2,411 research outputs found
Nonlinear Relaxation Dynamics in Elastic Networks and Design Principles of Molecular Machines
Analyzing nonlinear conformational relaxation dynamics in elastic networks
corresponding to two classical motor proteins, we find that they respond by
well-defined internal mechanical motions to various initial deformations and
that these motions are robust against external perturbations. We show that this
behavior is not characteristic for random elastic networks. However, special
network architectures with such properties can be designed by evolutionary
optimization methods. Using them, an example of an artificial elastic network,
operating as a cyclic machine powered by ligand binding, is constructed.Comment: 12 pages, 9 figure
Allo-network drugs: Extension of the allosteric drug concept to protein-protein interaction and signaling networks
Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same
protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design
Extended protein/water H-bond networks in photosynthetic water oxidation
Oxidation of water molecules in the photosystem II (PSII) protein complex
proceeds at the manganese–calcium complex, which is buried deeply in the
lumenal part of PSII. Understanding the PSII function requires knowledge of
the intricate coupling between the water-oxidation chemistry and the dynamic
proton management by the PSII protein matrix. Here we assess the structural
basis for long-distance proton transfer in the interior of PSII and for proton
management at its surface. Using the recent high-resolution crystal structure
of PSII, we investigate prominent hydrogen-bonded networks of the lumenal side
of PSII. This analysis leads to the identification of clusters of polar groups
and hydrogen-bonded networks consisting of amino acid residues and water
molecules. We suggest that long-distance proton transfer and conformational
coupling is facilitated by hydrogen-bonded networks that often involve more
than one protein subunit. Proton-storing Asp/Glu dyads, such as the
D1-E65/D2-E312 dyad connected to a complex water-wire network, may be
particularly important for coupling protonation states to the protein
conformation. Clusters of carboxylic amino acids could participate in proton
management at the lumenal surface of PSII. We propose that rather than having
a classical hydrophobic protein interior, the lumenal side of PSII resembles a
complex polyelectrolyte with evolutionary optimized hydrogen-bonding networks.
This article is part of a Special Issue entitled: Photosynthesis Research for
Sustainability: from Natural to Artificial
Exploring the Free Energy Landscape: From Dynamics to Networks and Back
The knowledge of the Free Energy Landscape topology is the essential key to
understand many biochemical processes. The determination of the conformers of a
protein and their basins of attraction takes a central role for studying
molecular isomerization reactions. In this work, we present a novel framework
to unveil the features of a Free Energy Landscape answering questions such as
how many meta-stable conformers are, how the hierarchical relationship among
them is, or what the structure and kinetics of the transition paths are.
Exploring the landscape by molecular dynamics simulations, the microscopic data
of the trajectory are encoded into a Conformational Markov Network. The
structure of this graph reveals the regions of the conformational space
corresponding to the basins of attraction. In addition, handling the
Conformational Markov Network, relevant kinetic magnitudes as dwell times or
rate constants, and the hierarchical relationship among basins, complete the
global picture of the landscape. We show the power of the analysis studying a
toy model of a funnel-like potential and computing efficiently the conformers
of a short peptide, the dialanine, paving the way to a systematic study of the
Free Energy Landscape in large peptides.Comment: PLoS Computational Biology (in press
Chance and Necessity in Evolution: Lessons from RNA
The relationship between sequences and secondary structures or shapes in RNA
exhibits robust statistical properties summarized by three notions: (1) the
notion of a typical shape (that among all sequences of fixed length certain
shapes are realized much more frequently than others), (2) the notion of shape
space covering (that all typical shapes are realized in a small neighborhood of
any random sequence), and (3) the notion of a neutral network (that sequences
folding into the same typical shape form networks that percolate through
sequence space). Neutral networks loosen the requirements on the mutation rate
for selection to remain effective. The original (genotypic) error threshold has
to be reformulated in terms of a phenotypic error threshold. With regard to
adaptation, neutrality has two seemingly contradictory effects: It acts as a
buffer against mutations ensuring that a phenotype is preserved. Yet it is
deeply enabling, because it permits evolutionary change to occur by allowing
the sequence context to vary silently until a single point mutation can become
phenotypically consequential. Neutrality also influences predictability of
adaptive trajectories in seemingly contradictory ways. On the one hand it
increases the uncertainty of their genotypic trace. At the same time neutrality
structures the access from one shape to another, thereby inducing a topology
among RNA shapes which permits a distinction between continuous and
discontinuous shape transformations. To the extent that adaptive trajectories
must undergo such transformations, their phenotypic trace becomes more
predictable.Comment: 37 pages, 14 figures; 1998 CNLS conference; high quality figures at
http://www.santafe.edu/~walte
Energy landscapes, supergraphs, and "folding funnels" in spin systems
Dynamical connectivity graphs, which describe dynamical transition rates
between local energy minima of a system, can be displayed against the
background of a disconnectivity graph which represents the energy landscape of
the system. The resulting supergraph describes both dynamics and statics of the
system in a unified coarse-grained sense. We give examples of the supergraphs
for several two dimensional spin and protein-related systems. We demonstrate
that disordered ferromagnets have supergraphs akin to those of model proteins
whereas spin glasses behave like random sequences of aminoacids which fold
badly.Comment: REVTeX, 9 pages, two-column, 13 EPS figures include
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