3D Off-lattice Model Research Articles

Elitist Animal Migration Optimization for Protein Structure Prediction based on 3D Off-Lattice Model

Predicting the structure of protein has been the center of attraction for the researchers. The aim is to make a reliable prediction of the protein structure by obtaining the minimum energy values among amino acids interactions. According to the generated shape of amino acids, the functionality of the proteins can be determined. However, it is known as one of the most challenging tasks in the field of bioinformatics considering its high computation complexity. Metaheuristic algorithms are mainly preferred by researchers from various fields, since their performances are quite satisfactory in solving such complex problems. Animal Migration Optimization (AMO) algorithm is a metaheuristic approach which mimics the behavior of animals during the migration process. However, in this research to reach a high solution quality, an elitist version of Animal Migration Optimization (ELAMO) algorithm is considered and in particular it is applied to Protein Structure Prediction (PSP) problem. The performance of ELAMO is tested on some well-studied artificial and real protein sequences, and then compared with powerful optimization algorithms which are specially designed for solving PSP problem. The results show that ELAMO is quite capable in solving this problem. Hence, it can be used as an efficient optimizer for solving complex problems that require better solution quality in the field of bioinformatics.

A growth model for the generation of particle aggregates with tunable fractal dimension

A new 3D off-lattice model is proposed to generate porous and controlled fractal aggregate structures. The method is a variation of the Eden Model with the additional capability of randomly inactivating particles with a given inactivation probability. As this probability increases from 0 to 1, the proposed model follows a continuous transition from an off-lattice Eden Model leading to a core-dense random structure to a self-avoiding random walk model leading to a linear chain with partially constrained growth directions. The resulting aggregates have been characterized in terms of their radius of gyration, shape factor, mass and surface fractal dimensions, pore volume fraction, specific surface area and pore size distribution. A large range of microstructural properties can be controlled by adjusting the inactivation probability and the number of particles (e.g. mass fractal dimension from 1.6 to 3). In addition the proposed approach is convenient to implement and highly computationally efficient. The morphology (radius of gyration and mass fractal dimension) of silica aggregates characterized by small angle X-ray scattering in the literature was successfully reproduced to demonstrate the capabilities of the method.

Two hypotheses and test assumptions based on Quantum-behaved Particle Swarm Optimization (QPSO)

In this paper, two hypotheses are proposed to explain protein folding, aiming at this hypotheses, an improved 3D Off-lattice model and Quantum-behaved Particle Swarm Optimization based protein folding algorithm (c-QPSO) for predicting the protein folding structure are also proposed. The results are sufficiently lowest energy better than the results determined by the other algorithms for verifying the two hypotheses. For Fibonacci sequence with size 13, 21 and 34, our result is roughly twice times than that of ELP and 1.5 times for sequence with size 55. It can also be seen that c-QPSO algorithm takes significantly less time than PSO method. For real protein sequences, the structures predicted by c-QPSO can approximately simulate the real protein to some extent. Experiments show that the two hypotheses is partially correct, and the improved 3D Off-lattice model is beneficial for c-QPSO algorithm to reduce the computation time and easily get the 3D coordinate of each amino acid. Some proteins fold faster than they elongate, and it is reasonable to assume that nascent chains can adopt secondary or tertiary structures cotranslationally.

Selection of appropriate metaheuristic algorithms for protein structure prediction in AB off-lattice model: a perspective from fitness landscape analysis

Protein structure prediction (PSP) from its primary sequence is a challenging task in computational biology. PSP is an optimization problem that determines the stable or native structure with minimum free energy. Several researchers have applied various heuristic algorithms and/or their variants to solve this problem. However, the mechanism to select a particular algorithm is not known a priori. Fitness landscape analysis (FLA) is a technique to determine the characteristics of a problem or its structural features based on which the most appropriate algorithm can be recommended for solving the problem. The aim of this study is two-fold while considering the PSP problem. Firstly, the structural features are determined by using the standard FLA techniques and secondly, the performance of some of the well-known optimization algorithms are analyzed based on the structural features as an illustration of the usefulness of the former research agenda. In this paper, we determine structural features of the PSP problem by analyzing the landscapes generated by using the quasi-random sampling technique and city block distance. Comprehensive simulations are carried out on both artificial and real protein sequences in 2D and 3D AB off-lattice model. Numerical results indicate that the complexity of the PSP problem increases with protein sequence length. We calculate the Pearson correlation coefficient between the FLA measures, separately for 2D and 3D off-lattice model and significant differences are identified among the measures. Six well-known real-coded optimization algorithms are evaluated over the same set of protein sequences and the performances are subsequently analyzed based on the structural features. Finally, we suggest the most appropriate algorithms for solving different classes of PSP problem.

Effect of polymer rigidity on the phase behaviour of polymer adsorption on to planar surface.

We study the process of a semiflexible polymer chain adsorption on to planar surface by the dynamic Monte Carlo (DMC) method, based on the 3D off-lattice model. Both the strength of attractive monomer–surface interaction (εa) and bending energy (b) have pronounced effect on the adsorption and shape of semiflexible polymer chain. The semiflexible polymer can just fully adsorb on to the surface at certain εa, which is defined as critical εa. The essential features of the semiflexible polymer adsorption on to surface are that (i) the critical εa increases with increase in b; (ii) the shape of the fully adsorbed semiflexible polymer chain is film-like toroid, and the toroid becomes more and more perfect with increase in b. In addition, the size of toroid and the number of turns of toroid can be controlled by the b and εa.

Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm

Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

Protein structure prediction with local adjust tabu search algorithm.

BackgroundProtein folding structure prediction is one of the most challenging problems in the bioinformatics domain. Because of the complexity of the realistic protein structure, the simplified structure model and the computational method should be adopted in the research. The AB off-lattice model is one of the simplification models, which only considers two classes of amino acids, hydrophobic (A) residues and hydrophilic (B) residues.ResultsThe main work of this paper is to discuss how to optimize the lowest energy configurations in 2D off-lattice model and 3D off-lattice model by using Fibonacci sequences and real protein sequences. In order to avoid falling into local minimum and faster convergence to the global minimum, we introduce a novel method (SATS) to the protein structure problem, which combines simulated annealing algorithm and tabu search algorithm. Various strategies, such as the new encoding strategy, the adaptive neighborhood generation strategy and the local adjustment strategy, are adopted successfully for high-speed searching the optimal conformation corresponds to the lowest energy of the protein sequences. Experimental results show that some of the results obtained by the improved SATS are better than those reported in previous literatures, and we can sure that the lowest energy folding state for short Fibonacci sequences have been found.ConclusionsAlthough the off-lattice models is not very realistic, they can reflect some important characteristics of the realistic protein. It can be found that 3D off-lattice model is more like native folding structure of the realistic protein than 2D off-lattice model. In addition, compared with some previous researches, the proposed hybrid algorithm can more effectively and more quickly search the spatial folding structure of a protein chain.

Protein Folding Prediction Based on Levy Flight Particle Swarm Optimization

PSO has slow convergence speed and low precision. In this paper, we combine Particle Swarm Optimization (PSO) and levy flight for the task of predicting protein folding based on the 3D off-lattice model. We introduce levy flight into PSO to improve the precision and enhance the capability of jumping out of the local optima through particle mutation mechanism.Experiments show that the proposed method outperforms other algorithms on the accuracy of calculating the protein sequence energy value, which is turned to be an effective way to analyze protein structure.