An immune algorithm with hyper-macromutations for the Dill's 2D hydrophobic-hydrophilic model (original) (raw)
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M.: “An Immune Algorithm with HyperMacromutations for the Dill’s 2D
2004
— This paper presents an Immune Algorithm (IA) based on Clonal Selection Principle using a new mutation operator, the hypermacromutation, and an aging process to tackle the protein structure prediction problem (PSP) in the 2D Hydrophilic-Hydrophobic (HP) model. The IA presented has only three parameters. To correctly set these parameters we compute the parameter surfaces, the 3D plots of IA success rate in function of the cloning paramater and the maximum age allowed to each B cell. The parameter surfaces show that hypermacromutation and aging operators are key features for generating diversity and searching more properly the funnel landscape of the PSP problem. Experiments show that the Immune Algorithm we propose is very competitive with the state-of-art algorithms for the PSP. I.
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.
Analysis of an immune algorithm for protein structure prediction
2008
The aim of a protein folding simulation is to determine the native state of a protein from its amino acid sequence. In this paper we describe the development and application of an Immune Algorithm (IA) to find the lowest energy conformations for the 2D (square) HP lattice bead protein model. Here we introduce a modified chain growth constructor to produce the initial population, where intermediate infeasible structures are recorded, thereby reducing the risk of attempting to perform wasteful point mutations during the mutation phase. We also investigate various approaches for population diversity tracking, ultimately allowing a greater understanding of the progress of the optimization.
Applications on Evolutionary Computing, 2005
In this work we investigate the applicability of a multiobjective formulation of the Ab-Initio Protein Structure Prediction (PSP) to medium size protein sequences (46-70 residues). In particular, we introduce a modified version of Pareto Archived Evolution Strategy (PAES) which makes use of immune inspired computing principles and which we will denote by "I-PAES". Experimental results on the test bed of five proteins from PDB show that PAES, (1+1)-PAES and its modified version I-PAES, are optimal multiobjective optimization algorithms and the introduced mutation operators, mut1 and mut2, are effective for the PSP problem. The proposed I-PAES is comparable with other evolutionary algorithms proposed in literature, both in terms of best solution found and computational cost.
Evolutionary algorithm to ab initio protein structure prediction with hydrophobic interactions
2007
Proteins are polymers whose chains are composed of 20 different monomers, called amino acids. The problem of Protein Structure Prediction (PSP) is the determination of protein 3D conformation from its amino acid sequence. Two main strategies are usually employed to work with PSP: homology and Ab initio approaches. This paper presents an Evolutionary Algorithm to PSP using an Ab initio approach (ProtPred). The predictions are evaluated using fitness functions based on potential energies (electrostatic and van der Waals) and hydrophobic interactions. The proposed approach uses dihedral angles and main angles of the lateral chains to model a protein structure. ProtPred is evaluated using relatively complex cases for an Ab initio approach. Results have shown that ProtPred is a consistent approach.
An enhanced genetic algorithm for protein structure prediction using the 2D hydrophobic-polar model
Artificial Evolution, 2006
This paper presents an enhanced genetic algorithm for the protein structure prediction problem. A new fitness function, that uses the concept of radius of gyration, is proposed. Also, a novel operator called partial optimization, together with different strategies for performance improvement, are described. Tests were done with five different amino acid chains from 20 to 85 residues long and better results were obtained, when compared with those in the current literature. Results are promising and suggest the suitability of the proposed method for protein structure prediction using the 2D HP model. Further experiments shall be done with longer amino acid chains as well as with realworld proteins.
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.
2005
We present an Immune Algorithm (IA) based on clonal selection principle and which uses memory B cells, to face the protein structure prediction problem (PSP) a particular example of the String Folding Problem in 2D and 3D lattice. Memory B cells with a longer life span are used to partition the funnel landscape of PSP, so to properly explore the search space. The designed IA shows its ability to tackle standard benchmarks instances substantially better than other IA's.
On Discrete Models and Immunological Algorithms for Protein Structure Prediction
Discrete models for protein structure prediction embed the protein amino acid sequence into a discrete spatial structure, usually a lattice, where an optimal tertiary structure is predicted on the basis of simple assumptions relating to the hydrophobic-hydrophilic character of amino acids in the sequence and to relevant interactions for free energy minimization. While the prediction problem is known to be NP complete even in the simple setting of Dill's model with a 2D-lattice, a variety of bio-inspired algorithms for this problem have been proposed in the literature. Immunological algorithms are inspired by the kind of optimization that immune systems perform when identifying and promoting the replication of the most effective antibodies against given antigens. A quick, state-of-the-art survey of discrete models and immunological algorithms for protein structure prediction is presented in this paper, and the main design and performance features of an immunological algorithm for this problem are illustrated in a tutorial fashion.
Exploring the Capability of Immune Algorithms: A Characterization of Hypermutation Operators
2004
In this paper, an important class of hypermutation operators are discussed and quantitatively compared with respect to their success rate and computational cost. We use a standard Immune Algorithm (IA), based on the clonal selection principle to investigate the searching capability of the designed hypermutation operators. We computed the parameter surface for each variation operator to predict the best parameter setting for each operator and their combination. The experimental investigation in which we use a standard clonal selection algorithm with different hypermutation operators on a complex “toy problem”, the trap functions, and a complex NP-complete problem, the 2D HP model for the protein structure prediction problem, clarifies that only few really different and useful hypermutation operators exist, namely: inversely proportional hypermutation, static hypermutation and hypermacromutation operators. The combination of static and inversely proportional Hypermutation and hypermacromutation showed the best experimental results for the “toy problem” and the NP-complete problem.