Programming co-assembled peptide nanofiber morphology via anionic amino acid type: Insights from molecular dynamics simulations.
Co-assembling peptides can be crafted into supramolecular biomaterials for use in biotechnological applications, such as cell culture scaffolds, drug delivery, biosensors, and tissue engineering. Peptide co-assembly refers to the spontaneous organization of two different peptides into a supramolecul...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2023-12-01
|
Series: | PLoS Computational Biology |
Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011685&type=printable |
Summary: | Co-assembling peptides can be crafted into supramolecular biomaterials for use in biotechnological applications, such as cell culture scaffolds, drug delivery, biosensors, and tissue engineering. Peptide co-assembly refers to the spontaneous organization of two different peptides into a supramolecular architecture. Here we use molecular dynamics simulations to quantify the effect of anionic amino acid type on co-assembly dynamics and nanofiber structure in binary CATCH(+/-) peptide systems. CATCH peptide sequences follow a general pattern: CQCFCFCFCQC, where all C's are either a positively charged or a negatively charged amino acid. Specifically, we investigate the effect of substituting aspartic acid residues for the glutamic acid residues in the established CATCH(6E-) molecule, while keeping CATCH(6K+) unchanged. Our results show that structures consisting of CATCH(6K+) and CATCH(6D-) form flatter β-sheets, have stronger interactions between charged residues on opposing β-sheet faces, and have slower co-assembly kinetics than structures consisting of CATCH(6K+) and CATCH(6E-). Knowledge of the effect of sidechain type on assembly dynamics and fibrillar structure can help guide the development of advanced biomaterials and grant insight into sequence-to-structure relationships. |
---|---|
ISSN: | 1553-734X 1553-7358 |