Extracellular calcium functions as a molecular glue for transmembrane helices to activate the scramblase Xkr4.

The "eat me" signal, phosphatidylserine is exposed on the surface of dying cells by phospholipid scrambling. Previously, we showed that the Xkr family protein Xkr4 is activated by caspase-mediated cleavage and binding of the XRCC4 fragment. Here, we show that extracellular calcium is an additional factor needed to activate Xkr4. The constitutively active mutant of Xkr4 is found to induce phospholipid scrambling in an extracellular, but not intracellular, calcium-dependent manner. Importantly, other Xkr family members also require extracellular calcium for activation. Alanine scanning shows that D123 and D127 of TM1 and E310 of TM3 coordinate calcium binding. Moreover, lysine scanning demonstrates that the E310K mutation-mediated salt bridge between TM1 and TM3 bypasses the requirement of calcium. Cysteine scanning proves that disulfide bond formation between TM1 and TM3 also activates phospholipid scrambling without calcium. Collectively, this study shows that extracellular calcium functions as a molecular glue for TM1 and TM3 of Xkr proteins for activation, thus demonstrating a regulatory mechanism for multi-transmembrane region-containing proteins.

Exciton diffusion in amorphous organic semiconductors: Reducing simulation overheads with machine learning.

Simulations of exciton and charge hopping in amorphous organic materials involve numerous physical parameters. Each of these parameters must be computed from costly ab initio calculations before the simulation can commence, resulting in a significant computational overhead for studying exciton diffusion, especially in large and complex material datasets. While the idea of using machine learning to quickly predict these parameters has been explored previously, typical machine learning models require long training times, which ultimately contribute to simulation overheads. In this paper, we present a new machine learning architecture for building predictive models for intermolecular exciton coupling parameters. Our architecture is designed in such a way that the total training time is reduced compared to ordinary Gaussian process regression or kernel ridge regression models. Based on this architecture, we build a predictive model and use it to estimate the coupling parameters which enter into an exciton hopping simulation in amorphous pentacene. We show that this hopping simulation is able to achieve excellent predictions for exciton diffusion tensor elements and other properties as compared to a simulation using coupling parameters computed entirely from density functional theory. This result, along with the short training times afforded by our architecture, shows how machine learning can be used to reduce the high computational overheads associated with exciton and charge diffusion simulations in amorphous organic materials.

Discovery of a phase-separating small molecule that selectively sequesters tubulin in cells.

Phase-separated membraneless organelles or biomolecular condensates play diverse functions in cells, however recapturing their characteristics using small organic molecules has been a challenge. In the present study, cell-lysate-based screening of 843 self-assembling small molecules led to the discovery of a simple organic molecule, named huezole, that forms liquid droplets to selectively sequester tubulin. Remarkably, this small molecule enters cultured human cells and prevents cell mitosis by forming tubulin-concentrating condensates in cells. The present study demonstrates the feasibility of producing a synthetic condensate out of non-peptidic small molecules for exogenous control of cellular processes. The modular structure of huezole provides a framework for designing a class of organelle-emulating small molecules.

Caspase cleavage releases a nuclear protein fragment that stimulates phospholipid scrambling at the plasma membrane.

Phospholipid scrambling in dying cells promotes phosphatidylserine exposure, a critical process for efferocytosis. We previously identified the Xkr family protein Xkr4 as a phospholipid-scrambling protein, but its activation mechanisms remain unknown. Here we show that Xkr4 is activated in two steps: dimer formation by caspase-mediated cleavage and structural change caused by activating factors. To identify the factors, we developed a new screening system, "revival screening," using a CRISPR sgRNA library. Applying this system, we identified the nuclear protein XRCC4 as the single candidate for the Xkr4 activator. Upon apoptotic stimuli, XRCC4, contained in the DNA repair complex, is cleaved by caspases, and its C-terminal fragment with an intrinsically disordered region is released into the cytoplasm. Protein interaction screening showed that the fragment interacts directly with the Xkr4 dimer to activate it. This study demonstrates that caspase-mediated cleavage releases a nuclear protein fragment for direct regulation of lipid dynamics on the plasma membrane.

Exploring the configuration spaces of surface materials using time-dependent diffraction patterns and unsupervised learning.

Computational methods for exploring the atomic configuration spaces of surface materials will lead to breakthroughs in nanotechnology and beyond. In order to develop such methods, especially ones utilizing machine learning approaches, descriptors which encode the structural features of the candidate configurations are required. In this paper, we propose the use of time-dependent electron diffraction simulations to create descriptors for the configurations of surface materials. Our proposal utilizes the fact that the sub-femtosecond time-dependence of electron diffraction patterns are highly sensitive to the arrangement of atoms in the surface region of the material, allowing one to distinguish configurations which possess identical symmetry but differ in the locations of the atoms in the unit cell. We demonstrate the effectiveness of this approach by considering the simple cases of copper(111) and an organic self-assembled monolayer system, and use it to search for metastable configurations of these materials.

Disorder-robust bands from anisotropic orbitals in a coordination polymer semiconductor.

While the effects of structural disorder on the electronic properties of solids are poorly understood, it is widely accepted that spatially isotropic orbitals lead to robustness against disorder. In this paper, we use first-principles calculations to show that a cluster of occupied bands in the coordination polymer semiconductor ß-copper(I) thiocyanate undergo relatively little fluctuation in the presence of thermal disorder-a surprising finding given that these bands are composed of spatially anisotropic d-orbitals. Analysis with the tight-binding method and a stochastic network model suggests that the robustness of these bands to the thermal disorder can be traced to the way in which these orbitals are aligned with respect to each other. This special alignment causes strong inverse statistical correlations between orbital-orbital distances, making these bands robust to random fluctuations of these distances. As well as proving that disorder-robust electronic properties can be achieved even with anisotropic orbitals, our results provide a concrete example of when simple 'averaging' methods can be used to treat thermal disorder in electronic structure calculations.

Materials informatics for self-assembly of functionalized organic precursors on metal surfaces.

Bottom-up fabrication via on-surface molecular self-assembly is a way to create defect-free, low-dimensional nanomaterials. For bottom-up fabrication to succeed, precursor molecules which correctly assemble into the target structure must be first identified. Here we present an informatics technique which connects self-assembled structures with particular chemical properties of the precursor molecules. Application of this method produces a visual output (a dendrogram) that functions much like the periodic table, but whereas the periodic table puts atoms into categories according to the way in which they bond to each other, the dendrogram put molecules into categories according to the way in which they arrange in a self-assembled structure. By applying this method to the case of functionalized bianthracene precursors adsorbed to copper(111), we identify the functional groups needed to assemble one-dimensional chains, two-dimensional tilings, and other shapes. This methodology can therefore help to identify appropriate precursor molecules for forming target nanomaterials via bottom-up fabrication.

A Hydroxamic Acid Anchoring Group for Durable Dye-Sensitized Solar Cells Incorporating a Cobalt Redox Shuttle.

A hydroxamic acid group has been employed for the first time as an anchoring group for cobalt-based dye-sensitized solar cells (DSSCs). The porphyrin dye YD2-o-C8HA including a hydroxamic acid anchoring group exhibited a power conversion efficiency (η) of 6.4 %, which is close to that of YD2-o-C8, a representative porphyrin dye incorporating a conventional carboxylic acid. More importantly, YD2-o-C8HA was found to be superior to YD2-o-C8 in terms of both binding ability to TiO2 and durability of cobalt-based DSSCs. Notably, YD2-o-C8HA photocells revealed a higher η-value (4.1 %) than YD2-o-C8 (2.8 %) after 500 h illumination. These results suggest that the hydroxamic acid can be used for DSSCs with other transition-metal-based redox shuttle to ensure high cell durability as well as excellent photovoltaic performance.

Chemical and entropic control on the molecular self-assembly process.

Molecular self-assembly refers to the spontaneous assembly of molecules into larger structures. In order to exploit molecular self-assembly for the bottom-up synthesis of nanomaterials, the effects of chemical control (strength of the directionality in the intermolecular interaction) and entropic control (temperature) on the self-assembly process should be clarified. Here we present a theoretical methodology that unambiguously distinguishes the effects of chemical and entropic control on the self-assembly of molecules adsorbed to metal surfaces. While chemical control simply increases the formation probability of ordered structures, entropic control induces a variety of effects. These effects range from fine structure modulation of ordered structures, through to degrading large, amorphous structures into short, chain-shaped structures. Counterintuitively, the latter effect shows that entropic control can improve molecular ordering. By identifying appropriate levels of chemical and entropic control, our methodology can, therefore, identify strategies for optimizing the yield of desired nanostructures from the molecular self-assembly process.

State-space reduction and equivalence class sampling for a molecular self-assembly model.

Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.

Charge transport in organic crystals: critical role of correlated fluctuations unveiled by analysis of Feynman diagrams.

Organic crystals have unique charge transport properties that lie somewhere between delocalised band-type transport and localised hopping transport. In this paper, we use a stochastic tight-binding model to explore how dynamical disorder in organic crystals affects charge transport. By analysing the model in terms of Feynman diagrams (virtual processes), we expose the crucial role of correlated dynamical disorder to the charge transport dynamics in the model at short times in the order of a few hundred femtoseconds. Under correlated dynamical disorder, the random motions of molecules in the crystal allow for low-energy "bonding"-type interactions between neighboring molecular orbitals can persist over long periods of time. On the other hand, the dependence of charge transport on correlated dynamical disorder also tends to localize the charge, as correlated disorder cannot persist far in space. This concept of correlation may be the "missing link" for describing the intermediate regime between band transport and hopping transport that occurs in organic crystals.

Effects of atomic collisions on the stoichiometry of thin films prepared by pulsed laser deposition.

We present an analytical model to quantitatively study the effect of collisions between the atoms of a plume and the molecules of a surrounding gas on the nonstoichiometry of lithium-containing oxide thin films deposited using pulsed laser deposition. A comparison of the experimental data and the model ascertain the inevitable loss of the lighter cation, leading to a nonstoichiometric reduction in the content of lighter cations in the films. Our model is the first analytic model of collision-induced plume expansion that can explain the partial oxygen pressure dependence of the Li content of a thin film. These studies have important implications for collision effects that affect the growth of thin films containing both light and heavy elements.

Dephasing by a continuous-time random walk process.

Stochastic treatments of magnetic resonance spectroscopy and optical spectroscopy require evaluations of functions such as (exp(i ∫(0)(t) Q(s)ds)), where t is time, Q(s) is the value of a stochastic process at time s, and the angular brackets denote ensemble averaging. This paper gives an exact evaluation of these functions for the case where Q is a continuous-time random walk process. The continuous-time random walk describes an environment that undergoes slow steplike changes in time. It also has a well-defined Gaussian limit and so allows for non-Gaussian and Gaussian stochastic dynamics to be studied within a single framework. We apply the results to extract qubit-lattice interaction parameters from dephasing data of P-doped Si semiconductors (data collected elsewhere) and to calculate the two-dimensional spectrum of a three-level harmonic oscillator undergoing random frequency modulations.

Non-Gaussian stochastic dynamics of spins and oscillators: a continuous-time random walk approach.

We consider separately a spin and an oscillator that are coupled to their environment. After a finite interval of random length, the state of the environment changes, and each change causes a random change in the resonance frequency of the spin or vibrational frequency of the oscillator. Mathematically, the evolution of these frequencies is described by a continuous-time random walk. Physically, the stochastic dynamics can be understood as non-Gaussian because the frequency of the system and state of the environment change on comparable time scales. These dynamics are also nonstationary, and so might apply to a nonequilibrium environment. The resonance and vibrational spectra of the spin and oscillator, as well as the ensemble-averaged displacement of the oscillator, are investigated in detail. We observe some distinct non-Gaussian features of the dynamics, such as the narrow, leptokurtic shape of the resonance spectrum of the spin and beating of the average oscillator displacement. The convergence to Gaussian dynamics as changes in the environment occur with increasing frequency is also considered. Among other results, we observe narrowing of the resonance and vibrational lines in the Gaussian limit due to a weakening of the system-environment interaction.

A stochastic, local mode study of neon-liquid surface collision dynamics.

Equations of motion for a fast, light rare gas atom passing over a liquid surface are derived and used to infer the dynamics of neon collisions with squalane and perfluorinated polyether surfaces from experimental data. The equations incorporate the local mode model of a liquid surface via a stochastic process and explicitly account for impulsive collisional energy loss to the surface. The equations predict angular distributions for scattering of neon that are in good quantitative agreement with experimental data. Our key dynamical conclusions are that experimental angular distributions derive mainly from local mode surface topography rather than from structural features of individual surface molecules, and that the available data for these systems can be accounted for almost exclusively by single collisions between neon atoms and the liquid surface.

A stochastic, local mode treatment of high-energy gas-liquid collisions.

The scattering angle distributions of high-energy molecular beams at the surfaces of three different liquids are treated in terms local mode theory. This is achieved by setting up a stochastic process modeling the effect of a superposition of local mode surface displacements on the incoming particle's trajectory. The results are found to be in good qualitative agreement with experiment, and directions for further work are indicated.