Genomic data mining approaches for the discovery of anticancer peptides from Ganoderma sinense

Drug discovery from traditional Chinese medicine (TCM) typically involves the extraction of active ingredients from natural products with high biological activity and function from the vast repertoire of traditional Chinese medicine. This strategy cannot fully exploit the vast resources of TCM. Known as the longevity mushroom, Ganoderma spp. has been used as medicine for thousands of years. Recent studies have demonstrated its anticancer activity. While most research on Ganoderma spp. has focused on their polysaccharides or small molecules as potential anticancer components, possible anticancer peptides (ACPs) or proteins have been neglected. In this study, genomic data mining approaches were used to discover potential ACPs from Ganoderma sinense. A search against known ACPs identified 477 proteins in the G. sinense proteome that possess putative ACP sequences and that thus may serve as parent proteins. After in silico digestion by trypsin, 34 G. sinense proteins were predicted to release putative ACPs (by the mACPpred program). A subsequent sequence similarity comparison against known ACPs identified 15 trypsin-digested fragments as possible ACPs, of which 3 sequences were identical to known ACPs. The results indicated that ACPs may be involved in the anticancer activity of G. sinense and that genomic mining approaches can be effective strategies for discovering active components in TCM resources. The accumulation of genomic and proteomic data will undoubtedly accelerate drug discovery from TCM resources.