A Methodological Approach to Relate Pentapeptide Sequence, Atomistic Self-Assembly, and Gelation Behavior.

Area of Science:

• Biomaterials science
• Tissue engineering
• Regenerative medicine

Background:

• Injectable peptide hydrogels are promising for stem cell delivery and differentiation.
• Rational design of peptide hydrogels is hindered by a lack of understanding of self-assembly mechanisms.
• Current computational methods focusing on aggregation propensity are limited and can be misleading.

Purpose of the Study:

• To develop a systematic computational approach for designing peptide hydrogels based on molecular dynamics simulations.
• To identify novel atomistic descriptors that capture the nuances of peptide self-assembly beyond simple aggregation.
• To enable a more rational design strategy for peptide hydrogels with predictable self-assembly properties.

Main Methods:

• Utilized molecular dynamics simulations to study peptide self-assembly.
• Introduced new atomistic descriptors: end-to-end distance, π-π stacking interactions, and residue-specific contacts.
• Applied these descriptors to predict self-assembling peptide sequences and validated findings with ANOVA.

Main Results:

• Identified key interactions among hydrophobic, aromatic, and charged residues that reliably predict gel formation.
• Successfully predicted a novel, robust self-assembling peptide sequence (KYYYL).
• Demonstrated that the new parameters significantly differentiate sequences, unlike aggregation propensity.

Conclusions:

• Amino acid selection and position critically influence peptide hydrogel self-assembly.
• The developed computational approach enables a more rational design of supramolecular hydrogels.
• This work provides a robust framework for expanding the peptide sequence space for hydrogel design.