An Immune Algorithm for Protein Structure Prediction on Lattice Models

Cutello, Vincenzo; Nicosia, Giuseppe; Pavone, Mario; Timmis, Jon

doi:10.1109/tevc.2006.880328

Cited by 205 publications

(154 citation statements)

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“…In other words, each paratope on an antibody can not only recognize a foreign antigen, but also can be recognized by external idiotopes. Motivated by this mechanism, the lateral mutation (LM) [34], [35], [43] is implemented as:…”

Section: Lateral Mutationmentioning

confidence: 99%

A Multi-Learning Immune Algorithm for Numerical Optimization

Wang

Gao

Aorigele

et al. 2015

IEICE Trans. Fundamentals

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SUMMARYThe emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

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Section: Lateral Mutationmentioning

confidence: 99%

A Multi-Learning Immune Algorithm for Numerical Optimization

Wang

Gao

Aorigele

et al. 2015

IEICE Trans. Fundamentals

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“…The protein structure prediction was investigated with AIS for models based on Dill's lattice approach [108,109] and with three-dimensional models [110]. In the following sections we present briefly the AIRS, CLONALG, CSCA, and IMMUNOS algorithms, and we evaluate their ability to predict the phospholipidosis inducing potential.…”

Section: Artificial Immune Systemsmentioning

confidence: 99%

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

Ivanciuc

2008

CTMC

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Abstract:The drug discovery and development process is lengthy and expensive, and bringing a drug to market may take up to 18 years and may cost up to 2 billion $US. The extensive use of computer-assisted drug design techniques may considerably increase the chances of finding valuable drug candidates, thus decreasing the drug discovery time and costs. The most important computational approach is represented by structure-activity relationships that can discriminate between sets of chemicals that are active/inactive towards a certain biological receptor. An adverse effect of some cationic amphiphilic drugs is phospholipidosis that manifests as an intracellular accumulation of phospholipids and formation of concentric lamellar bodies. Here we present structure-activity relationships (SAR) computed with a wide variety of machine learning algorithms trained to identify drugs that have phospholipidosis inducing potential. All SAR models are developed with the machine learning software Weka, and include both classical algorithms, such as k-nearest neighbors and decision trees, as well as recently introduced methods, such as support vector machines and artificial immune systems. The best predictions are obtained with support vector machines, followed by a perceptron artificial neural network, logistic regression, and k-nearest neighbors.

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“…For solving ab initio PSP using the lattice model numerous nondeterministic approaches have been investigated: Monte Carlo (MC) simulation, Evolutionary MC (EMC) [12,13], Simulated Annealing (SA), Tabu Search with Genetic Algorithm (GTB) [14], Ant Colony Optimisation [15], and Immune Algorithm (IA) based on Artificial Immune System (AIS) [44]. Due to their simplicity and search effectiveness, Genetic Algorithms (GAs) [11,26,32,[45][46][47][48] are the most attractive.…”

Section: Nondeterministic Conformational Search Algorithmsmentioning

confidence: 99%

DFS Based Partial Pathways in GA for Protein Structure Prediction

Hoque

Chetty

Lewis

et al. 2008

Pattern Recognition in Bioinformatics

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Abstract. Nondeterministic conformational search techniques, such as Genetic Algorithms (GAs) are promising for solving protein structure prediction (PSP) problem. The crossover operator of a GA can underpin the formation of potential conformations by exchanging and sharing potential sub-conformations, which is promising for solving PSP. However, the usual nature of an optimum PSP conformation being compact can produce many invalid conformations (by having non-self-avoiding-walk) using crossover. While a crossover-based converging conformation suffers from limited pathways, combining it with depthfirst search (DFS) can partially reveal potential pathways. DFS generates random conformations increasingly quickly with increasing length of the protein sequences compared to random-move-only-based conformation generation. Random conformations are frequently applied for maintaining diversity as well as for initialization in many GA variations.

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An Immune Algorithm for Protein Structure Prediction on Lattice Models

Cited by 205 publications

References 50 publications

A Multi-Learning Immune Algorithm for Numerical Optimization

A Multi-Learning Immune Algorithm for Numerical Optimization

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

DFS Based Partial Pathways in GA for Protein Structure Prediction

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