Abstract

Anticancer peptides (ACPs) are polycationic amphiphiles capable of preferentially killing a widespectrum of cancer cells relative to non-cancerous cells. Their primary mode of action is aninteraction with the cell membrane and subsequent activation of lytic effects, however it remainscontroversial the exact mechanism responsible for this mode of action. It has in previous studies been shown that utilizing zeta potential analyses it was possible to demonstrate the interaction of a small anticancer peptide with membrane modelsystems and cancer cells. Electrostatic interactions have a pivotal role in the cell killing processand in contrast to the AMPs action cell death occurs without achieving full neutralization of themembrane charge. The advent of microarray technology has revolutionized the search for genes that are differentially expressed across a range of cell types or experimental conditions. Traditional clustering methods, such as hierarchical clustering, are often difficult to deploy effectively since genes rarely exhibit similar expression pattern across a wide range of conditions. Web-enabled service called GEMS (Gene Expression Mining Server) for biclustering microarray data where Users may upload expression data and specify a set of criteria.GEMS performs bicluster mining based on a Gibbs sampling paradigm of multi-mimotopic algorithmic approach for biclustering analysis of In silico designed expression data on an Anticancer Peptide SVS-1 multipharmacophore as a potential drug-like efficator in Preceding Membrane Neutralization.

Keywords

In silico designed; Anticancer Peptide SVS-1; multipharmacophore; drug-like; efficator in Preceding Membrane Neutralization; multi-mimotopic algorithmic approach; biclustering analysis; expression data.