The Metal-Insulator Transition in Vanadium Oxide Nanofilms Enables Microkelvin-Resolution Thermometry.

High-resolution thermometry is critical for probing nanoscale energy transport. Here, we demonstrate how high-resolution thermometry can be accomplished using vanadium oxide (VOx), which features a sizable temperature-dependence of its resistance at room temperature and an even stronger dependence at its metal-insulator-transition (MIT) temperature. We microfabricate VOx nanofilm-based electrical resistance thermometers that undergo a metal-insulator-transition at ∼337 K and systematically quantify their temperature-dependent resistance, noise characteristics, and temperature resolution. We show that VOx sensors can achieve, in a bandwidth of ∼16 mHz, a temperature resolution of ∼5 µK at room temperature (∼300 K) and a temperature resolution of ∼1 µK at the MIT (∼337 K) when the amplitude of temperature perturbations is in the microkelvin range, which, in contrast to larger perturbations, is found to avoid hysteric resistance responses. These results demonstrate that VOx-based thermometers offer a ∼10-50-fold improvement in resolution over widely used Pt-based thermometers.

Synergistic computational and experimental studies of a phosphoglycosyl transferase membrane/ligand ensemble.

Complex glycans serve essential functions in all living systems. Many of these intricate and byzantine biomolecules are assembled employing biosynthetic pathways wherein the constituent enzymes are membrane-associated. A signature feature of the stepwise assembly processes is the essentiality of unusual linear long-chain polyprenol phosphate-linked substrates of specific isoprene unit geometry, such as undecaprenol phosphate (UndP) in bacteria. How these enzymes and substrates interact within a lipid bilayer needs further investigation. Here, we focus on a small enzyme, PglC from Campylobacter, structurally characterized for the first time in 2018 as a detergent-solubilized construct. PglC is a monotopic phosphoglycosyl transferase that embodies the functional core structure of the entire enzyme superfamily and catalyzes the first membrane-committed step in a glycoprotein assembly pathway. The size of the enzyme is significant as it enables high-level computation and relatively facile, for a membrane protein, experimental analysis. Our ensemble computational and experimental results provided a high-level view of the membrane-embedded PglC/UndP complex. The findings suggested that it is advantageous for the polyprenol phosphate to adopt a conformation in the same leaflet where the monotopic membrane protein resides as opposed to additionally disrupting the opposing leaflet of the bilayer. Further, the analysis showed that electrostatic steering acts as a major driving force contributing to the recognition and binding of both UndP and the soluble nucleotide sugar substrate. Iterative computational and experimental mutagenesis support a specific interaction of UndP with phosphoglycosyl transferase cationic residues and suggest a role for critical conformational transitions in substrate binding and specificity.

Probing the Origins of the Disorder-to-Order Transition of a Modified Cholesterol in Ternary Lipid Bilayers.

In a recent study, spectroscopic observations of modified cholesterol in both lipid-coated nanoparticles and liposomes provided evidence for a disorder-to-order orientational transition with increasing temperature. Below a critical temperature, in a membrane composed of modified cholesterol, saturated (DPPC) lipid, and anionic (DOPS) lipid, a roughly equal population of head-out and head-in conformations was observed. Surprisingly, as temperature was increased the modified cholesterol presented an abrupt transition to a population of all head-in orientations. Additionally, when saturated DPPC lipids were replaced by unsaturated DOPC the disorder-to-order transition was eliminated. To gain insight into this curious transition, we use all-atom molecular dynamics simulations to characterize the structure and fluctuations of lipid bilayers composed of saturated and unsaturated lipids, in the presence of normal and modified cholesterol. Free energy differences between head-out and head-in conformations are computed as a function of varying lipid membrane composition for normal and modified cholesterol. In bilayers primarily composed of DPPC, the orientation of modified cholesterol is observed to depend sensitively on the orientation of the surrounding normal or modified cholesterol molecules, suggesting cooperative Ising-like interactions favoring an ordered state. In bilayers primarily composed of DOPC, spontaneous flip-flop of modified cholesterol is observed, consistent with the measured small free energy barrier separating the head-in and head-out orientations. This combined experimental and computational study effectively characterizes the orientational dimorphism and provides novel insight into the fundamental nature of cholesterol interactions in membrane.

Interfacial hydration determines orientational and functional dimorphism of sterol-derived Raman tags in lipid-coated nanoparticles.

Lipid-coated noble metal nanoparticles (L-NPs) combine the biomimetic surface properties of a self-assembled lipid membrane with the plasmonic properties of a nanoparticle (NP) core. In this work, we investigate derivatives of cholesterol, which can be found in high concentrations in biological membranes, and other terpenoids, as tunable, synthetic platforms to functionalize L-NPs. Side chains of different length and polarity, with a terminal alkyne group as Raman label, are introduced into cholesterol and betulin frameworks. The synthesized tags are shown to coexist in two conformations in the lipid layer of the L-NPs, identified as "head-out" and "head-in" orientations, whose relative ratio is determined by their interactions with the lipid-water hydrogen-bonding network. The orientational dimorphism of the tags introduces orthogonal functionalities into the NP surface for selective targeting and plasmon-enhanced Raman sensing, which is utilized for the identification and Raman imaging of epidermal growth factor receptor-overexpressing cancer cells.

Opto-thermoelectric trapping of fluorescent nanodiamonds on plasmonic nanostructures.

Deterministic optical manipulation of fluorescent nanodiamonds (FNDs) in fluids has emerged as an experimental challenge in multimodal biological imaging. Designing and developing nano-optical trapping strategies to serve this purpose is an important task. In this Letter, we show how chemically prepared gold nanoparticles and silver nanowires can facilitate an opto-thermoelectric force to trap individual entities of FNDs using a long working distance lens, low power-density illumination (532-nm laser, 12 µW/µm2). Our trapping configuration combines the thermoplasmonic fields generated by individual plasmonic nanoparticles and the opto-thermoelectric effect facilitated by the surfactant to realize a nano-optical trap down to a single FND that is 120 nm in diameter. We use the same trapping excitation source to capture the spectral signatures of single FNDs and track their position. By tracking the FND, we observe the differences in the dynamics of the FND around different plasmonic structures. We envisage that our drop-casting platform can be extrapolated to perform targeted, low-power trapping, manipulation, and multimodal imaging of FNDs inside biological systems such as cells.

The role of structural heterogeneity in the homodimerization of transmembrane proteins.

The equilibrium association of transmembrane proteins plays a fundamental role in membrane protein function and cellular signaling. While the study of the equilibrium binding of single pass transmembrane proteins has received significant attention in experiment and simulation, the accurate assessment of equilibrium association constants remains a challenge to experiment and simulation. In experiment, there remain wide variations in association constants derived from experimental studies of the most widely studied transmembrane proteins. In simulation, state-of-the art methods have failed to adequately sample the thermodynamically relevant structures of the dimer state ensembles using coarse-grained models. In addition, all-atom force fields often fail to accurately assess the relative free energies of the dimer and monomer states. Given the importance of this fundamental biophysical process, it is essential to address these shortcomings. In this work, we establish an effective computational protocol for the calculation of equilibrium association constants for transmembrane homodimer formation. A set of transmembrane protein homodimers, used in the parameterization of the MARTINI v3 force field, are simulated using metadynamics, based on three collective variables. The method is found to be accurate and computationally efficient, providing a standard to be used in the future simulation studies using coarse-grained or all-atom models.

Machine Learning Derived Collective Variables for the Study of Protein Homodimerization in Membrane.

The accurate calculation of equilibrium constants for protein-protein association is of fundamental importance to quantitative biology and remains an outstanding challenge for computational biophysics. Traditionally, equilibrium constants have been computed from one-dimensional free energy surfaces derived from sampling along a single collective variable. Importantly, recent advances in enhanced sampling methodology have facilitated the characterization of multidimensional free energy landscapes, often exposing multiple thermodynamically important minima missed by more restrictive sampling methods. A key to the effectiveness of this multidimensional sampling approach is the identification of collective variables that effectively define the configurational space of dissociated and associated states. Here we present the application of two machine learning methods for the unbiased determination of collective variables for enhanced sampling and analysis of protein-protein association. Our results both validate prior work, based on intuition derived collective variables, and demonstrate the effectiveness of the machine learning methods for the identification of collective variables for association reactions in complex biomolecular systems.

A nanoscale photonic thermal transistor for sub-second heat flow switching.

Control of heat flow is critical for thermal logic devices and thermal management and has been explored theoretically. However, experimental progress on active control of heat flow has been limited. Here, we describe a nanoscale radiative thermal transistor that comprises of a hot source and a cold drain (both are ~250 nm-thick silicon nitride membranes), which are analogous to the source and drain electrodes of a transistor. The source and drain are in close proximity to a vanadium oxide (VOx)-based planar gate electrode, whose dielectric properties can be adjusted by changing its temperature. We demonstrate that when the gate is located close ( < ~1 µm) to the source-drain device and undergoes a metal-insulator transition, the radiative heat transfer between the source and drain can be changed by a factor of three. More importantly, our nanomembrane-based thermal transistor features fast switching times ( ~ 500 ms as opposed to minutes for past three-terminal thermal transistors) due to its small thermal mass. Our experiments are supported by detailed calculations that highlight the mechanism of thermal modulation. We anticipate that the advances reported here will open new opportunities for designing thermal circuits or thermal logic devices for advanced thermal management.

On Computing Equilibrium Binding Constants for Protein-Protein Association in Membranes.

Protein association in lipid membranes is fundamental to membrane protein function and of great biomedical relevance. All-atom and coarse-grained models have been extensively used to understand the protein-protein interactions in the membrane and to compute equilibrium association constants. However, slow translational and rotational diffusion of protein in membrane presents challenges to the effective sampling of conformations defining the ensembles of free and bound states contributing to the association equilibrium and the free energy of dimerization. We revisit the homodimerization equilibrium of the TM region of glycophorin A. Conformational sampling is performed using umbrella sampling along previously proposed one-dimensional collective variables and compared with sampling over a two-dimensional collective variable space using the MARTINI v2.2 force field. We demonstrate that the one-dimensional collective variables suffer from restricted sampling of the native homodimer conformations leading to a biased free energy landscape. Conversely, simulations along the two-dimensional collective variable effectively characterize the thermodynamically relevant native and non-native interactions contributing to the association equilibrium. These results demonstrate the challenges associated with accurately characterizing binding equilibria when multiple poses contribute to the bound state ensemble.

Molecular typing of dengue virus in Mizoram, Northeast India.

INTRODUCTION: Dengue is an emerging vector-borne public health threat and characterization at the molecular level is important for proper management of the disease. The aim of the study is to examine the diversity of the dengue viral serotypes from a hilly mountainous region of Northeast India. METHODOLOGY: Thirty-six blood samples that were positive for dengue virus IgM antibodies identified by the enzyme-linked immunosorbent assay (ELISA) method were collected and quantified for the IL6 gene expression by using reverse transcriptase polymerase chain reaction (RT-PCR). RESULTS: All the patients had dengue hemorrhagic fever (DHF); 12 samples had a monotypic infection and 14 samples had dual infection with various dengue virus (DENV) serotypes; one sample had triple infection with DENV-1, DENV-2, and DENV-3. CONCLUSIONS: This study identified DENV-1 as the major serotype in the state of Mizoram and it is the first report on the molecular typing of the dengue virus from the hilly mountainous state located in the Indo-Burma region bordering Myanmar and Bangladesh.

Addressing the Excessive Aggregation of Membrane Proteins in the MARTINI Model.

The MARTINI model is a widely used coarse-grained force field popular for its capacity to represent a diverse array of complex biomolecules. However, efforts to simulate increasingly realistic models of membranes, involving complex lipid mixtures and multiple proteins, suggest that membrane protein aggregates are overstabilized by the MARTINI v2.2 force field. In this study, we address this shortcoming of the MARTINI model. We determined the free energy of dimerization of four transmembrane protein systems using the nonpolarizable MARTINI model. Comparison with experimental FRET-based estimates of the dimerization free energy was used to quantify the significant overstabilization of each protein homodimer studied. To improve the agreement between simulation and experiment, a single uniform scaling factor, α, was used to enhance the protein-lipid Lennard-Jones interaction. A value of α = 1.04-1.045 was found to provide the best fit to the dimerization free energies for the proteins studied while maintaining the specificity of contacts at the dimer interface. To further validate the modified force field, we performed a multiprotein simulation using both MARTINI v2.2 and the reparameterized MARTINI model. While the original MARTINI model predicts oligomerization of protein into a single aggregate, the reparameterized MARTINI model maintains a dynamic equilibrium between monomers and dimers as predicted by experimental studies. The proposed reparameterization is an alternative to the standard MARTINI model for use in simulations of realistic models of a biological membrane containing diverse lipids and proteins.

Computational screening of antimicrobial peptides for Acinetobacter baumannii.

Acinetobacter baumannii, has been developing resistance to even the last line of drugs. Antimicrobial peptides (AMPs) to which bacteria do not develop resistance easily may be the last hope. A few independent experimental studies have designed and studied the activity of AMPs on A. baumannii, however the number of such studies are still limited. With the goal of developing a rational approach to the screening of AMPs against A. baumannii, we carefully curated the drug activity data from 75 cationic AMPs, all measured with a similar protocol, and on the same ATCC 19606 strain. A quantitative model developed and validated with a part of the data. While the model may be used for predicting the activity of any designed AMPs, in this work, we perform an in silico screening for the entire database of naturally occurring AMPs, to provide a rational guidance in this urgently needed drug development.