Analysis of an immune algorithm for protein structure prediction (original) (raw)
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Development and optimisation of a novel genetic algorithm for studying model protein folding
Theoretical Chemistry Accounts, 2004
Determination of the native state of a protein from its amino acid sequence is the goal of protein folding simulations, with potential applications in gene therapy and drug design. Location of the global minimum structure for a given sequence, however, is a difficult optimisation problem. In this paper, we describe the development and application of a genetic algorithm (GA) to find the lowest-energy conformations for the 2D HP lattice bead protein model. Optimisation of the parameters of our ''standard'' GA program reveals that the GA is most successful (at finding the lowest-energy conformations) for high rates of mating and mutation and relatively high elitism. We have also introduced a number of new genetic operators: a duplicate predator-which maintains population diversity by eliminating duplicate structures; brood selection-where two ''parent'' structures undergo crossover and give rise to a brood of (not just two) offspring; and a Monte Carlo based local search algorithm-to explore the neighbourhood of all members of the population. It is shown that these operators lead to significant improvements in the success and efficiency of the GA, both compared with our standard GA and with previously published GA studies for benchmark HP sequences with up to 50 beads.
An immune algorithm for protein structure prediction on lattice models
2007
Abstract We present an immune algorithm (IA) inspired by the clonal selection principle, which has been designed for the protein structure prediction problem (PSP). The proposed IA employs two special mutation operators, hypermutation and hypermacromutation to allow effective searching, and an aging mechanism which is a new immune inspired operator that is devised to enforce diversity in the population during evolution.
Generating folded protein structures with a lattice chain growth algorithm
The Journal of Chemical Physics, 2000
We present a new application of the chain growth algorithm to lattice generation of protein structure and thermodynamics. Given the difficulty of ab initio protein structure prediction, this approach provides an alternative to current folding algorithms. The chain growth algorithm, unlike Metropolis folding algorithms, generates independent protein structures to achieve rapid and efficient exploration of configurational space. It is a modified version of the Rosenbluth algorithm where the chain growth transition probability is a normalized Boltzmann factor; it was previously applied only to simple polymers and protein models with two residue types. The independent protein configurations, generated segment-by-segment on a refined cubic lattice, are based on a single interaction site for each amino acid and a statistical interaction energy derived by Miyazawa and Jernigan. We examine for several proteins the algorithm's ability to produce nativelike folds and its effectiveness for calculating protein thermodynamics. Thermal transition profiles associated with the internal energy, entropy, and radius of gyration show characteristic folding/unfolding transitions and provide evidence for unfolding via partially unfolded ͑molten-globule͒ states. From the configurational ensembles, the protein structures with the lowest distance root-mean-square deviations ͑dRMSD͒ vary between 2.2 to 3.8 Å, a range comparable to results of an exhaustive enumeration search. Though the ensemble-averaged dRMSD values are about 1.5 to 2 Å larger, the lowest dRMSD structures have similar overall folds to the native proteins. These results demonstrate that the chain growth algorithm is a viable alternative to protein simulations using the whole chain.
2007
Abstract Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algorithms have been used to predict the native structure with the lowest energy conformation of the primary sequence of a given protein. Successful structure prediction requires a free energy function sufficiently close to the true potential for the native state, as well as a method for exploring the conformational space.
An efficient genetic algorithm for predicting protein tertiary structures in the 2D HP model
Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05, 2005
Given the amino acid sequence of a protein, predicting its tertiary structure is known as the protein folding problem. This problem has been widely studied under the HP model in which each amino acid is classified, based on its hydrophobicity, as an H (hydrophobic or non-polar) or a P (hydrophilic or polar). Conformation of a protein in the HP model is embedded as a self-avoiding walk in either a two-dimensional or a three-dimensional lattice. The protein folding problem in the HP model is to find a lowest energy conformation. This problem is known to be NPhard in both two-dimensional and three-dimensional square lattices. In this paper, we present an efficient genetic algorithm for the protein folding problem under the HP model in the two-dimensional square lattice. A special feature of this algorithm is its usage of secondary structures, that the algorithm evolves, as building blocks for the conformation. Experimental results on benchmark sequences show that the algorithm performs very well against existing evolutionary algorithms and Monte Carlo algorithms.
A Test of Lattice Protein Folding Algorithms
Proceedings of The National Academy of Sciences, 1995
We report a blind test of lattice-model-based search strategies for finding global minima of model protein chains. One of us (E.I.S.) selected 10 compact conformations of 48-mer chains on the three-dimensional cubic lattice and used their inverse folding algorithm to design HP (H, hydrophobic; P, polar) sequences that should fold to those "target" structures. The sequences, but not the structures, were sent to the UCSF group (K.Y., K.M.F., P.D.T., H.S.C., and K.A.D.), who used two methods to attempt to find the globally optimal conformations: "hydrophobic zippers" and a constraintbased hydrophobic core construction (CHCC) method. The CHCC method found global minima in all cases, and the hydrophobic zippers method found global minima in some cases, in minutes to hours on workstations. In 9 out of 10 sequences, the CHCC method found lower energy conformations than the 48-mers were designed to fold to. Thus the search strategies succeed for the HP model but the design strategy does not. For every sequence the global energy minimum was found to have multiple degeneracy with 103 to 106 conformations. We discuss the implications of these
Genetic Algorithms for Protein Folding Simulations
Journal of Molecular Biology, 1993
Genetic algorithms methods utilize the same optimization procedures as natural genetic evolution, in which a population is gradually improved by selection. We have developed a genetic algorithm search procedure suitable for use in protein folding simulations. A population of conformations of the polypeptide chain is maintained, and conformations are changed bx mutation, in the form of conventional Monte Carlo steps, and crossovers in which parts of the polypeptide chain are interchanged between conformations. For folding on a simple two-dimensional lattice it is found that the genetic algorithm is dramatically superior to conventional Monte Carlo methods.
Evolutionary Algorithms for Protein Structure Prediction in Lattice Models
Analele Universitatii de Vest din Timisoara, 2011
Protein structure prediction is a computationally challenging problem aiming to find a protein structure having minimum energy starting from an unfolded chain of amino acids. Detecting such a structure represents an NP-hard problem even in simplified lattice protein models which abstract away many of the details of protein folding. Various evolutionary models are analysed for protein structure prediction in the hydrophobic-polar protein model. The starting point of these variant models is represented by an evolutionary model based ...
Efficient conformational space exploration in ab initio protein folding simulation
Royal Society Open Science, 2015
Ab initio protein folding simulation largely depends on knowledge-based energy functions that are derived from known protein structures using statistical methods. These knowledge-based energy functions provide us with a good approximation of real protein energetics. However, these energy functions are not very informative for search algorithms and fail to distinguish the types of amino acid interactions that contribute largely to the energy function from those that do not. As a result, search algorithms frequently get trapped into the local minima. On the other hand, the hydrophobicpolar (HP) model considers hydrophobic interactions only. The simplified nature of HP energy function makes it limited only to a low-resolution model. In this paper, we present a strategy to derive a non-uniform scaled version of the real 20 × 20 pairwise energy function. The non-uniform scaling helps tackle the difficulty faced by a real energy function, whereas the integration of 20 × 20 pairwise information overcomes the limitations faced by the HP energy function. Here, we have applied a derived energy function with a genetic algorithm on discrete lattices. On a standard set of benchmark protein sequences, our approach significantly outperforms the state-ofthe-art methods for similar models. Our approach has been able to explore regions of the conformational space which all the previous methods have failed to explore. Effectiveness of the derived energy function is presented by showing qualitative differences and similarities of the sampled structures to the native structures. Number of objective function evaluation in a single run of the algorithm is used as a comparison metric to demonstrate efficiency.
Simulating Protein Conformations through Global Optimization
2008
Many researches have been working on the protein folding problem from more than half century. Protein folding is indeed one of the major unsolved problems in science. In this work, we discuss a model for the simulation of protein conformations. This simple model is based on the idea of imposing few geometric requirements on chains of atoms representing the backbone of a protein conformation. The model leads to the formulation of a global optimization problem, whose solutions correspond to conformations satisfying the desired requirements. The global optimization problem is solved by the recently proposed Monkey Search algorithm. The simplicity of the optimization problem and the effectiveness of the used meta-heuristic search allowed the simulation of a large set of high-quality conformations. We show that, even though only few geometric requirements are imposed, some of the simulated conformation results to be similar (in terms of RMSD) to conformations real proteins actually have in nature.