Fig. 4

Two-objective genetic algorithm-informed design suggestions for experimental validation. a Pareto front (output) of the optimization results for the OBOC peptide library having 5 positions where variability was introduced (r = 5), with m = 7 and \(x_i={s,e,r,w,a,G,i}\) and two fixed positions, being \(x_3=p\) and \(x_7=y\). In the zoom of the pareto front, in the 70–100% mass diversity range, we chose five best solutions: BS 1 (100%), BS 2 (98%), BS 3 (86%), BS 4 (77%) and BS 6 (70%). b Sequence logo representation of the BS 2, showing the maximally diverse random peptide library design we would choose for further studies. c Sequence logos of BS 1, BS 3, BS 4 and BS 6 suggesting various synthetic possibilities and pointing out possible synthetic challenges. Several other design suggestions are available, but we show only these five for simplicity