Computationally Guided Tuning of Amino Acid Configuration Influences the Chiroptical Properties of Supramolecular Peptide-π-Peptide Nanostructures

Evolution of π-Peptide Self-Assembly: From Understanding to Prediction and Control

Supramolecular materials derived from the self-assembly of engineered molecules continue to garner tremendous scientific and technological interest. Recent innovations include the realization of nano- and mesoscale particles (0D), rods and fibrils (1D), sheets (2D), and even extended lattices (3D). Our research groups have focused attention over the past 15 years on one particular class of supramolecular materials derived from oligopeptides with embedded π-electron units, where the oligopeptides can be viewed as substituents or side chains to direct the assembly of the central π-electron cores. Upon assembly, the π-systems are driven into close cofacial architectures that facilitate a variety of energy migration processes within the nanomaterial volume, including exciton transport, voltage transmission, and photoinduced electron transfer. Like many practitioners of supramolecular materials science, many of our initial molecular designs were designed with substantial inspiration from biologically occurring self-assembly coupled with input from chemical intuition and molecular modeling and simulation. In this feature article, we summarize our current understanding of the π-peptide self-assembly process as documented through our body of publications in this area. We address fundamental spectroscopic and computational tools used to extract information regarding the internal structures and energetics of the π-peptide assemblies, and we address the current state of the art in terms of recent applications of data science tools in conjunction with high-throughput computational screening and experimental assays to guide the efficient traversal of the π-peptide molecular design space. The abstract image details our integrated program of chemical synthesis, spectroscopic and functional characterization, multiscale simulation, and machine learning which has advanced the understanding and control of the assembly of synthetic π-conjugated peptides into supramolecular nanostructures with energy and biomedical applications.

Self-Assembling Hydrogel Structures for Neural Tissue Repair

Hydrogel materials have been employed as biological scaffolds for tissue regeneration across a wide range of applications. Their versatility and biomimetic properties make them an optimal choice for treating the complex and delicate milieu of neural tissue damage. Aside from finely tailored hydrogel properties, which aim to mimic healthy physiological tissue, a minimally invasive delivery method is essential to prevent off-target and surgery-related complications. The specific class of injectable hydrogels termed self-assembling peptides (SAPs), provide an ideal combination of in situ polymerization combined with versatility for biofunctionlization, tunable physicochemical properties, and high cytocompatibility. This review identifies design criteria for neural scaffolds based upon key cellular interactions with the neural extracellular matrix (ECM), with emphasis on aspects that are reproducible in a biomaterial environment. Examples of the most recent SAPs and modification methods are presented, with a focus on biological, mechanical, and topographical cues. Furthermore, SAP electrical properties and methods to provide appropriate electrical and electrochemical cues are widely discussed, in light of the endogenous electrical activity of neural tissue as well as the clinical effectiveness of stimulation treatments. Recent applications of SAP materials in neural repair and electrical stimulation therapies are highlighted, identifying research gaps in the field of hydrogels for neural regeneration.

Computationally Guided Tuning of Peptide-Conjugated Perylene Diimide Self-Assembly

Peptide-π-conjugated materials are important for biointerfacing charge-transporting applications due to their aqueous compatibility and formation of long-range π-electron networks. Perylene diimides (PDIs), well-established charge-transporting π systems, can self-assemble in aqueous solutions when conjugated with amino acids. In this work, we leveraged computational guidance from our previous work to access two different self-assembled architectures from PDI-amino acid conjugates. Furthermore, we expanded the design rule to other sequences to learn that the closest amino acids to the π core have a significant effect on the photophysical properties of the resulting assemblies. By simply altering glycine to alanine at the closest residue position, we observed significantly different electronic properties as revealed through UV-vis, photoluminescence, and circular dichroism spectroscopies. Accompanying molecular dynamics simulations revealed two distinct types of self-assembled architectures: cofacial structures when the smaller glycine residue is at the closest residue position to the π core versus rotationally shifted structures when glycine is substituted for the larger alanine. This study illustrates the use of tandem computations and experiments to unearth and understand new design rules for supramolecular materials and exposes a modest amino acid substitution as a means to predictably modulate the supramolecular organization and engineer the photophysical properties of π-conjugated peptidic materials.