1,874 research outputs found
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
Monte Carlo Procedure for Protein Design
A new method for sequence optimization in protein models is presented. The
approach, which has inherited its basic philosophy from recent work by Deutsch
and Kurosky [Phys. Rev. Lett. 76, 323 (1996)] by maximizing conditional
probabilities rather than minimizing energy functions, is based upon a novel
and very efficient multisequence Monte Carlo scheme. By construction, the
method ensures that the designed sequences represent good folders
thermodynamically. A bootstrap procedure for the sequence space search is
devised making very large chains feasible. The algorithm is successfully
explored on the two-dimensional HP model with chain lengths N=16, 18 and 32.Comment: 7 pages LaTeX, 4 Postscript figures; minor change
A Hybrid Monte Carlo Ant Colony Optimization Approach for Protein Structure Prediction in the HP Model
The hydrophobic-polar (HP) model has been widely studied in the field of
protein structure prediction (PSP) both for theoretical purposes and as a
benchmark for new optimization strategies. In this work we introduce a new
heuristics based on Ant Colony Optimization (ACO) and Markov Chain Monte Carlo
(MCMC) that we called Hybrid Monte Carlo Ant Colony Optimization (HMCACO). We
describe this method and compare results obtained on well known HP instances in
the 3 dimensional cubic lattice to those obtained with standard ACO and
Simulated Annealing (SA). All methods were implemented using an unconstrained
neighborhood and a modified objective function to prevent the creation of
overlapping walks. Results show that our methods perform better than the other
heuristics in all benchmark instances.Comment: In Proceedings Wivace 2013, arXiv:1309.712
Flat histogram simulation of lattice polymer systems
We demonstrate the use of a new algorithm called the Flat Histogram sampling
algorithm for the simulation of lattice polymer systems. Thermodynamics
properties, such as average energy or entropy and other physical quantities
such as end-to-end distance or radius of gyration can be easily calculated
using this method. Ground-state energy can also be determined. We also explore
the accuracy and limitations of this method.
Key words: Monte Carlo algorithms, flat histogram sampling, HP model, lattice
polymer systemsComment: 7 RevTeX two-column page
A Solvable Model of Secondary Structure Formation in Random Hetero-Polymers
We propose and solve a simple model describing secondary structure formation
in random hetero-polymers. It describes monomers with a combination of
one-dimensional short-range interactions (representing steric forces and
hydrogen bonds) and infinite range interactions (representing polarity forces).
We solve our model using a combination of mean field and random field
techniques, leading to phase diagrams exhibiting second-order transitions
between folded, partially folded and unfolded states, including regions where
folding depends on initial conditions. Our theoretical results, which are in
excellent agreement with numerical simulations, lead to an appealing physical
picture of the folding process: the polarity forces drive the transition to a
collapsed state, the steric forces introduce monomer specificity, and the
hydrogen bonds stabilise the conformation by damping the frustration-induced
multiplicity of states.Comment: 24 pages, 14 figure
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