46,475 research outputs found
Multiscale virtual particle based elastic network model (MVP-ENM) for biomolecular normal mode analysis
In this paper, a multiscale virtual particle based elastic network model
(MVP-ENM) is proposed for biomolecular normal mode analysis. The multiscale
virtual particle model is proposed for the discretization of biomolecular
density data in different scales. Essentially, the model works as the
coarse-graining of the biomolecular structure, so that a delicate balance
between biomolecular geometric representation and computational cost can be
achieved. To form "connections" between these multiscale virtual particles, a
new harmonic potential function, which considers the influence from both mass
distributions and distance relations, is adopted between any two virtual
particles. Unlike the previous ENMs that use a constant spring constant, a
particle-dependent spring parameter is used in MVP-ENM. Two independent models,
i.e., multiscale virtual particle based Gaussian network model (MVP-GNM) and
multiscale virtual particle based anisotropic network model (MVP-ANM), are
proposed. Even with a rather coarse grid and a low resolution, the MVP-GNM is
able to predict the Debye-Waller factors (B-factors) with considerable good
accuracy. Similar properties have also been observed in MVP-ANM. More
importantly, in B-factor predictions, the mismatch between the predicted
results and experimental ones is predominantly from higher fluctuation regions.
Further, it is found that MVP-ANM can deliver a very consistent low-frequency
eigenmodes in various scales. This demonstrates the great potential of MVP-ANM
in the deformation analysis of low resolution data. With the multiscale
rigidity function, the MVP-ENM can be applied to biomolecular data represented
in density distribution and atomic coordinates. Further, the great advantage of
my MVP-ENM model in computational cost has been demonstrated by using two
poliovirus virus structures. Finally, the paper ends with a conclusion.Comment: 15 figures; 25 page
Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks
<p>Abstract</p> <p>Background</p> <p>Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.</p> <p>Results</p> <p>In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent <it>hypotheses </it>that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.</p> <p>Conclusion</p> <p>Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.</p
An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.
Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.Comment: 20 pages, 8 figures, 5 table
Pressure effects on collective density fluctuations in water and protein solutions
Neutron Brillouin scattering and molecular dynamics simulations have been used to investigate protein hydration water density fluctuations as a function of pressure. Our results show significant differences between the pressure and density dependence of collective dynamics in bulk water and in concentrated protein solutions. Pressure-induced changes in the tetrahedral order of the water HB network have direct consequences for the high-frequency sound velocity and damping coefficients, which we find to be a sensitive probe for changes in the HB network structure as well as the wetting of biomolecular surfaces
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