Peptideinformatics-establishment of newly screening and design method of functional peptide-

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Peptides have attracted attention for their ability to regulate a variety of cellular events by interacting with receptors on the cell surface. A large number of biomaterials that conjugate functional peptides have been developed for medicine, drug delivery, tissue engineering, bio-imaging, and food additives. However, identifying functional peptides by exhaustive screening that covers all the possible combinations of 20 amino acids is terribly time-consuming.In this research, “Peptideinformatics" was proposed, which is combined new peptide screening method combined with computational analysis. The method is based on the concept that screening efficiency can be enhanced from even limited data by use of a model derived from computational analysis that serves as a guide to screening and combining it with subsequent repeated experiments. Here we focus on cell adhesion peptides as a model application of this peptide-screening strategy. Cell adhesion peptides were screened by use of a cell-based assay of a peptide array. Starting with the screening data obtained from a limited, random 5-mer library (643 sequences), a rule regarding structural characteristics of cell adhesion peptides was extracted by fuzzy neural network (FMN) analysis. According to this rule, peptides with nnfavored residues in certain positions that led to inefficient binding were eliminated from the random sequences. In the restricted, second random library (273 sequences), the yield of cell adhesion peptides having an adhesion rate more than 1.5-fold to that of the basal array-support was significantly high (31%) compared to the unrestricted random library (20%). In the restricted third library (50 sequences), the yield of cell adhesion peptides increased to 84%. We conclude that a repeated cycle of experiments screening limited numbers of peptides can be assisted by the rule-extracting feature of FNN.