Hilbert space filling curves for interpretable point mutation effects on peptide conformational dynamics

Spontaneous self-organization of solid-binding peptides on single-layer atomic materials offers enormous potential in employing these systems for practical technological and medical applications. Molecular self-organization of peptides depends highly on their sequences which, in turn, affect their conformational behavior under aqueous conditions. Traditional ways of computationally studying the effect of mutations on the conformation states involves dimension reduction on cosine and sine transformed torsion angles, often represented as Ramachandran plots. Although these studies successfully cluster conformation states, they fail to intuitively characterize the effect of the point mutation(s) directly, necessitating further data analysis. Here, we apply Hilbert Space-Filling-Curve (HSFC) on the torsion angles and demonstrate intuitive visualization for the effect of point mutations on conformation states and secondary structure dynamics along a reaction coordinate. We perform molecular dynamics (MD) simulation on wild-type graphene binding peptide (WT-GrBP5). The 12-amino acid long peptide was selected by directed evolution and known to self-organize on atomically flat surface of graphene only under low-neutral pH at room temperature. A charge neutral mutant, M9-GrBP5, on the other hand, assembles at a broader range of pHs at room temperature, as expected. The HSFC shows clearly that the mutated amino acids in M9 do not correlate with the reaction coordinate of pH change, unlike that of WT, confirming heuristic knowledge. Understanding the effect of specific amino acid {varphi}-{psi} pairs that contribute most to the changes in the conformational space of the peptide with changing conditions, will help in analyzing effects of point mutations in peptide sequences. The knowledge of the conformational behavior of solid binding peptides, in general, and its effect on their self-organization propensities on solid surfaces would lead to the rational design of sequences that form soft bio/nano hybrid interfaces in the future towards robust strategies for surface biofunctionalization, in general, and bioelectronics and biosensors, in particular.