Thermodynamics, morphology, and kinetics of early-stage self-assembly of π-conjugated oligopeptides

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

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.

Peptide framework for screening the effects of amino acids on assembly

Discovery of peptide domains with unique intermolecular interactions is essential for engineering peptide-based materials. Rather than attempting a brute-force approach, we instead identify a previously unexplored strategy for discovery and study of intermolecular interactions: “co-assembly of oppositely charged peptide” (CoOP), a framework that “encourages” peptide assembly by mixing two oppositely charged hexapeptides. We used an integrated computational and experimental approach, probed the free energy of association and probability of amino acid contacts during co-assembly with atomic-resolution simulations, and correlated them to the physical properties of the aggregates. We introduce CoOP with three examples: dialanine, ditryptophan, and diisoleucine. Our results indicated that the opposite charges initiate the assembly, and the subsequent stability is enhanced by the presence of an undisturbed hydrophobic core. CoOP represents a unique, simple, and elegant framework that can be used to identify the structure-property relationships of self-assembling peptide-based materials.

Chiral structure fluctuations predicted by a coarse-grained model of peptide aggregation

This work reports on the chiral structure fluctuations of peptide clusters at the early stages of aggregation in a coarse-grained peptide model. Our model reproduces a variety of aggregate structures, from disordered to crystal-like, that are observed experimentally. Unexpectedly, our molecular dynamics simulations showed that the small peptide cluster undergoes chiral structure fluctuations although the underlying implicit solvent model does not assume the chirality of peptides. The chiral fluctuations are quantified through a cluster twist parameter. A simple model is presented where the twist parameter undergoes a stochastic diffusion on a 1D potential surface. The shape of the potential surface changes with the cluster size. The model shows semi-quantitative agreement with the simulations. We hypothesize that the chiral fluctuations at the early stages of peptide aggregation can contribute to the selection of the final fibril structures.

Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

Electronically active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from π-conjugated oligopeptides composed of an aromatic core flanked by oligopeptide wings offer emergent optoelectronic properties within a water-soluble and biocompatible substrate. Nanoaggregate properties can be controlled by tuning core chemistry and peptide composition, but the sequence-structure-function relations remain poorly characterized. In this work, we employ coarse-grained molecular dynamics simulations within an active learning protocol employing deep representational learning and Bayesian optimization to efficiently identify molecules capable of assembling pseudo-1D nanoaggregates with good stacking of the electronically active π-cores. We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an Asp residue and OPV3 is an oligophenylenevinylene oligomer (1,4-distyrylbenzene), to identify the top performing XXX tripeptides within all 20³ = 8000 possible sequences. By direct simulation of only 2.3% of this space, we identify molecules predicted to exhibit superior assembly relative to those reported in prior work. Spectral clustering of the top candidates reveals new design rules governing assembly. This work establishes new understanding of DXXX-OPV3-XXXD assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems.

Revealing the Sequence-Structure-Electronic Property Relation of Self-Assembling π-Conjugated Oligopeptides by Molecular and Quantum Mechanical Modeling

Self-assembled nanoaggregates of π-conjugated synthetic peptides present a biocompatible and highly tunable alternative to silicon-based optical and electronic materials. Understanding the relationship between structural morphology and electronic properties of these assemblies is critical for understanding and controlling their mechanical, optical, and electronic responses. In this work, we combine all-atom classical molecular simulations with quantum mechanical electronic structure calculations to ascertain the sequence-structure-electronic property relationship within a family of Asp-X-X-quaterthiophene-X-X-Asp (DXX-OT4-XXD) oligopeptides in which X is one of the five amino acids {Ala, Phe, Gly, Ile, Val} ({A, F, G, I, V}). Molecular dynamics simulations reveal that smaller amino acid substituents (A, G) favor linear stacking within a peptide dimer, whereas larger groups (F, I, V) induce larger twist angles between the peptides. Density functional theory calculations on the dimer show the absorption spectrum to be dominated by transitions between carbon and sulfur p orbitals. Although the absorption spectrum is largely insensitive to the relative twist angle, the highest occupied molecular orbital strongly localizes onto one molecule within the dimer at large twist angles, impeding the efficiency of transport between molecules. Our results provide a fundamental understanding of the relation between peptide orientation and electronic structure and offer design precepts for rational engineering of these systems.

Controlling Supramolecular Chirality in Peptide-π-Peptide Networks by Variation of the Alkyl Spacer Length

Self-assembled supramolecular organic materials with π-functionalities are of great interest because of their applications as biocompatible nanoelectronics. A detailed understanding of molecular parameters to modulate the formation of hierarchical structures can inform design principles for materials with engineered optical and electronic properties. In this work, we combine molecular-level characterization techniques with all-atom molecular simulations to investigate the subtle relationship between the chemical structure of peptide-π-peptide molecules and the emergent supramolecular chirality of their spontaneously self-assembled nanoaggregates. We demonstrate through circular dichroism measurements that we can modulate the chirality by incorporating alkyl spacers of various lengths in between the peptides and thienylene-phenylene π-system chromophores: even numbers of alkyl carbons in the spacer units (0, 2) induce M-type helical character whereas odd numbers (1, 3) induce P-type. Corroborating molecular dynamics simulations and explicating machine learning analysis techniques identify hydrogen bonding and hydrophobic packing to be the principal discriminants of the observed chirality switches. Our results present a molecular-level design rule to engineer chirality into optically and electronically active nanoaggregates of these peptidic building blocks by exploiting systematic variations in the alkyl spacer length.

Evidence for Prenucleated Fibrilogenesis of Acid-Mediated Self-Assembling Oligopeptides via Molecular Simulation and Fluorescence Correlation Spectroscopy

An important step in controlling biomimetic amyloid systems is understanding the self-assembly reaction kinetics. We are interested in a family of such materials characterized by symmetric sequences of amino acids flanking a π-conjugated functional core. Many of these materials rapidly self-assemble into long fibers upon protonation in an acidic environment. Despite extensive investigation of these materials' properties, little is yet understood regarding their reaction kinetics. Based on previous studies, we have chosen DFAG-4T-GAFD as a representative system and conducted molecular dynamics simulations to show that although large-scale assembly is induced by lowering pH, some degree of assembly is thermodynamically favorable in high-pH nonprotonating environments. These results are consistent with findings for other systems such as DFAG-OPV-GAFD. The nonprotonated aggregation also appears to be concentration dependent, occurring at concentrations of 100 nM and above. Single molecule measurements using fluorescence correlation spectroscopy provide experimental support for these computational predictions. We find evidence of spontaneous aggregation in aqueous solutions of peptides with concentrations as low as 100 nM; however, 10 nM solutions appear to be largely homogeneous solutions of unassembled monomer. These results indicate that the simplest explanations for kinetics of acid-mediated assembly-protonation-induced nucleation by monomeric addition followed by subsequent stages of aggregation and elongation-are inappropriate in this system. In fact, the system only exists as pure monomer in very low concentrations, nucleation actually occurs in the absence of protonating elements at concentrations typically used for experiments, and pH triggered assembly proceeds from these preassembled aggregates. Accordingly, triggered assembly must be considered to operate outside the domain of nucleation-dependent models.

Molecular simulations of self-assembling bio-inspired supramolecular systems and their connection to experiments

In bionanotechnology, the field of creating functional materials consisting of bio-inspired molecules, the function and shape of a nanostructure only appear through the assembly of many small molecules together. The large number of building blocks required to define a nanostructure combined with the many degrees of freedom in packing small molecules has long precluded molecular simulations, but recent advances in computational hardware as well as software have made classical simulations available to this strongly expanding field. Here, we review the state of the art in simulations of self-assembling bio-inspired supramolecular systems. We will first discuss progress in force fields, simulation protocols and enhanced sampling techniques using recent examples. Secondly, we will focus on efforts to enable the comparison of experimentally accessible observables and computational results. Experimental quantities that can be measured by microscopy, spectroscopy and scattering can be linked to simulation output either directly or indirectly, via quantum mechanical or semi-empirical techniques. Overall, we aim to provide an overview of the various computational approaches to understand not only the molecular architecture of nanostructures, but also the mechanism of their formation.

Multicomponent peptide assemblies

Self-assembled peptide nanostructures have been increasingly exploited as functional materials for applications in biomedicine and energy. The emergent properties of these nanomaterials determine the applications for which they can be exploited. It has recently been appreciated that nanomaterials composed of multicomponent coassembled peptides often display unique emergent properties that have the potential to dramatically expand the functional utility of peptide-based materials. This review presents recent efforts in the development of multicomponent peptide assemblies. The discussion includes multicomponent assemblies derived from short low molecular weight peptides, peptide amphiphiles, coiled coil peptides, collagen, and β-sheet peptides. The design, structure, emergent properties, and applications for these multicomponent assemblies are presented in order to illustrate the potential of these formulations as sophisticated next-generation bio-inspired materials.

Photon management in supramolecular peptide nanomaterials

Self-assembling peptides with covalent pi-electron functionality offer new ways to create delocalized conduits within protein-based nanomaterials. My group's recent research is summarized in this regard, detailing foundational self-assembly and photophysical characterizations that validate the electronic couplings existing within the resulting peptidic nanomaterials. Using these initial studies as a benchmark, ongoing studies to create even more complex photonic energy delocalization schemes are presented, spanning excitonic and Förster energy transfer to low-bandgap dopant sites (whereby 46% of the observed photoluminescence could be quenched by the addition of 1 mol% of an energy acceptor), the creation of charge separated states following photoinduced electron transfer that persisted for over a nanosecond, and use of kinetic control to dictate self-sorting (at long time scales, ca. several hours) or intimate coassembly (at short time scales, ca. several seconds) of multiple peptide components. Peptide coassemblies are described that exhibit both directed exciton migration to low-energy sites and follow-up charge separation events, very much in mimicry with relevant photosynthetic processes.

Nonresonant and Local Field Effects in Peptidic Nanostructures Bearing Oligo(p-phenylenevinylene) Units

Peptide nanostructures with built-in electronic functions offer a new platform for biomaterial science. In this report, we interrogate the influences of the immediate peptide environment around oligo(p-phenylenevinylene) (OPV3) electronic units embedded within one-dimensional peptide nanostructures on the resulting photophysics as assessed by UV-vis, photoluminescence (PL), and circular dichroism spectroscopies. To do so, we studied peptide-core-peptide molecules where the core was either OPV3 or an aliphatic n-decyl chain. Coassemblies of these molecules wherein the π-core was diluted as a minority component within a majority aliphatic matrix allowed for the variation of interchromophore exciton coupling commonly found in homoassemblies of peptide-OPV3-peptide monomers. Upon coassembly of the peptides, a hydrophilic tripeptide sequence (Asp-Asp-Asp-, DDD-) promoted the dilution/isolation of the peptide-π-peptide molecules in the majority peptide-decyl-peptide matrix whereas a hydrophobic tripeptide sequence (Asp-Val-Val-, DVV-) promoted the formation of self-associated stacks within the nanostructures. We also performed temperature variation studies to induce the reorganization of π-electron units in the spatially constrained n-decyl environment. This study elucidates the nonresonant (e.g., conformational) and local peptide field effects enforced within the internal environment of peptide nanomaterials and how they lead to varied photophysical properties of the embedded π-electron cores. It offers new insights on tuning the optoelectronic properties of these types of materials on the basis of the local electronic and steric environment available within the nanostructures.

Mesoscale Simulation of Asphaltene Aggregation

Asphaltenes constitute a heavy aromatic crude oil fraction with a propensity to aggregate and precipitate out of solution during petroleum processing. Aggregation is thought to proceed according to the Yen-Mullins hierarchy, but the molecular mechanisms underlying mesoscopic assembly remain poorly understood. By combining coarse-grained molecular models parametrized using all-atom data with high-performance GPU hardware, we have performed molecular dynamics simulations of the aggregation of hundreds of asphaltenes over microsecond time scales. Our simulations reveal a hierarchical self-assembly mechanism consistent with the Yen-Mullins model, but the details are sensitive and depend on asphaltene chemistry and environment. At low concentrations asphaltenes exist predominantly as dispersed monomers. Upon increasing concentration, we first observe parallel stacking into 1D rod-like nanoaggregates, followed by the formation of clusters of nanoaggregates associated by offset, T-shaped, and edge-edge stacking. Asphaltenes possessing long aliphatic side chains cannot form nanoaggregate clusters due to steric repulsions between their aliphatic coronae. At very high concentrations, we observe a porous percolating network of rod-like nanoaggregates suspended in a sea of interpenetrating aliphatic side chains with a fractal dimension of ∼2. The lifetime of the rod-like aggregates is described by an exponential distribution reflecting a dynamic equilibrium between coagulation and fragmentation.