Non-negligible Outer-Shell Reorganization Energy for Charge Transfer in Nonpolar Systems.

Many charge-transporting molecular systems function as ordered or disordered arrays of solid state materials composed of nonpolar (or weakly polar) molecules. Due to low dielectric constants for nonpolar systems, it is common to ignore the effects of outer-shell reorganization energy (λout). However, ignoring λout has not been properly supported and it can severely impact predictions and insights derived. Here, we estimate λout by two means: from experimental ultraviolet photoelectron spectra, in which vibronic progression in these spectra can be fitted with the widths of peaks determining the low-frequency component in reorganization energy, regarded to be closely associated with λout, and from molecular dynamic (MD) simulation of nonpolar molecules, in which disorder or fluctuation statistics for energies of charged molecules are calculated. An upper bound for λout was obtained as 505 and 549 meV for crystalline anthracene (140 K) and pentacene (50 K), respectively, by fitting of experimental data, and 212 and 170 meV, respectively, from MD simulations. These values are comparable to the inner-sphere reorganization energy (λin) arising from intramolecular vibration. With corresponding spectral density functions calculated, we found that λout is influenced both by low- and high-frequency dynamics, in which the former arises from constrained translational and rotational motions of surrounding molecules. In an amorphous state, about half of the λout's obtained are from high-frequency components, which is quite different from the conventional polar solvation. Moreover, crystalline systems exhibit super-Ohmic spectral density, whereas amorphous systems are sub-Ohmic.

A whole-cell platform for discovering synthetic cell adhesion molecules in bacteria.

Developing programmable bacterial cell-cell adhesion is of significant interest due to its versatile applications. Current methods that rely on presenting cell adhesion molecules (CAMs) on bacterial surfaces are limited by the lack of a generalizable strategy to identify such molecules targeting bacterial membrane proteins in their natural states. Here, we introduce a whole-cell screening platform designed to discover CAMs targeting bacterial membrane proteins within a synthetic bacteria-displayed nanobody library. Leveraging the potency of the bacterial type IV secretion system-a contact-dependent DNA delivery nanomachine-we have established a positive feedback mechanism to selectively enrich for bacteria displaying nanobodies that target antigen-expressing cells. Our platform successfully identified functional CAMs capable of recognizing three distinct outer membrane proteins (TraN, OmpA, OmpC), demonstrating its efficacy in CAM discovery. This approach holds promise for engineering bacterial cell-cell adhesion, such as directing the antibacterial activity of programmed inhibitor cells toward target bacteria in mixed populations.

Predicting Selectivity with a Bifurcating Surface: Inaccurate Model or Inaccurate Statistics of Dynamics?

Reactions on post-transition-state bifurcation (PTSB) energy surfaces are an important class of reaction in which classical rate theories, such as the transition state theory, fail to account for the selectivity. Quasiclassical trajectory molecular dynamic (QCT-MD) simulation is an important computational approach to understanding reactions mechanisms, especially for reactions that cannot be predicted from conventional rate theories. However, the applicability of direct dynamic simulations is hampered by huge computational costs for collecting a statistically meaningful set of trajectories, making it difficult to compare simulation results with theoretical or physical insights-based predictions (non-MD predictions). In this work, we examine the PTSB of Schmidt-Aubé reactions studied by Tantillo and co-workers. With machine-learning using kernel-ridge regression (KRR) to predict atomic forces, statistical reliability was enhanced by significantly increasing the number of trajectories. With KRR, the bottleneck of simulating dynamics (atomic forces in QCT-MD with density functional theory) was accelerated more than 100-fold. We found that this KRR-aided QCT-MD approach is successful in predicting branching ratios with a much larger number of trajectories. With our approach, statistical errors are greatly reduced, and hypothetical non-MD models for predicting selectivity are tested with much higher confidence. By comparison with non-MD models, dynamical properties that affect branching ratios become more clearly described.

Excited-State Charge Transfer Coupling from Quasiparticle Energy Density Functional Theory.

The recently developed Quasiparticle Energy (QE) scheme, based on a DFT calculation with one more (or less) electron, offers a good description of excitation energies, even with charge transfer characters. In this work, QE is further extended to calculate electron transfer (ET) couplings involving two excited states. We tested it with a donor-acceptor complex, consisting of a furan and a 1,1-dicyanoethylene (DCNE), in which two low lying charge transfer and local excitation states are involved. With generalized Mülliken-Hush and fragment charge-difference schemes, couplings from the QE approach generally agree well with those obtained from TDDFT, except that QE couplings exhibit better exponential distance dependence. Couplings from half-energy gaps with an external field are also calculated and reported. Our results show that the QE scheme is robust in calculating ET couplings with greatly reduced computational time.

Two-Step Machine Learning Approach for Charge-Transfer Coupling with Structurally Diverse Data.

Electronic coupling is important in determining charge-transfer rates and dynamics. Coupling strength is sensitive to both intermolecular, e.g., orientation or distance, and intramolecular degrees of freedom. Hence, it is challenging to build an accurate machine learning model to predict electronic coupling of molecular pairs, especially for those derived from the amorphous phase, for which intermolecular configurations are much more diverse than those derived from crystals. In this work, we devise a new prediction algorithm that employs two consecutive KRR models. The first model predicts molecular orbitals (MOs) from structural variation for each fragment, and coupling is further predicted by using the overlap integral included in a second model. With our two-step procedure, we achieved mean absolute errors of 0.27 meV for an ethylene dimer and 1.99 meV for a naphthalene pair, much improved accuracy amounting to 14-fold and 3-fold error reductions, respectively. In addition, MOs from the first model can also be the starting point to obtain other quantum chemical properties from atomistic structures. This approach is also compatible with a MO predictor with sufficient accuracy.

Machine-learned dynamic disorder of electron transfer coupling.

Electron transfer (ET) is a fundamental process in chemistry and biochemistry, and electronic coupling is an important determinant of the rate of ET. However, the electronic coupling is sensitive to many nuclear degrees of freedom, particularly those involved in intermolecular movements, making its characterization challenging. As a result, dynamic disorder in electron transfer coupling has rarely been investigated, hindering our understanding of charge transport dynamics in complex chemical and biological systems. In this work, we employed molecular dynamic simulations and machine-learning models to study dynamic disorder in the coupling of hole transfer between neighboring ethylene and naphthalene dimer. Our results reveal that low-frequency modes dominate these dynamics, resulting primarily from intermolecular movements such as rotation and translation. Interestingly, we observed an increasing contribution of translational motion as temperature increased. Moreover, we found that coupling is sub-Ohmic in its spectral density character, with cut-off frequencies in the range of 102 cm-1. Machine-learning models allow direct study of dynamics of electronic coupling in charge transport with sufficient ensemble trajectories, providing further new insights into charge transporting dynamics.

DNA tension assays reveal that force-dependent integrin activation regulates neurite outgrowth in primary cortical neurons.

Biomechanical inputs are ubiquitously present in biological systems and are known to regulate various cell functions. In particular, neural cell development is sensitive to mechanical regulation, as these cells reside in one of the softest microenvironments in the body. To fully characterize and comprehend how mechanical force modulates early neuronal processes, we prepared substrates functionalized with DNA probes displaying integrin ligands, including cRGD and laminin, to quantify integrin-mediated molecular tension during neurite initiation in primary cortical neurons. Our live-cell imaging analysis reveals that integrin-mediated tension force is highly dynamic and distributed across the cell body, with the overall tension signal gradually increasing during neurite outgrowth. Notably, we detected a consistent level of mechanical force (amplitude = 4.7-12 piconewtons, pN) for cell integrin-ligand interactions. Further quantifications reveal that neurons exhibit faster cell spreading and neurite outgrowth upon interacting with ligands functionalized with 4.7 pN relative to 12 pN probes. These findings indicate that the magnitude of integrin-mediated mechanical feedback regulates neuronal activity during early neuritogenesis. Additionally, we observed that mechanical tension is correlated with calcium signaling, since inhibiting calcium influx substantially reduced mechanical tension. Thus, our findings support that the magnitude of integrin-mediated mechanical feedback regulates neuronal activity during early neuritogenesis and that mechanical force is an essential element complementing well-known biochemical regulatory mechanisms orchestrating the integrin activation machinery and controlled neurite outgrowth in cortical neurons.

Localization of Noise in Biochemical Networks.

Noise, or uncertainty in biochemical networks, has become an important aspect of many biological problems. Noise can arise and propagate from external factors and probabilistic chemical reactions occurring in small cellular compartments. For species survival, it is important to regulate such uncertainties in executing vital cell functions. Regulated noise can improve adaptability, whereas uncontrolled noise can cause diseases. Simulation can provide a detailed analysis of uncertainties, but parameters such as rate constants and initial conditions are usually unknown. A general understanding of noise dynamics from the perspective of network structure is highly desirable. In this study, we extended the previously developed law of localization for characterizing noise in terms of (co)variances and developed noise localization theory. With linear noise approximation, we can expand a biochemical network into an extended set of differential equations representing a fictitious network for pseudo-components consisting of variances and covariances, together with chemical species. Through localization analysis, perturbation responses at the steady state of pseudo-components can be summarized into a sensitivity matrix that only requires knowledge of network topology. Our work allows identification of buffering structures at the level of species, variances, and covariances and can provide insights into noise flow under non-steady-state conditions in the form of a pseudo-chemical reaction. We tested noise localization in various systems, and here we discuss its implications and potential applications. Results show that this theory is potentially applicable in discriminating models, scanning network topologies with interesting noise behavior, and designing and perturbing networks with the desired response.

TADF-based NIR-II semiconducting polymer dots for in vivo 3D bone imaging.

Intraoperative fluorescence imaging in the second near-infrared (NIR-II) region heralds a new era in image-guided surgery since the success in the first-in-human liver-tumor surgery guided by NIR-II fluorescence. Limited by the conventional small organic NIR dyes such as FDA-approved indocyanine green with suboptimal NIR-II fluorescence and non-targeting ability, the resulting shallow penetration depth and high false positive diagnostic values have been challenging. Described here is the design of NIR-II emissive semiconducting polymer dots (Pdots) incorporated with thermally activated delayed fluorescence (TADF) moieties to exhibit emission maxima of 1064-1100 nm and fluorescence quantum yields of 0.40-1.58% in aqueous solutions. To further understand how the TADF units affect the molecular packing and the resulting optical properties of Pdots, in-depth and thorough density-functional theory calculations were carried out to better understand the underlying mechanisms. We then applied these Pdots for in vivo 3D bone imaging in mice. This work provides a direction for future designs of NIR-II Pdots and holds promising applications for bone-related diseases.

Luteolin Phosphate Derivatives Generated by Cultivating Bacillus subtilis var. Natto BCRC 80517 with Luteolin.

Luteolin (LUT), a plant-derived flavone, exhibits various bioactivities; however, the poor aqueous solubility hampers its applications. Here, we revealed bioconversion of LUT by Bacillus subtilis BCRC 80517, yielding three water-soluble phosphate conjugates. These derivatives were identified as luteolin 4'-O-phosphate (L4'P), luteolin 3'-O-phosphate (L3'P), and luteolin 7-O-phosphate (L7P) by LC-ESI-MS/MS and NMR. Besides, we found that Bacillus subtilis BCRC 80517 was able to convert different levels of LUT but showed a limited conversion rate. By observing bacterial morphology with transmission electron microscopy and confocal fluorescence microscopy, we found that LUT disrupted the bacterial membrane integrity, which explained the incomplete conversion. Additionally, we revealed a spontaneous intramolecular transesterification of L4'P to L3'P, the thermodynamically more stable form, under acidic conditions and proposed the possible mechanism involving a cyclic phosphate as the intermediate. This study provides insight into development of a potent structural modification strategy to enhance the solubility of LUT through biophosphorylation.

Reformulation of thermally assisted-occupation density functional theory in the Kohn-Sham framework.

We reformulate the thermally assisted-occupation density functional theory (TAO-DFT) into the Kohn-Sham single-determinant framework and construct two new post-self-consistent field (post-SCF) static correlation correction schemes, named rTAO and rTAO-1. In contrast to the original TAO-DFT with the density in an ensemble form, in which each orbital density is weighted with a fractional occupation number, the ground-state density is given by a single-determinant wavefunction, a regular Kohn-Sham (KS) density, and total ground state energy is expressed in the normal KS form with a static correlation energy formulated in terms of the KS orbitals. In post-SCF calculations with rTAO functionals, an efficient energy scanning to quantitatively determine θ is also proposed. The rTAOs provide a promising method to simulate systems with strong static correlation as original TAO, but simpler and more efficient. We show that both rTAO and rTAO-1 is capable of reproducing most results from TAO-DFT without the additional functional Eθ used in TAO-DFT. Furthermore, our numerical results support that, without the functional Eθ, both rTAO and rTAO-1 can capture correct static correlation profiles in various systems.

Noise reduction by upstream open reading frames.

Gene expression is prone to burst production, making it a highly noisy process that requires additional controls. Upstream open reading frames (uORFs) are widely present in the 5' leader sequences of 30-50% of eukaryotic messenger RNAs1-3. The translation of uORFs can repress the translation efficiency of the downstream main coding sequences. Whether the low translation efficiency leads to a different variation, or noise, in gene expression has not been investigated, nor has the direct biological impact of uORF-repressed translation. Here we show that uORFs achieve low but precise protein production in plant cells, possibly by reducing the protein production rate. We also demonstrate that, by buffering a stable TIMING OF CAB EXPRESSION 1 (TOC1) protein production level, uORFs contribute to the robust operation of the plant circadian clock. Our results provide both an action model and the biological impact of uORFs in translational control to mitigate transcriptional noise for precise protein production.

BioSANS: A software package for symbolic and numeric biological simulation.

Modeling biochemical systems can provide insights into behaviors that are difficult to observe or understand. It requires software, programming, and understanding of the system to build a model and study it. Softwares exist for systems biology modeling, but most support only certain types of modeling tasks. Desirable features including ease in preparing input, symbolic or analytical computation, parameter estimation, graphical user interface, and systems biology markup language (SBML) support are not seen concurrently in one software package. In this study, we developed a python-based software that supports these features, with both deterministic and stochastic propagations. The software can be used by graphical user interface, command line, or as a python import. We also developed a semi-programmable and intuitively easy topology input method for the biochemical reactions. We tested the software with semantic and stochastic SBML test cases. Tests on symbolic solution and parameter estimation were also included. The software we developed is reliable, well performing, convenient to use, and compliant with most of the SBML tests. So far it is the only systems biology software that supports symbolic, deterministic, and stochastic modeling in one package that also features parameter estimation and SBML support. This work offers a comprehensive set of tools and allows for better availability and accessibility for studying kinetics and dynamics in biochemical systems.

Skin cells undergo asynthetic fission to expand body surfaces in zebrafish.

As an animal's surface area expands during development, skin cell populations must quickly respond to maintain sufficient epithelial coverage. Despite much progress in understanding of skin cell behaviours in vivo1,2, it remains unclear how cells collectively act to satisfy coverage demands at an organismic level. Here we created a multicolour cell membrane tagging system, palmskin, to monitor the entire population of superficial epithelial cells (SECs) in developing zebrafish larvae. Using time-lapse imaging, we found that many SECs readily divide on the animal body surface; during a specific developmental window, a single SEC can produce a maximum of four progeny cells over its lifetime on the surface of the animal. Remarkably, EdU assays, DNA staining and hydroxyurea treatment showed that these terminally differentiated skin cells continue splitting despite an absence of DNA replication, causing up to 50% of SECs to exhibit reduced genome size. On the basis of a simple mathematical model and quantitative analyses of cell volumes and apical surface areas, we propose that 'asynthetic fission' is used as an efficient mechanism for expanding epithelial coverage during rapid growth. Furthermore, global or local manipulation of body surface growth affects the extent and mode of SEC division, presumably through tension-mediated activation of stretch-activated ion channels. We speculate that this frugal yet flexible mode of cell proliferation might also occur in contexts other than zebrafish skin expansion.

Enhancing Singlet Fission Coupling with Nonbonding Orbitals.

Singlet fission (SF) is a process where a singlet exciton is split into a pair of triplet excitons. The increase in the excitonic generation can be exploited to enhance the efficiency of solar cells. Molecules with conjugated π bonds are commonly developed for optoelectronic applications including SF, due to their low energy gaps. The electronic coupling for SF in such well-stacked π-conjugated molecule pairs can be rather limited due to the orthogonal π and π* orbital overlaps that are involved in the coupling elements, leading to a large cancellation in the coupling. In the present work, we show that such limits can be removed by involving triplet states of different origins, such as those with nonbonding n orbitals. We demonstrate such an effect for formaldehyde and methylenimine dimers, with a low-lying n-π* triplet state (T1) in addition to the π-π* triplet (T2). We show that the coupling can be enhanced by 40 times or more for the formaldehyde dimer, and 15 times or more for the methylenimine dimer, with the T1-T2 state as the end product of SF. With 1759 randomly oriented pairs of formaldehyde derived from a molecular dynamics simulation, the coupling from a singlet exciton to this T1-T2 state is, on an average, almost two times larger than that for a regular T1-T1 state. We investigated a few families that have been shown to be prospective candidates for SF, using our proposed strategy. However, our unfavorable results indicate that there are clear difficulties in fulfilling the ES1 ≳ ET1 + ET2 energy criterion. Nevertheless, our results provide a new molecular design concept for better SF (and triplet-triplet annihilation, TTA) materials that allows future development.

Molecular and nano structures of chiral PEDOT derivatives influence the enantiorecognition of biomolecules. In silico analysis of chiral recognition.

In this study we investigated the synergistic effects of the chirality (molecular structure) and surface morphology (nanostructure) of a newly designed sensing platform for the stereoselective recognition of biomolecules. We synthesized 3,4-ethylenedioxythiophene monomers presenting an OH functional group on the side chain (EDOT-OH) with either R or S chirality and then electropolymerized them in a template-free manner to engineer poly(EDOT-OH) nanotubes and smooth films with R or S chirality. We used a quartz crystal microbalance (QCM) to examine the differential binding of fetal bovine serum, RGD peptide, insulin, and (R)- and (S)-mandelic acid (MA) on these chiral polymeric platforms. All of these biomolecules bound stereoselectively and with greater affinity toward the nanotubes than to the smooth films. The sensitive chiral recognition of (S)- and (R)-MA on the (R)-poly(EDOT-OH) nanotube surface occurred with the highest chiral discrepancy ratio of 1.80. In vitro experiments revealed a greater degree of protein deposition from MCF-7 cells on the chiral nanotube surfaces. We employed ab initio molecular dynamics simulations and density functional theory calculations to investigate the mechanism underlying the sensitive chiral recognition between the chiral sensing platforms and the chiral analyte molecules.

Molecular Design of Ultrabright Semiconducting Polymer Dots with High NIR-II Fluorescence for 3D Tumor Mapping.

Fluorescence probes emitting in the second near-infrared (NIR-II, 1000-1700 nm) window with the ability for deep-tissue imaging in mammals herald a new era in surgical methodology. However, the brightness of these NIR-II probes is still far from satisfactory due to their low fluorescence quantum yields (QYs), preventing the observation of high-resolution images such as whole-organ vascular networks in real time. Described here is the molecular engineering of a series of semiconducting polymer dots (Pdots) incorporated with aggregation-induced emission moieties to exhibit the QYs as high as 14% in the NIR-II window. Benefiting from the ultrahigh brightness, a 1400 nm long-pass filter is utilized to realize in vivo 3D tumor mapping in mice. To further understand how the geometrical and electron structures of the semiconducting polymers affect their optical properties, the in-depth and thorough density-functional theory calculations are performed to interpret the experimental results. This study lays the groundwork for further molecular design of highly bright NIR-II Pdots.

Low-Cost Hole-Transporting Materials Based on Carbohelicene for High-Performance Perovskite Solar Cells.

Two hole-transporting materials (HTMs) based on carbohelicene cores, CH1 and CH2, are developed and used in fabricating efficient and stable perovskite solar cells (PSCs). Owing to the rigid conformation of the helicene core, both compounds possess unique CH-π interactions in the crystalline packing pattern and good phase stability, which are distinct from the π-π intermolecular interactions of conventional planar and spiro-type molecules. PSCs based on CH1 and CH2 as HTMs deliver excellent device efficiencies of 19.36 and 18.71%, respectively, outperforming the control device fabricated with spiro-OMeTAD (18.45%). Furthermore, both PSCs exhibit better ambient stability, with 90% of initial performance retained after aging with a 50-60% relative humidity at 25 °C for 500 h. Due to the low production cost of both compounds, these newly designed carbohelicene-type HTMs have the potential for the future commercialization of PSCs.