Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

Conformational Sampling of a Biomolecular Rugged Energy Landscape

The protein structure refinement using conformational sampling is important in hitherto protein studies. In this paper, we examined the protein structure refinement by means of potential energy minimization using immune computing as a method of sampling conformations. The method was tested on the x-ray structure and 30 decoys of the mutant of [Leu]Enkephalin, a paradigmatic example of the biomolecular multiple-minima problem. In order to score the refined conformations, we used a standard potential energy function with the OPLSAA force field. The effectiveness of the search was assessed using a variety of methods. The robustness of sampling was checked by the energy yield function which measures quantitatively the number of the peptide decoys residing in an energetic funnel. Furthermore, the potential energy-dependent Pareto fronts were calculated to elucidate dissimilarities between peptide conformations and the native state as observed by x-ray crystallography. Our results showed that the probed potential energy landscape of [Leu]Enkephalin is self-similar on different metric scales and that the local potential energy minima of the peptide decoys are metastable, thus they can be refined to conformations whose potential energy is decreased by approximately 250 kJ/mol.

Integrating Clonal Selection and Deterministic Sampling for Efficient Associative Classification

Traditional Associative Classification (AC) algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly find a set of high-stakes association rules that potentially builds a highly accurate classifier. This paper introduces AC-CS, an AC algorithm that integrates the clonal selection of the immune system along with deterministic data sampling. Upon picking a representative sample of the original data, it proceeds in an evolutionary fashion to populate only rules that are likely to yield good classification accuracy. Empirical results on several real datasets show that the approach generates dramatically less rules than traditional AC algorithms. In addition, the proposed approach is significantly more efficient than traditional AC algorithms while achieving a competitive accuracy.