface potential computed from molecular dynamics simulations of the nonpolar vacuum-methane system and the polar vacuum-water interface system. The computed vacuum-methane interface potential from a molecular dynamics simulation (-220 mV) is captured with quantitative precision by the model. For the vacuum-water interface system, the model predicts a potential of -400 mV compared to -510 mV, calculated from a molecular dynamics simulation. The physical implications of this isotropic contribution to the interface potential is examined using the example of ion solvation in liquid methane.

Characteristics of the mixing volume model with the interactions among spatially distributed particles for Lagrangian simulations of turbulent mixing

NASA Astrophysics Data System (ADS)

Watanabe, Tomoaki; Nagata, Koji

2016-11-01

The mixing volume model (MVM), which is a mixing model for molecular diffusion in Lagrangian simulations of turbulent mixing problems, is proposed based on the interactions among spatially distributed particles in a finite volume. The mixing timescale in the MVM is derived by comparison between the model and the subgrid scale scalar variance equation. A-priori test of the MVM is conducted based on the direct numerical simulations of planar jets. The MVM is shown to predict well the mean effects of the molecular diffusion under various conditions. However, a predicted value of the molecular diffusion term is positively correlated to the exact value in the DNS only when the number of the mixing particles is larger than two. Furthermore, the MVM is tested in the hybrid implicit large-eddy-simulation/Lagrangian-particle-simulation (ILES/LPS). The ILES/LPS with the present mixing model predicts well the decay of the scalar variance in planar jets. This work was supported by JSPS KAKENHI Nos. 25289030 and 16K18013. The numerical simulations presented in this manuscript were carried out on the high performance computing system (NEC SX-ACE) in the Japan Agency for Marine-Earth Science and Technology.

Supercomputer applications in molecular modeling.

PubMed

Gund, T M

1988-01-01

An overview of the functions performed by molecular modeling is given. Molecular modeling techniques benefiting from supercomputing are described, namely, conformation, search, deriving bioactive conformations, pharmacophoric pattern searching, receptor mapping, and electrostatic properties. The use of supercomputers for problems that are computationally intensive, such as protein structure prediction, protein dynamics and reactivity, protein conformations, and energetics of binding is also examined. The current status of supercomputing and supercomputer resources are discussed.

Model-based confirmation of alternative substrates of mitochondrial electron transport chain.

PubMed

Kleessen, Sabrina; Araújo, Wagner L; Fernie, Alisdair R; Nikoloski, Zoran

2012-03-30

Discrimination of metabolic models based on high throughput metabolomics data, reflecting various internal and external perturbations, is essential for identifying the components that contribute to the emerging behavior of metabolic processes. Here, we investigate 12 different models of the mitochondrial electron transport chain (ETC) in Arabidopsis thaliana during dark-induced senescence in order to elucidate the alternative substrates to this metabolic pathway. Our findings demonstrate that the coupling of the proposed computational approach, based on dynamic flux balance analysis, with time-resolved metabolomics data results in model-based confirmations of the hypotheses that, during dark-induced senescence in Arabidopsis, (i) under conditions where the main substrate for the ETC are not fully available, isovaleryl-CoA dehydrogenase and 2-hydroxyglutarate dehydrogenase are able to donate electrons to the ETC, (ii) phytanoyl-CoA does not act even as an indirect substrate of the electron transfer flavoprotein/electron-transfer flavoprotein:ubiquinone oxidoreductase complex, and (iii) the mitochondrial γ-aminobutyric acid transporter has functional significance in maintaining mitochondrial metabolism. Our study provides a basic framework for future in silico studies of alternative pathways in mitochondrial metabolism under extended darkness whereby the role of its components can be computationally discriminated based on available molecular profile data.

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

Kadoura, Ahmad, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Sun, Shuyu, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Siripatana, Adil, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa

In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jones (LJ) particles. The main advantage of such surrogates, once generated, is the capability of accurately computing the needed thermodynamic quantities in a few seconds, thus efficiently replacing the computationally expensive MC molecular simulations. Benefiting from the tremendous computational time reduction, the PC surrogates were used to conduct large-scale optimization in order to propose single-site LJ models for several simple molecules. Experimental data, a set of supercriticalmore » isotherms, and part of the two-phase envelope, of several pure components were used for tuning the LJ parameters (ε, σ). Based on the conducted optimization, excellent fit was obtained for different noble gases (Ar, Kr, and Xe) and other small molecules (CH{sub 4}, N{sub 2}, and CO). On the other hand, due to the simplicity of the LJ model used, dramatic deviations between simulation and experimental data were observed, especially in the two-phase region, for more complex molecules such as CO{sub 2} and C{sub 2} H{sub 6}.« less

Computer Aided Drug Design: Success and Limitations.

PubMed

Baig, Mohammad Hassan; Ahmad, Khurshid; Roy, Sudeep; Ashraf, Jalaluddin Mohammad; Adil, Mohd; Siddiqui, Mohammad Haris; Khan, Saif; Kamal, Mohammad Amjad; Provazník, Ivo; Choi, Inho

2016-01-01

Over the last few decades, computer-aided drug design has emerged as a powerful technique playing a crucial role in the development of new drug molecules. Structure-based drug design and ligand-based drug design are two methods commonly used in computer-aided drug design. In this article, we discuss the theory behind both methods, as well as their successful applications and limitations. To accomplish this, we reviewed structure based and ligand based virtual screening processes. Molecular dynamics simulation, which has become one of the most influential tool for prediction of the conformation of small molecules and changes in their conformation within the biological target, has also been taken into account. Finally, we discuss the principles and concepts of molecular docking, pharmacophores and other methods used in computer-aided drug design.

URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries.

PubMed

Drawert, Brian; Engblom, Stefan; Hellander, Andreas

2012-06-22

Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both computationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling. We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development. In this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. Through the combined efforts of algorithm development and improved modeling accuracy, increasingly complex biological models become feasible to study through computational methods. URDME is freely available at http://www.urdme.org.

Quantum wavepacket ab initio molecular dynamics: an approach for computing dynamically averaged vibrational spectra including critical nuclear quantum effects.

PubMed

Sumner, Isaiah; Iyengar, Srinivasan S

2007-10-18

We have introduced a computational methodology to study vibrational spectroscopy in clusters inclusive of critical nuclear quantum effects. This approach is based on the recently developed quantum wavepacket ab initio molecular dynamics method that combines quantum wavepacket dynamics with ab initio molecular dynamics. The computational efficiency of the dynamical procedure is drastically improved (by several orders of magnitude) through the utilization of wavelet-based techniques combined with the previously introduced time-dependent deterministic sampling procedure measure to achieve stable, picosecond length, quantum-classical dynamics of electrons and nuclei in clusters. The dynamical information is employed to construct a novel cumulative flux/velocity correlation function, where the wavepacket flux from the quantized particle is combined with classical nuclear velocities to obtain the vibrational density of states. The approach is demonstrated by computing the vibrational density of states of [Cl-H-Cl]-, inclusive of critical quantum nuclear effects, and our results are in good agreement with experiment. A general hierarchical procedure is also provided, based on electronic structure harmonic frequencies, classical ab initio molecular dynamics, computation of nuclear quantum-mechanical eigenstates, and employing quantum wavepacket ab initio dynamics to understand vibrational spectroscopy in hydrogen-bonded clusters that display large degrees of anharmonicities.

Overview of the SAMPL5 host–guest challenge: Are we doing better?

PubMed Central

Yin, Jian; Henriksen, Niel M.; Slochower, David R.; Shirts, Michael R.; Chiu, Michael W.; Mobley, David L.; Gilson, Michael K.

2016-01-01

The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein–ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host–guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host–guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host–guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements. PMID:27658802

Overview of the SAMPL5 host-guest challenge: Are we doing better?

PubMed

Yin, Jian; Henriksen, Niel M; Slochower, David R; Shirts, Michael R; Chiu, Michael W; Mobley, David L; Gilson, Michael K

2017-01-01

The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein-ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host-guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host-guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host-guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements.

Thermal Rate Coefficients for the Astrochemical Process C + CH+ → C2+ + H by Ring Polymer Molecular Dynamics.

PubMed

Rampino, Sergio; Suleimanov, Yury V

2016-12-22

Thermal rate coefficients for the astrochemical reaction C + CH + → C 2 + + H were computed in the temperature range 20-300 K by using novel rate theory based on ring polymer molecular dynamics (RPMD) on a recently published bond-order based potential energy surface and compared with previous Langevin capture model (LCM) and quasi-classical trajectory (QCT) calculations. Results show that there is a significant discrepancy between the RPMD rate coefficients and the previous theoretical results that can lead to overestimation of the rate coefficients for the title reaction by several orders of magnitude at very low temperatures. We argue that this can be attributed to a very challenging energy profile along the reaction coordinate for the title reaction, not taken into account in extenso by either the LCM or QCT approximation. In the absence of any rigorous quantum mechanical or experimental results, the computed RPMD rate coefficients represent state-of-the-art estimates to be included in astrochemical databases and kinetic networks.

Multi-Dielectric Brownian Dynamics and Design-Space-Exploration Studies of Permeation in Ion Channels.

PubMed

Siksik, May; Krishnamurthy, Vikram

2017-09-01

This paper proposes a multi-dielectric Brownian dynamics simulation framework for design-space-exploration (DSE) studies of ion-channel permeation. The goal of such DSE studies is to estimate the channel modeling-parameters that minimize the mean-squared error between the simulated and expected "permeation characteristics." To address this computational challenge, we use a methodology based on statistical inference that utilizes the knowledge of channel structure to prune the design space. We demonstrate the proposed framework and DSE methodology using a case study based on the KcsA ion channel, in which the design space is successfully reduced from a 6-D space to a 2-D space. Our results show that the channel dielectric map computed using the framework matches with that computed directly using molecular dynamics with an error of 7%. Finally, the scalability and resolution of the model used are explored, and it is shown that the memory requirements needed for DSE remain constant as the number of parameters (degree of heterogeneity) increases.

Towards computational materials design from first principles using alchemical changes and derivatives.

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

von Lilienfeld-Toal, Otto Anatole

2010-11-01

The design of new materials with specific physical, chemical, or biological properties is a central goal of much research in materials and medicinal sciences. Except for the simplest and most restricted cases brute-force computational screening of all possible compounds for interesting properties is beyond any current capacity due to the combinatorial nature of chemical compound space (set of stoichiometries and configurations). Consequently, when it comes to computationally optimizing more complex systems, reliable optimization algorithms must not only trade-off sufficient accuracy and computational speed of the models involved, they must also aim for rapid convergence in terms of number of compoundsmore » 'visited'. I will give an overview on recent progress on alchemical first principles paths and gradients in compound space that appear to be promising ingredients for more efficient property optimizations. Specifically, based on molecular grand canonical density functional theory an approach will be presented for the construction of high-dimensional yet analytical property gradients in chemical compound space. Thereafter, applications to molecular HOMO eigenvalues, catalyst design, and other problems and systems shall be discussed.« less

Modelling the spread of innovation in wild birds.

PubMed

Shultz, Thomas R; Montrey, Marcel; Aplin, Lucy M

2017-06-01

We apply three plausible algorithms in agent-based computer simulations to recent experiments on social learning in wild birds. Although some of the phenomena are simulated by all three learning algorithms, several manifestations of social conformity bias are simulated by only the approximate majority (AM) algorithm, which has roots in chemistry, molecular biology and theoretical computer science. The simulations generate testable predictions and provide several explanatory insights into the diffusion of innovation through a population. The AM algorithm's success raises the possibility of its usefulness in studying group dynamics more generally, in several different scientific domains. Our differential-equation model matches simulation results and provides mathematical insights into the dynamics of these algorithms. © 2017 The Author(s).

Computational challenges in modeling gene regulatory events.

PubMed

Pataskar, Abhijeet; Tiwari, Vijay K

2016-10-19

Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating "omics" data and subsequently modeling the gene-regulatory events. Toward this end, computational biology should keep pace with evolving experimental procedures and data availability. This article gives an exemplified account of the current computational challenges in molecular biology.

Adiabatic quantum computing with spin qubits hosted by molecules.

PubMed

Yamamoto, Satoru; Nakazawa, Shigeaki; Sugisaki, Kenji; Sato, Kazunobu; Toyota, Kazuo; Shiomi, Daisuke; Takui, Takeji

2015-01-28

A molecular spin quantum computer (MSQC) requires electron spin qubits, which pulse-based electron spin/magnetic resonance (ESR/MR) techniques can afford to manipulate for implementing quantum gate operations in open shell molecular entities. Importantly, nuclear spins, which are topologically connected, particularly in organic molecular spin systems, are client qubits, while electron spins play a role of bus qubits. Here, we introduce the implementation for an adiabatic quantum algorithm, suggesting the possible utilization of molecular spins with optimized spin structures for MSQCs. We exemplify the utilization of an adiabatic factorization problem of 21, compared with the corresponding nuclear magnetic resonance (NMR) case. Two molecular spins are selected: one is a molecular spin composed of three exchange-coupled electrons as electron-only qubits and the other an electron-bus qubit with two client nuclear spin qubits. Their electronic spin structures are well characterized in terms of the quantum mechanical behaviour in the spin Hamiltonian. The implementation of adiabatic quantum computing/computation (AQC) has, for the first time, been achieved by establishing ESR/MR pulse sequences for effective spin Hamiltonians in a fully controlled manner of spin manipulation. The conquered pulse sequences have been compared with the NMR experiments and shown much faster CPU times corresponding to the interaction strength between the spins. Significant differences are shown in rotational operations and pulse intervals for ESR/MR operations. As a result, we suggest the advantages and possible utilization of the time-evolution based AQC approach for molecular spin quantum computers and molecular spin quantum simulators underlain by sophisticated ESR/MR pulsed spin technology.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

PubMed Central

Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

2015-01-01

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310–323. doi: 10.1002/wcms.1220 PMID:26753008

Toward ab initio molecular dynamics modeling for sum-frequency generation spectra; an efficient algorithm based on surface-specific velocity-velocity correlation function.

PubMed

Ohto, Tatsuhiko; Usui, Kota; Hasegawa, Taisuke; Bonn, Mischa; Nagata, Yuki

2015-09-28

Interfacial water structures have been studied intensively by probing the O-H stretch mode of water molecules using sum-frequency generation (SFG) spectroscopy. This surface-specific technique is finding increasingly widespread use, and accordingly, computational approaches to calculate SFG spectra using molecular dynamics (MD) trajectories of interfacial water molecules have been developed and employed to correlate specific spectral signatures with distinct interfacial water structures. Such simulations typically require relatively long (several nanoseconds) MD trajectories to allow reliable calculation of the SFG response functions through the dipole moment-polarizability time correlation function. These long trajectories limit the use of computationally expensive MD techniques such as ab initio MD and centroid MD simulations. Here, we present an efficient algorithm determining the SFG response from the surface-specific velocity-velocity correlation function (ssVVCF). This ssVVCF formalism allows us to calculate SFG spectra using a MD trajectory of only ∼100 ps, resulting in the substantial reduction of the computational costs, by almost an order of magnitude. We demonstrate that the O-H stretch SFG spectra at the water-air interface calculated by using the ssVVCF formalism well reproduce those calculated by using the dipole moment-polarizability time correlation function. Furthermore, we applied this ssVVCF technique for computing the SFG spectra from the ab initio MD trajectories with various density functionals. We report that the SFG responses computed from both ab initio MD simulations and MD simulations with an ab initio based force field model do not show a positive feature in its imaginary component at 3100 cm(-1).

Ab initio RNA folding by discrete molecular dynamics: From structure prediction to folding mechanisms

PubMed Central

Ding, Feng; Sharma, Shantanu; Chalasani, Poornima; Demidov, Vadim V.; Broude, Natalia E.; Dokholyan, Nikolay V.

2008-01-01

RNA molecules with novel functions have revived interest in the accurate prediction of RNA three-dimensional (3D) structure and folding dynamics. However, existing methods are inefficient in automated 3D structure prediction. Here, we report a robust computational approach for rapid folding of RNA molecules. We develop a simplified RNA model for discrete molecular dynamics (DMD) simulations, incorporating base-pairing and base-stacking interactions. We demonstrate correct folding of 150 structurally diverse RNA sequences. The majority of DMD-predicted 3D structures have <4 Å deviations from experimental structures. The secondary structures corresponding to the predicted 3D structures consist of 94% native base-pair interactions. Folding thermodynamics and kinetics of tRNAPhe, pseudoknots, and mRNA fragments in DMD simulations are in agreement with previous experimental findings. Folding of RNA molecules features transient, non-native conformations, suggesting non-hierarchical RNA folding. Our method allows rapid conformational sampling of RNA folding, with computational time increasing linearly with RNA length. We envision this approach as a promising tool for RNA structural and functional analyses. PMID:18456842

Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein-Ligand Docking Method.

PubMed

Shin, Woong-Hee; Kihara, Daisuke

2018-01-01

Virtual screening is a computational technique for predicting a potent binding compound for a receptor protein from a ligand library. It has been a widely used in the drug discovery field to reduce the efforts of medicinal chemists to find hit compounds by experiments.Here, we introduce our novel structure-based virtual screening program, PL-PatchSurfer, which uses molecular surface representation with the three-dimensional Zernike descriptors, which is an effective mathematical representation for identifying physicochemical complementarities between local surfaces of a target protein and a ligand. The advantage of the surface-patch description is its tolerance on a receptor and compound structure variation. PL-PatchSurfer2 achieves higher accuracy on apo form and computationally modeled receptor structures than conventional structure-based virtual screening programs. Thus, PL-PatchSurfer2 opens up an opportunity for targets that do not have their crystal structures. The program is provided as a stand-alone program at http://kiharalab.org/plps2 . We also provide files for two ligand libraries, ChEMBL and ZINC Drug-like.

A COMPUTER MODELING STUDY OF BINDING PROPERTIES OF CHIRAL NUCLEOPEPTIDE FOR BIOMEDICAL APPLICATIONS.

PubMed

Pirtskhalava, M; Egoyan, A; Mirtskhulava, M; Roviello, G

2017-12-01

Nucleopeptides often show interesting properties of molecular binding that render them good candidates for development of innovative drugs for anticancer and antiviral therapies. In this work we present results of computer modeling of interactions between the molecules of hexathymine nucleopeptide (T6) and poly rA RNA (A18). The results of geometry optimization calculated using Hyperchem software and our own computer program for molecular docking show that molecules establish stable complexes due to the complementary-nucleobase interaction and the electrostatic interaction between the negative phosphate group of poly rA and the positively-charged residues present in the cationic nucleopeptide structure. Computer modeling makes it possible to find the optimal binding configuration of the molecules of a nucleopeptide and poly rA RNA and to estimate the binding energy between the molecules.

Combining Rosetta with molecular dynamics (MD): A benchmark of the MD-based ensemble protein design.

PubMed

Ludwiczak, Jan; Jarmula, Adam; Dunin-Horkawicz, Stanislaw

2018-07-01

Computational protein design is a set of procedures for computing amino acid sequences that will fold into a specified structure. Rosetta Design, a commonly used software for protein design, allows for the effective identification of sequences compatible with a given backbone structure, while molecular dynamics (MD) simulations can thoroughly sample near-native conformations. We benchmarked a procedure in which Rosetta design is started on MD-derived structural ensembles and showed that such a combined approach generates 20-30% more diverse sequences than currently available methods with only a slight increase in computation time. Importantly, the increase in diversity is achieved without a loss in the quality of the designed sequences assessed by their resemblance to natural sequences. We demonstrate that the MD-based procedure is also applicable to de novo design tasks started from backbone structures without any sequence information. In addition, we implemented a protocol that can be used to assess the stability of designed models and to select the best candidates for experimental validation. In sum our results demonstrate that the MD ensemble-based flexible backbone design can be a viable method for protein design, especially for tasks that require a large pool of diverse sequences. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

Mobile modeling in the molecular sciences

EPA Science Inventory

The art of modeling in the molecular sciences is highly dependent on both the available computational technology, underlying data, and ability to collaborate. With the ever increasing market share of mobile devices, it is assumed by many that tablets will overtake laptops as the...

Testing the limits of sensitivity in a solid-state structural investigation by combined X-ray powder diffraction, solid-state NMR, and molecular modelling.

PubMed

Filip, Xenia; Borodi, Gheorghe; Filip, Claudiu

2011-10-28

A solid state structural investigation of ethoxzolamide is performed on microcrystalline powder by using a multi-technique approach that combines X-ray powder diffraction (XRPD) data analysis based on direct space methods with information from (13)C((15)N) solid-state Nuclear Magnetic Resonance (SS-NMR) and molecular modeling. Quantum chemical computations of the crystal were employed for geometry optimization and chemical shift calculations based on the Gauge Including Projector Augmented-Wave (GIPAW) method, whereas a systematic search in the conformational space was performed on the isolated molecule using a molecular mechanics (MM) approach. The applied methodology proved useful for: (i) removing ambiguities in the XRPD crystal structure determination process and further refining the derived structure solutions, and (ii) getting important insights into the relationship between the complex network of non-covalent interactions and the induced supra-molecular architectures/crystal packing patterns. It was found that ethoxzolamide provides an ideal case study for testing the accuracy with which this methodology allows to distinguish between various structural features emerging from the analysis of the powder diffraction data. This journal is © the Owner Societies 2011

SpectraPlot.com: Integrated spectroscopic modeling of atomic and molecular gases

NASA Astrophysics Data System (ADS)

Goldenstein, Christopher S.; Miller, Victor A.; Mitchell Spearrin, R.; Strand, Christopher L.

2017-10-01

SpectraPlot is a web-based application for simulating spectra of atomic and molecular gases. At the time this manuscript was written, SpectraPlot consisted of four primary tools for calculating: (1) atomic and molecular absorption spectra, (2) atomic and molecular emission spectra, (3) transition linestrengths, and (4) blackbody emission spectra. These tools currently employ the NIST ASD, HITRAN2012, and HITEMP2010 databases to perform line-by-line simulations of spectra. SpectraPlot employs a modular, integrated architecture, enabling multiple simulations across multiple databases and/or thermodynamic conditions to be visualized in an interactive plot window. The primary objective of this paper is to describe the architecture and spectroscopic models employed by SpectraPlot in order to provide its users with the knowledge required to understand the capabilities and limitations of simulations performed using SpectraPlot. Further, this manuscript discusses the accuracy of several underlying approximations used to decrease computational time, in particular, the use of far-wing cutoff criteria.

Analytic derivative couplings and first-principles exciton/phonon coupling constants for an ab initio Frenkel-Davydov exciton model: Theory, implementation, and application to compute triplet exciton mobility parameters for crystalline tetracene.

PubMed

Morrison, Adrian F; Herbert, John M

2017-06-14

Recently, we introduced an ab initio version of the Frenkel-Davydov exciton model for computing excited-state properties of molecular crystals and aggregates. Within this model, supersystem excited states are approximated as linear combinations of excitations localized on molecular sites, and the electronic Hamiltonian is constructed and diagonalized in a direct-product basis of non-orthogonal configuration state functions computed for isolated fragments. Here, we derive and implement analytic derivative couplings for this model, including nuclear derivatives of the natural transition orbital and symmetric orthogonalization transformations that are part of the approximation. Nuclear derivatives of the exciton Hamiltonian's matrix elements, required in order to compute the nonadiabatic couplings, are equivalent to the "Holstein" and "Peierls" exciton/phonon couplings that are widely discussed in the context of model Hamiltonians for energy and charge transport in organic photovoltaics. As an example, we compute the couplings that modulate triplet exciton transport in crystalline tetracene, which is relevant in the context of carrier diffusion following singlet exciton fission.

Accuracy assessment of the linear Poisson-Boltzmann equation and reparametrization of the OBC generalized Born model for nucleic acids and nucleic acid-protein complexes.

PubMed

Fogolari, Federico; Corazza, Alessandra; Esposito, Gennaro

2015-04-05

The generalized Born model in the Onufriev, Bashford, and Case (Onufriev et al., Proteins: Struct Funct Genet 2004, 55, 383) implementation has emerged as one of the best compromises between accuracy and speed of computation. For simulations of nucleic acids, however, a number of issues should be addressed: (1) the generalized Born model is based on a linear model and the linearization of the reference Poisson-Boltmann equation may be questioned for highly charged systems as nucleic acids; (2) although much attention has been given to potentials, solvation forces could be much less sensitive to linearization than the potentials; and (3) the accuracy of the Onufriev-Bashford-Case (OBC) model for nucleic acids depends on fine tuning of parameters. Here, we show that the linearization of the Poisson Boltzmann equation has mild effects on computed forces, and that with optimal choice of the OBC model parameters, solvation forces, essential for molecular dynamics simulations, agree well with those computed using the reference Poisson-Boltzmann model. © 2015 Wiley Periodicals, Inc.

Effect of Material Ion Exchanges on the Mechanical Stiffness Properties and Shear Deformation of Hydrated Cement Material Chemistry Structure C-S-H Jennit - A Computational Modeling Study

DTIC Science & Technology

2014-01-01

Study Material properties and performance are governed by material molecular chemistry structures and molecular level interactions. Methods to...understand relationships between the material properties and performance and their correlation to the molecular level chemistry and morphology, and thus...find ways of manipulating and adjusting matters at the atomistic level in order to improve material performance are required. A computational material

The computation of lipophilicities of ⁶⁴Cu PET systems based on a novel approach for fluctuating charges.

PubMed

Comba, Peter; Martin, Bodo; Sanyal, Avik; Stephan, Holger

2013-08-21

A QSPR scheme for the computation of lipophilicities of ⁶⁴Cu complexes was developed with a training set of 24 tetraazamacrocylic and bispidine-based Cu(II) compounds and their experimentally available 1-octanol-water distribution coefficients. A minimum number of physically meaningful parameters were used in the scheme, and these are primarily based on data available from molecular mechanics calculations, using an established force field for Cu(II) complexes and a recently developed scheme for the calculation of fluctuating atomic charges. The developed model was also applied to an independent validation set and was found to accurately predict distribution coefficients of potential ⁶⁴Cu PET (positron emission tomography) systems. A possible next step would be the development of a QSAR-based biodistribution model to track the uptake of imaging agents in different organs and tissues of the body. It is expected that such simple, empirical models of lipophilicity and biodistribution will be very useful in the design and virtual screening of positron emission tomography (PET) imaging agents.

[Study on the dynamic model with supercritical CO2 fluid extracting the lipophilic components in Panax notoginseng].

PubMed

Duan, Xian-Chun; Wang, Yong-Zhong; Zhang, Jun-Ru; Luo, Huan; Zhang, Heng; Xia, Lun-Zhu

2011-08-01

To establish a dynamics model for extracting the lipophilic components in Panax notoginseng with supercritical carbon dioxide (CO2). Based on the theory of counter-flow mass transfer and the molecular mass transfer between the material and the supercritical CO2 fluid under differential mass-conservation equation, a dynamics model was established and computed to compare forecasting result with the experiment process. A dynamics model has been established for supercritical CO2 to extract the lipophilic components in Panax notoginseng, the computed result of this model was consistent with the experiment process basically. The supercritical fluid extract dynamics model established in this research can expound the mechanism in the extract process of which lipophilic components of Panax notoginseng dissolve the mass transfer and is tallied with the actual extract process. This provides certain instruction for the supercritical CO2 fluid extract' s industrialization enlargement.

Complex molecular assemblies at hand via interactive simulations.

PubMed

Delalande, Olivier; Férey, Nicolas; Grasseau, Gilles; Baaden, Marc

2009-11-30

Studying complex molecular assemblies interactively is becoming an increasingly appealing approach to molecular modeling. Here we focus on interactive molecular dynamics (IMD) as a textbook example for interactive simulation methods. Such simulations can be useful in exploring and generating hypotheses about the structural and mechanical aspects of biomolecular interactions. For the first time, we carry out low-resolution coarse-grain IMD simulations. Such simplified modeling methods currently appear to be more suitable for interactive experiments and represent a well-balanced compromise between an important gain in computational speed versus a moderate loss in modeling accuracy compared to higher resolution all-atom simulations. This is particularly useful for initial exploration and hypothesis development for rare molecular interaction events. We evaluate which applications are currently feasible using molecular assemblies from 1900 to over 300,000 particles. Three biochemical systems are discussed: the guanylate kinase (GK) enzyme, the outer membrane protease T and the soluble N-ethylmaleimide-sensitive factor attachment protein receptors complex involved in membrane fusion. We induce large conformational changes, carry out interactive docking experiments, probe lipid-protein interactions and are able to sense the mechanical properties of a molecular model. Furthermore, such interactive simulations facilitate exploration of modeling parameters for method improvement. For the purpose of these simulations, we have developed a freely available software library called MDDriver. It uses the IMD protocol from NAMD and facilitates the implementation and application of interactive simulations. With MDDriver it becomes very easy to render any particle-based molecular simulation engine interactive. Here we use its implementation in the Gromacs software as an example. Copyright 2009 Wiley Periodicals, Inc.

Computational Nanomechanics of Carbon Nanotubes and Composites

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Wei, Chenyu; Cho, Kyeongjae; Biegel, Bryan (Technical Monitor)

2002-01-01

Nanomechanics of individual carbon and boron-nitride nanotubes and their application as reinforcing fibers in polymer composites has been reviewed with interplay of theoretical modeling, computer simulations and experimental observations. The emphasis in this work is on elucidating the multi-length scales of the problems involved, and of different simulation techniques that are needed to address specific characteristics of individual nanotubes and nanotube polymer-matrix interfaces. Classical molecular dynamics simulations are shown to be sufficient to describe the generic behavior such as strength and stiffness modulus but are inadequate to describe elastic limit and nature of plastic buckling at large strength. Quantum molecular dynamics simulations are shown to bring out explicit atomic nature dependent behavior of these nanoscale materials objects that are not accessible either via continuum mechanics based descriptions or through classical molecular dynamics based simulations. As examples, we discus local plastic collapse of carbon nanotubes under axial compression and anisotropic plastic buckling of boron-nitride nanotubes. Dependence of the yield strain on the strain rate is addressed through temperature dependent simulations, a transition-state-theory based model of the strain as a function of strain rate and simulation temperature is presented, and in all cases extensive comparisons are made with experimental observations. Mechanical properties of nanotube-polymer composite materials are simulated with diverse nanotube-polymer interface structures (with van der Waals interaction). The atomistic mechanisms of the interface toughening for optimal load transfer through recycling, high-thermal expansion and diffusion coefficient composite formation above glass transition temperature, and enhancement of Young's modulus on addition of nanotubes to polymer are discussed and compared with experimental observations.

Molecular modelling of protein-protein/protein-solvent interactions

NASA Astrophysics Data System (ADS)

Luchko, Tyler

The inner workings of individual cells are based on intricate networks of protein-protein interactions. However, each of these individual protein interactions requires a complex physical interaction between proteins and their aqueous environment at the atomic scale. In this thesis, molecular dynamics simulations are used in three theoretical studies to gain insight at the atomic scale about protein hydration, protein structure and tubulin-tubulin (protein-protein) interactions, as found in microtubules. Also presented, in a fourth project, is a molecular model of solvation coupled with the Amber molecular modelling package, to facilitate further studies without the need of explicitly modelled water. Basic properties of a minimally solvated protein were calculated through an extended study of myoglobin hydration with explicit solvent, directly investigating water and protein polarization. Results indicate a close correlation between polarization of both water and protein and the onset of protein function. The methodology of explicit solvent molecular dynamics was further used to study tubulin and microtubules. Extensive conformational sampling of the carboxy-terminal tails of 8-tubulin was performed via replica exchange molecular dynamics, allowing the characterisation of the flexibility, secondary structure and binding domains of the C-terminal tails through statistical analysis methods. Mechanical properties of tubulin and microtubules were calculated with adaptive biasing force molecular dynamics. The function of the M-loop in microtubule stability was demonstrated in these simulations. The flexibility of this loop allowed constant contacts between the protofilaments to be maintained during simulations while the smooth deformation provided a spring-like restoring force. Additionally, calculating the free energy profile between the straight and bent tubulin configurations was used to test the proposed conformational change in tubulin, thought to cause microtubule destabilization. No conformational change was observed but a nucleotide dependent 'softening' of the interaction was found instead, suggesting that an entropic force in a microtubule configuration could be the mechanism of microtubule collapse. Finally, to overcome much of the computational costs associated with explicit soIvent calculations, a new combination of molecular dynamics with the 3D-reference interaction site model (3D-RISM) of solvation was integrated into the Amber molecular dynamics package. Our implementation of 3D-RISM shows excellent agreement with explicit solvent free energy calculations. Several optimisation techniques, including a new multiple time step method, provide a nearly 100 fold performance increase, giving similar computational performance to explicit solvent.

Examination of the nature of lattice matched III V semiconductor interfaces using computer simulated molecular beam epitaxial growth I. AC/BC interfaces

NASA Astrophysics Data System (ADS)

Thomsen, M.; Ghaisas, S. V.; Madhukar, A.

1987-07-01

A previously developed computer simulation of molecular beam epitaxial growth of III-V semiconductors based on the configuration dependent reactive incorporation (CDRI) model is extended to allow for two different cation species. Attention is focussed on examining the nature of interfaces formed in lattice matched quantum well structures of the form AC/BC/AC(100). We consider cation species with substantially different effective diffusion lengths, as is the case with Al and Ga during the growth of their respective As compounds. The degree of intermixing occuring at the interface is seen to be dependent upon, among other growth parameters, the pressure of the group V species during growth. Examination of an intraplanar order parameter at the interfaces reveals the existence of short range clustering of the cation species.

Dicopper(II) metallacyclophanes as multifunctional magnetic devices: a joint experimental and computational study.

PubMed

Castellano, María; Ruiz-García, Rafael; Cano, Joan; Ferrando-Soria, Jesús; Pardo, Emilio; Fortea-Pérez, Francisco R; Stiriba, Salah-Eddine; Julve, Miguel; Lloret, Francesc

2015-03-17

Metallosupramolecular complexes constitute an important advance in the emerging fields of molecular spintronics and quantum computation and a useful platform in the development of active components of spintronic circuits and quantum computers for applications in information processing and storage. The external control of chemical reactivity (electro- and photochemical) and physical properties (electronic and magnetic) in metallosupramolecular complexes is a current challenge in supramolecular coordination chemistry, which lies at the interface of several other supramolecular disciplines, including electro-, photo-, and magnetochemistry. The specific control of current flow or spin delocalization through a molecular assembly in response to one or many input signals leads to the concept of developing a molecule-based spintronics that can be viewed as a potential alternative to the classical molecule-based electronics. A great variety of factors can influence over these electronically or magnetically coupled, metallosupramolecular complexes in a reversible manner, electronic or photonic external stimuli being the most promising ones. The response ability of the metal centers and/or the organic bridging ligands to the application of an electric field or light irradiation, together with the geometrical features that allow the precise positioning in space of substituent groups, make these metal-organic systems particularly suitable to build highly integrated molecular spintronic circuits. In this Account, we describe the chemistry and physics of dinuclear copper(II) metallacyclophanes with oxamato-containing dinucleating ligands featuring redox- and photoactive aromatic spacers. Our recent works on dicopper(II) metallacyclophanes and earlier ones on related organic cyclophanes are now compared in a critical manner. Special focus is placed on the ligand design as well as in the combination of experimental and computational methods to demonstrate the multifunctionality nature of these metallosupramolecular complexes. This new class of oxamato-based dicopper(II) metallacyclophanes affords an excellent synthetic and theoretical set of models for both chemical and physical fundamental studies on redox- and photo-triggered, long-distance electron exchange phenomena, which are two major topics in molecular magnetism and molecular electronics. Apart from their use as ground tests for the fundamental research on the relative importance of the spin delocalization and spin polarization mechanisms of the electron exchange interaction through extended π-conjugated aromatic ligands in polymetallic complexes, oxamato-based dicopper(II) metallacyclophanes possessing spin-containing electro- and chromophores at the metal and/or the ligand counterparts emerge as potentially active (magnetic and electronic) molecular components to build a metal-based spintronic circuit. They are thus unique examples of multifunctional magnetic complexes to get single-molecule spintronic devices by controlling and allowing the spin communication, when serving as molecular magnetic couplers and wires, or by exhibiting bistable spin behavior, when acting as molecular magnetic rectifiers and switches. Oxamato-based dicopper(II) metallacyclophanes also emerge as potential candidates for the study of coherent electron transport through single molecules, both experimentally and theoretically. The results presented herein, which are a first step in the metallosupramolecular approach to molecular spintronics, intend to attract the attention of physicists and materials scientists with a large expertice in the manipulation and measurement of single-molecule electron transport properties, as well as in the processing and addressing of molecules on different supports.

eSBMTools 1.0: enhanced native structure-based modeling tools.

PubMed

Lutz, Benjamin; Sinner, Claude; Heuermann, Geertje; Verma, Abhinav; Schug, Alexander

2013-11-01

Molecular dynamics simulations provide detailed insights into the structure and function of biomolecular systems. Thus, they complement experimental measurements by giving access to experimentally inaccessible regimes. Among the different molecular dynamics techniques, native structure-based models (SBMs) are based on energy landscape theory and the principle of minimal frustration. Typically used in protein and RNA folding simulations, they coarse-grain the biomolecular system and/or simplify the Hamiltonian resulting in modest computational requirements while achieving high agreement with experimental data. eSBMTools streamlines running and evaluating SBM in a comprehensive package and offers high flexibility in adding experimental- or bioinformatics-derived restraints. We present a software package that allows setting up, modifying and evaluating SBM for both RNA and proteins. The implemented workflows include predicting protein complexes based on bioinformatics-derived inter-protein contact information, a standardized setup of protein folding simulations based on the common PDB format, calculating reaction coordinates and evaluating the simulation by free-energy calculations with weighted histogram analysis method or by phi-values. The modules interface with the molecular dynamics simulation program GROMACS. The package is open source and written in architecture-independent Python2. http://sourceforge.net/projects/esbmtools/. alexander.schug@kit.edu. Supplementary data are available at Bioinformatics online.

Computational identification of gene–social environment interaction at the human IL6 locus

PubMed Central

Cole, Steven W.; Arevalo, Jesusa M. G.; Takahashi, Rie; Sloan, Erica K.; Lutgendorf, Susan K.; Sood, Anil K.; Sheridan, John F.; Seeman, Teresa E.

2010-01-01

To identify genetic factors that interact with social environments to impact human health, we used a bioinformatic strategy that couples expression array–based detection of environmentally responsive transcription factors with in silico discovery of regulatory polymorphisms to predict genetic loci that modulate transcriptional responses to stressful environments. Tests of one predicted interaction locus in the human IL6 promoter (SNP rs1800795) verified that it modulates transcriptional response to β-adrenergic activation of the GATA1 transcription factor in vitro. In vivo validation studies confirmed links between adverse social conditions and increased transcription of GATA1 target genes in primary neural, immune, and cancer cells. Epidemiologic analyses verified the health significance of those molecular interactions by documenting increased 10-year mortality risk associated with late-life depressive symptoms that occurred solely for homozygous carriers of the GATA1-sensitive G allele of rs1800795. Gating of depression-related mortality risk by IL6 genotype pertained only to inflammation-related causes of death and was associated with increased chronic inflammation as indexed by plasma C-reactive protein. Computational modeling of molecular interactions, in vitro biochemical analyses, in vivo animal modeling, and human molecular epidemiologic analyses thus converge in identifying β-adrenergic activation of GATA1 as a molecular pathway by which social adversity can alter human health risk selectively depending on individual genetic status at the IL6 locus. PMID:20176930

Partially Glycosylated Dendrimers Block MD-2 and Prevent TLR4-MD-2-LPS Complex Mediated Cytokine Responses

PubMed Central

Barata, Teresa S.; Teo, Ian; Brocchini, Steve; Zloh, Mire; Shaunak, Sunil

2011-01-01

The crystal structure of the TLR4-MD-2-LPS complex responsible for triggering powerful pro-inflammatory cytokine responses has recently become available. Central to cell surface complex formation is binding of lipopolysaccharide (LPS) to soluble MD-2. We have previously shown, in biologically based experiments, that a generation 3.5 PAMAM dendrimer with 64 peripheral carboxylic acid groups acts as an antagonist of pro-inflammatory cytokine production after surface modification with 8 glucosamine molecules. We have also shown using molecular modelling approaches that this partially glycosylated dendrimer has the flexibility, cluster density, surface electrostatic charge, and hydrophilicity to make it a therapeutically useful antagonist of complex formation. These studies enabled the computational study of the interactions of the unmodified dendrimer, glucosamine, and of the partially glycosylated dendrimer with TLR4 and MD-2 using molecular docking and molecular dynamics techniques. They demonstrate that dendrimer glucosamine forms co-operative electrostatic interactions with residues lining the entrance to MD-2's hydrophobic pocket. Crucially, dendrimer glucosamine interferes with the electrostatic binding of: (i) the 4′phosphate on the di-glucosamine of LPS to Ser118 on MD-2; (ii) LPS to Lys91 on MD-2; (iii) the subsequent binding of TLR4 to Tyr102 on MD-2. This is followed by additional co-operative interactions between several of the dendrimer glucosamine's carboxylic acid branches and MD-2. Collectively, these interactions block the entry of the lipid chains of LPS into MD-2's hydrophobic pocket, and also prevent TLR4-MD-2-LPS complex formation. Our studies have therefore defined the first nonlipid-based synthetic MD-2 antagonist using both animal model-based studies of pro-inflammatory cytokine responses and molecular modelling studies of a whole dendrimer with its target protein. Using this approach, it should now be possible to computationally design additional macromolecular dendrimer based antagonists for other Toll Like Receptors. They could be useful for treating a spectrum of infectious, inflammatory and malignant diseases. PMID:21738462

Quasi-classical modeling of molecular quantum-dot cellular automata multidriver gates

NASA Astrophysics Data System (ADS)

Rahimi, Ehsan; Nejad, Shahram Mohammad

2012-05-01

Molecular quantum-dot cellular automata (mQCA) has received considerable attention in nanoscience. Unlike the current-based molecular switches, where the digital data is represented by the on/off states of the switches, in mQCA devices, binary information is encoded in charge configuration within molecular redox centers. The mQCA paradigm allows high device density and ultra-low power consumption. Digital mQCA gates are the building blocks of circuits in this paradigm. Design and analysis of these gates require quantum chemical calculations, which are demanding in computer time and memory. Therefore, developing simple models to probe mQCA gates is of paramount importance. We derive a semi-classical model to study the steady-state output polarization of mQCA multidriver gates, directly from the two-state approximation in electron transfer theory. The accuracy and validity of this model are analyzed using full quantum chemistry calculations. A complete set of logic gates, including inverters and minority voters, are implemented to provide an appropriate test bench in the two-dot mQCA regime. We also briefly discuss how the QCADesigner tool could find its application in simulation of mQCA devices.

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

Zhang, Na; Zhang, Peng; Kang, Wei

Multiscale simulations of fluids such as blood represent a major computational challenge of coupling the disparate spatiotemporal scales between molecular and macroscopic transport phenomena characterizing such complex fluids. In this paper, a coarse-grained (CG) particle model is developed for simulating blood flow by modifying the Morse potential, traditionally used in Molecular Dynamics for modeling vibrating structures. The modified Morse potential is parameterized with effective mass scales for reproducing blood viscous flow properties, including density, pressure, viscosity, compressibility and characteristic flow dynamics of human blood plasma fluid. The parameterization follows a standard inverse-problem approach in which the optimal micro parameters aremore » systematically searched, by gradually decoupling loosely correlated parameter spaces, to match the macro physical quantities of viscous blood flow. The predictions of this particle based multiscale model compare favorably to classic viscous flow solutions such as Counter-Poiseuille and Couette flows. It demonstrates that such coarse grained particle model can be applied to replicate the dynamics of viscous blood flow, with the advantage of bridging the gap between macroscopic flow scales and the cellular scales characterizing blood flow that continuum based models fail to handle adequately.« less

Parallel Simulation of Unsteady Turbulent Flames

NASA Technical Reports Server (NTRS)

Menon, Suresh

1996-01-01

Time-accurate simulation of turbulent flames in high Reynolds number flows is a challenging task since both fluid dynamics and combustion must be modeled accurately. To numerically simulate this phenomenon, very large computer resources (both time and memory) are required. Although current vector supercomputers are capable of providing adequate resources for simulations of this nature, the high cost and their limited availability, makes practical use of such machines less than satisfactory. At the same time, the explicit time integration algorithms used in unsteady flow simulations often possess a very high degree of parallelism, making them very amenable to efficient implementation on large-scale parallel computers. Under these circumstances, distributed memory parallel computers offer an excellent near-term solution for greatly increased computational speed and memory, at a cost that may render the unsteady simulations of the type discussed above more feasible and affordable.This paper discusses the study of unsteady turbulent flames using a simulation algorithm that is capable of retaining high parallel efficiency on distributed memory parallel architectures. Numerical studies are carried out using large-eddy simulation (LES). In LES, the scales larger than the grid are computed using a time- and space-accurate scheme, while the unresolved small scales are modeled using eddy viscosity based subgrid models. This is acceptable for the moment/energy closure since the small scales primarily provide a dissipative mechanism for the energy transferred from the large scales. However, for combustion to occur, the species must first undergo mixing at the small scales and then come into molecular contact. Therefore, global models cannot be used. Recently, a new model for turbulent combustion was developed, in which the combustion is modeled, within the subgrid (small-scales) using a methodology that simulates the mixing and the molecular transport and the chemical kinetics within each LES grid cell. Finite-rate kinetics can be included without any closure and this approach actually provides a means to predict the turbulent rates and the turbulent flame speed. The subgrid combustion model requires resolution of the local time scales associated with small-scale mixing, molecular diffusion and chemical kinetics and, therefore, within each grid cell, a significant amount of computations must be carried out before the large-scale (LES resolved) effects are incorporated. Therefore, this approach is uniquely suited for parallel processing and has been implemented on various systems such as: Intel Paragon, IBM SP-2, Cray T3D and SGI Power Challenge (PC) using the system independent Message Passing Interface (MPI) compiler. In this paper, timing data on these machines is reported along with some characteristic results.

Generative Recurrent Networks for De Novo Drug Design.

PubMed

Gupta, Anvita; Müller, Alex T; Huisman, Berend J H; Fuchs, Jens A; Schneider, Petra; Schneider, Gisbert

2018-01-01

Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Molecular interaction of selected phytochemicals under the charged environment of Plasmodium falciparum chloroquine resistance transporter (PfCRT) model.

PubMed

Patel, Saumya K; Khedkar, Vijay M; Jha, Prakash C; Jasrai, Yogesh T; Pandya, Himanshu A; George, Linz-Buoy; Highland, Hyacinth N; Skelton, Adam A

2016-01-01

Phytochemicals of Catharanthus roseus Linn. and Tylophora indica have been known for their inhibition of malarial parasite, Plasmodium falciparum in cell culture. Resistance to chloroquine (CQ), a widely used antimalarial drug, is due to the CQ resistance transporter (CRT) system. The present study deals with computational modeling of Plasmodium falciparum chloroquine resistance transporter (PfCRT) protein and development of charged environment to mimic a condition of resistance. The model of PfCRT was developed using Protein homology/analogy engine (PHYRE ver 0.2) and was validated based on the results obtained using PSI-PRED. Subsequently, molecular interactions of selected phytochemicals extracted from C. roseus Linn. and T. indica were studied using multiple-iterated genetic algorithm-based docking protocol in order to investigate the translocation of these legends across the PfCRT protein. Further, molecular dynamics studies exhibiting interaction energy estimates of these compounds within the active site of the protein showed that compounds are more selective toward PfCRT. Clusters of conformations with the free energy of binding were estimated which clearly demonstrated the potential channel and by this means the translocation across the PfCRT is anticipated.

Computing the Absorption and Emission Spectra of 5-Methylcytidine in Different Solvents: A Test-Case for Different Solvation Models.

PubMed

Martínez-Fernández, L; Pepino, A J; Segarra-Martí, J; Banyasz, A; Garavelli, M; Improta, R

2016-09-13

The optical spectra of 5-methylcytidine in three different solvents (tetrahydrofuran, acetonitrile, and water) is measured, showing that both the absorption and the emission maximum in water are significantly blue-shifted (0.08 eV). The absorption spectra are simulated based on CAM-B3LYP/TD-DFT calculations but including solvent effects with three different approaches: (i) a hybrid implicit/explicit full quantum mechanical approach, (ii) a mixed QM/MM static approach, and (iii) a QM/MM method exploiting the structures issuing from molecular dynamics classical simulations. Ab-initio Molecular dynamics simulations based on CAM-B3LYP functionals have also been performed. The adopted approaches all reproduce the main features of the experimental spectra, giving insights on the chemical-physical effects responsible for the solvent shifts in the spectra of 5-methylcytidine and providing the basis for discussing advantages and limitations of the adopted solvation models.

Notes on quantitative structure-properties relationships (QSPR) (1): A discussion on a QSPR dimensionality paradox (QSPR DP) and its quantum resolution.

PubMed

Carbó-Dorca, Ramon; Gallegos, Ana; Sánchez, Angel J

2009-05-01

Classical quantitative structure-properties relationship (QSPR) statistical techniques unavoidably present an inherent paradoxical computational context. They rely on the definition of a Gram matrix in descriptor spaces, which is used afterwards to reduce the original dimension via several possible kinds of algebraic manipulations. From there, effective models for the computation of unknown properties of known molecular structures are obtained. However, the reduced descriptor dimension causes linear dependence within the set of discrete vector molecular representations, leading to positive semi-definite Gram matrices in molecular spaces. To resolve this QSPR dimensionality paradox (QSPR DP) here is proposed to adopt as starting point the quantum QSPR (QQSPR) computational framework perspective, where density functions act as infinite dimensional descriptors. The fundamental QQSPR equation, deduced from employing quantum expectation value numerical evaluation, can be approximately solved in order to obtain models exempt of the QSPR DP. The substitution of the quantum similarity matrix by an empirical Gram matrix in molecular spaces, build up with the original non manipulated discrete molecular descriptor vectors, permits to obtain classical QSPR models with the same characteristics as in QQSPR, that is: possessing a certain degree of causality and explicitly independent of the descriptor dimension. 2008 Wiley Periodicals, Inc.

Simulating Self-Assembly with Simple Models

NASA Astrophysics Data System (ADS)

Rapaport, D. C.

Results from recent molecular dynamics simulations of virus capsid self-assembly are described. The model is based on rigid trapezoidal particles designed to form polyhedral shells of size 60, together with an atomistic solvent. The underlying bonding process is fully reversible. More extensive computations are required than in previous work on icosahedral shells built from triangular particles, but the outcome is a high yield of closed shells. Intermediate clusters have a variety of forms, and bond counts provide a useful classification scheme

Theoretical aspects and modelling of cellular decision making, cell killing and information-processing in photodynamic therapy of cancer.

PubMed

Gkigkitzis, Ioannis

2013-01-01

The aim of this report is to provide a mathematical model of the mechanism for making binary fate decisions about cell death or survival, during and after Photodynamic Therapy (PDT) treatment, and to supply the logical design for this decision mechanism as an application of rate distortion theory to the biochemical processing of information by the physical system of a cell. Based on system biology models of the molecular interactions involved in the PDT processes previously established, and regarding a cellular decision-making system as a noisy communication channel, we use rate distortion theory to design a time dependent Blahut-Arimoto algorithm where the input is a stimulus vector composed of the time dependent concentrations of three PDT related cell death signaling molecules and the output is a cell fate decision. The molecular concentrations are determined by a group of rate equations. The basic steps are: initialize the probability of the cell fate decision, compute the conditional probability distribution that minimizes the mutual information between input and output, compute the cell probability of cell fate decision that minimizes the mutual information and repeat the last two steps until the probabilities converge. Advance to the next discrete time point and repeat the process. Based on the model from communication theory described in this work, and assuming that the activation of the death signal processing occurs when any of the molecular stimulants increases higher than a predefined threshold (50% of the maximum concentrations), for 1800s of treatment, the cell undergoes necrosis within the first 30 minutes with probability range 90.0%-99.99% and in the case of repair/survival, it goes through apoptosis within 3-4 hours with probability range 90.00%-99.00%. Although, there is no experimental validation of the model at this moment, it reproduces some patterns of survival ratios of predicted experimental data. Analytical modeling based on cell death signaling molecules has been shown to be an independent and useful tool for prediction of cell surviving response to PDT. The model can be adjusted to provide important insights for cellular response to other treatments such as hyperthermia, and diseases such as neurodegeneration.

An Ensemble-Based Protocol for the Computational Prediction of Helix-Helix Interactions in G Protein-Coupled Receptors using Coarse-Grained Molecular Dynamics.

PubMed

Altwaijry, Nojood A; Baron, Michael; Wright, David W; Coveney, Peter V; Townsend-Nicholson, Andrea

2017-05-09

The accurate identification of the specific points of interaction between G protein-coupled receptor (GPCR) oligomers is essential for the design of receptor ligands targeting oligomeric receptor targets. A coarse-grained molecular dynamics computer simulation approach would provide a compelling means of identifying these specific protein-protein interactions and could be applied both for known oligomers of interest and as a high-throughput screen to identify novel oligomeric targets. However, to be effective, this in silico modeling must provide accurate, precise, and reproducible information. This has been achieved recently in numerous biological systems using an ensemble-based all-atom molecular dynamics approach. In this study, we describe an equivalent methodology for ensemble-based coarse-grained simulations. We report the performance of this method when applied to four different GPCRs known to oligomerize using error analysis to determine the ensemble size and individual replica simulation time required. Our measurements of distance between residues shown to be involved in oligomerization of the fifth transmembrane domain from the adenosine A 2A receptor are in very good agreement with the existing biophysical data and provide information about the nature of the contact interface that cannot be determined experimentally. Calculations of distance between rhodopsin, CXCR4, and β 1 AR transmembrane domains reported to form contact points in homodimers correlate well with the corresponding measurements obtained from experimental structural data, providing an ability to predict contact interfaces computationally. Interestingly, error analysis enables identification of noninteracting regions. Our results confirm that GPCR interactions can be reliably predicted using this novel methodology.

Computational challenges in modeling gene regulatory events

PubMed Central

Pataskar, Abhijeet; Tiwari, Vijay K.

2016-01-01

ABSTRACT Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating “omics” data and subsequently modeling the gene-regulatory events. Toward this end, computational biology should keep pace with evolving experimental procedures and data availability. This article gives an exemplified account of the current computational challenges in molecular biology. PMID:27390891

Analog synthetic biology.

PubMed

Sarpeshkar, R

2014-03-28

We analyse the pros and cons of analog versus digital computation in living cells. Our analysis is based on fundamental laws of noise in gene and protein expression, which set limits on the energy, time, space, molecular count and part-count resources needed to compute at a given level of precision. We conclude that analog computation is significantly more efficient in its use of resources than deterministic digital computation even at relatively high levels of precision in the cell. Based on this analysis, we conclude that synthetic biology must use analog, collective analog, probabilistic and hybrid analog-digital computational approaches; otherwise, even relatively simple synthetic computations in cells such as addition will exceed energy and molecular-count budgets. We present schematics for efficiently representing analog DNA-protein computation in cells. Analog electronic flow in subthreshold transistors and analog molecular flux in chemical reactions obey Boltzmann exponential laws of thermodynamics and are described by astoundingly similar logarithmic electrochemical potentials. Therefore, cytomorphic circuits can help to map circuit designs between electronic and biochemical domains. We review recent work that uses positive-feedback linearization circuits to architect wide-dynamic-range logarithmic analog computation in Escherichia coli using three transcription factors, nearly two orders of magnitude more efficient in parts than prior digital implementations.

Analog synthetic biology

PubMed Central

Sarpeshkar, R.

2014-01-01

We analyse the pros and cons of analog versus digital computation in living cells. Our analysis is based on fundamental laws of noise in gene and protein expression, which set limits on the energy, time, space, molecular count and part-count resources needed to compute at a given level of precision. We conclude that analog computation is significantly more efficient in its use of resources than deterministic digital computation even at relatively high levels of precision in the cell. Based on this analysis, we conclude that synthetic biology must use analog, collective analog, probabilistic and hybrid analog–digital computational approaches; otherwise, even relatively simple synthetic computations in cells such as addition will exceed energy and molecular-count budgets. We present schematics for efficiently representing analog DNA–protein computation in cells. Analog electronic flow in subthreshold transistors and analog molecular flux in chemical reactions obey Boltzmann exponential laws of thermodynamics and are described by astoundingly similar logarithmic electrochemical potentials. Therefore, cytomorphic circuits can help to map circuit designs between electronic and biochemical domains. We review recent work that uses positive-feedback linearization circuits to architect wide-dynamic-range logarithmic analog computation in Escherichia coli using three transcription factors, nearly two orders of magnitude more efficient in parts than prior digital implementations. PMID:24567476

Molecular dynamics-based refinement and validation for sub-5 Å cryo-electron microscopy maps

PubMed Central

Singharoy, Abhishek; Teo, Ivan; McGreevy, Ryan; Stone, John E; Zhao, Jianhua; Schulten, Klaus

2016-01-01

Two structure determination methods, based on the molecular dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5 Å cryo-electron microscopy (EM) maps with either single structures or ensembles of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution. Application of sequential re-refinement enables MDFF to achieve a radius of convergence of ~25 Å demonstrated with the accurate modeling of β-galactosidase and TRPV1 proteins at 3.2 Å and 3.4 Å resolution, respectively. The MDFF refinements uniquely offer map-model validation and B-factor determination criteria based on the inherent dynamics of the macromolecules studied, captured by means of local root mean square fluctuations. The MDFF tools described are available to researchers through an easy-to-use and cost-effective cloud computing resource on Amazon Web Services. DOI: http://dx.doi.org/10.7554/eLife.16105.001 PMID:27383269

Quantification of the effect of cross-shear and applied nominal contact pressure on the wear of moderately cross-linked polyethylene.

PubMed

Abdelgaied, Abdellatif; Brockett, Claire L; Liu, Feng; Jennings, Louise M; Fisher, John; Jin, Zhongmin

2013-01-01

Polyethylene wear is a great concern in total joint replacement. It is now considered a major limiting factor to the long life of such prostheses. Cross-linking has been introduced to reduce the wear of ultra-high-molecular-weight polyethylene (UHMWPE). Computational models have been used extensively for wear prediction and optimization of artificial knee designs. However, in order to be independent and have general applicability and predictability, computational wear models should be based on inputs from independent experimentally determined wear parameters (wear factors or wear coefficients). The objective of this study was to investigate moderately cross-linked UHMWPE, using a multidirectional pin-on-plate wear test machine, under a wide range of applied nominal contact pressure (from 1 to 11 MPa) and under five different kinematic inputs, varying from a purely linear track to a maximum rotation of +/- 55 degrees. A computational model, based on a direct simulation of the multidirectional pin-on-plate wear tester, was developed to quantify the degree of cross-shear (CS) of the polyethylene pins articulating against the metallic plates. The moderately cross-linked UHMWPE showed wear factors less than half of that reported in the literature for, the conventional UHMWPE, under the same loading and kinematic inputs. In addition, under high applied nominal contact stress, the moderately crosslinked UHMWPE wear showed lower dependence on the degree of CS compared to that under low applied nominal contact stress. The calculated wear coefficients were found to be independent of the applied nominal contact stress, in contrast to the wear factors that were shown to be highly pressure dependent. This study provided independent wear data for inputs into computational models for moderately cross-linked polyethylene and supported the application of wear coefficient-based computational wear models.

Ab initio molecular simulations with numeric atom-centered orbitals

NASA Astrophysics Data System (ADS)

Blum, Volker; Gehrke, Ralf; Hanke, Felix; Havu, Paula; Havu, Ville; Ren, Xinguo; Reuter, Karsten; Scheffler, Matthias

2009-11-01

We describe a complete set of algorithms for ab initio molecular simulations based on numerically tabulated atom-centered orbitals (NAOs) to capture a wide range of molecular and materials properties from quantum-mechanical first principles. The full algorithmic framework described here is embodied in the Fritz Haber Institute "ab initio molecular simulations" (FHI-aims) computer program package. Its comprehensive description should be relevant to any other first-principles implementation based on NAOs. The focus here is on density-functional theory (DFT) in the local and semilocal (generalized gradient) approximations, but an extension to hybrid functionals, Hartree-Fock theory, and MP2/GW electron self-energies for total energies and excited states is possible within the same underlying algorithms. An all-electron/full-potential treatment that is both computationally efficient and accurate is achieved for periodic and cluster geometries on equal footing, including relaxation and ab initio molecular dynamics. We demonstrate the construction of transferable, hierarchical basis sets, allowing the calculation to range from qualitative tight-binding like accuracy to meV-level total energy convergence with the basis set. Since all basis functions are strictly localized, the otherwise computationally dominant grid-based operations scale as O(N) with system size N. Together with a scalar-relativistic treatment, the basis sets provide access to all elements from light to heavy. Both low-communication parallelization of all real-space grid based algorithms and a ScaLapack-based, customized handling of the linear algebra for all matrix operations are possible, guaranteeing efficient scaling (CPU time and memory) up to massively parallel computer systems with thousands of CPUs.

Local rules simulation of the kinetics of virus capsid self-assembly.

PubMed

Schwartz, R; Shor, P W; Prevelige, P E; Berger, B

1998-12-01

A computer model is described for studying the kinetics of the self-assembly of icosahedral viral capsids. Solution of this problem is crucial to an understanding of the viral life cycle, which currently cannot be adequately addressed through laboratory techniques. The abstract simulation model employed to address this is based on the local rules theory of. Proc. Natl. Acad. Sci. USA. 91:7732-7736). It is shown that the principle of local rules, generalized with a model of kinetics and other extensions, can be used to simulate complicated problems in self-assembly. This approach allows for a computationally tractable molecular dynamics-like simulation of coat protein interactions while retaining many relevant features of capsid self-assembly. Three simple simulation experiments are presented to illustrate the use of this model. These show the dependence of growth and malformation rates on the energetics of binding interactions, the tolerance of errors in binding positions, and the concentration of subunits in the examples. These experiments demonstrate a tradeoff within the model between growth rate and fidelity of assembly for the three parameters. A detailed discussion of the computational model is also provided.

Theoretical Modeling of Hydrogen Bonding in omolecular Solutions: The Combination of Quantum Mechanics and Molecular Mechanics

NASA Astrophysics Data System (ADS)

Ma, Jing; Jiang, Nan; Li, Hui

Hydrogen bonding interaction takes an important position in solutions. The non-classic nature of hydrogen bonding requires the resource-demanding quantum mechanical (QM) calculations. The molecular mechanics (MM) method, with much lower computational load, is applicable to the large-sized system. The combination of QM and MM is an efficient way in the treatment of solution. Taking advantage of the low-cost energy-based fragmentation QM approach (in which the o-molecule is divided into several subsystems, and QM calculation is carried out on each subsystem that is embedded in the environment of background charges of distant parts), the fragmentation-based QM/MM and polarization models have been implemented for the modeling of o-molecule in aqueous solutions, respectively. Within the framework of the fragmentation-based QM/MM hybrid model, the solute is treated by the fragmentation QM calculation while the numerous solvent molecules are described by MM. In the polarization model, the polarizability is considered by allowing the partial charges and fragment-centered dipole moments to be variables, with values coming from the energy-based fragmentation QM calculations. Applications of these two methods to the solvated long oligomers and cyclic peptides have demonstrated that the hydrogen bonding interaction affects the dynamic change in chain conformations of backbone.

The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research.

PubMed

Cano, Isaac; Tényi, Ákos; Schueller, Christine; Wolff, Martin; Huertas Migueláñez, M Mercedes; Gomez-Cabrero, David; Antczak, Philipp; Roca, Josep; Cascante, Marta; Falciani, Francesco; Maier, Dieter

2014-11-28

Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data. The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice. The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.

Computational modeling of epidermal cell fate determination systems.

PubMed

Ryu, Kook Hui; Zheng, Xiaohua; Huang, Ling; Schiefelbein, John

2013-02-01

Cell fate decisions are of primary importance for plant development. Their simple 'either-or' outcome and dynamic nature has attracted the attention of computational modelers. Recent efforts have focused on modeling the determination of several epidermal cell types in the root and shoot of Arabidopsis where many molecular components have been defined. Results of integrated modeling and molecular biology experimentation in these systems have highlighted the importance of competitive positive and negative factors and interconnected feedback loops in generating flexible yet robust mechanisms for establishing distinct gene expression programs in neighboring cells. These models have proven useful in judging hypotheses and guiding future research. Copyright © 2012 Elsevier Ltd. All rights reserved.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus.

PubMed

Ekins, Sean; Freundlich, Joel S; Coffee, Megan

2014-01-01

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus

PubMed Central

Ekins, Sean; Freundlich, Joel S.; Coffee, Megan

2014-01-01

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested. PMID:25653841

Generation of Well-Relaxed All-Atom Models of Large Molecular Weight Polymer Melts: A Hybrid Particle-Continuum Approach Based on Particle-Field Molecular Dynamics Simulations.

PubMed

De Nicola, Antonio; Kawakatsu, Toshihiro; Milano, Giuseppe

2014-12-09

A procedure based on Molecular Dynamics (MD) simulations employing soft potentials derived from self-consistent field (SCF) theory (named MD-SCF) able to generate well-relaxed all-atom structures of polymer melts is proposed. All-atom structures having structural correlations indistinguishable from ones obtained by long MD relaxations have been obtained for poly(methyl methacrylate) (PMMA) and poly(ethylene oxide) (PEO) melts. The proposed procedure leads to computational costs mainly related on system size rather than to the chain length. Several advantages of the proposed procedure over current coarse-graining/reverse mapping strategies are apparent. No parametrization is needed to generate relaxed structures of different polymers at different scales or resolutions. There is no need for special algorithms or back-mapping schemes to change the resolution of the models. This characteristic makes the procedure general and its extension to other polymer architectures straightforward. A similar procedure can be easily extended to the generation of all-atom structures of block copolymer melts and polymer nanocomposites.

Multi-scale genetic dynamic modelling I : an algorithm to compute generators.

PubMed

Kirkilionis, Markus; Janus, Ulrich; Sbano, Luca

2011-09-01

We present a new approach or framework to model dynamic regulatory genetic activity. The framework is using a multi-scale analysis based upon generic assumptions on the relative time scales attached to the different transitions of molecular states defining the genetic system. At micro-level such systems are regulated by the interaction of two kinds of molecular players: macro-molecules like DNA or polymerases, and smaller molecules acting as transcription factors. The proposed genetic model then represents the larger less abundant molecules with a finite discrete state space, for example describing different conformations of these molecules. This is in contrast to the representations of the transcription factors which are-like in classical reaction kinetics-represented by their particle number only. We illustrate the method by considering the genetic activity associated to certain configurations of interacting genes that are fundamental to modelling (synthetic) genetic clocks. A largely unknown question is how different molecular details incorporated via this more realistic modelling approach lead to different macroscopic regulatory genetic models which dynamical behaviour might-in general-be different for different model choices. The theory will be applied to a real synthetic clock in a second accompanying article (Kirkilioniset al., Theory Biosci, 2011).

Structure and dynamics of human vimentin intermediate filament dimer and tetramer in explicit and implicit solvent models.

PubMed

Qin, Zhao; Buehler, Markus J

2011-01-01

Intermediate filaments, in addition to microtubules and microfilaments, are one of the three major components of the cytoskeleton in eukaryotic cells, and play an important role in mechanotransduction as well as in providing mechanical stability to cells at large stretch. The molecular structures, mechanical and dynamical properties of the intermediate filament basic building blocks, the dimer and the tetramer, however, have remained elusive due to persistent experimental challenges owing to the large size and fibrillar geometry of this protein. We have recently reported an atomistic-level model of the human vimentin dimer and tetramer, obtained through a bottom-up approach based on structural optimization via molecular simulation based on an implicit solvent model (Qin et al. in PLoS ONE 2009 4(10):e7294, 9). Here we present extensive simulations and structural analyses of the model based on ultra large-scale atomistic-level simulations in an explicit solvent model, with system sizes exceeding 500,000 atoms and simulations carried out at 20 ns time-scales. We report a detailed comparison of the structural and dynamical behavior of this large biomolecular model with implicit and explicit solvent models. Our simulations confirm the stability of the molecular model and provide insight into the dynamical properties of the dimer and tetramer. Specifically, our simulations reveal a heterogeneous distribution of the bending stiffness along the molecular axis with the formation of rather soft and highly flexible hinge-like regions defined by non-alpha-helical linker domains. We report a comparison of Ramachandran maps and the solvent accessible surface area between implicit and explicit solvent models, and compute the persistence length of the dimer and tetramer structure of vimentin intermediate filaments for various subdomains of the protein. Our simulations provide detailed insight into the dynamical properties of the vimentin dimer and tetramer intermediate filament building blocks, which may guide the development of novel coarse-grained models of intermediate filaments, and could also help in understanding assembly mechanisms.

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

PubMed Central

Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2013-01-01

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

United polarizable multipole water model for molecular mechanics simulation

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

Qi, Rui; Wang, Qiantao; Ren, Pengyu, E-mail: pren@mail.utexas.edu

2015-07-07

We report the development of a united AMOEBA (uAMOEBA) polarizable water model, which is computationally 3–5 times more efficient than the three-site AMOEBA03 model in molecular dynamics simulations while providing comparable accuracy for gas-phase and liquid properties. In this coarse-grained polarizable water model, both electrostatic (permanent and induced) and van der Waals representations have been reduced to a single site located at the oxygen atom. The permanent charge distribution is described via the molecular dipole and quadrupole moments and the many-body polarization via an isotropic molecular polarizability, all located at the oxygen center. Similarly, a single van der Waals interactionmore » site is used for each water molecule. Hydrogen atoms are retained only for the purpose of defining local frames for the molecular multipole moments and intramolecular vibrational modes. The parameters have been derived based on a combination of ab initio quantum mechanical and experimental data set containing gas-phase cluster structures and energies, and liquid thermodynamic properties. For validation, additional properties including dimer interaction energy, liquid structures, self-diffusion coefficient, and shear viscosity have been evaluated. The results demonstrate good transferability from the gas to the liquid phase over a wide range of temperatures, and from nonpolar to polar environments, due to the presence of molecular polarizability. The water coordination, hydrogen-bonding structure, and dynamic properties given by uAMOEBA are similar to those derived from the all-atom AMOEBA03 model and experiments. Thus, the current model is an accurate and efficient alternative for modeling water.« less

On-the-Fly Kinetic Monte Carlo Simulation of Aqueous Phase Advanced Oxidation Processes.

PubMed

Guo, Xin; Minakata, Daisuke; Crittenden, John

2015-08-04

We have developed an on-the-fly kinetic Monte Carlo (KMC) model to predict the degradation mechanisms and fates of intermediates and byproducts that are produced during aqueous-phase advanced oxidation processes (AOPs). The on-the-fly KMC model is composed of a reaction pathway generator, a reaction rate constant estimator, a mechanistic reduction module, and a KMC solver. The novelty of this work is that we develop the pathway as we march forward in time rather than developing the pathway before we use the KMC method to solve the equations. As a result, we have fewer reactions to consider, and we have greater computational efficiency. We have verified this on-the-fly KMC model for the degradation of polyacrylamide (PAM) using UV light and titanium dioxide (i.e., UV/TiO2). Using the on-the-fly KMC model, we were able to predict the time-dependent profiles of the average molecular weight for PAM. The model provided detailed and quantitative insights into the time evolution of the molecular weight distribution and reaction mechanism. We also verified our on-the-fly KMC model for the destruction of (1) acetone, (2) trichloroethylene (TCE), and (3) polyethylene glycol (PEG) for the ultraviolet light and hydrogen peroxide AOP. We demonstrated that the on-the-fly KMC model can achieve the same accuracy as the computer-based first-principles KMC (CF-KMC) model, which has already been validated in our earlier work. The on-the-fly KMC is particularly suitable for molecules with large molecular weights (e.g., polymers) because the degradation mechanisms for large molecules can result in hundreds of thousands to even millions of reactions. The ordinary differential equations (ODEs) that describe the degradation pathways cannot be solved using traditional numerical methods, but the KMC can solve these equations.

Interactive Visualization of Infrared Spectral Data: Synergy of Computation, Visualization, and Experiment for Learning Spectroscopy

NASA Astrophysics Data System (ADS)

Lahti, Paul M.; Motyka, Eric J.; Lancashire, Robert J.

2000-05-01

A straightforward procedure is described to combine computation of molecular vibrational modes using commonly available molecular modeling programs with visualization of the modes using advanced features of the MDL Information Systems Inc. Chime World Wide Web browser plug-in. Minor editing of experimental spectra that are stored in the JCAMP-DX format allows linkage of IR spectral frequency ranges to Chime molecular display windows. The spectra and animation files can be combined by Hypertext Markup Language programming to allow interactive linkage between experimental spectra and computationally generated vibrational displays. Both the spectra and the molecular displays can be interactively manipulated to allow the user maximum control of the objects being viewed. This procedure should be very valuable not only for aiding students through visual linkage of spectra and various vibrational animations, but also by assisting them in learning the advantages and limitations of computational chemistry by comparison to experiment.

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

PubMed Central

Merk, Daniel; Friedrich, Lukas; Grisoni, Francesca

2018-01-01

Abstract Generative artificial intelligence offers a fresh view on molecular design. We present the first‐time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine‐tuned on recognizing retinoid X and peroxisome proliferator‐activated receptor agonists. We synthesized five top‐ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low‐micromolar receptor modulatory activity in cell‐based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry. PMID:29319225

COMPUTATIONAL TOXICOLOGY: FRAMEWORK, PARTNERSHIPS, AND PROGRAM DEVELOPMENT

EPA Science Inventory

Computational toxicology is a new research initiative being developed within the Office of Research and Development (ORD) of the US Environmental Protection Agency (EPA). Operationally, it is defined as the application of mathematical and computer models together with molecular c...

Exposure Science and the US EPA National Center for Computational Toxicology

EPA Science Inventory

The emerging field of computational toxicology applies mathematical and computer models and molecular biological and chemical approaches to explore both qualitative and quantitative relationships between sources of environmental pollutant exposure and adverse health outcomes. The...

Theoretical modeling of UV-Vis absorption and emission spectra in liquid state systems including vibrational and conformational effects: explicit treatment of the vibronic transitions.

PubMed

D'Abramo, Marco; Aschi, Massimiliano; Amadei, Andrea

2014-04-28

Here, we extend a recently introduced theoretical-computational procedure [M. D'Alessandro, M. Aschi, C. Mazzuca, A. Palleschi, and A. Amadei, J. Chem. Phys. 139, 114102 (2013)] to include quantum vibrational transitions in modelling electronic spectra of atomic molecular systems in condensed phase. The method is based on the combination of Molecular Dynamics simulations and quantum chemical calculations within the Perturbed Matrix Method approach. The main aim of the presented approach is to reproduce as much as possible the spectral line shape which results from a subtle combination of environmental and intrinsic (chromophore) mechanical-dynamical features. As a case study, we were able to model the low energy UV-vis transitions of pyrene in liquid acetonitrile in good agreement with the experimental data.

Molecular modeling on streptolysin-O of multidrug resistant Streptococcus pyogenes and computer aided screening and in vitro assay for novel herbal inhibitors.

PubMed

Skariyachan, Sinosh; Narayan, Naik Sowmyalaxmi; Aggimath, Tejaswini S; Nagaraj, Sushmitha; Reddy, Monika S; Narayanappa, Rajeswari

2014-03-01

Streptococcus pyogenes is a notorious pathogenic bacterium which causes various human diseases ranging from localized infections to life threatening invasive diseases. Streptolysin-O (SLO), pore-forming thiol-activated cytolysin, is the major virulent factor for streptococcal infections. Present therapies against streptococcal infections are limited as most of the strains have developed multi-drug resistance to present generation of drugs. Hence, there is a need for alternative therapeutic substances. Structure based virtual screening is a novel platform to select lead molecules with better pharmacokinetic properties. The 3D structure of SLO (not available in native form), essential for such studies, was computationally generated and this homology model was used as probable drug target. Based on literature survey, several phytoligands from 25 medicinal plants were selected. Out of these, leads from 11 plants showed better pharmacokinetic properties. The best lead molecules were screened based on computer aided drug likeness and pharmacokinetic predictions. The inhibitory properties of selected herbal leads against SLO were studied by molecular docking. An in vitro assay was further carried out and variations observed were found to be significant (p<0.05). Antibiotic sensitivity testing was also performed with the clinical strain of Streptococcus pyogenes with conventional drugs. The clinical strain showed multi-drug resistance to conventional drugs. Our study revealed that numerous phytoligands have better inhibitory properties towards the toxin. We noticed that incorporation of selected herbal extracts in blood agar medium showed significant reduction in hemolysis (MIC 300μl/plate), indicating inhibition of SLO. Furthermore, the butanol extracts of selected herbal preparation based on computer aided screening showed significant inhibitory properties at 250 mcg/disc concentration. We also noticed that selected herbal formulations have better antimicrobial properties at MIC range of 300- 400μl. Hence, our study suggests that these herbal extracts have better inhibitory properties against the toxin as well as drug resistant Streptococcus pyogenes.

Coarse-graining of proteins based on elastic network models

NASA Astrophysics Data System (ADS)

Sinitskiy, Anton V.; Voth, Gregory A.

2013-08-01

To simulate molecular processes on biologically relevant length- and timescales, coarse-grained (CG) models of biomolecular systems with tens to even hundreds of residues per CG site are required. One possible way to build such models is explored in this article: an elastic network model (ENM) is employed to define the CG variables. Free energy surfaces are approximated by Taylor series, with the coefficients found by force-matching. CG potentials are shown to undergo renormalization due to roughness of the energy landscape and smoothing of it under coarse-graining. In the case study of hen egg-white lysozyme, the entropy factor is shown to be of critical importance for maintaining the native structure, and a relationship between the proposed ENM-mode-based CG models and traditional CG-bead-based models is discussed. The proposed approach uncovers the renormalizable character of CG models and offers new opportunities for automated and computationally efficient studies of complex free energy surfaces.

First-principles study of the infrared spectra of the ice Ih (0001) surface

DOE PAGES

Pham, T. Anh; Huang, P.; Schwegler, E.; ...

2012-08-22

Here, we present a study of the infrared (IR) spectra of the (0001) deuterated ice surface based on first-principles molecular dynamics simulations. The computed spectra show a good agreement with available experimental IR measurements. We identified the bonding configurations associated with specific features in the spectra, allowing us to provide a detailed interpretation of IR signals. We computed the spectra of several proton ordered and disordered models of the (0001) surface of ice, and we found that IR spectra do not appear to be a sensitive probe of the microscopic arrangement of protons at ice surfaces.

Model Reduction in Biomechanics

NASA Astrophysics Data System (ADS)

Feng, Yan

The mechanical characteristic of the cell is primarily performed by the cytoskeleton. Microtubules, actin, and intermediate filaments are the three main cytoskeletal polymers. Of these, microtubules are the stiffest and have multiple functions within a cell that include: providing tracks for intracellular transport, transmitting the mechanical force necessary for cell division during mitosis, and providing sufficient stiffness for propulsion in flagella and cilia. Microtubule mechanics has been studied by a variety of methods: detailed molecular dynamics (MD), coarse-grained models, engineering type models, and elastic continuum models. In principle, atomistic MD simulations should be able to predict all desired mechanical properties of a single molecule, however, in practice the large computational resources are required to carry out a simulation of larger biomolecular system. Due to the limited accessibility using even the most ambitious all-atom models and the demand for the multiscale molecular modeling and simulation, the emergence of the reduced models is critically important to provide the capability for investigating the biomolecular dynamics that are critical to many biological processes. Then the coarse-grained models, such as elastic network models and anisotropic network models, have been shown to bequite accurate in predicting microtubule mechanical response, but still requires significant computational resources. On the other hand, the microtubule is treated as comprising materials with certain continuum material properties. Such continuum models, especially Euler-Bernoulli beam models, are often used to extract mechanical parameters from experimental results. The microtubule is treated as comprising materials with certain continuum material properties. Such continuum models, especially Euler-Bernoulli beam models in which the biomolecular system is assumed as homogeneous isotropic materials with solid cross-sections, are often used to extract mechanical parameters from experimental results. However, in real biological world, these homogeneous and isotropic assumptions are usually invalidate. Thus, instead of using hypothesized model, a specific continuum model at mesoscopic scale can be introduced based upon data reduction of the results from molecular simulations at atomistic level. Once a continuum model is established, it can provide details on the distribution of stresses and strains induced within the biomolecular system which is useful in determining the distribution and transmission of these forces to the cytoskeletal and sub-cellular components, and help us gain a better understanding in cell mechanics. A data-driven model reduction approach to the problem of microtubule mechanics as an application is present, a beam element is constructed for microtubules based upon data reduction of the results from molecular simulation of the carbon backbone chain of alphabeta-tubulin dimers. The data base of mechanical responses to various types of loads from molecular simulation is reduced to dominant modes. The dominant modes are subsequently used to construct the stiffness matrix of a beam element that captures the anisotropic behavior and deformation mode coupling that arises from a microtubule's spiral structure. In contrast to standard Euler-Bernoulli or Timoshenko beam elements, the link between forces and node displacements results not from hypothesized deformation behavior, but directly from the data obtained by molecular scale simulation. Differences between the resulting microtubule data-driven beam model (MTDDBM) and standard beam elements are presented, with a focus on coupling of bending, stretch, shear deformations. The MTDDBM is just as economical to use as a standard beam element, and allows accurate reconstruction of the mechanical behavior of structures within a cell as exemplified in a simple model of a component element of the mitotic spindle.

Cutoff size need not strongly influence molecular dynamics results for solvated polypeptides.

PubMed

Beck, David A C; Armen, Roger S; Daggett, Valerie

2005-01-18

The correct treatment of van der Waals and electrostatic nonbonded interactions in molecular force fields is essential for performing realistic molecular dynamics (MD) simulations of solvated polypeptides. The most computationally tractable treatment of nonbonded interactions in MD utilizes a spherical distance cutoff (typically, 8-12 A) to reduce the number of pairwise interactions. In this work, we assess three spherical atom-based cutoff approaches for use with all-atom explicit solvent MD: abrupt truncation, a CHARMM-style electrostatic shift truncation, and our own force-shifted truncation. The chosen system for this study is an end-capped 17-residue alanine-based alpha-helical peptide, selected because of its use in previous computational and experimental studies. We compare the time-averaged helical content calculated from these MD trajectories with experiment. We also examine the effect of varying the cutoff treatment and distance on energy conservation. We find that the abrupt truncation approach is pathological in its inability to conserve energy. The CHARMM-style shift truncation performs quite well but suffers from energetic instability. On the other hand, the force-shifted spherical cutoff method conserves energy, correctly predicts the experimental helical content, and shows convergence in simulation statistics as the cutoff is increased. This work demonstrates that by using proper and rigorous techniques, it is possible to correctly model polypeptide dynamics in solution with a spherical cutoff. The inherent computational advantage of spherical cutoffs over Ewald summation (and related) techniques is essential in accessing longer MD time scales.

Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC).

PubMed

Cockrell, Robert Chase; Christley, Scott; Chang, Eugene; An, Gary

2015-01-01

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.

Fragment-based 13C nuclear magnetic resonance chemical shift predictions in molecular crystals: An alternative to planewave methods

NASA Astrophysics Data System (ADS)

Hartman, Joshua D.; Monaco, Stephen; Schatschneider, Bohdan; Beran, Gregory J. O.

2015-09-01

We assess the quality of fragment-based ab initio isotropic 13C chemical shift predictions for a collection of 25 molecular crystals with eight different density functionals. We explore the relative performance of cluster, two-body fragment, combined cluster/fragment, and the planewave gauge-including projector augmented wave (GIPAW) models relative to experiment. When electrostatic embedding is employed to capture many-body polarization effects, the simple and computationally inexpensive two-body fragment model predicts both isotropic 13C chemical shifts and the chemical shielding tensors as well as both cluster models and the GIPAW approach. Unlike the GIPAW approach, hybrid density functionals can be used readily in a fragment model, and all four hybrid functionals tested here (PBE0, B3LYP, B3PW91, and B97-2) predict chemical shifts in noticeably better agreement with experiment than the four generalized gradient approximation (GGA) functionals considered (PBE, OPBE, BLYP, and BP86). A set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided based on these benchmark calculations. Statistical cross-validation procedures are used to demonstrate the robustness of these fits.

Fragment-based (13)C nuclear magnetic resonance chemical shift predictions in molecular crystals: An alternative to planewave methods.

PubMed

Hartman, Joshua D; Monaco, Stephen; Schatschneider, Bohdan; Beran, Gregory J O

2015-09-14

We assess the quality of fragment-based ab initio isotropic (13)C chemical shift predictions for a collection of 25 molecular crystals with eight different density functionals. We explore the relative performance of cluster, two-body fragment, combined cluster/fragment, and the planewave gauge-including projector augmented wave (GIPAW) models relative to experiment. When electrostatic embedding is employed to capture many-body polarization effects, the simple and computationally inexpensive two-body fragment model predicts both isotropic (13)C chemical shifts and the chemical shielding tensors as well as both cluster models and the GIPAW approach. Unlike the GIPAW approach, hybrid density functionals can be used readily in a fragment model, and all four hybrid functionals tested here (PBE0, B3LYP, B3PW91, and B97-2) predict chemical shifts in noticeably better agreement with experiment than the four generalized gradient approximation (GGA) functionals considered (PBE, OPBE, BLYP, and BP86). A set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided based on these benchmark calculations. Statistical cross-validation procedures are used to demonstrate the robustness of these fits.

The Use of Molecular Modeling Programs in Medicinal Chemistry Instruction.

ERIC Educational Resources Information Center

Harrold, Marc W.

1992-01-01

This paper describes and evaluates the use of a molecular modeling computer program (Alchemy II) in a pharmaceutical education program. Provided are the hardware requirements and basic program features as well as several examples of how this program and its features have been applied in the classroom. (GLR)

Preliminary Modulus and Breakage Calculations on Cellulose Models

USDA-ARS?s Scientific Manuscript database

The Young’s modulus of polymers can be calculated by stretching molecular models with the computer. The molecule is stretched and the derivative of the changes in stored potential energy for several displacements, divided by the molecular cross-section area, is the stress. The modulus is the slope o...

Yoink: An interaction-based partitioning API.

PubMed

Zheng, Min; Waller, Mark P

2018-05-15

Herein, we describe the implementation details of our interaction-based partitioning API (application programming interface) called Yoink for QM/MM modeling and fragment-based quantum chemistry studies. Interactions are detected by computing density descriptors such as reduced density gradient, density overlap regions indicator, and single exponential decay detector. Only molecules having an interaction with a user-definable QM core are added to the QM region of a hybrid QM/MM calculation. Moreover, a set of molecule pairs having density-based interactions within a molecular system can be computed in Yoink, and an interaction graph can then be constructed. Standard graph clustering methods can then be applied to construct fragments for further quantum chemical calculations. The Yoink API is licensed under Apache 2.0 and can be accessed via yoink.wallerlab.org. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Computational study on UV curing characteristics in nanoimprint lithography: Stochastic simulation

NASA Astrophysics Data System (ADS)

Koyama, Masanori; Shirai, Masamitsu; Kawata, Hiroaki; Hirai, Yoshihiko; Yasuda, Masaaki

2017-06-01

A computational simulation model of UV curing in nanoimprint lithography based on a simplified stochastic approach is proposed. The activated unit reacts with a randomly selected monomer within a critical reaction radius. Cluster units are chained to each other. Then, another monomer is activated and the next chain reaction occurs. This process is repeated until a virgin monomer disappears within the reaction radius or until the activated monomers react with each other. The simulation model well describes the basic UV curing characteristics, such as the molecular weight distributions of the reacted monomers and the effect of the initiator concentration on the conversion ratio. The effects of film thickness on UV curing characteristics are also studied by the simulation.

Equilibrating high-molecular-weight symmetric and miscible polymer blends with hierarchical back-mapping.

PubMed

Ohkuma, Takahiro; Kremer, Kurt; Daoulas, Kostas

2018-05-02

Understanding properties of polymer alloys with computer simulations frequently requires equilibration of samples comprised of microscopically described long molecules. We present the extension of an efficient hierarchical backmapping strategy, initially developed for homopolymer melts, to equilibrate high-molecular-weight binary blends. These mixtures present significant interest for practical applications and fundamental polymer physics. In our approach, the blend is coarse-grained into models representing polymers as chains of soft blobs. Each blob stands for a subchain with N b microscopic monomers. A hierarchy of blob-based models with different resolution is obtained by varying N b . First the model with the largest N b is used to obtain an equilibrated blend. This configuration is sequentially fine-grained, reinserting at each step the degrees of freedom of the next in the hierarchy blob-based model. Once the blob-based description is sufficiently detailed, the microscopic monomers are reinserted. The hard excluded volume is recovered through a push-off procedure and the sample is re-equilibrated with molecular dynamics (MD), requiring relaxation on the order of the entanglement time. For the initial method development we focus on miscible blends described on microscopic level through a generic bead-spring model, which reproduces hard excluded volume, strong covalent bonds, and realistic liquid density. The blended homopolymers are symmetric with respect to molecular architecture and liquid structure. To parameterize the blob-based models and validate equilibration of backmapped samples, we obtain reference data from independent hybrid simulations combining MD and identity exchange Monte Carlo moves, taking advantage of the symmetry of the blends. The potential of the backmapping strategy is demonstrated by equilibrating blend samples with different degree of miscibility, containing 500 chains with 1000 monomers each. Equilibration is verified by comparing chain conformations and liquid structure in backmapped blends with the reference data. Possible directions for further methodological developments are discussed.

Equilibrating high-molecular-weight symmetric and miscible polymer blends with hierarchical back-mapping

NASA Astrophysics Data System (ADS)

Ohkuma, Takahiro; Kremer, Kurt; Daoulas, Kostas

2018-05-01

Understanding properties of polymer alloys with computer simulations frequently requires equilibration of samples comprised of microscopically described long molecules. We present the extension of an efficient hierarchical backmapping strategy, initially developed for homopolymer melts, to equilibrate high-molecular-weight binary blends. These mixtures present significant interest for practical applications and fundamental polymer physics. In our approach, the blend is coarse-grained into models representing polymers as chains of soft blobs. Each blob stands for a subchain with N b microscopic monomers. A hierarchy of blob-based models with different resolution is obtained by varying N b. First the model with the largest N b is used to obtain an equilibrated blend. This configuration is sequentially fine-grained, reinserting at each step the degrees of freedom of the next in the hierarchy blob-based model. Once the blob-based description is sufficiently detailed, the microscopic monomers are reinserted. The hard excluded volume is recovered through a push-off procedure and the sample is re-equilibrated with molecular dynamics (MD), requiring relaxation on the order of the entanglement time. For the initial method development we focus on miscible blends described on microscopic level through a generic bead-spring model, which reproduces hard excluded volume, strong covalent bonds, and realistic liquid density. The blended homopolymers are symmetric with respect to molecular architecture and liquid structure. To parameterize the blob-based models and validate equilibration of backmapped samples, we obtain reference data from independent hybrid simulations combining MD and identity exchange Monte Carlo moves, taking advantage of the symmetry of the blends. The potential of the backmapping strategy is demonstrated by equilibrating blend samples with different degree of miscibility, containing 500 chains with 1000 monomers each. Equilibration is verified by comparing chain conformations and liquid structure in backmapped blends with the reference data. Possible directions for further methodological developments are discussed.

Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique

NASA Astrophysics Data System (ADS)

Shevade, Abhijit V.; Ryan, Margaret A.; Homer, Margie L.; Zhou, Hanying; Manfreda, Allison M.; Lara, Liana M.; Yen, Shiao-Pin S.; Jewell, April D.; Manatt, Kenneth S.; Kisor, Adam K.

We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.

RNA nanotechnology for computer design and in vivo computation

PubMed Central

Qiu, Meikang; Khisamutdinov, Emil; Zhao, Zhengyi; Pan, Cheryl; Choi, Jeong-Woo; Leontis, Neocles B.; Guo, Peixuan

2013-01-01

Molecular-scale computing has been explored since 1989 owing to the foreseeable limitation of Moore's law for silicon-based computation devices. With the potential of massive parallelism, low energy consumption and capability of working in vivo, molecular-scale computing promises a new computational paradigm. Inspired by the concepts from the electronic computer, DNA computing has realized basic Boolean functions and has progressed into multi-layered circuits. Recently, RNA nanotechnology has emerged as an alternative approach. Owing to the newly discovered thermodynamic stability of a special RNA motif (Shu et al. 2011 Nat. Nanotechnol. 6, 658–667 (doi:10.1038/nnano.2011.105)), RNA nanoparticles are emerging as another promising medium for nanodevice and nanomedicine as well as molecular-scale computing. Like DNA, RNA sequences can be designed to form desired secondary structures in a straightforward manner, but RNA is structurally more versatile and more thermodynamically stable owing to its non-canonical base-pairing, tertiary interactions and base-stacking property. A 90-nucleotide RNA can exhibit 490 nanostructures, and its loops and tertiary architecture can serve as a mounting dovetail that eliminates the need for external linking dowels. Its enzymatic and fluorogenic activity creates diversity in computational design. Varieties of small RNA can work cooperatively, synergistically or antagonistically to carry out computational logic circuits. The riboswitch and enzymatic ribozyme activities and its special in vivo attributes offer a great potential for in vivo computation. Unique features in transcription, termination, self-assembly, self-processing and acid resistance enable in vivo production of RNA nanoparticles that harbour various regulators for intracellular manipulation. With all these advantages, RNA computation is promising, but it is still in its infancy. Many challenges still exist. Collaborations between RNA nanotechnologists and computer scientists are necessary to advance this nascent technology. PMID:24000362

RNA nanotechnology for computer design and in vivo computation.

PubMed

Qiu, Meikang; Khisamutdinov, Emil; Zhao, Zhengyi; Pan, Cheryl; Choi, Jeong-Woo; Leontis, Neocles B; Guo, Peixuan

2013-10-13

Molecular-scale computing has been explored since 1989 owing to the foreseeable limitation of Moore's law for silicon-based computation devices. With the potential of massive parallelism, low energy consumption and capability of working in vivo, molecular-scale computing promises a new computational paradigm. Inspired by the concepts from the electronic computer, DNA computing has realized basic Boolean functions and has progressed into multi-layered circuits. Recently, RNA nanotechnology has emerged as an alternative approach. Owing to the newly discovered thermodynamic stability of a special RNA motif (Shu et al. 2011 Nat. Nanotechnol. 6, 658-667 (doi:10.1038/nnano.2011.105)), RNA nanoparticles are emerging as another promising medium for nanodevice and nanomedicine as well as molecular-scale computing. Like DNA, RNA sequences can be designed to form desired secondary structures in a straightforward manner, but RNA is structurally more versatile and more thermodynamically stable owing to its non-canonical base-pairing, tertiary interactions and base-stacking property. A 90-nucleotide RNA can exhibit 4⁹⁰ nanostructures, and its loops and tertiary architecture can serve as a mounting dovetail that eliminates the need for external linking dowels. Its enzymatic and fluorogenic activity creates diversity in computational design. Varieties of small RNA can work cooperatively, synergistically or antagonistically to carry out computational logic circuits. The riboswitch and enzymatic ribozyme activities and its special in vivo attributes offer a great potential for in vivo computation. Unique features in transcription, termination, self-assembly, self-processing and acid resistance enable in vivo production of RNA nanoparticles that harbour various regulators for intracellular manipulation. With all these advantages, RNA computation is promising, but it is still in its infancy. Many challenges still exist. Collaborations between RNA nanotechnologists and computer scientists are necessary to advance this nascent technology.

Reconstruction algorithms based on l1-norm and l2-norm for two imaging models of fluorescence molecular tomography: a comparative study.

PubMed

Yi, Huangjian; Chen, Duofang; Li, Wei; Zhu, Shouping; Wang, Xiaorui; Liang, Jimin; Tian, Jie

2013-05-01

Fluorescence molecular tomography (FMT) is an important imaging technique of optical imaging. The major challenge of the reconstruction method for FMT is the ill-posed and underdetermined nature of the inverse problem. In past years, various regularization methods have been employed for fluorescence target reconstruction. A comparative study between the reconstruction algorithms based on l1-norm and l2-norm for two imaging models of FMT is presented. The first imaging model is adopted by most researchers, where the fluorescent target is of small size to mimic small tissue with fluorescent substance, as demonstrated by the early detection of a tumor. The second model is the reconstruction of distribution of the fluorescent substance in organs, which is essential to drug pharmacokinetics. Apart from numerical experiments, in vivo experiments were conducted on a dual-modality FMT/micro-computed tomography imaging system. The experimental results indicated that l1-norm regularization is more suitable for reconstructing the small fluorescent target, while l2-norm regularization performs better for the reconstruction of the distribution of fluorescent substance.

Water models based on a single potential energy surface and different molecular degrees of freedom

NASA Astrophysics Data System (ADS)

Saint-Martin, Humberto; Hernández-Cobos, Jorge; Ortega-Blake, Iván

2005-06-01

Up to now it has not been possible to neatly assess whether a deficient performance of a model is due to poor parametrization of the force field or the lack of inclusion of enough molecular properties. This work compares several molecular models in the framework of the same force field, which was designed to include many-body nonadditive effects: (a) a polarizable and flexible molecule with constraints that account for the quantal nature of the vibration [B. Hess, H. Saint-Martin, and H. J. C. Berendsen, J. Chem. Phys. 116, 9602 (2002), H. Saint-Martin, B. Hess, and H. J. C. Berendsen, J. Chem. Phys. 120, 11133 (2004)], (b) a polarizable and classically flexible molecule [H. Saint-Martin, J. Hernández-Cobos, M. I. Bernal-Uruchurtu, I. Ortega-Blake, and H. J. C. Berendsen, J. Chem. Phys. 113, 10899 (2000)], (c) a polarizable and rigid molecule, and finally (d) a nonpolarizable and rigid molecule. The goal is to determine how significant the different molecular properties are. The results indicate that all factors—nonadditivity, polarizability, and intramolecular flexibility—are important. Still, approximations can be made in order to diminish the computational cost of the simulations with a small decrease in the accuracy of the predictions, provided that those approximations are counterbalanced by the proper inclusion of an effective molecular property, that is, an average molecular geometry or an average dipole. Hence instead of building an effective force field by parametrizing it in order to reproduce the properties of a specific phase, a building approach is proposed that is based on adequately restricting the molecular flexibility and/or polarizability of a model potential fitted to unimolecular properties, pair interactions, and many-body nonadditive contributions. In this manner, the same parental model can be used to simulate the same substance under a wide range of thermodynamic conditions. An additional advantage of this approach is that, as the force field improves by the quality of the molecular calculations, all levels of modeling can be improved.

Modeling bioluminescent photon transport in tissue based on Radiosity-diffusion model

NASA Astrophysics Data System (ADS)

Sun, Li; Wang, Pu; Tian, Jie; Zhang, Bo; Han, Dong; Yang, Xin

2010-03-01

Bioluminescence tomography (BLT) is one of the most important non-invasive optical molecular imaging modalities. The model for the bioluminescent photon propagation plays a significant role in the bioluminescence tomography study. Due to the high computational efficiency, diffusion approximation (DA) is generally applied in the bioluminescence tomography. But the diffusion equation is valid only in highly scattering and weakly absorbing regions and fails in non-scattering or low-scattering tissues, such as a cyst in the breast, the cerebrospinal fluid (CSF) layer of the brain and synovial fluid layer in the joints. A hybrid Radiosity-diffusion model is proposed for dealing with the non-scattering regions within diffusing domains in this paper. This hybrid method incorporates a priori information of the geometry of non-scattering regions, which can be acquired by magnetic resonance imaging (MRI) or x-ray computed tomography (CT). Then the model is implemented using a finite element method (FEM) to ensure the high computational efficiency. Finally, we demonstrate that the method is comparable with Mont Carlo (MC) method which is regarded as a 'gold standard' for photon transportation simulation.

Theoretical modeling of multiprotein complexes by iSPOT: Integration of small-angle X-ray scattering, hydroxyl radical footprinting, and computational docking.

PubMed

Huang, Wei; Ravikumar, Krishnakumar M; Parisien, Marc; Yang, Sichun

2016-12-01

Structural determination of protein-protein complexes such as multidomain nuclear receptors has been challenging for high-resolution structural techniques. Here, we present a combined use of multiple biophysical methods, termed iSPOT, an integration of shape information from small-angle X-ray scattering (SAXS), protection factors probed by hydroxyl radical footprinting, and a large series of computationally docked conformations from rigid-body or molecular dynamics (MD) simulations. Specifically tested on two model systems, the power of iSPOT is demonstrated to accurately predict the structures of a large protein-protein complex (TGFβ-FKBP12) and a multidomain nuclear receptor homodimer (HNF-4α), based on the structures of individual components of the complexes. Although neither SAXS nor footprinting alone can yield an unambiguous picture for each complex, the combination of both, seamlessly integrated in iSPOT, narrows down the best-fit structures that are about 3.2Å and 4.2Å in RMSD from their corresponding crystal structures, respectively. Furthermore, this proof-of-principle study based on the data synthetically derived from available crystal structures shows that the iSPOT-using either rigid-body or MD-based flexible docking-is capable of overcoming the shortcomings of standalone computational methods, especially for HNF-4α. By taking advantage of the integration of SAXS-based shape information and footprinting-based protection/accessibility as well as computational docking, this iSPOT platform is set to be a powerful approach towards accurate integrated modeling of many challenging multiprotein complexes. Copyright © 2016 Elsevier Inc. All rights reserved.

Peridynamics with LAMMPS : a user guide.

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

Lehoucq, Richard B.; Silling, Stewart Andrew; Plimpton, Steven James

2008-01-01

Peridynamics is a nonlocal formulation of continuum mechanics. The discrete peridynamic model has the same computational structure as a molecular dynamic model. This document details the implementation of a discrete peridynamic model within the LAMMPS molecular dynamic code. This document provides a brief overview of the peridynamic model of a continuum, then discusses how the peridynamic model is discretized, and overviews the LAMMPS implementation. A nontrivial example problem is also included.

Computational modeling and molecular imprinting for the development of acrylic polymers with high affinity for bile salts.

PubMed

Yañez, Fernando; Chianella, Iva; Piletsky, Sergey A; Concheiro, Angel; Alvarez-Lorenzo, Carmen

2010-02-05

This work has focused on the rational development of polymers capable of acting as traps of bile salts. Computational modeling was combined with molecular imprinting technology to obtain networks with high affinity for cholate salts in aqueous medium. The screening of a virtual library of 18 monomers, which are commonly used for imprinted networks, identified N-(3-aminopropyl)-methacrylate hydrochloride (APMA.HCl), N,N-diethylamino ethyl methacrylate (DEAEM) and ethyleneglycol methacrylate phosphate (EGMP) as suitable functional monomers with medium-to-high affinity for cholic acid. The polymers were prepared with a fix cholic acid:functional monomer mole ratio of 1:4, but with various cross-linking densities. Compared to polymers prepared without functional monomer, both imprinted and non-imprinted microparticles showed a high capability to remove sodium cholate from aqueous medium. High affinity APMA-based particles even resembled the performance of commercially available cholesterol-lowering granules. The imprinting effect was evident in most of the networks prepared, showing that computational modeling and molecular imprinting can act synergistically to improve the performance of certain polymers. Nevertheless, both the imprinted and non-imprinted networks prepared with the best monomer (APMA.HCl) identified by the modeling demonstrated such high affinity for the template that the imprinting effect was less important. The fitting of adsorption isotherms to the Freundlich model indicated that, in general, imprinting increases the population of high affinity binding sites, except when the affinity of the functional monomer for the target molecule is already very high. The cross-linking density was confirmed as a key parameter that determines the accessibility of the binding points to sodium cholate. Materials prepared with 9% mol APMA and 91% mol cross-linker showed enough affinity to achieve binding levels of up to 0.4 mmol g(-1) (i.e., 170 mg g(-1)) under flow (1 mL min(-1)) of 0.2 mM sodium cholate solution. Copyright 2009 Elsevier B.V. All rights reserved.

Development and application of hybrid structure based method for efficient screening of ligands binding to G-protein coupled receptors

NASA Astrophysics Data System (ADS)

Kortagere, Sandhya; Welsh, William J.

2006-12-01

G-protein coupled receptors (GPCRs) comprise a large superfamily of proteins that are targets for nearly 50% of drugs in clinical use today. In the past, the use of structure-based drug design strategies to develop better drug candidates has been severely hampered due to the absence of the receptor's three-dimensional structure. However, with recent advances in molecular modeling techniques and better computing power, atomic level details of these receptors can be derived from computationally derived molecular models. Using information from these models coupled with experimental evidence, it has become feasible to build receptor pharmacophores. In this study, we demonstrate the use of the Hybrid Structure Based (HSB) method that can be used effectively to screen and identify prospective ligands that bind to GPCRs. Essentially; this multi-step method combines ligand-based methods for building enriched libraries of small molecules and structure-based methods for screening molecules against the GPCR target. The HSB method was validated to identify retinal and its analogues from a random dataset of ˜300,000 molecules. The results from this study showed that the 9 top-ranking molecules are indeed analogues of retinal. The method was also tested to identify analogues of dopamine binding to the dopamine D2 receptor. Six of the ten top-ranking molecules are known analogues of dopamine including a prodrug, while the other thirty-four molecules are currently being tested for their activity against all dopamine receptors. The results from both these test cases have proved that the HSB method provides a realistic solution to bridge the gap between the ever-increasing demand for new drugs to treat psychiatric disorders and the lack of efficient screening methods for GPCRs.

Integrating Computational Chemistry into the Physical Chemistry Curriculum

ERIC Educational Resources Information Center

Johnson, Lewis E.; Engel, Thomas

2011-01-01

Relatively few undergraduate physical chemistry programs integrate molecular modeling into their quantum mechanics curriculum owing to concerns about limited access to computational facilities, the cost of software, and concerns about increasing the course material. However, modeling exercises can be integrated into an undergraduate course at a…

Large-scale molecular dynamics simulation of DNA: implementation and validation of the AMBER98 force field in LAMMPS.

PubMed

Grindon, Christina; Harris, Sarah; Evans, Tom; Novik, Keir; Coveney, Peter; Laughton, Charles

2004-07-15

Molecular modelling played a central role in the discovery of the structure of DNA by Watson and Crick. Today, such modelling is done on computers: the more powerful these computers are, the more detailed and extensive can be the study of the dynamics of such biological macromolecules. To fully harness the power of modern massively parallel computers, however, we need to develop and deploy algorithms which can exploit the structure of such hardware. The Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is a scalable molecular dynamics code including long-range Coulomb interactions, which has been specifically designed to function efficiently on parallel platforms. Here we describe the implementation of the AMBER98 force field in LAMMPS and its validation for molecular dynamics investigations of DNA structure and flexibility against the benchmark of results obtained with the long-established code AMBER6 (Assisted Model Building with Energy Refinement, version 6). Extended molecular dynamics simulations on the hydrated DNA dodecamer d(CTTTTGCAAAAG)(2), which has previously been the subject of extensive dynamical analysis using AMBER6, show that it is possible to obtain excellent agreement in terms of static, dynamic and thermodynamic parameters between AMBER6 and LAMMPS. In comparison with AMBER6, LAMMPS shows greatly improved scalability in massively parallel environments, opening up the possibility of efficient simulations of order-of-magnitude larger systems and/or for order-of-magnitude greater simulation times.

DEVELOPMENT OF A MODEL THAT CONTAINS BOTH MULTIPOLE MOMENTS AND GAUSSIANS FOR THE CALCULATION OF MOLECULAR ELECTROSTATIC POTENTIALS

EPA Science Inventory

The electrostatic interaction is a critical component of intermolecular interactions in biological processes. Rapid methods for the computation and characterization of the molecular electrostatic potential (MEP) that segment the molecular charge distribution and replace this cont...

Simulation tools for particle-based reaction-diffusion dynamics in continuous space

PubMed Central

2014-01-01

Particle-based reaction-diffusion algorithms facilitate the modeling of the diffusional motion of individual molecules and the reactions between them in cellular environments. A physically realistic model, depending on the system at hand and the questions asked, would require different levels of modeling detail such as particle diffusion, geometrical confinement, particle volume exclusion or particle-particle interaction potentials. Higher levels of detail usually correspond to increased number of parameters and higher computational cost. Certain systems however, require these investments to be modeled adequately. Here we present a review on the current field of particle-based reaction-diffusion software packages operating on continuous space. Four nested levels of modeling detail are identified that capture incrementing amount of detail. Their applicability to different biological questions is discussed, arching from straight diffusion simulations to sophisticated and expensive models that bridge towards coarse grained molecular dynamics. PMID:25737778

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks

PubMed Central

Liao, Shuohao; Vejchodský, Tomáš; Erban, Radek

2015-01-01

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org. PMID:26063822

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks.

PubMed

Liao, Shuohao; Vejchodský, Tomáš; Erban, Radek

2015-07-06

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.

Computational design of intrinsic molecular rectifiers based on asymmetric functionalization of N-phenylbenzamide

DOE PAGES

Ding, Wendu; Koepf, Matthieu; Koenigsmann, Christopher; ...

2015-11-03

Here, we report a systematic computational search of molecular frameworks for intrinsic rectification of electron transport. The screening of molecular rectifiers includes 52 molecules and conformers spanning over 9 series of structural motifs. N-Phenylbenzamide is found to be a promising framework with both suitable conductance and rectification properties. A targeted screening performed on 30 additional derivatives and conformers of N-phenylbenzamide yielded enhanced rectification based on asymmetric functionalization. We demonstrate that electron-donating substituent groups that maintain an asymmetric distribution of charge in the dominant transport channel (e.g., HOMO) enhance rectification by raising the channel closer to the Fermi level. These findingsmore » are particularly valuable for the design of molecular assemblies that could ensure directionality of electron transport in a wide range of applications, from molecular electronics to catalytic reactions.« less

Computational Nanoelectronics and Nanotechnology at NASA ARC

NASA Technical Reports Server (NTRS)

Saini, Subhash; Kutler, Paul (Technical Monitor)

1998-01-01

Both physical and economic considerations indicate that the scaling era of CMOS will run out of steam around the year 2010. However, physical laws also indicate that it is possible to compute at a rate of a billion times present speeds with the expenditure of only one Watt of electrical power. NASA has long-term needs where ultra-small semiconductor devices are needed for critical applications: high performance, low power, compact computers for intelligent autonomous vehicles and Petaflop computing technology are some key examples. To advance the design, development, and production of future generation micro- and nano-devices, IT Modeling and Simulation Group has been started at NASA Ames with a goal to develop an integrated simulation environment that addresses problems related to nanoelectronics and molecular nanotechnology. Overview of nanoelectronics and nanotechnology research activities being carried out at Ames Research Center will be presented. We will also present the vision and the research objectives of the IT Modeling and Simulation Group including the applications of nanoelectronic based devices relevant to NASA missions.

Computational Nanoelectronics and Nanotechnology at NASA ARC

NASA Technical Reports Server (NTRS)

Saini, Subhash

1998-01-01

Both physical and economic considerations indicate that the scaling era of CMOS will run out of steam around the year 2010. However, physical laws also indicate that it is possible to compute at a rate of a billion times present speeds with the expenditure of only one Watt of electrical power. NASA has long-term needs where ultra-small semiconductor devices are needed for critical applications: high performance, low power, compact computers for intelligent autonomous vehicles and Petaflop computing technolpgy are some key examples. To advance the design, development, and production of future generation micro- and nano-devices, IT Modeling and Simulation Group has been started at NASA Ames with a goal to develop an integrated simulation environment that addresses problems related to nanoelectronics and molecular nanotecnology. Overview of nanoelectronics and nanotechnology research activities being carried out at Ames Research Center will be presented. We will also present the vision and the research objectives of the IT Modeling and Simulation Group including the applications of nanoelectronic based devices relevant to NASA missions.

Atomistic simulation of solid-liquid coexistence for molecular systems: application to triazole and benzene.

PubMed

Eike, David M; Maginn, Edward J

2006-04-28

A method recently developed to rigorously determine solid-liquid equilibrium using a free-energy-based analysis has been extended to analyze multiatom molecular systems. This method is based on using a pseudosupercritical transformation path to reversibly transform between solid and liquid phases. Integration along this path yields the free energy difference at a single state point, which can then be used to determine the free energy difference as a function of temperature and therefore locate the coexistence temperature at a fixed pressure. The primary extension reported here is the introduction of an external potential field capable of inducing center of mass order along with secondary orientational order for molecules. The method is used to calculate the melting point of 1-H-1,2,4-triazole and benzene. Despite the fact that the triazole model gives accurate bulk densities for the liquid and crystal phases, it is found to do a poor job of reproducing the experimental crystal structure and heat of fusion. Consequently, it yields a melting point that is 100 K lower than the experimental value. On the other hand, the benzene model has been parametrized extensively to match a wide range of properties and yields a melting point that is only 20 K lower than the experimental value. Previous work in which a simple "direct heating" method was used actually found that the melting point of the benzene model was 50 K higher than the experimental value. This demonstrates the importance of using proper free energy methods to compute phase behavior. It also shows that the melting point is a very sensitive measure of force field quality that should be considered in parametrization efforts. The method described here provides a relatively simple approach for computing melting points of molecular systems.

Bacterial molecular networks: bridging the gap between functional genomics and dynamical modelling.

PubMed

van Helden, Jacques; Toussaint, Ariane; Thieffry, Denis

2012-01-01

This introductory review synthesizes the contents of the volume Bacterial Molecular Networks of the series Methods in Molecular Biology. This volume gathers 9 reviews and 16 method chapters describing computational protocols for the analysis of metabolic pathways, protein interaction networks, and regulatory networks. Each protocol is documented by concrete case studies dedicated to model bacteria or interacting populations. Altogether, the chapters provide a representative overview of state-of-the-art methods for data integration and retrieval, network visualization, graph analysis, and dynamical modelling.

Model-based Confirmation of Alternative Substrates of Mitochondrial Electron Transport Chain

PubMed Central

Kleessen, Sabrina; Araújo, Wagner L.; Fernie, Alisdair R.; Nikoloski, Zoran

2012-01-01

Discrimination of metabolic models based on high throughput metabolomics data, reflecting various internal and external perturbations, is essential for identifying the components that contribute to the emerging behavior of metabolic processes. Here, we investigate 12 different models of the mitochondrial electron transport chain (ETC) in Arabidopsis thaliana during dark-induced senescence in order to elucidate the alternative substrates to this metabolic pathway. Our findings demonstrate that the coupling of the proposed computational approach, based on dynamic flux balance analysis, with time-resolved metabolomics data results in model-based confirmations of the hypotheses that, during dark-induced senescence in Arabidopsis, (i) under conditions where the main substrate for the ETC are not fully available, isovaleryl-CoA dehydrogenase and 2-hydroxyglutarate dehydrogenase are able to donate electrons to the ETC, (ii) phytanoyl-CoA does not act even as an indirect substrate of the electron transfer flavoprotein/electron-transfer flavoprotein:ubiquinone oxidoreductase complex, and (iii) the mitochondrial γ-aminobutyric acid transporter has functional significance in maintaining mitochondrial metabolism. Our study provides a basic framework for future in silico studies of alternative pathways in mitochondrial metabolism under extended darkness whereby the role of its components can be computationally discriminated based on available molecular profile data. PMID:22334689

Parameterizing the Morse Potential for Coarse-Grained Modeling of Blood Plasma

PubMed Central

Zhang, Na; Zhang, Peng; Kang, Wei; Bluestein, Danny; Deng, Yuefan

2014-01-01

Multiscale simulations of fluids such as blood represent a major computational challenge of coupling the disparate spatiotemporal scales between molecular and macroscopic transport phenomena characterizing such complex fluids. In this paper, a coarse-grained (CG) particle model is developed for simulating blood flow by modifying the Morse potential, traditionally used in Molecular Dynamics for modeling vibrating structures. The modified Morse potential is parameterized with effective mass scales for reproducing blood viscous flow properties, including density, pressure, viscosity, compressibility and characteristic flow dynamics of human blood plasma fluid. The parameterization follows a standard inverse-problem approach in which the optimal micro parameters are systematically searched, by gradually decoupling loosely correlated parameter spaces, to match the macro physical quantities of viscous blood flow. The predictions of this particle based multiscale model compare favorably to classic viscous flow solutions such as Counter-Poiseuille and Couette flows. It demonstrates that such coarse grained particle model can be applied to replicate the dynamics of viscous blood flow, with the advantage of bridging the gap between macroscopic flow scales and the cellular scales characterizing blood flow that continuum based models fail to handle adequately. PMID:24910470

T.D.S. spectroscopic databank for spherical tops: DOS version

NASA Astrophysics Data System (ADS)

Tyuterev, V. G.; Babikov, Yu. L.; Tashkun, S. A.; Perevalov, V. I.; Nikitin, A.; Champion, J.-P.; Wenger, C.; Pierre, C.; Pierre, G.; Hilico, J.-C.; Loete, M.

1994-10-01

T.D.S. (Traitement de Donnees Spectroscopiques or Tomsk-Dijon-Spectroscopy project) is a computer package concerned with high resolution spectroscopy of spherical top molecules like CH4, CF4, SiH4, SiF4, SnH4, GeH4, SF6, etc. T.D.S. contains information, fundamental spectroscopic data (energies, transition moments, spectroscopic constants) recovered from comprehensive modeling and simultaneous fitting of experimental spectra, and associated software written in C. The T.D.S. goal is to provide an access to all available information on vibration-rotation molecular states and transitions including various spectroscopic processes (Stark, Raman, etc.) under extended conditions based on extrapolations of laboratory measurements using validated theoretical models. Applications for T.D.S. may include: education/training in molecular physics, quantum chemistry, laser physics; spectroscopic applications (analysis, laser spectroscopy, atmospheric optics, optical standards, spectroscopic atlases); applications to environment studies and atmospheric physics (remote sensing); data supply for specific databases; and to photochemistry (laser excitation, multiphoton processes). The reported DOS-version is designed for IBM and compatible personal computers.

The binding domain of the HMGB1 inhibitor carbenoxolone: Theory and experiment

NASA Astrophysics Data System (ADS)

Mollica, Luca; Curioni, Alessandro; Andreoni, Wanda; Bianchi, Marco E.; Musco, Giovanna

2008-05-01

We present a combined computational and experimental study of the interaction of the Box A of the HMGB1 protein and carbenoxolone, an inhibitor of its pro-inflammatory activity. The computational approach consists of classical molecular dynamics (MD) simulations based on the GROMOS force field with quantum-refined (QRFF) atomic charges for the ligand. Experimental data consist of fluorescence intensities, chemical shift displacements, saturation transfer differences and intermolecular Nuclear Overhauser Enhancement signals. Good agreement is found between observations and the conformation of the ligand-protein complex resulting from QRFF-MD. In contrast, simple docking procedures and MD based on the unrefined force field provide models inconsistent with experiment. The ligand-protein binding is dominated by non-directional interactions.

[Construction and application of bioinformatic analysis platform for aquatic pathogen based on the MilkyWay-2 supercomputer].

PubMed

Fang, Xiang; Li, Ning-qiu; Fu, Xiao-zhe; Li, Kai-bin; Lin, Qiang; Liu, Li-hui; Shi, Cun-bin; Wu, Shu-qin

2015-07-01

As a key component of life science, bioinformatics has been widely applied in genomics, transcriptomics, and proteomics. However, the requirement of high-performance computers rather than common personal computers for constructing a bioinformatics platform significantly limited the application of bioinformatics in aquatic science. In this study, we constructed a bioinformatic analysis platform for aquatic pathogen based on the MilkyWay-2 supercomputer. The platform consisted of three functional modules, including genomic and transcriptomic sequencing data analysis, protein structure prediction, and molecular dynamics simulations. To validate the practicability of the platform, we performed bioinformatic analysis on aquatic pathogenic organisms. For example, genes of Flavobacterium johnsoniae M168 were identified and annotated via Blast searches, GO and InterPro annotations. Protein structural models for five small segments of grass carp reovirus HZ-08 were constructed by homology modeling. Molecular dynamics simulations were performed on out membrane protein A of Aeromonas hydrophila, and the changes of system temperature, total energy, root mean square deviation and conformation of the loops during equilibration were also observed. These results showed that the bioinformatic analysis platform for aquatic pathogen has been successfully built on the MilkyWay-2 supercomputer. This study will provide insights into the construction of bioinformatic analysis platform for other subjects.

Multiscale Modeling of Multiphase Fluid Flow

DTIC Science & Technology

2016-08-01

the disparate time and length scales involved in modeling fluid flow and heat transfer. Molecular dynamics simulations were carried out to provide a...fluid dynamics methods were used to investigate the heat transfer process in open-cell micro-foam with phase change material; enhancement of natural...Computational fluid dynamics, Heat transfer, Phase change material in Micro-foam, Molecular Dynamics, Multiphase flow, Multiscale modeling, Natural

A molecular dynamics approach for predicting the glass transition temperature and plasticization effect in amorphous pharmaceuticals.

PubMed

Gupta, Jasmine; Nunes, Cletus; Jonnalagadda, Sriramakamal

2013-11-04

The objectives of this study were as follows: (i) To develop an in silico technique, based on molecular dynamics (MD) simulations, to predict glass transition temperatures (Tg) of amorphous pharmaceuticals. (ii) To computationally study the effect of plasticizer on Tg. (iii) To investigate the intermolecular interactions using radial distribution function (RDF). Amorphous sucrose and water were selected as the model compound and plasticizer, respectively. MD simulations were performed using COMPASS force field and isothermal-isobaric ensembles. The specific volumes of amorphous cells were computed in the temperature range of 440-265 K. The characteristic "kink" observed in volume-temperature curves, in conjunction with regression analysis, defined the Tg. The MD computed Tg values were 367 K, 352 K and 343 K for amorphous sucrose containing 0%, 3% and 5% w/w water, respectively. The MD technique thus effectively simulated the plasticization effect of water; and the corresponding Tg values were in reasonable agreement with theoretical models and literature reports. The RDF measurements revealed strong hydrogen bond interactions between sucrose hydroxyl oxygens and water oxygen. Steric effects led to weak interactions between sucrose acetal oxygens and water oxygen. MD is thus a powerful predictive tool for probing temperature and water effects on the stability of amorphous systems during drug development.

Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations.

PubMed

Gupta, Jasmine; Nunes, Cletus; Vyas, Shyam; Jonnalagadda, Sriramakamal

2011-03-10

The objectives of this study were (i) to develop a computational model based on molecular dynamics technique to predict the miscibility of indomethacin in carriers (polyethylene oxide, glucose, and sucrose) and (ii) to experimentally verify the in silico predictions by characterizing the drug-carrier mixtures using thermoanalytical techniques. Molecular dynamics (MD) simulations were performed using the COMPASS force field, and the cohesive energy density and the solubility parameters were determined for the model compounds. The magnitude of difference in the solubility parameters of drug and carrier is indicative of their miscibility. The MD simulations predicted indomethacin to be miscible with polyethylene oxide and to be borderline miscible with sucrose and immiscible with glucose. The solubility parameter values obtained using the MD simulations values were in reasonable agreement with those calculated using group contribution methods. Differential scanning calorimetry showed melting point depression of polyethylene oxide with increasing levels of indomethacin accompanied by peak broadening, confirming miscibility. In contrast, thermal analysis of blends of indomethacin with sucrose and glucose verified general immiscibility. The findings demonstrate that molecular modeling is a powerful technique for determining the solubility parameters and predicting miscibility of pharmaceutical compounds. © 2011 American Chemical Society

A collaborative molecular modeling environment using a virtual tunneling service.

PubMed

Lee, Jun; Kim, Jee-In; Kang, Lin-Woo

2012-01-01

Collaborative researches of three-dimensional molecular modeling can be limited by different time zones and locations. A networked virtual environment can be utilized to overcome the problem caused by the temporal and spatial differences. However, traditional approaches did not sufficiently consider integration of different computing environments, which were characterized by types of applications, roles of users, and so on. We propose a collaborative molecular modeling environment to integrate different molecule modeling systems using a virtual tunneling service. We integrated Co-Coot, which is a collaborative crystallographic object-oriented toolkit, with VRMMS, which is a virtual reality molecular modeling system, through a collaborative tunneling system. The proposed system showed reliable quantitative and qualitative results through pilot experiments.

Development and application of QM/MM methods to study the solvation effects and surfaces

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

Dibya, Pooja Arora

2010-01-01

Quantum mechanical (QM) calculations have the advantage of attaining high-level accuracy, however QM calculations become computationally inefficient as the size of the system grows. Solving complex molecular problems on large systems and ensembles by using quantum mechanics still poses a challenge in terms of the computational cost. Methods that are based on classical mechanics are an inexpensive alternative, but they lack accuracy. A good trade off between accuracy and efficiency is achieved by combining QM methods with molecular mechanics (MM) methods to use the robustness of the QM methods in terms of accuracy and the MM methods to minimize themore » computational cost. Two types of QM combined with MM (QM/MM) methods are the main focus of the present dissertation: the application and development of QM/MM methods for solvation studies and reactions on the Si(100) surface. The solvation studies were performed using a discreet solvation model that is largely based on first principles called the effective fragment potential method (EFP). The main idea of combining the EFP method with quantum mechanics is to accurately treat the solute-solvent and solvent-solvent interactions, such as electrostatic, polarization, dispersion and charge transfer, that are important in correctly calculating solvent effects on systems of interest. A second QM/MM method called SIMOMM (surface integrated molecular orbital molecular mechanics) is a hybrid QM/MM embedded cluster model that mimics the real surface.3 This method was employed to calculate the potential energy surfaces for reactions of atomic O on the Si(100) surface. The hybrid QM/MM method is a computationally inexpensive approach for studying reactions on larger surfaces in a reasonably accurate and efficient manner. This thesis is comprised of four chapters: Chapter 1 describes the general overview and motivation of the dissertation and gives a broad background of the computational methods that have been employed in this work. Chapter 2 illustrates the methodology of the interface of the EFP method with the configuration interaction with single excitations (CIS) method to study solvent effects in excited states. Chapter 3 discusses the study of the adiabatic electron affinity of the hydroxyl radical in aqueous solution and in micro-solvated clusters using a QM/EFP method. Chapter 4 describes the study of etching and diffusion of oxygen atom on a reconstructed Si(100)-2 x 1 surface using a hybrid QM/MM embedded cluster model (SIMOMM). Chapter 4 elucidates the application of the EFP method towards the understanding of the aqueous ionization potential of Na atom. Finally, a general conclusion of this dissertation work and prospective future direction are presented in Chapter 6.« less

Evaluation of a grid based molecular dynamics approach for polypeptide simulations.

PubMed

Merelli, Ivan; Morra, Giulia; Milanesi, Luciano

2007-09-01

Molecular dynamics is very important for biomedical research because it makes possible simulation of the behavior of a biological macromolecule in silico. However, molecular dynamics is computationally rather expensive: the simulation of some nanoseconds of dynamics for a large macromolecule such as a protein takes very long time, due to the high number of operations that are needed for solving the Newton's equations in the case of a system of thousands of atoms. In order to obtain biologically significant data, it is desirable to use high-performance computation resources to perform these simulations. Recently, a distributed computing approach based on replacing a single long simulation with many independent short trajectories has been introduced, which in many cases provides valuable results. This study concerns the development of an infrastructure to run molecular dynamics simulations on a grid platform in a distributed way. The implemented software allows the parallel submission of different simulations that are singularly short but together bring important biological information. Moreover, each simulation is divided into a chain of jobs to avoid data loss in case of system failure and to contain the dimension of each data transfer from the grid. The results confirm that the distributed approach on grid computing is particularly suitable for molecular dynamics simulations thanks to the elevated scalability.

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

NASA Astrophysics Data System (ADS)

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; Stuehn, Torsten

2017-11-01

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.

Computational Modeling and Simulation of Developmental ...

EPA Pesticide Factsheets

Standard practice for assessing developmental toxicity is the observation of apical endpoints (intrauterine death, fetal growth retardation, structural malformations) in pregnant rats/rabbits following exposure during organogenesis. EPA’s computational toxicology research program (ToxCast) generated vast in vitro cellular and molecular effects data on >1858 chemicals in >600 high-throughput screening (HTS) assays. The diversity of assays has been increased for developmental toxicity with several HTS platforms, including the devTOX-quickPredict assay from Stemina Biomarker Discovery utilizing the human embryonic stem cell line (H9). Translating these HTS data into higher order-predictions of developmental toxicity is a significant challenge. Here, we address the application of computational systems models that recapitulate the kinematics of dynamical cell signaling networks (e.g., SHH, FGF, BMP, retinoids) in a CompuCell3D.org modeling environment. Examples include angiogenesis (angiodysplasia) and dysmorphogenesis. Being numerically responsive to perturbation, these models are amenable to data integration for systems Toxicology and Adverse Outcome Pathways (AOPs). The AOP simulation outputs predict potential phenotypes based on the in vitro HTS data ToxCast. A heuristic computational intelligence framework that recapitulates the kinematics of dynamical cell signaling networks in the embryo, together with the in vitro profiling data, produce quantitative predic

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

Nabeel, A.; Khan, M.A.; Husain, S.

Coal is the most abundant source of energy. However, there is a need to develop cleaner, and more efficient, economical, and convenient coal conversion technologies. It is important to understand the organic chemical structure of coal for achieving real breakthroughs in the development of such coal conversion technologies. A novel computer-assisted modeling technique based on the analysis of {sup 13}C NMR and gel permeation chromatography has been applied to predict the average molecular structure of the acetylated product of a depolymerized bituminous Indian coal. The proposed molecular structure may be of practical use in understanding the mechanism of coal conversionsmore » during the processes of liquefaction, gasification, combustion, and carbonization.« less

Coarse-Grained Structural Modeling of Molecular Motors Using Multibody Dynamics

PubMed Central

Parker, David; Bryant, Zev; Delp, Scott L.

2010-01-01

Experimental and computational approaches are needed to uncover the mechanisms by which molecular motors convert chemical energy into mechanical work. In this article, we describe methods and software to generate structurally realistic models of molecular motor conformations compatible with experimental data from different sources. Coarse-grained models of molecular structures are constructed by combining groups of atoms into a system of rigid bodies connected by joints. Contacts between rigid bodies enforce excluded volume constraints, and spring potentials model system elasticity. This simplified representation allows the conformations of complex molecular motors to be simulated interactively, providing a tool for hypothesis building and quantitative comparisons between models and experiments. In an example calculation, we have used the software to construct atomically detailed models of the myosin V molecular motor bound to its actin track. The software is available at www.simtk.org. PMID:20428469

MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

PubMed Central

2011-01-01

Background To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. Results To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease. Conclusions MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis. PMID:21375730