27,826 research outputs found
CPSP-tools – Exact and complete algorithms for high-throughput 3D lattice protein studies
<p>Abstract</p> <p>Background</p> <p>The principles of protein folding and evolution pose problems of very high inherent complexity. Often these problems are tackled using simplified protein models, e.g. lattice proteins. The CPSP-tools package provides programs to solve exactly and completely the problems typical of studies using 3D lattice protein models. Among the tasks addressed are the prediction of (all) globally optimal and/or suboptimal structures as well as sequence design and neutral network exploration.</p> <p>Results</p> <p>In contrast to stochastic approaches, which are not capable of answering many fundamental questions, our methods are based on fast, non-heuristic techniques. The resulting tools are designed for high-throughput studies of 3D-lattice proteins utilising the Hydrophobic-Polar (HP) model. The source bundle is freely available <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p> <p>Conclusion</p> <p>The CPSP-tools package is the first set of exact and complete methods for extensive, high-throughput studies of non-restricted 3D-lattice protein models. In particular, our package deals with cubic and face centered cubic (FCC) lattices.</p
Sequential Monte Carlo Methods for Protein Folding
We describe a class of growth algorithms for finding low energy states of
heteropolymers. These polymers form toy models for proteins, and the hope is
that similar methods will ultimately be useful for finding native states of
real proteins from heuristic or a priori determined force fields. These
algorithms share with standard Markov chain Monte Carlo methods that they
generate Gibbs-Boltzmann distributions, but they are not based on the strategy
that this distribution is obtained as stationary state of a suitably
constructed Markov chain. Rather, they are based on growing the polymer by
successively adding individual particles, guiding the growth towards
configurations with lower energies, and using "population control" to eliminate
bad configurations and increase the number of "good ones". This is not done via
a breadth-first implementation as in genetic algorithms, but depth-first via
recursive backtracking. As seen from various benchmark tests, the resulting
algorithms are extremely efficient for lattice models, and are still
competitive with other methods for simple off-lattice models.Comment: 10 pages; published in NIC Symposium 2004, eds. D. Wolf et al. (NIC,
Juelich, 2004
Importance of chirality and reduced flexibility of protein side chains: A study with square and tetrahedral lattice models
In simple models side chains are often represented implicitly (e.g., by
spin-states) or simplified as one atom. We study side chain effects using
square lattice and tetrahedral lattice models, with explicitly side chains of
two atoms. We distinguish effects due to chirality and effects due to side
chain flexibilities, since residues in proteins are L-residues, and their side
chains adopt different rotameric states. Short chains are enumerated
exhaustively. For long chains, we sample effectively rare events (eg, compact
conformations) and obtain complete pictures of ensemble properties of these
models at all compactness region. We find that both chirality and reduced side
chain flexibility lower the folding entropy significantly for globally compact
conformations, suggesting that they are important properties of residues to
ensure fast folding and stable native structure. This corresponds well with our
finding that natural amino acid residues have reduced effective flexibility, as
evidenced by analysis of rotamer libraries and side chain rotatable bonds. We
further develop a method calculating the exact side-chain entropy for a given
back bone structure. We show that simple rotamer counting often underestimates
side chain entropy significantly, and side chain entropy does not always
correlate well with main chain packing. Among compact backbones with maximum
side chain entropy, helical structures emerges as the dominating
configurations. Our results suggest that side chain entropy may be an important
factor contributing to the formation of alpha helices for compact
conformations.Comment: 16 pages, 15 figures, 2 tables. Accepted by J. Chem. Phy
Design of Sequences with Good Folding Properties in Coarse-Grained Protein Models
Background: Designing amino acid sequences that are stable in a given target
structure amounts to maximizing a conditional probability. A straightforward
approach to accomplish this is a nested Monte Carlo where the conformation
space is explored over and over again for different fixed sequences, which
requires excessive computational demand. Several approximate attempts to remedy
this situation, based on energy minimization for fixed structure or high-
expansions, have been proposed. These methods are fast but often not accurate
since folding occurs at low .
Results: We develop a multisequence Monte Carlo procedure, where both
sequence and conformation space are simultaneously probed with efficient
prescriptions for pruning sequence space. The method is explored on
hydrophobic/polar models. We first discuss short lattice chains, in order to
compare with exact data and with other methods. The method is then successfully
applied to lattice chains with up to 50 monomers, and to off-lattice 20-mers.
Conclusions: The multisequence Monte Carlo method offers a new approach to
sequence design in coarse-grained models. It is much more efficient than
previous Monte Carlo methods, and is, as it stands, applicable to a fairly wide
range of two-letter models.Comment: 23 pages, 7 figure
Simulating chemistry using quantum computers
The difficulty of simulating quantum systems, well-known to quantum chemists,
prompted the idea of quantum computation. One can avoid the steep scaling
associated with the exact simulation of increasingly large quantum systems on
conventional computers, by mapping the quantum system to another, more
controllable one. In this review, we discuss to what extent the ideas in
quantum computation, now a well-established field, have been applied to
chemical problems. We describe algorithms that achieve significant advantages
for the electronic-structure problem, the simulation of chemical dynamics,
protein folding, and other tasks. Although theory is still ahead of experiment,
we outline recent advances that have led to the first chemical calculations on
small quantum information processors.Comment: 27 pages. Submitted to Ann. Rev. Phys. Che
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