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The unfolding dynamics of ubiquitin were studied using a combination of x-ray solution scattering (XSS) and molecular dynamics (MD) simulations. The kinetic analysis of the XSS ubiquitin signals showed that the protein unfolds through a two-state process, independent of the presence of destabilizing salts. In order to characterize the ensemble of unfolded states in atomic detail, the experimental XSS results were used as a constraint in the MD simulations through the incorporation of x-ray scattering derived potential to drive the folded ubiquitin structure toward sampling unfolded states consistent with the XSS signals. We detail how biased MD simulations provide insight into unfolded states that are otherwise difficult to resolve and underscore how experimental XSS data can be combined with MD to efficiently sample structures away from the native state. Our results indicate that ubiquitin samples unfolded in states with a high degree of loss in secondary structure yet without a collapse to a molten globule or fully solvated extended chain. Finally, we propose how using biased-MD can significantly decrease the computational time and resources required to sample experimentally relevant nonequilibrium states. [ABSTRACT FROM AUTHOR]

Organic solar cells (OSCs) using non-fullerene acceptors (NFAs) afford exceptional photovoltaic performance metrics, however, their stability remains a significant challenge. Existing OSC stability studies focus on understanding degradation rate-performance relationships, improving interfacial layers, and suppressing degradative chemical reaction pathways. Nevertheless, there is a knowledge gap concerning how such degradation affects crystal structure, electronic states, and recombination dynamics that ultimately impact NFA performance. Here we seek a quantitative relationship between OSC metrics and blend morphology, trap density of states, charge carrier mobility, and recombination processes during the UV-light-induced degradation of PBDB-TF:Y6 inverted solar cells as the PCE (power conversion efficiency) falls from 17.3 to 5.0%. Temperature-dependent electrical and impedance measurements reveal deep traps at 0.48 eV below the conduction band that are unaffected by Y6 degradation, and shallow traps at 0.15 eV below the conduction band that undergo a three-fold density of states increase at the PCE degradation onset. Computational analysis correlates vinyl oxidation with a new trap state at 0.25 eV below the conduction band, likely involving charge transfer from the UV-absorbing ZnO electron transport layer. In situ integrated photocurrent analysis and transient absorption spectroscopy reveal that these traps lower electron mobility and increase recombination rates during degradation. Grazing-incidence wide-angle X-ray scattering and computational analysis reveal that the degraded Y6 crystallite morphology is largely preserved but that <1% of degraded Y6 molecules cause OSC PCE performance degradation by ≈50%. Together the detailed electrical, impedance, morphological, ultrafast spectroscopic, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) spectroscopy, and computational data reveal that the trap state energies and densities accompanying Y6 vinyl oxidation are primarily responsible for the PCE degradation in these operating NFA-OSCs. [ABSTRACT FROM AUTHOR]

Organic semiconductors (OSCs) are making great progress as active components in alternative energy and flexible electronics technologies that are of interest to many undergraduates. However, in materials governed by the confluence of multiple length scales (molecular (Angstrom), intermolecular (nm), domain (100s nm)), providing a pedagogically accessible pathway to incorporating OSCs into undergraduate education can be difficult. Here, we provide a multiscale description of OSCs that relies only on concepts covered in typical undergraduate chemistry and chemical engineering curricula: a tight-binding description of molecular orbitals using Hückel theory, the miscibility of intermolecular domains using thermodynamic principles based on the Ising spin model, the incorporation of thermal disorder of both the electronic and molecular states using the Boltzmann distribution, and a simple description of charge percolation using low-level graph theory. We illustrate these topics on a small organic molecule, 1,3,5-hexatriene, simple enough to be utilized in an undergraduate course without resorting to opaque metrics.

[Figurre: see text]. Protein aggregation can be defined as the sacrifice of stabilizing intrachain contacts of the functional state that are replaced with interchain contacts to form non-functional states. The resulting aggregate morphologies range from amorphous structures without long-range order typical of nondisease proteins involved in inclusion bodies to highly structured fibril assemblies typical of amyloid disease proteins. In this Account, we describe the development and application of computational models for the investigation of nondisease and disease protein aggregation as illustrated for the proteins L and G and the Alzheimer's Abeta systems. In each case, we validate the models against relevant experimental observables and then expand on the experimental window to better elucidate the link between molecular properties and aggregation outcomes. Our studies show that each class of protein exhibits distinct aggregation mechanisms that are dependent on protein sequence, protein concentration, and solution conditions. Nondisease proteins can have native structural elements in the denatured state ensemble or rapidly form early folding intermediates, which offers avenues of protection against aggregation even at relatively high concentrations. The possibility that early folding intermediates may be evolutionarily selected for their protective role against unwanted aggregation could be a useful strategy for reengineering sequences to slow aggregation and increase folding yield in industrial protein production. The observed oligomeric aggregates that we see for nondisease proteins L and G may represent the nuclei for larger aggregates, not just for large amorphous inclusion bodies, but potentially as the seeds of ordered fibrillar aggregates, since most nondisease proteins can form amyloid fibrils under conditions that destabilize the native state. By contrast, amyloidogenic protein sequences such as Abeta 1-40,42 and the familial Alzheimer's disease (FAD) mutants favor aggregation into ordered fibrils once the free-energy barrier for forming a critical nucleus is crossed. However, the structural characteristics and oligomer size of the soluble nucleation species have yet to be determined experimentally for any disease peptide sequence, and the molecular mechanism of polymerization that eventually delineates a mature fibril is unknown. This is in part due to the limited experimental access to very low peptide concentrations that are required to characterize these early aggregation events, providing an opportunity for theoretical studies to bridge the gap between the monomer and fibril end points and to develop testable hypotheses. Our model shows that Abeta 1-40 requires as few as 6-10 monomer chains (depending on sequence) to begin manifesting the cross-beta order that is a signature of formation of amyloid filaments or fibrils assessed in dye-binding kinetic assays. The richness of the oligomeric structures and viable filament and fibril polymorphs that we observe may offer structural clues to disease virulence variations that are seen for the WT and hereditary mutants.

Using a coarse-grained model of the Abeta peptide, we analyze the Arctic (E22G), Dutch (E22Q), and Flemish (A21G) familial Alzheimer's disease (FAD) mutants for any changes in the stability of amyloid assemblies with respect to the wild-type (WT) sequence. Based on a structural reference state of two protofilaments aligned to create the "agitated" protofibril as determined by solid-state NMR, we determine free energy trends for Abeta assemblies for the WT and FAD familial sequences. We find that the structural characteristics and oligomer size of the critical nucleus vary dramatically among the hereditary mutants. The Arctic mutant's disorder in the turn region introduces new stabilizing interactions that better align the two protofilaments, yielding a well-defined protofibril axis at relatively small oligomer sizes with respect to WT. By contrast, the critical nucleus for the Flemish mutant is beyond the 20 chains characterized in this study, thereby showing a strong shift in the equilibrium toward monomers with respect to larger protofibril assemblies. The Dutch mutant forms more ordered protofilaments than WT, but exhibits greater disorder in protofibril structure that includes an alternative polymorph of the WT fibril. An important conclusion of this work is that the Dutch mutant does not support the agitated protofibril assembly. We discuss the implications of the structural ensembles and free energy profiles for the FAD mutants in regards to interpretation of the kinetics of fibril assembly using chromatography and dye-binding experiments.

The protein folding process often proceeds through partially folded transient states. Therefore, a structural understanding of these disordered states is crucial for developing mechanistic models of the folding process. Characterization of unfolded states remains challenging due to their disordered nature, and incorporating multiple methods is necessary. Combining the time-resolved x-ray solution scattering (TRXSS) signal with molecular dynamics (MD), we are able to characterize transient partially folded states of bovine α-lactalbumin, a model system widely used for investigation of molten globule states, during its unfolding triggered by a temperature jump. We track the unfolding process between 20 µs and 70 ms and demonstrate that it passes through three distinct kinetic states. The scattering signals associated with these transient species are then analyzed with TRXSS constrained MD simulations to produce protein structures that are compatible with the input signals. Without utilizing any experimentally extracted kinetic information, the constrained MD simulation successfully drove the protein to an intermediate molten globule state; signals for two later disordered states are refined to terminal unfolded states. From our examination of the structural characteristics of these disordered states, we discuss the implications disordered states have on the folding process, especially on the folding pathway. Finally, we discuss the potential applications and limitations of this method. [ABSTRACT FROM AUTHOR]

In the past few decades, prediction of macromolecular structures beyond the native conformation has been aided by the development of molecular dynamics (MD) protocols aimed at exploration of the energetic landscape of proteins. Yet, the computed structures do not always agree with experimental observables, calling for further development of the MD strategies to bring the computations and experiments closer together. Here, we report a scalable, efficient MD simulation approach that incorporates an x-ray solution scattering signal as a driving force for the conformational search of stable structural configurations outside of the native basin. We further demonstrate the importance of inclusion of the hydration layer effect for a precise description of the processes involving large changes in the solvent exposed area, such as unfolding. Utilization of the graphics processing unit allows for an efficient all-atom calculation of scattering patterns on-the-fly, even for large biomolecules, resulting in a speed-up of the calculation of the associated driving force. The utility of the methodology is demonstrated on two model protein systems, the structural transition of lysine-, arginine-, ornithine-binding protein and the folding of deca-alanine. We discuss how the present approach will aid in the interpretation of dynamical scattering experiments on protein folding and association. [ABSTRACT FROM AUTHOR]

We develop a lattice model utilizing coarse-grained molecular sites to study charge transport in molecular semiconducting materials. The model bridges atomistic descriptions and structureless lattice models by mapping molecular structure onto sets of spatial vectors isomorphic with spin vectors in a classical n-vector Heisenberg model. Specifically, this model incorporates molecular topologydependent orientational and intermolecular coupling preferences, including the direct inclusion of spatially correlated transfer integrals and site energy disorder. This model contains the essential physics required to explicitly simulate the interplay of molecular topology and correlated structural disorder, and their effect on charge transport. As a demonstration of its utility, we apply this model to analyze the effects of long-range orientational correlations, molecular topology, and intermolecular interaction strength on charge motion in bulk molecular semiconductors. [ABSTRACT FROM AUTHOR]

Photonic crystals have been widely studied due to their broad technological applications in lasers, sensors, optical telecommunications, and display devices. Typically, photonic crystals are periodic structures of touching dielectric materials with alternating high and low refractive indices, and to date, the variables of interest have focused primarily on crystal symmetry and the refractive indices of the constituent materials, primarily polymers and semiconductors. In contrast, finite difference time domain (FDTD) simulations suggest that plasmonic nanoparticle superlattices with spacer groups offer an alternative route to photonic crystals due to the controllable spacing of the nanoparticles and the high refractive index of the lattices, even far away from the plasmon frequency where losses are low. Herein, the stopband features of 13 Bravais lattices are characterized and compared, resulting in paradigm-shifting design principles for photonic crystals. Based on these design rules, a simple cubic structure with an ~130-nm lattice parameter is predicted to have a broad photonic stopband, a property confirmed by synthesizing the structure via DNA programmable assembly and characterizing it by reflectance measurements. We show through simulation that a maximum reflectance of more than 0.99 can be achieved in these plasmonic photonic crystals by optimizing the nanoparticle composition and structural parameters. [ABSTRACT FROM AUTHOR]