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Communication and re-use of chemical information in bioscience
Peter Murray-Rust, John BO Mitchell, Henry S Rzepa
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-180
Abstract: In a previous article [1] we have argued the value of extracting the chemical information in bioscientific research, transforming it to XML and redisseminating it openly. The present article expands on the technical and cultural infrastructure required to support this. The technical aspects have been solved to proof-of-concept stage and we are starting to embark on experiments in the social domain. In this we thank BMC for inviting us to submit this and we present a model here which we believe could be attractive for bioscience publishers and their community.We concentrate on the current publication of chemistry in bioscience. This includes:1. mention of chemical compounds.2. details of synthesis (in vivo and in vitro) of compounds.3. proof of structure (spectra and analytical data).4. Methods and reagents in bioscience bio-protocols5. properties of compounds.6. reactions and their properties, both in enzymes and enzyme-free systems.This type of chemistry is very well understood and has a simple ontology which has not changed over decades[2]. Unlike much bioscience, where ontological tools are an essential part of reconciling the domain-dependent approaches, much chemistry has an implicitly agreed abstract description. The problems are primarily reconciling syntax and semantics. This is because chemists use abbreviated and lazy methods of communicating data, relying on trained readers to add information from the context. We have reviewed current problems of machine-understanding of chemistry[3] in a typical chemistry journal, many of which are perpetuated by the graphical orientation of conventional publishing houses. Here we take the view that a committed publishing house can create a cost-effective and human-tolerable system for authoring semantically correct chemistry in (bio)scientific documents.We know from experience that Utopian visions do not sell themselves. The enormous and accepted value of the sequence and structures databases arose not from the demands
Fast method for quantum mechanical molecular dynamics  [PDF]
Anders M. N. Niklasson,Marc J. Cawkwell
Physics , 2012, DOI: 10.1103/PhysRevB.86.174308
Abstract: With the continuous growth of processing power for scientific computing, first principles Born-Oppenheimer molecular dynamics (MD) simulations are becoming increasingly popular for the study of a wide range of problems in materials science, chemistry and biology. Nevertheless, the computational cost still remains prohibitively large in many cases, particularly in comparison to classical MD simulations using empirical force fields. Here we show how to circumvent the major computational bottleneck in Born-Oppenheimer MD simulations arising from the self-consistent-charge optimization. The optimization-free quantum mechanical MD method is demonstrated for density functional tight-binding theory. The molecular trajectories are almost indistinguishable from an "exact" microcanonical Born-Oppenheimer MD simulation even when linear scaling sparse matrix algebra is used. Our findings drastically reduce the computational gap between classical and quantum mechanical MD simulations.
Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System  [PDF]
Nicolas Rapin,Ole Lund,Massimo Bernaschi,Filippo Castiglione
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0009862
Abstract: We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein–protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system.
Instabilities and Non-Reversibility of Molecular Dynamics Trajectories  [PDF]
R. G. Edwards,Ivan Horváth,A. D. Kennedy
Physics , 1996, DOI: 10.1016/S0550-3213(96)00618-9
Abstract: The theoretical justification of the Hybrid Monte Carlo algorithm depends upon the molecular dynamics trajectories within it being exactly reversible. If computations were carried out with exact arithmetic then it would be easy to ensure such reversibility, but the use of approximate floating point arithmetic inevitably introduces violations of reversibility. In the absence of evidence to the contrary, we are usually prepared to accept that such rounding errors can be made small enough to be innocuous, but in certain circumstances they are exponentially amplified and lead to blatantly erroneous results. We show that there are two types of instability of the molecular dynamics trajectories which lead to this behavior, instabilities due to insufficiently accurate numerical integration of Hamilton's equations, and intrinsic chaos in the underlying continuous fictitious time equations of motion themselves. We analyze the former for free field theory, and show that it is essentially a finite volume effect. For the latter we propose a hypothesis as to how the Liapunov exponent describing the chaotic behavior of the fictitious time equations of motion for an asymptotically free quantum field theory behaves as the system is taken to its continuum limit, and explain why this means that instabilities in molecular dynamics trajectories are not a significant problem for Hybrid Monte Carlo computations. We present data for pure $SU(3)$ gauge theory and for QCD with dynamical fermions on small lattices to illustrate and confirm some of our results.
Extended Lagrangian Born-Oppenheimer molecular dynamics in the limit of vanishing self-consistent field optimization  [PDF]
Petros Souvatzis,Anders M. N. Niklasson
Physics , 2013, DOI: 10.1063/1.4834015
Abstract: We present an efficient general approach to first principles molecular dynamics simulations based on extended Lagrangian Born-Oppenheimer molecular dynamics in the limit of vanishing self-consistent field optimization. The reduction of the optimization requirement reduces the computational cost to a minimum, but without causing any significant loss of accuracy or longterm energy drift. The optimization-free first principles molecular dynamics requires only one single diagonalization per time step and yields trajectories at the same level of accuracy as "exact", fully converged, Born-Oppenheimer molecular dynamics simulations. The optimization-free limit of extended Lagrangian Born-Oppenheimer molecular dynamics therefore represents an ideal starting point for a robust and efficient formulation of a new generation first principles quantum mechanical molecular dynamics simulation schemes.
Using Computational Verbs to Cluster Trajectories and Curves
Tao Yang
International Journal of Computational Cognition , 2006,
Abstract: The first step of building a computational verbmodel of any a dynamical system is to find the representativecomputational verbs, which are also called template computationalverbs, from the historical records. In this paper, clusteringalgorithms were used to find centers of quantitatively differentclusters, of which each was represented by a template computationalverbs. The clustering space was constructed basedon verb similarities between observed curves or waveformsand some “ideal” computational verbs such as increase anddecrease, which had linear evolving functions. As an example,the peak-valley and valley-peak waveforms generated by a chaoticsystem called Chua’s circuit were clustered into 18 templatecomputational verbs.
Non-Reversibility of Molecular Dynamics Trajectories  [PDF]
R. G. Edwards,Ivan Horvath,A. D. Kennedy
Physics , 1996, DOI: 10.1016/S0920-5632(96)00830-4
Abstract: We study the non-reversibility of molecular dynamics trajectories arising from the amplification of rounding errors. We analyse the causes of such behaviour and give arguments, indicating that this does not pose a significant problem for Hybrid Monte Carlo computations. We present data for pure SU(3) gauge theory and for QCD with dynamical fermions on small lattices to illustrate and to support some of our ideas.
Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps
Domenico Fraccalvieri, Alessandro Pandini, Fabio Stella, Laura Bonati
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-158
Abstract: The conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions.The use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from other sources.Protein dynamics plays a central role in cell life. In many cases biological function involves molecular motion [1] and it was recently suggested that intrinsic dynamics also defines the ability of proteins to adapt and evolve new functions [2]. Therefore, a full understanding of protein function and evolution will require a deeper insight into biomolecular atomistic dynamics.Significant contributions in this direction have come from computational methods, in particular from Molecular dynamics (MD) simulations [3,4], by which a large ensemble of molecular structures can be generated to sample the accessible conformational space of a protein. Analysis of this ensemble ca
Quantum Mechanics with Trajectories: Quantum Trajectories and Adaptive Grids  [PDF]
Robert E. Wyatt,Eric R. Bittner
Physics , 2003,
Abstract: Although the foundations of the hydrodynamical formulation of quantum mechanics were laid over 50 years ago, it has only been within the past few years that viable computational implementations have been developed. One approach to solving the hydrodynamic equations uses quantum trajectories as the computational tool. The trajectory equations of motion are described and methods for implementation are discussed, including fitting of the fields to gaussian clusters.
Molecular Trajectories Leading to the Alternative Fates of Duplicate Genes  [PDF]
Michael Marotta, Helen Piontkivska, Hisashi Tanaka
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0038958
Abstract: Gene duplication generates extra gene copies in which mutations can accumulate without risking the function of pre-existing genes. Such mutations modify duplicates and contribute to evolutionary novelties. However, the vast majority of duplicates appear to be short-lived and experience duplicate silencing within a few million years. Little is known about the molecular mechanisms leading to these alternative fates. Here we delineate differing molecular trajectories of a relatively recent duplication event between humans and chimpanzees by investigating molecular properties of a single duplicate: DNA sequences, gene expression and promoter activities. The inverted duplication of the Glutathione S-transferase Theta 2 (GSTT2) gene had occurred at least 7 million years ago in the common ancestor of African great apes and is preserved in chimpanzees (Pan troglodytes), whereas a deletion polymorphism is prevalent in humans. The alternative fates are associated with expression divergence between these species, and reduced expression in humans is regulated by silencing mutations that have been propagated between duplicates by gene conversion. In contrast, selective constraint preserved duplicate divergence in chimpanzees. The difference in evolutionary processes left a unique DNA footprint in which dying duplicates are significantly more similar to each other (99.4%) than preserved ones. Such molecular trajectories could provide insights for the mechanisms underlying duplicate life and death in extant genomes.
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