Screening of peptides with a high affinity for ZnO using spot-synthesized peptide arrays and computational analysis

Metal-binding peptides have attracted attention for their usefulness in biomineralization processes. In this work screening of high affinity peptides against ZnO was performed using a combinatorial library approach with a peptide array and computational analysis. The computationally assisted peptide screening and design enabled identification of linear peptides with affinity for ZnO with limited experimentation. Starting with the screening data obtained from a random 6-mer library of 420 sequences, the characteristics of the peptides with high ZnO affinity were analyzed using a fuzzy neural network algorithm by comparison of high and low affinity peptides. Three physical properties of amino acids (hydrophobicity, isoelectric point and size) and positional information of each residue were analyzed and a peptide rule with restricted amino acids at certain positions was extracted. The average affinity for ZnO increased 2.0-fold when 300 sequences were synthesized according to the restricted random library, compared with the non-restricted library. In addition, for a peptide library with the amino acids at the restricted sites exchanged the average binding capacity decreased to 0.7-fold. Interestingly, the peptides with high ZnO affinity obtained exhibited binding specificity for ZnO. Computationally assisted screening is an invaluable means for finding peptides with limited experimentation.