339 research outputs found
Mean first-passage time of quantum transition processes
In this paper, we consider the problem of mean first-passage time (MFPT) in
quantum mechanics; the MFPT is the average time of the transition from a given
initial state, passing through some intermediate states, to a given final state
for the first time. We apply the method developed in statistical mechanics for
calculating the MFPT of random walks to calculate the MFPT of a transition
process. As applications, we (1) calculate the MFPT for multiple-state systems,
(2) discuss transition processes occurring in an environment background, (3)
consider a roundabout transition in a hydrogen atom, and (4) apply the approach
to laser theory.Comment: 11 pages, no figur
Anisotropic coarse-grained statistical potentials improve the ability to identify native-like protein structures
We present a new method to extract distance and orientation dependent
potentials between amino acid side chains using a database of protein
structures and the standard Boltzmann device. The importance of orientation
dependent interactions is first established by computing orientational order
parameters for proteins with alpha-helical and beta-sheet architecture.
Extraction of the anisotropic interactions requires defining local reference
frames for each amino acid that uniquely determine the coordinates of the
neighboring residues. Using the local reference frames and histograms of the
radial and angular correlation functions for a standard set of non-homologue
protein structures, we construct the anisotropic pair potentials. The
performance of the orientation dependent potentials was studied using a large
database of decoy proteins. The results demonstrate that the new distance and
orientation dependent residue-residue potentials present a significantly
improved ability to recognize native folds from a set of native and decoy
protein structures.Comment: Submitted to "The Journal of Chemical Physics
Fibril elongation mechanisms of HET-s prion-forming domain: Topological evidence for growth polarity
The prion-forming C-terminal domain of the fungal prion HET-s forms
infectious amyloid fibrils at physiological pH. The conformational switch from
the non-prion soluble form to the prion fibrillar form is believed to have a
functional role, since HET-s in its prion form participates in a recognition
process of different fungal strains. Based on the knowledge of the
high-resolution structure of HET-s(218-289) (the prion forming-domain) in its
fibrillar form, we here present a numerical simulation of the fibril growth
process which emphasizes the role of the topological properties of the
fibrillar structure. An accurate thermodynamic analysis of the way an
intervening HET-s chain is recruited to the tip of the growing fibril suggests
that elongation proceeds through a dock and lock mechanism. First, the chain
docks onto the fibril by forming the longest -strands. Then, the
re-arrangement in the fibrillar form of all the rest of molecule takes place.
Interestingly, we predict also that one side of the HET-s fibril is more
suitable for substaining its growth with respect to the other. The resulting
strong polarity of fibril growth is a consequence of the complex topology of
HET-s fibrillar structure, since the central loop of the intervening chain
plays a crucially different role in favouring or not the attachment of the
C-terminus tail to the fibril, depending on the growth side.Comment: 16 pages, 10 figure
Empirical Potential Function for Simplified Protein Models: Combining Contact and Local Sequence-Structure Descriptors
An effective potential function is critical for protein structure prediction
and folding simulation. Simplified protein models such as those requiring only
or backbone atoms are attractive because they enable efficient
search of the conformational space. We show residue specific reduced discrete
state models can represent the backbone conformations of proteins with small
RMSD values. However, no potential functions exist that are designed for such
simplified protein models. In this study, we develop optimal potential
functions by combining contact interaction descriptors and local
sequence-structure descriptors. The form of the potential function is a
weighted linear sum of all descriptors, and the optimal weight coefficients are
obtained through optimization using both native and decoy structures. The
performance of the potential function in test of discriminating native protein
structures from decoys is evaluated using several benchmark decoy sets. Our
potential function requiring only backbone atoms or atoms have
comparable or better performance than several residue-based potential functions
that require additional coordinates of side chain centers or coordinates of all
side chain atoms. By reducing the residue alphabets down to size 5 for local
structure-sequence relationship, the performance of the potential function can
be further improved. Our results also suggest that local sequence-structure
correlation may play important role in reducing the entropic cost of protein
folding.Comment: 20 pages, 5 figures, 4 tables. In press, Protein
Generic Mechanism of Emergence of Amyloid Protofilaments from Disordered Oligomeric aggregates
The presence of oligomeric aggregates, which is often observed during the
process of amyloid formation, has recently attracted much attention since it
has been associated with neurodegenerative conditions such as Alzheimer's and
Parkinson's diseases. We provide a description of a sequence-indepedent
mechanism by which polypeptide chains aggregate by forming metastable
oligomeric intermediate states prior to converting into fibrillar structures.
Our results illustrate how the formation of ordered arrays of hydrogen bonds
drives the formation of beta-sheets within the disordered oligomeric aggregates
that form early under the effect of hydrophobic forces. Initially individual
beta-sheets form with random orientations, which subsequently tend to align
into protofilaments as their lengths increases. Our results suggest that
amyloid aggregation represents an example of the Ostwald step rule of first
order phase transitions by showing that ordered cross-beta structures emerge
preferentially from disordered compact dynamical intermediate assemblies.Comment: 14 pages, 4 figure
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
Knowledge-based energy functions for computational studies of proteins
This chapter discusses theoretical framework and methods for developing
knowledge-based potential functions essential for protein structure prediction,
protein-protein interaction, and protein sequence design. We discuss in some
details about the Miyazawa-Jernigan contact statistical potential,
distance-dependent statistical potentials, as well as geometric statistical
potentials. We also describe a geometric model for developing both linear and
non-linear potential functions by optimization. Applications of knowledge-based
potential functions in protein-decoy discrimination, in protein-protein
interactions, and in protein design are then described. Several issues of
knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe
A Condensation-Ordering Mechanism in Nanoparticle-Catalyzed Peptide Aggregation
Nanoparticles introduced in living cells are capable of strongly promoting
the aggregation of peptides and proteins. We use here molecular dynamics
simulations to characterise in detail the process by which nanoparticle
surfaces catalyse the self- assembly of peptides into fibrillar structures. The
simulation of a system of hundreds of peptides over the millisecond timescale
enables us to show that the mechanism of aggregation involves a first phase in
which small structurally disordered oligomers assemble onto the nanoparticle
and a second phase in which they evolve into highly ordered beta-sheets as
their size increases
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