Screening of peptides with a high affinity to bile acids using peptide arrays and a computational analysis

Bile acid binding peptides have attracted attention for the improvement and prevention of hypercholesterolemia. In this study, screening of bile acid high affinity peptides was investigated using computationally-assisted peptide array analysis. Starting with the screening data obtained from a limited, random 6-mer library (2212 sequences), the peptides with a high affinity to bile acid were characterized by comparison of high- and low-affinity peptides using fuzzy neural network (FNN) analysis. The physical properties of amino acids at specific positions that contribute to bile acid binding activity were extracted as the structural rule; optimization was carried out using three repeated screening cycles of the rule extraction. The extracted structural rule indicates that Trp, Tyr, Phe, Leu, Ile and Val are enriched in bile acid binding peptides. The yields of bile acid binding peptides with an affinity of above the VAWWMY peptide (soystatin, control sequence) were significantly higher in the optimized structural rule (32.5%) compared to that of the random library (3.1%), and 6 peptides were obtained with above 2.0-fold increased binding activity.