Localized Soft Vibrational Modes and Coherent Structural Phase Transformations in Rutile TiO2 Nanoparticles under Negative Pressure.

We study the effect of size on the vibrational modes and frequencies of nanoparticles, by applying a newly developed, robust, and efficient first-principles-based method that we present in outline. We focus on rutile TiO2, a technologically important material whose bulk exhibits a softening of a transverse acoustic mode close to q=(12,12,14), which becomes unstable with the application of negative pressure. We demonstrate that, under these conditions, nanoparticles above a critical size exhibit unstable localized modes and we calculate their characteristic localization length and decomposition with respect to bulk phonons. We propose that such localized soft modes could initiate coherent structural phase transformations in small nanoparticles above a critical size.

Investigating the quasi-liquid layer on ice surfaces: a comparison of order parameters.

Ice surfaces are characterized by pre-melted quasi-liquid layers (QLLs), which mediate both crystal growth processes and interactions with external agents. Understanding QLLs at the molecular level is necessary to unravel the mechanisms of ice crystal formation. Computational studies of the QLLs heavily rely on the accuracy of the methods employed for identifying the local molecular environment and arrangements, discriminating between solid-like and liquid-like water molecules. Here we compare the results obtained using different order parameters to characterize the QLLs on hexagonal ice (Ih) and cubic ice (Ic) model surfaces investigated with molecular dynamics (MD) simulations in a range of temperatures. For the classification task, in addition to the traditional Steinhardt order parameters in different flavours, we select an entropy fingerprint and a deep learning neural network approach (DeepIce), which are conceptually different methodologies. We find that all the analysis methods give qualitatively similar trends for the behaviours of the QLLs on ice surfaces with temperature, with some subtle differences in the classification sensitivity limited to the solid-liquid interface. The thickness of QLLs on the ice surface increases gradually as the temperature increases. The trends of the QLL size and of the values of the order parameters as a function of temperature for the different facets may be linked to surface growth rates which, in turn, affect crystal morphologies at lower vapour pressure. The choice of the order parameter can be therefore informed by computational convenience except in cases where a very accurate determination of the liquid-solid interface is important.

Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.

The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive and time consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free-energy profiles of the ligand unbinding process, focusing on the free-energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (ICs) that form contacts between the protein and the ligand; it then iteratively updates these interactions during a series of biased molecular dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite-temperature string simulations to obtain the free-energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable the further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased "downhill" trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is cyclin-dependent kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for the potential further development of these compounds. We compared the free-energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.

Extraction and high-throughput sequencing of oak heartwood DNA: Assessing the feasibility of genome-wide DNA methylation profiling.

Tree ring features are affected by environmental factors and therefore are the basis for dendrochronological studies to reconstruct past environmental conditions. Oak wood often provides the data for these studies because of the durability of oak heartwood and hence the availability of samples spanning long time periods of the distant past. Wood formation is regulated in part by epigenetic mechanisms such as DNA methylation. Studies of the methylation state of DNA preserved in oak heartwood thus could identify epigenetic tree ring features informing on past environmental conditions. In this study, we aimed to establish protocols for the extraction of DNA, the high-throughput sequencing of whole-genome DNA libraries (WGS) and the profiling of DNA methylation by whole-genome bisulfite sequencing (WGBS) for oak (Quercus robur) heartwood drill cores taken from the trunks of living standing trees spanning the AD 1776-2014 time period. Heartwood contains little DNA, and large amounts of phenolic compounds known to hinder the preparation of high-throughput sequencing libraries. Whole-genome and DNA methylome library preparation and sequencing consistently failed for oak heartwood samples more than 100 and 50 years of age, respectively. DNA fragmentation increased with sample age and was exacerbated by the additional bisulfite treatment step during methylome library preparation. Relative coverage of the non-repetitive portion of the oak genome was sparse. These results suggest that quantitative methylome studies of oak hardwood will likely be limited to relatively recent samples and will require a high sequencing depth to achieve sufficient genome coverage.

Photopharmacology of Ion Channels through the Light of the Computational Microscope.

The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach.

Cyclopropyl Substituents Transform the Viscosity-Sensitive BODIPY Molecular Rotor into a Temperature Sensor.

A quantitative fluorescent probe that responds to changes in temperature is highly desirable for studies of biological environments, particularly in cellulo. Here, we report new cell-permeable fluorescence probes based on the BODIPY moiety that respond to environmental temperature. The new probes were developed on the basis of a well-established BODIPY-based viscosity probe by functionalization with cyclopropyl substituents at α and ß positions of the BODIPY core. In contrast to the parent BODIPY fluorophore, α-cyclopropyl-substituted fluorophore displays temperature-dependent time-resolved fluorescence decays showing greatly diminished viscosity dependence, making it an attractive sensor to be used with fluorescence lifetime imaging microscopy (FLIM). We performed theoretical calculations that help rationalize the effect of the cyclopropyl substituents on the photophysical behavior of the new BODIPYs. In summary, we designed an attractive new quantitative FLIM-based temperature probe that can be used for temperature sensing in live cells.

A Single Mutation in the Outer Lipid-Facing Helix of a Pentameric Ligand-Gated Ion Channel Affects Channel Function Through a Radially-Propagating Mechanism.

Pentameric ligand-gated ion channels (pLGICs) mediate fast synaptic transmission and are crucial drug targets. Their gating mechanism is triggered by ligand binding in the extracellular domain that culminates in the opening of a hydrophobic gate in the transmembrane domain. This domain is made of four α-helices (M1 to M4). Recently the outer lipid-facing helix (M4) has been shown to be key to receptor function, however its role in channel opening is still poorly understood. It could act through its neighboring helices (M1/M3), or via the M4 tip interacting with the pivotal Cys-loop in the extracellular domain. Mutation of a single M4 tyrosine (Y441) to alanine renders one pLGIC-the 5-HT3A receptor-unable to function despite robust ligand binding. Using Y441A as a proxy for M4 function, we here predict likely paths of Y441 action using molecular dynamics, and test these predictions with functional assays of mutant receptors in HEK cells and Xenopus oocytes using fluorescent membrane potential sensitive dye and two-electrode voltage clamp respectively. We show that Y441 does not act via the M4 tip or Cys-loop, but instead connects radially through M1 to a residue near the ion channel hydrophobic gate on the pore-lining helix M2. This demonstrates the active role of the M4 helix in channel opening.

Cholesterol content in the membrane promotes key lipid-protein interactions in a pentameric serotonin-gated ion channel.

Pentameric ligand-gated ion channels (pLGICs), embedded in the lipid membranes of nerve cells, mediate fast synaptic transmission and are major pharmaceutical targets. Because of their complexity and the limited knowledge of their structure, their working mechanisms have still to be fully unraveled at the molecular level. Over the past few years, evidence that the lipid membrane may modulate the function of membrane proteins, including pLGICs, has emerged. Here, we investigate, by means of molecular dynamics simulations, the behavior of the lipid membrane at the interface with the 5-HT3A receptor (5-HT3AR), a representative pLGIC which is the target of nausea-suppressant drugs, in a nonconductive state. Three lipid compositions are studied, spanning different concentrations of the phospholipids, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine, and of cholesterol, hence a range of viscosities. A variety of lipid interactions and persistent binding events to different parts of the receptor are revealed in the investigated models, providing snapshots of the dynamical environment at the membrane-receptor interface. Some of these events result in lipid intercalation within the transmembrane domain, and others reach out to protein key sections for signal transmission and receptor activation, such as the Cys-loop and the M2-M3 loop. In particular, phospholipids, with their long hydrophobic tails, play an important role in these interactions, potentially providing a bridge between these two structures. A higher cholesterol content appears to promote lipid persistent binding to the receptor.

Proline isomerization effects in the amyloidogenic protein ß2-microglobulin.

The protein ß2-microglobulin (ß2-m) can aggregate in insoluble amyloid fibrils, which deposit in the skeletal muscle system of patients undergoing long-term haemodialysis. The molecular mechanisms of such amyloidogenesis are still not fully understood. A potential, although debated, triggering factor is the cis to trans isomerization of a specific proline (Pro32) in ß2-m. Here we investigate this process in the native protein and in the aggregation-prone mutant D76N by means of molecular dynamics and the enhanced sampling method metadynamics. Our simulations, including the estimation of the free energy difference between the cis and trans isomers, are in good agreement with in vitro experiments and highlight the importance of the hydrogen bond and hydrophobic interaction network around the critical Pro32 in stabilizing and de-stabilizing the two isomers.

Time-Resolved Fluorescence Anisotropy and Molecular Dynamics Analysis of a Novel GFP Homo-FRET Dimer.

Förster resonance energy transfer (FRET) is a powerful tool to investigate the interaction between proteins in living cells. Fluorescence proteins, such as the green fluorescent protein (GFP) and its derivatives, are coexpressed in cells linked to proteins of interest. Time-resolved fluorescence anisotropy is a popular tool to study homo-FRET of fluorescent proteins as an indicator of dimerization, in which its signature consists of a very short component at the beginning of the anisotropy decay. In this work, we present an approach to study GFP homo-FRET via a combination of time-resolved fluorescence anisotropy, the stretched exponential decay model, and molecular dynamics simulations. We characterize a new, to our knowledge, FRET standard formed by two enhanced GFPs (eGFPs) and a flexible linker of 15 aminoacids (eGFP15eGFP) with this protocol, which is validated by using an eGFP monomer as a reference. An excellent agreement is found between the FRET efficiency calculated from the fit of the eGFP15eGFP fluorescence anisotropy decays with a stretched exponential decay model (〈EFRETexp〉 = 0.25 ± 0.05) and those calculated from the molecular dynamics simulations (〈EFRETMD〉 = 0.18 ± 0.14). The relative dipole orientation between the GFPs is best described by the orientation factors 〈κ2〉 = 0.17 ± 0.16 and 〈|κ|〉 = 0.35 ± 0.20, contextualized within a static framework in which the linker hinders the free rotation of the fluorophores and excludes certain configurations. The combination of time- and polarization-resolved fluorescence spectroscopy with molecular dynamics simulations is shown to be a powerful tool for the study and interpretation of homo-FRET.

Time-Resolved Fluorescence Anisotropy of a Molecular Rotor Resolves Microscopic Viscosity Parameters in Complex Environments.

Understanding viscosity in complex environments remains a largely unanswered question despite its importance in determining reaction rates in vivo. Here, time-resolved fluorescence anisotropy imaging (TR-FAIM) is combined with fluorescent molecular rotors (FMRs) to simultaneously determine two non-equivalent viscosity-related parameters in complex heterogeneous environments. The parameters, FMR rotational correlation time and lifetime, are extracted from fluorescence anisotropy decays, which in heterogeneous environments show dip-and-rise behavior due to multiple dye populations. Decays of this kind are found both in artificially constructed adiposomes and in live cell lipid droplet organelles. Molecular dynamics simulations are used to assign each population to nano-environments within the lipid systems. The less viscous population corresponds to the state showing an average 25° tilt to the lipid membrane normal, and the more viscous population to the state showing an average 55° tilt. This combined experimental and simulation approach enables a comprehensive description of the FMR probe behavior within viscous nano-environments in complex, biological systems.

Mutagenesis computer experiments in pentameric ligand-gated ion channels: the role of simulation tools with different resolution.

Pentameric ligand-gated ion channels (pLGICs) are an important class of widely expressed membrane neuroreceptors, which play a crucial role in fast synaptic communications and are involved in several neurological conditions. They are activated by the binding of neurotransmitters, which trigger the transmission of an electrical signal via facilitated ion flux. They can also be activated, inhibited or modulated by a number of drugs. Mutagenesis electrophysiology experiments, with natural or unnatural amino acids, have provided a large body of functional data that, together with emerging structural information from X-ray spectroscopy and cryo-electron microscopy, are helping unravel the complex working mechanisms of these neuroreceptors. Computer simulations are complementing these mutagenesis experiments, with insights at various levels of accuracy and resolution. Here, we review how a selection of computational tools, including first principles methods, classical molecular dynamics and enhanced sampling techniques, are contributing to construct a picture of how pLGICs function and can be pharmacologically targeted to treat the disorders they are responsible for.

DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules.

Computer simulation studies of multiphase systems rely on the accurate identification of local molecular structures and arrangements in order to extract useful insights. Local order parameters, such as Steinhardt parameters, are widely used for this identification task; however, the parameters are often tailored to specific local structural geometries and generalize poorly to new structures and distorted or undercoordinated bonding environments. Motivated by the desire to simplify the process and improve the accuracy, we introduce DeepIce, a novel deep neural network designed to identify ice and water molecules, which can be generalized to new structures where multiple bonding environments are present. DeepIce demonstrates that the characteristics of a crystalline or liquid molecule can be classified using as input simply the Cartesian coordinates of the nearest neighbors without compromising the accuracy. The network is flexible and capable of inferring rotational invariance and produces a high predictive accuracy compared to the Steinhardt approach, the tetrahedral order parameter and polyhedral template matching in the detection of the phase of molecules in premelted ice surfaces.

Trans- Cis Proline Switches in a Pentameric Ligand-Gated Ion Channel: How They Are Affected by and How They Affect the Biomolecular Environment.

Pentameric ligand-gated ion channels (pLGICs) are important neuroreceptors, embedded in neuronal membranes, that mediate fast synaptic transmission. The molecular details of their working mechanisms have still to be fully unravelled due to their complexity and limited structural information available. Here we focus on a potential molecular switch in a prototypical pLGIC, the serotonin-activated 5-HT3 receptor, consisting of the trans- cis isomerization of a proline at the interface between the extracellular and transmembrane domain. Mutagenesis electrophysiology experiments previously showed that if such isomerization could not take place, the channel would not open, but the hypothetical role of this mechanism as key to channel gating is still debated. We investigate this switch within the receptor with molecular dynamics and enhanced sampling simulations. We analyze how the isomerization free energy landscape is affected by the receptor environment in comparison to simplified models. Moreover, we reveal how the isomerization, in turn, affects the structural and electrostatic properties of the receptor at the extracellular-transmembrane domain interface, e.g., by tuning the ion selectivity filter.

Calculating Kinetic Rates and Membrane Permeability from Biased Simulations.

We present a simple approach to calculate the kinetic properties of lipid membrane crossing processes from biased molecular dynamics simulations. We demonstrate that by using biased simulations, one can obtain highly accurate kinetic information with significantly reduced computational time with respect to unbiased simulations. We describe how to conveniently calculate the transition rates to enter, cross, and exit the membrane in terms of the mean first passage times. To obtain free energy barriers and relaxation times from biased simulations only, we constructed Markov models using the dynamic histogram analysis method (DHAM). The permeability coefficients that are calculated from the relaxation times are found to correlate highly with experimentally evaluated values. We show that more generally, certain calculated kinetic properties linked to the crossing of the membrane layer (e.g., barrier height and barrier crossing rates) are good indicators of ordering drugs by permeability. Extending the analysis to a 2D Markov model provides a physical description of the membrane crossing mechanism.

Mapping the conformational free energy of aspartic acid in the gas phase and in aqueous solution.

The conformational free energy landscape of aspartic acid, a proteogenic amino acid involved in a wide variety of biological functions, was investigated as an example of the complexity that multiple rotatable bonds produce even in relatively simple molecules. To efficiently explore such a landscape, this molecule was studied in the neutral and zwitterionic forms, in the gas phase and in water solution, by means of molecular dynamics and the enhanced sampling method metadynamics with classical force-fields. Multi-dimensional free energy landscapes were reduced to bi-dimensional maps through the non-linear dimensionality reduction algorithm sketch-map to identify the energetically stable conformers and their interconnection paths. Quantum chemical calculations were then performed on the minimum free energy structures. Our procedure returned the low energy conformations observed experimentally in the gas phase with rotational spectroscopy [M. E. Sanz et al., Phys. Chem. Chem. Phys. 12, 3573 (2010)]. Moreover, it provided information on higher energy conformers not accessible to experiments and on the conformers in water. The comparison between different force-fields and quantum chemical data highlighted the importance of the underlying potential energy surface to accurately capture energy rankings. The combination of force-field based metadynamics, sketch-map analysis, and quantum chemical calculations was able to produce an exhaustive conformational exploration in a range of significant free energies that complements the experimental data. Similar protocols can be applied to larger peptides with complex conformational landscapes and would greatly benefit from the next generation of accurate force-fields.

Unravelling the Roles of Size, Ligands, and Pressure in the Piezochromic Properties of CdS Nanocrystals.

Understanding the effects of pressure-induced deformations on the optoelectronic properties of nanomaterials is important not only from the fundamental point of view but also for potential applications such as stress sensors and electromechanical devices. Here, we describe the novel insights into these piezochromic effects gained from using a linear-scaling density functional theory framework and an electronic enthalpy scheme, which allow us to accurately characterize the electronic structure of CdS nanocrystals with a zincblende-like core of experimentally relevant size. In particular, we focus on unravelling the complex interplay of size and surface (phenyl) ligands with pressure. We show that pressure-induced deformations are not simple isotropic scaling of the original structures and that the change in HOMO-LUMO gap with pressure results from two competing factors: (i) a bulk-like linear increase due to compression, which is offset by (ii) distortions and disorder and, to a lesser extent, orbital hybridization induced by ligands affecting the frontier orbitals. Moreover, we observe that the main peak in the optical absorption spectra is systematically red-shifted or blue-shifted, as pressure is increased up to 5 GPa, depending on the presence or absence of phenyl ligands. These heavily hybridize the frontier orbitals, causing a reduction in overlap and oscillator strength, so that at zero pressure, the lowest energy transition involves deeper hole orbitals than in the case of hydrogen-capped nanocrystals; the application of pressure induces greater delocalization over the whole nanocrystals bringing the frontier hole orbitals into play and resulting in an unexpected red shift for the phenyl-capped nanocrystals, in part caused by distortions. In response to a growing interest in relatively small nanocrystals that can be difficult to accurately characterize with experimental techniques, this work exemplifies the detailed understanding of structure-property relationships under pressure that can be obtained for realistic nanocrystals with state-of-the-art first-principles methods and used for the characterization and design of devices based on these and similar nanomaterials.

The Free Energy Landscape of GABA Binding to a Pentameric Ligand-Gated Ion Channel and Its Disruption by Mutations.

Pentameric ligand-gated ion channels (pLGICs) of the Cys-loop superfamily are important neuroreceptors that mediate fast synaptic transmission. They are activated by the binding of a neurotransmitter, but the details of this process are still not fully understood. As a prototypical pLGIC, here we choose the insect resistance to dieldrin (RDL) receptor involved in resistance to insecticides and investigate the binding of the neurotransmitter GABA to its extracellular domain at the atomistic level. We achieve this by means of µ-sec funnel-metadynamics simulations, which efficiently enhance the sampling of bound and unbound states by using a funnel-shaped restraining potential to limit the exploration in the solvent. We reveal the sequence of events in the binding process from the capture of GABA from the solvent to its pinning between the charged residues Arg111 and Glu204 in the binding pocket. We characterize the associated free energy landscapes in the wild-type RDL receptor and in two mutant forms, where the key residues Arg111 and Glu204 are mutated to Ala. Experimentally these mutations produce nonfunctional channels, which is reflected in the reduced ligand binding affinities due to the loss of essential interactions. We also analyze the dynamical behavior of the crucial loop C, whose opening allows the access of GABA to the binding site and closure locks the ligand into the protein. The RDL receptor shares structural and functional features with other pLGICs; hence, our work outlines a valuable protocol to study the binding of ligands to pLGICs beyond conventional docking and molecular dynamics techniques.

Pressure-Induced Amorphization and a New High Density Amorphous Metallic Phase in Matrix-Free Ge Nanoparticles.

Over the last two decades, it has been demonstrated that size effects have significant consequences for the atomic arrangements and phase behavior of matter under extreme pressure. Furthermore, it has been shown that an understanding of how size affects critical pressure-temperature conditions provides vital guidance in the search for materials with novel properties. Here, we report on the remarkable behavior of small (under ~5 nm) matrix-free Ge nanoparticles under hydrostatic compression that is drastically different from both larger nanoparticles and bulk Ge. We discover that the application of pressure drives surface-induced amorphization leading to Ge-Ge bond overcompression and eventually to a polyamorphic semiconductor-to-metal transformation. A combination of spectroscopic techniques together with ab initio simulations were employed to reveal the details of the transformation mechanism into a new high density phase-amorphous metallic Ge.

Structural Properties of Green Tea Catechins.

Green tea catechins are polyphenols which are believed to provide health benefits; they are marketed as health supplements and are studied for their potential effects on a variety of medical conditions. However, their mechanisms of action and interaction with the environment at the molecular level are still not well-understood. Here, by means of atomistic simulations, we explore the structural properties of four green tea catechins, in the gas phase and water solution: specifically, (-)-epigallocatechin-3-gallate, which is the most abundant, (-)-epicatechin-3-gallate, (-)-epigallocatechin-3-O-(3-O-methyl)-gallate, and (-)-epigallocatechin. We characterize the free energy conformational landscapes of these catechins at ambient conditions, as a function of the torsional degrees of freedom of the pholyphenolic rings, determining the stable conformers and their connections. We show that these free energy landscapes are only subtly influenced by the interactions with the solvent and by the structural details of the polyphenolic rings. However, the number and position of the hydroxyl groups (or their sustituents) and the presence/absence of the galloyl moiety have significant impact on the selected catechin solvation shells and hydrogen bond capabilities, which are ultimately linked to their ability to interact with and affect the biological environment.