1,345 research outputs found
Sequence-based Multiscale Model (SeqMM) for High-throughput chromosome conformation capture (Hi-C) data analysis
In this paper, I introduce a Sequence-based Multiscale Model (SeqMM) for the
biomolecular data analysis. With the combination of spectral graph method, I
reveal the essential difference between the global scale models and local scale
ones in structure clustering, i.e., different optimization on Euclidean (or
spatial) distances and sequential (or genomic) distances. More specifically,
clusters from global scale models optimize Euclidean distance relations. Local
scale models, on the other hand, result in clusters that optimize the genomic
distance relations. For a biomolecular data, Euclidean distances and sequential
distances are two independent variables, which can never be optimized
simultaneously in data clustering. However, sequence scale in my SeqMM can work
as a tuning parameter that balances these two variables and deliver different
clusterings based on my purposes. Further, my SeqMM is used to explore the
hierarchical structures of chromosomes. I find that in global scale, the
Fiedler vector from my SeqMM bears a great similarity with the principal vector
from principal component analysis, and can be used to study genomic
compartments. In TAD analysis, I find that TADs evaluated from different scales
are not consistent and vary a lot. Particularly when the sequence scale is
small, the calculated TAD boundaries are dramatically different. Even for
regions with high contact frequencies, TAD regions show no obvious consistence.
However, when the scale value increases further, although TADs are still quite
different, TAD boundaries in these high contact frequency regions become more
and more consistent. Finally, I find that for a fixed local scale, my method
can deliver very robust TAD boundaries in different cluster numbers.Comment: 22 PAGES, 13 FIGURE
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
Protein folding tames chaos
Protein folding produces characteristic and functional three-dimensional
structures from unfolded polypeptides or disordered coils. The emergence of
extraordinary complexity in the protein folding process poses astonishing
challenges to theoretical modeling and computer simulations. The present work
introduces molecular nonlinear dynamics (MND), or molecular chaotic dynamics,
as a theoretical framework for describing and analyzing protein folding. We
unveil the existence of intrinsically low dimensional manifolds (ILDMs) in the
chaotic dynamics of folded proteins. Additionally, we reveal that the
transition from disordered to ordered conformations in protein folding
increases the transverse stability of the ILDM. Stated differently, protein
folding reduces the chaoticity of the nonlinear dynamical system, and a folded
protein has the best ability to tame chaos. Additionally, we bring to light the
connection between the ILDM stability and the thermodynamic stability, which
enables us to quantify the disorderliness and relative energies of folded,
misfolded and unfolded protein states. Finally, we exploit chaos for protein
flexibility analysis and develop a robust chaotic algorithm for the prediction
of Debye-Waller factors, or temperature factors, of protein structures
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