18,121 research outputs found
Dry and wet interfaces: Influence of solvent particles on molecular recognition
We present a coarse-grained lattice model to study the influence of water on
the recognition process of two rigid proteins. The basic model is formulated in
terms of the hydrophobic effect. We then investigate several modifications of
our basic model showing that the selectivity of the recognition process can be
enhanced by considering the explicit influence of single solvent particles.
When the number of cavities at the interface of a protein-protein complex is
fixed as an intrinsic geometric constraint, there typically exists a
characteristic fraction that should be filled with water molecules such that
the selectivity exhibits a maximum. In addition the optimum fraction depends on
the hydrophobicity of the interface so that one has to distinguish between dry
and wet interfaces.Comment: 11 pages, 7 figure
A structural view of microRNA-target recognition
It is well established that the correct identification of the messenger RNA targeted by a given microRNA (miRNA) is a difficult problem, and that available methods all suffer from low specificity. We hypothesize that the correct identification of the pairing should take into account the effect of the Argonaute protein (AGO), an essential catalyst of the recognition process. Therefore, we developed a strategy named MiREN for building and scoring three-dimensional models of the ternary complex formed by AGO, a miRNA and 22 nt of a target mRNA that putatively interacts with it. We show here that MiREN can be used to assess the likelihood that an RNA molecule is the target of a given miRNA and that this approach is more accurate than other existing methods, usually based on sequence or sequence-related features. Our results also suggest that AGO plays a relevant role in the selection of the miRNA targets. Our method can represent an additional step for refining predictions made by faster but less accurate classical methods for the identification of miRNA targets
Protein secondary structure: Entropy, correlations and prediction
Is protein secondary structure primarily determined by local interactions
between residues closely spaced along the amino acid backbone, or by non-local
tertiary interactions? To answer this question we have measured the entropy
densities of primary structure and secondary structure sequences, and the local
inter-sequence mutual information density. We find that the important
inter-sequence interactions are short ranged, that correlations between
neighboring amino acids are essentially uninformative, and that only 1/4 of the
total information needed to determine the secondary structure is available from
local inter-sequence correlations. Since the remaining information must come
from non-local interactions, this observation supports the view that the
majority of most proteins fold via a cooperative process where secondary and
tertiary structure form concurrently. To provide a more direct comparison to
existing secondary structure prediction methods, we construct a simple hidden
Markov model (HMM) of the sequences. This HMM achieves a prediction accuracy
comparable to other single sequence secondary structure prediction algorithms,
and can extract almost all of the inter-sequence mutual information. This
suggests that these algorithms are almost optimal, and that we should not
expect a dramatic improvement in prediction accuracy. However, local
correlations between secondary and primary structure are probably of
under-appreciated importance in many tertiary structure prediction methods,
such as threading.Comment: 8 pages, 5 figure
Protein structural variation in computational models and crystallographic data
Normal mode analysis offers an efficient way of modeling the conformational
flexibility of protein structures. Simple models defined by contact topology,
known as elastic network models, have been used to model a variety of systems,
but the validation is typically limited to individual modes for a single
protein. We use anisotropic displacement parameters from crystallography to
test the quality of prediction of both the magnitude and directionality of
conformational variance. Normal modes from four simple elastic network model
potentials and from the CHARMM forcefield are calculated for a data set of 83
diverse, ultrahigh resolution crystal structures. While all five potentials
provide good predictions of the magnitude of flexibility, the methods that
consider all atoms have a clear edge at prediction of directionality, and the
CHARMM potential produces the best agreement. The low-frequency modes from
different potentials are similar, but those computed from the CHARMM potential
show the greatest difference from the elastic network models. This was
illustrated by computing the dynamic correlation matrices from different
potentials for a PDZ domain structure. Comparison of normal mode results with
anisotropic temperature factors opens the possibility of using ultrahigh
resolution crystallographic data as a quantitative measure of molecular
flexibility. The comprehensive evaluation demonstrates the costs and benefits
of using normal mode potentials of varying complexity. Comparison of the
dynamic correlation matrices suggests that a combination of topological and
chemical potentials may help identify residues in which chemical forces make
large contributions to intramolecular coupling.Comment: 17 pages, 4 figure
Ab initio RNA folding
RNA molecules are essential cellular machines performing a wide variety of
functions for which a specific three-dimensional structure is required. Over
the last several years, experimental determination of RNA structures through
X-ray crystallography and NMR seems to have reached a plateau in the number of
structures resolved each year, but as more and more RNA sequences are being
discovered, need for structure prediction tools to complement experimental data
is strong. Theoretical approaches to RNA folding have been developed since the
late nineties when the first algorithms for secondary structure prediction
appeared. Over the last 10 years a number of prediction methods for 3D
structures have been developed, first based on bioinformatics and data-mining,
and more recently based on a coarse-grained physical representation of the
systems. In this review we are going to present the challenges of RNA structure
prediction and the main ideas behind bioinformatic approaches and physics-based
approaches. We will focus on the description of the more recent physics-based
phenomenological models and on how they are built to include the specificity of
the interactions of RNA bases, whose role is critical in folding. Through
examples from different models, we will point out the strengths of
physics-based approaches, which are able not only to predict equilibrium
structures, but also to investigate dynamical and thermodynamical behavior, and
the open challenges to include more key interactions ruling RNA folding.Comment: 28 pages, 18 figure
Molecular dynamics of folding of secondary structures in Go-type models of proteins
We consider six different secondary structures of proteins and construct two
types of Go-type off-lattice models: with the steric constraints and without.
The basic aminoacid-aminoacid potential is Lennard Jones for the native
contacts and a soft repulsion for the non-native contacts. The interactions are
chosen to make the target secondary structure be the native state of the
system. We provide a thorough equilibrium and kinetic characterization of the
sequences through the molecular dynamics simulations with the Langevin noise.
Models with the steric constraints are found to be better folders and to be
more stable, especially in the case of the -structures. Phononic spectra
for vibrations around the native states have low frequency gaps that correlate
with the thermodynamic stability. Folding of the secondary structures proceeds
through a well defined sequence of events. For instance, -helices fold
from the ends first. The closer to the native state, the faster establishment
of the contacts. Increasing the system size deteriorates the folding
characteristics. We study the folding times as a function of viscous friction
and find a regime of moderate friction with the linear dependence. We also
consider folding when one end of a structure is pinned which imitates
instantaneous conditions when a protein is being synthesized. We find that,
under such circumstances, folding of helices is faster and of the
-sequences slower.Comment: REVTeX, 14 pages, EPS figures included, JCP in pres
Topological network alignment uncovers biological function and phylogeny
Sequence comparison and alignment has had an enormous impact on our
understanding of evolution, biology, and disease. Comparison and alignment of
biological networks will likely have a similar impact. Existing network
alignments use information external to the networks, such as sequence, because
no good algorithm for purely topological alignment has yet been devised. In
this paper, we present a novel algorithm based solely on network topology, that
can be used to align any two networks. We apply it to biological networks to
produce by far the most complete topological alignments of biological networks
to date. We demonstrate that both species phylogeny and detailed biological
function of individual proteins can be extracted from our alignments.
Topology-based alignments have the potential to provide a completely new,
independent source of phylogenetic information. Our alignment of the
protein-protein interaction networks of two very different species--yeast and
human--indicate that even distant species share a surprising amount of network
topology with each other, suggesting broad similarities in internal cellular
wiring across all life on Earth.Comment: Algorithm explained in more details. Additional analysis adde
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