A sequence-dependent rigid-base model of DNA

,
Amer Inst Physics, 2013
Journal paper

Abstract

A novel hierarchy of coarse-grain, sequence-dependent, rigid-base models of B-form DNA in solution is introduced. The hierarchy depends on both the assumed range of energetic couplings, and the extent of sequence dependence of the model parameters. A significant feature of the models is that they exhibit the phenomenon of frustration: each base cannot simultaneously minimize the energy of all of its interactions. As a consequence, an arbitrary DNA oligomer has an intrinsic or pre-existing stress, with the level of this frustration dependent on the particular sequence of the oligomer. Attention is focussed on the particular model in the hierarchy that has nearest-neighbor interactions and dimer sequence dependence of the model parameters. For a Gaussian version of this model, a complete coarse-grain parameter set is estimated. The parameterized model allows, for an oligomer of arbitrary length and sequence, a simple and explicit construction of an approximation to the configuration-space equilibrium probability density function for the oligomer in solution. The training set leading to the coarse-grain parameter set is itself extracted from a recent and extensive database of a large number of independent, atomic-resolution molecular dynamics (MD) simulations of short DNA oligomers immersed in explicit solvent. The Kullback-Leibler divergence between probability density functions is used to make several quantitative assessments of our nearest-neighbor, dimer-dependent model, which is compared against others in the hierarchy to assess various assumptions pertaining both to the locality of the energetic couplings and to the level of sequence dependence of its parameters. It is also compared directly against all-atom MD simulation to assess its predictive capabilities. The results show that the nearest-neighbor, dimer-dependent model can successfully resolve sequence effects both within and between oligomers. For example, due to the presence of frustration, the model can successfully predict the nonlocal changes in the minimum energy configuration of an oligomer that are consequent upon a local change of sequence at the level of a single point mutation. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4789411]

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,
Amer Inst Physics, 2013
Journal paper

Abstract

Official source

About this result

Related concepts (17)

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Atmospheric trace gases and aerosols and climate interact in many ways. A quantitative assessment of the influence of trace gases and aerosols on climate can only be achieved if the interactions and feedbacks among these three major components are accounted for. The goal of this thesis was to develop, evaluate and apply the ECHAM5-HAMMOZ chemistry-aerosol-climate model. The model includes fully interactive simulations of the NOx-Ox-hydrocarbons chemistry and of the aerosol chemistry and microphysics, the two simulations being embedded into the well established ECHAM5 climate model. In particular, on-line calculation of the photolysis rates (that accounts for aerosols and clouds) and heterogeneous reactions of trace gases on aerosols are accounted for in the model. 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Sulfate concentrations are represented fairly well, for all the regions considered, while SO2 is overestimated by a factor of 2 in general. Black carbon concentrations are underestimated over the TRACE-P region (mean absolute bias of 80%), most probably because of too low emissions, but well reproduced over north America. The aerosol optical depths compare well with observations at many sites in general, both in terms of annual means and seasonal variations. We show the results from a series of sensitivity simulations which goals are to assess the impact of heterogeneous reactions, photolysis reactions and sulfur chemistry on the regional and global trace gas distribution, and aerosol distribution, composition and optical properties. We found that heterogeneous reactions result in a reduction of 7% in the global O3 burden, and by up to 15% in surface O3 over regions rich in mineral dust. 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EPFL2007

A DNA Coarse-Grain Rigid Base Model and Parameter Estimation from Molecular Dynamics Simulations

Daiva Petkeviciute

Sequence dependent mechanics of DNA is believed to play a central role in the functioning of the cell through the expression of genetic information. Nucleosome positioning, gene regulation, DNA looping and packaging within the cell are only some of the processes that are believed to be at least partially governed by mechanical laws. Therefore it is important to understand how the sequence of DNA affects its mechanical properties. For exploring the mechanical properties of DNA, various discrete and continuum models have been, and continue to be, developed. A large family of these models, including the model considered in this work, assume that bases or base pairs of DNA are rigid bodies. The most standard are rigid base pair models, with parameters either obtained directly from experimental data or from Molecular Dynamics (MD) simulations. The drawback of current experimental data, such as crystal structures, is that only small ensembles of configurations are available for a small number of sequences. In contrast, MD simulations allow a much more detailed view of a larger number of DNA sequences. However, the drawback is that the results of these simulations depend on the choice of the simulation protocol and force field parameters. MD simulations also have sequence length limitations and are currently too intensive for (linear) molecules longer than a few tens of base pairs. The only way to simulate longer sequences is to construct a coarse-grain model. The goal of this work is to construct a small parameter set that can model a sequence- dependent equilibrium probability distribution for rigid base configurations of a DNA oligomer with any given sequence of any length. The model parameter sets previously available were for rigid base pair models ignoring all the couplings beyond nearest neighbour interactions. However it was shown in previous work, that this standard model of rigid base pair nearest neighbour interactions is inconsistent with a (then) large scale MD simulation of a single oligomer [36]. In contrast we here show that a rigid base nearest neighbour, dimer sequence dependent model is a quite good fit to many MD simulations of different duration and se- quence. In fact a hierarchy of rigid base models with different interaction range and length of sequence-dependence is discussed, and it is concluded that the nearest neighbour, dimer based model is a good compromise between accuracy and complexity of the model. A full parameter set for this model is estimated. An interesting feature is that despite the dimer dependence of the parameter set, due to the phenomenon of frustration, our model predicts non local changes in the oligomer shape as a function of local changes in the sequence, down to the level of a point mutation.

EPFL2012

We introduce a sequence-dependent coarse-grain model of double-stranded DNA with an explicit description of both the bases and the phosphate groups as interacting rigid-bodies. The model parameters are trained on extensive, state-of-the-art large scale molecular dynamics (MD) simulations. The model paradigm relies on three main approximations: 1) nucleic acid bases and phosphate groups are rigid, 2) interactions are nearest-neighbour and can be modelled with a quadratic energy, 3) model parameters have dimer sequence dependence. For an arbitrary sequence, the model predicts a sequence-dependent Gaussian equilibrium probability distribution. The parameter set comprises dimer-based elements, which are used to reconstruct mean configurations, called ground-states, which can have strong non-local sequence dependence, and precision matrices, or stiffness matrices, for any sequence of any length. This prediction step is sufficiently efficient that it is straightforward to construct probability density functions for millions of fragments each of length a few hundred base-pairs. The estimation of a parameter set consists in minimising the sum of Kullback-Leibler divergences between Gaussians predicted by the model and analogous Gaussians estimated directly from MD simulations of a training library of sequences. The training library comprises a short list of short palindromic DNA sequences. We designed the palindromic library using an ad hoc algorithm to include multiple instances of all independent tetramer sub-sequences. We exploit palindromic symmetry properties to study the convergence of the statistics extracted from MD simulations of palindromes and to define palindromically symmetrised estimators of first and second centred moments. The computation of the parameter set is delicate and needs the use of sophisticated numerics. We present an efficient and reliable procedure for estimating a complete parameter set which involves a generalisation of the classic Fisher information matrix and its relationship to the relative entropy, or Kullback-Leibler divergence. The model is a computationally efficient tool that allows the study of the mechanical properties of double-stranded DNA of arbitrary length and sequence. We use the model to study the sequence-dependent rigidity of DNA and we compute sequence-dependent apparent and dynamic persistence lengths. The explicit treatment of the phosphate group also allows computation of sequence-dependent grooves widths. Moreover, with fine-grained representation of predicted ground-states, we can also study sequence-dependence of sugar puckering modes and BI-BII backbone conformations.

EPFL2019

A DNA Coarse-Grain Rigid Base Model and Parameter Estimation from Molecular Dynamics Simulations

Daiva Petkeviciute

EPFL2012

EPFL2007

EPFL2019