3,651 research outputs found
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
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
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
Multicanonical Study of Coarse-Grained Off-Lattice Models for Folding Heteropolymers
We have performed multicanonical simulations of hydrophobic-hydrophilic
heteropolymers with two simple effective, coarse-grained off-lattice models to
study the influence of specific interactions in the models on conformational
transitions of selected sequences with 20 monomers. Another aspect of the
investigation was the comparison with the purely hydrophobic homopolymer and
the study of general conformational properties induced by the "disorder" in the
sequence of a heteropolymer. Furthermore, we applied an optimization algorithm
to sequences with up to 55 monomers and compared the global-energy minimum
found with lowest-energy states identified within the multicanonical
simulation. This was used to find out how reliable the multicanonical method
samples the free-energy landscape, in particular for low temperatures.Comment: 11 pages, RevTeX, 10 Postscript figures, Author Information under
http://www.physik.uni-leipzig.de/index.php?id=2
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
3D Protein structure prediction with genetic tabu search algorithm
Abstract Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively
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