Inverted nucleation for photoinduced nonequilibrium melting

Ultrafast photoinduced melting provides an essential platform for studying nonequilibrium phase transitions by linking the kinetics of electron dynamics to ionic motions. Knowledge of dynamic balance in their energetics is essential to understanding how the ionic reaction is influenced by femtosecond photoexcited electrons with notable time lag depending on reaction mechanisms. Here, by directly imaging fluctuating density distributions and evaluating the ionic pressure and Gibbs free energy from two-temperature molecular dynamics that verified experimental results, we uncovered that transient ionic pressure, triggered by photoexcited electrons, controls the overall melting kinetics. In particular, ultrafast nonequilibrium melting can be described by the reverse nucleation process with voids as nucleation seeds. The strongly driven solid-to-liquid transition of metallic gold is successfully explained by void nucleation facilitated by photoexcited electron-initiated ionic pressure, establishing a solid knowledge base for understanding ultrafast nonequilibrium kinetics.

Observing femtosecond orbital dynamics in ultrafast Ge melting with time-resolved resonant X-ray scattering

Photoinduced nonequilibrium phase transitions have stimulated interest in the dynamic interactions between electrons and crystalline ions, which have long been overlooked within the Born-Oppenheimer approximation. Ultrafast melting before lattice thermalization prompted researchers to revisit this issue to understand ultrafast photoinduced weakening of the crystal bonding. However, the absence of direct evidence demonstrating the role of orbital dynamics in lattice disorder leaves it elusive. By performing time-resolved resonant X-ray scattering with an X-ray free-electron laser, we directly monitored the ultrafast dynamics of bonding orbitals of Ge to drive photoinduced melting. Increased photoexcitation of bonding electrons amplifies the orbital disturbance to expedite the lattice disorder approaching the sub-picosecond scale of the nonthermal regime. The lattice disorder time shows strong nonlinear dependence on the laser fluence with a crossover behavior from thermal-driven to nonthermal-dominant kinetics, which is also verified by ab initio and two-temperature molecular dynamics simulations. This study elucidates the impact of bonding orbitals on lattice stability with a unifying interpretation on photoinduced melting.

Ultrafast Energy Transfer Process in Confined Gold Nanospheres Revealed by Femtosecond X-ray Imaging and Diffraction

Femtosecond laser pulses drive nonequilibrium phase transitions via reaction paths hidden in thermal equilibrium. This stimulates interest to understand photoinduced ultrafast melting processes, which remains incomplete due to challenges in resolving accompanied kinetics at the relevant space-time resolution. Here, by newly establishing a multiplexing femtosecond X-ray probe, we have successfully revealed ultrafast energy transfer processes in confined Au nanospheres. Real-time images of electron density distributions with the corresponding lattice structures elucidate that the energy transfer begins with subpicosecond melting at the specimen boundary earlier than the lattice thermalization, and proceeds by forming voids. Two temperature molecular dynamics simulations uncovered the presence of both heterogeneous melting with the melting front propagation from surface and grain boundaries and homogeneous melting with random melting seeds and nanoscale voids. Supported by experimental and theoretical results, we provide a comprehensive atomic-scale picture that accounts for the ultrafast laser-induced melting and evaporation kinetics.

Inducing thermodynamically blocked atomic ordering via strongly driven nonequilibrium kinetics

Ultrafast light-matter interactions enable inducing exotic material phases by promoting access to kinetic processes blocked in equilibrium. Despite potential opportunities, actively using nonequilibrium kinetics for material discovery is limited by the poor understanding on intermediate states of driven systems. Here, using single-pulse time-resolved imaging with x-ray free-electron lasers, we found intermediate states of photoexcited bismuth nanoparticles that showed kinetically reversed surface ordering during ultrafast melting. This entropy-lowering reaction was further investigated by molecular dynamics simulations to reveal that observed kinetics were thermodynamically buried in equilibrium, which emphasized the critical role of electron-mediated ultrafast free-energy modification in inducing exotic material phases. This study demonstrated that ultrafast photoexcitations of electrons provide an efficient strategy to induce hidden material phases by overcoming thermodynamic barriers via nonequilibrium reaction pathways.

High-Throughput 3D Ensemble Characterization of Individual Core-Shell Nanoparticles with X-ray Free Electron Laser Single-Particle Imaging

The structures as building blocks for designing functional nanomaterials have fueled the development of versatile nanoprobes to understand local structures of noncrystalline specimens. Progress in analyzing structures of individual specimens with atomic scale accuracy has been notable recently. In most cases, however, only a limited number of specimens are inspected lacking statistics to represent the systems with structural inhomogeneity. Here, by employing single-particle imaging with X-ray free electron lasers and algorithms for multiple-model 3D imaging, we succeeded in investigating several thousand specimens in a couple of hours and identified intrinsic heterogeneities with 3D structures. Quantitative analysis has unveiled 3D morphology, facet indices, and elastic strain. The 3D elastic energy distribution is further corroborated by molecular dynamics simulations to gain mechanical insight at the atomic level. This work establishes a route to high-throughput characterization of individual specimens in large ensembles, hence overcoming statistical deficiency while providing quantitative information at the nanoscale.

Topological defects: origin of nanopores and enhanced adsorption performance in nanoporous carbon

A scanning transmission electron microscopy investigation of two nanoporous carbon materials, wood-based ultramicroporous carbon and poly(furfuryl alcohol)-derived carbon, is reported. Atomic-resolution images demonstrate they comprise isotropic, three-dimensional networks of wrinkled one-atom-thick graphene sheets. In each graphene plane, nonhexagonal defects are frequently observed as connected five- and seven-atom rings. Atomic-level modeling shows that these topological defects induce localized rippling of graphene sheets, which interferes with their graphitic stacking and induces nanopores that lead to enhanced adsorption of H(2) molecules. The poly(furfuryl alcohol)-derived carbon contains larger regions of stacked layers, and shows significantly smaller surface area and pore volume than the ultramicroporous carbon.

The influence of dispersion interactions on the hydrogen adsorption properties of expanded graphite

We demonstrate the importance of London dispersion forces in defining the adsorption capacity within expanded graphite, a simple model of the more complex experimental geometries of activated carbon, using a combination of the non-local correlation functional of Dion et al paired with a recent exchange functional of Cooper (vdW-DF(C09x)) and a classical continuum model. Our results indicate that longer ranged interactions due to dispersion forces increase the volume over which molecules interact with a porous medium. This significantly enhances the adsorption density within a material, and explains recent experimental work showing that the densification of H(2) in carbon nanopores is sensitive to the pore size. Remarkably, our slit pore geometries give adsorption densities of up to 3 wt% at 298 K and 20 MPa which correlates well with experimental values for 9 Å pores-a value that could not be predicted using local density approximation (LDA) calculations. In its entirety, this work presents a powerful approach for assessing molecular uptake in porous media and may have serious impacts on efforts to optimize the properties of these materials.

Structural model of the Rev regulatory protein from equine infectious anemia virus

Rev is an essential regulatory protein in the equine infectious anemia virus (EIAV) and other lentiviruses, including HIV-1. It binds incompletely spliced viral mRNAs and shuttles them from the nucleus to the cytoplasm, a critical prerequisite for the production of viral structural proteins and genomic RNA. Despite its important role in production of infectious virus, the development of antiviral therapies directed against Rev has been hampered by the lack of an experimentally-determined structure of the full length protein. We have used a combined computational and biochemical approach to generate and evaluate a structural model of the Rev protein. The modeled EIAV Rev (ERev) structure includes a total of 6 helices, four of which form an anti-parallel four-helix bundle. The first helix contains the leucine-rich nuclear export signal (NES). An arginine-rich RNA binding motif, RRDRW, is located in a solvent-exposed loop region. An ERLE motif required for Rev activity is predicted to be buried in the core of modeled structure where it plays an essential role in stabilization of the Rev fold. This structural model is supported by existing genetic and functional data as well as by targeted mutagenesis of residues predicted to be essential for overall structural integrity. Our predicted structure should increase understanding of structure-function relationships in Rev and may provide a basis for the design of new therapies for lentiviral diseases.

A limited universe of membrane protein families and folds

One of the goals of structural genomics is to obtain a structural representative of almost every fold in nature. A recent estimate suggests that 70%-80% of soluble protein domains identified in the first 1000 genome sequences should be covered by about 25,000 structures-a reasonably achievable goal. As no current estimates exist for the number of membrane protein families, however, it is not possible to know whether family coverage is a realistic goal for membrane proteins. Here we find that virtually all polytopic helical membrane protein families are present in the already known sequences so we can make an estimate of the total number of families. We find that only approximately 700 polytopic membrane protein families account for 80% of structured residues and approximately 1700 cover 90% of structured residues. While apparently a finite and reachable goal, we estimate that it will likely take more than three decades to obtain the structures needed for 90% residue coverage, if current trends continue.

Codability criterion for picking proteinlike structures from random three-dimensional configurations

We show that the dominant eigenvectors of real protein structural contact matrices are highly correlated with their amino acid sequences. These results suggests that an ab initio sequence-independent profile exists for every protein structure and that this profile is highly effective in differentiating the ordering of amino acids in natural protein sequences from random sequences. This profile provides a structural code and is a key for understanding the unique behavior of protein structures. Using a lattice model, we show that there are special codable structures highly separated from random structures in the dominant eigenvector space of their structural contact matrices. As an example, we show our results provide a good explanation to the "designable principle" of protein structures.

Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

Background

Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks.

Results

In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods.

Conclusions

We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.