Sequence-Dependent Structural Stability of Self-Assembled Cylindrical Nanofibers by Peptide Amphiphiles

Abstract

Three-dimensional networks of nanofibers, which are formed through self-assembly of peptide amphiphiles, serve as a biomimetic hydrogel scaffold for tissue engineering. With an emphasis to improve hydrogel properties for cell-specific behavior, a better understanding between structural characteristics and physical properties of the macroscopic gel is sought. Large-scale molecular dynamics simulations were performed on two PA sequences with identical composition (palmitoyl-V 3 A 3 E 3 and palmitoyl-A 3 V 3 E 3 ) showing different self-assembly kinetic mechanisms. While both sequences yielded cylindrical nanofibers, these structures have contrasting internal arrangement with respect to the hydrophobic core; the former is continuous with predominately alkyl tails, whereas the latter is disjointed with interconnecting micelles. Two additional sequences (palmitoyl-V 6 E 3 and palmitoyl-A 6 E 3 ) were examined to determine the effects of a homogeneous β-sheet forming segment that is either strongly or mildly hydrophobic on self-assembly. Results from this study indicate that internal structural arrangement of nanofibers can provide a correlation with structural stability and mechanical behavior of hydrogel nanostructures.

Self-assembly of model short triblock amphiphiles in dilute solution

“…We studied the self-assembly of these triblock amphiphiles in diluted solution as a function of the architecture of the molecule and strength of the interactions. Peptide amphiphiles have been studied using atomistic 18,19 and coarse-grained [20][21][22] molecular dynamics simulations, but these works focused heavily on understanding structural details (at an atomistic scale) of a given type of aggregate rather than predicting the effect of the chemical structure of the amphiphiles (i.e. their molecular architecture) on the morphology behavior of the self-assembled structures.…”

“…their molecular architecture) on the morphology behavior of the self-assembled structures. Moreover, previous simulation work in the area of peptide amphiphiles [18][19][20][21][22] and generic short ABC-triblock copolymers 2,11,23 did not address the thermodynamic stability of the aggregates (with few exceptions 24 ), which is important as it allows to predict the most stable supramolecular structure for a given self-assembling molecule. Therefore, it is not clear whether the sizes and morphologies of the simulated aggregates correspond to the thermodynamically most stable nanostructures.…”

Elucidating Self‐Assembling Peptide Aggregation via Morphoscanner: A New Tool for Protein‐Peptide Structural Characterization

“…[19] Furthermore, molecular dynamics simulations are a powerful tool to study peptide self-assembly, and showed that molten peptide oligomers could act as incubators for β-structuring. [23][24][25][26][27][28][29][30][31][32] Nonetheless, at the molecular level it is still unclear how oligomer-to-fibril transition emerges. Indeed, currently available tools to analyze molecular dynamics do not allow to track key events of the self-assembling process such as the evolution of secondary structure patterns over time.…”

Molecular simulations of peptide amphiphiles

“…For example, the MARTINI 44 force field is a widely used CG tool for simulations of lipids, proteins and carbohydrates, as well as short peptide sequences 45–47 and PAs 23,32 . Two other CG force fields recently used for PA simulations are the Shinoda-DeVane-Klein (SDK) force field 43,48,49 as well as ePRIME, 24 an extension of PRIME (Protein Intermediate Resolution Model), 50 which was developed by Fu et al 24,27,28 to CG their PA monomer, Palmitoyl-VVVAAAEEE. However, CG MD simulations of such large systems have their own challenges which include: i) the accuracy of the force field for PAs, ii) loss of detailed chemical resolution with CG modelling, and iii) ability to computationally characterize structure and properties of these assemblies to compare with experimental results, validate computational force fields, and establish a feedback loop between experiment and computation.…”