Direct Calculation of Electron Transfer Rates with the Binless Dynamic Histogram Analysis Method.

Umbrella sampling molecular dynamics simulations are widely used to enhance sampling along the reaction coordinates of chemical reactions. The effect of the artificial bias can be removed using methods such as the dynamic weighted histogram analysis method (DHAM), which in addition to the global free energy profile also provides kinetic information about barrier-crossing rates directly from the Markov matrix. Here we present a binless formulation of DHAM that extends DHAM to high-dimensional and Hamiltonian-based biasing to allow the study of electron transfer (ET) processes, for which enhanced sampling is usually not possible based on simple geometric grounds. We show the capabilities of binless DHAM on examples such as aqueous ferrous-ferric ET and intramolecular ET in the radical anion of benzoquinone-tetrathiafulvalene-benzoquinone (Q-TTF-Q)-. From classical Hamiltonian-based umbrella sampling simulations and electronic coupling values from quantum chemistry calculations, binless DHAM provides ET rates for adiabatic and nonadiabatic ET reactions alike in excellent agreement with experimental results.

Mechanism-Based Redesign of GAP to Activate Oncogenic Ras.

Ras GTPases play a crucial role in cell signaling pathways. Mutations of the Ras gene occur in about one third of cancerous cell lines and are often associated with detrimental clinical prognosis. Hot spot residues Gly12, Gly13, and Gln61 cover 97% of oncogenic mutations, which impair the enzymatic activity in Ras. Using QM/MM free energy calculations, we present a two-step mechanism for the GTP hydrolysis catalyzed by the wild-type Ras.GAP complex. We found that the deprotonation of the catalytic water takes place via the Gln61 as a transient Brønsted base. We also determined the reaction profiles for key oncogenic Ras mutants G12D and G12C using QM/MM minimizations, matching the experimentally observed loss of catalytic activity, thereby validating our reaction mechanism. Using the optimized reaction paths, we devised a fast and accurate procedure to design GAP mutants that activate G12D Ras. We replaced GAP residues near the active site and determined the activation barrier for 190 single mutants. We furthermore built a machine learning for ultrafast screening, by fast prediction of the barrier heights, tested both on the single and double mutations. This work demonstrates that fast and accurate screening can be accomplished via QM/MM reaction path optimizations to design protein sequences with increased catalytic activity. Several GAP mutations are predicted to re-enable catalysis in oncogenic G12D, offering a promising avenue to overcome aberrant Ras-driven signal transduction by activating enzymatic activity instead of inhibition. The outlined computational screening protocol is readily applicable for designing ligands and cofactors analogously.

Strong structuring arising from weak cooperative O-H···π and C-H···O hydrogen bonding in benzene-methanol solution.

Weak hydrogen bonds, such as O-H···π and C-H···O, are thought to direct biochemical assembly, molecular recognition, and chemical selectivity but are seldom observed in solution. We have used neutron diffraction combined with H/D isotopic substitution to obtain a detailed spatial and orientational picture of the structure of benzene-methanol mixtures. Our analysis reveals that methanol fully solvates and surrounds each benzene molecule. The expected O-H···π interaction is highly localised and directional, with the methanol hydroxyl bond aligned normal to the aromatic plane and the hydrogen at a distance of 2.30 Å from the ring centroid. Simultaneously, the tendency of methanol to form chain and cyclic motifs in the bulk liquid is manifest in a highly templated solvation structure in the plane of the ring. The methanol molecules surround the benzene so that the O-H bonds are coplanar with the aromatic ring while the oxygens interact with C-H groups through simultaneous bifurcated hydrogen bonds. This demonstrates that weak hydrogen bonding can modulate existing stronger interactions to give rise to highly ordered cooperative structural motifs that persist in the liquid phase.

SERS Sensing of Dopamine with Fe(III)-Sensitized Nanogaps in Recleanable AuNP Monolayer Films.

Sensing of neurotransmitters (NTs) down to nm concentrations is demonstrated by utilizing self-assembled monolayers of plasmonic 60 nm Au nanoparticles in close-packed arrays immobilized onto glass substrates. Multiplicative surface-enhanced Raman spectroscopy enhancements are achieved by integrating Fe(III) sensitizers into the precisely-defined <1 nm nanogaps, to target dopamine (DA) sensing. The transparent glass substrates allow for efficient access from both sides of the monolayer aggregate films by fluid and light, allowing repeated sensing in different analytes. Repeated reusability after analyte sensing is shown through oxygen plasma cleaning protocols, which restore pristine conditions for the nanogaps. Examining binding competition in multiplexed sensing of two catecholamine NTs, DA and epinephrine, reveals their bidentate binding and their interactions. These systems are promising for widespread microfluidic integration enabling a wide range of continuous biofluid monitoring for applications in precision health.

Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor.

Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights into this inhibitor remain unclear; specifically, the elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide, show a consistent qualitative mechanism and allow us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved in the transition state. Additionally, our results point to relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3's activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid toward drug design and lead optimization.

Variational kinetic clustering of complex networks.

Efficiently identifying the most important communities and key transition nodes in weighted and unweighted networks is a prevalent problem in a wide range of disciplines. Here, we focus on the optimal clustering using variational kinetic parameters, linked to Markov processes defined on the underlying networks, namely, the slowest relaxation time and the Kemeny constant. We derive novel relations in terms of mean first passage times for optimizing clustering via the Kemeny constant and show that the optimal clustering boundaries have equal round-trip times to the clusters they separate. We also propose an efficient method that first projects the network nodes onto a 1D reaction coordinate and subsequently performs a variational boundary search using a parallel tempering algorithm, where the variational kinetic parameters act as an energy function to be extremized. We find that maximization of the Kemeny constant is effective in detecting communities, while the slowest relaxation time allows for detection of transition nodes. We demonstrate the validity of our method on several test systems, including synthetic networks generated from the stochastic block model and real world networks (Santa Fe Institute collaboration network, a network of co-purchased political books, and a street network of multiple cities in Luxembourg). Our approach is compared with existing clustering algorithms based on modularity and the robust Perron cluster analysis, and the identified transition nodes are compared with different notions of node centrality.

Dynamic regulation of RAS and RAS signaling.

RAS proteins regulate most aspects of cellular physiology. They are mutated in 30% of human cancers and 4% of developmental disorders termed Rasopathies. They cycle between active GTP-bound and inactive GDP-bound states. When active, they can interact with a wide range of effectors that control fundamental biochemical and biological processes. Emerging evidence suggests that RAS proteins are not simple on/off switches but sophisticated information processing devices that compute cell fate decisions by integrating external and internal cues. A critical component of this compute function is the dynamic regulation of RAS activation and downstream signaling that allows RAS to produce a rich and nuanced spectrum of biological outputs. We discuss recent findings how the dynamics of RAS and its downstream signaling is regulated. Starting from the structural and biochemical properties of wild-type and mutant RAS proteins and their activation cycle, we examine higher molecular assemblies, effector interactions and downstream signaling outputs, all under the aspect of dynamic regulation. We also consider how computational and mathematical modeling approaches contribute to analyze and understand the pleiotropic functions of RAS in health and disease.

In-Situ Spectro-Electrochemistry of Conductive Polymers Using Plasmonics to Reveal Doping Mechanisms.

Conducting polymers are a key component for developing wearable organic electronics, but tracking their redox processes at the nanoscale to understand their doping mechanism remains challenging. Here we present an in-situ spectro-electrochemical technique to observe redox dynamics of conductive polymers in an extremely localized volume (<100 nm3). Plasmonic nanoparticles encapsulated by thin shells of different conductive polymers provide actively tuned scattering color through switching their refractive index. Surface-enhanced Raman scattering in combination with cyclic voltammetry enables detailed studies of the redox/doping process. Our data intriguingly show that the doping mechanism varies with polymer conductivity: a disproportionation mechanism dominates in more conductive polymers, while sequential electron transfer prevails in less conductive polymers.

Collective Mid-Infrared Vibrations in Surface-Enhanced Raman Scattering.

Surface-enhanced Raman scattering (SERS) is typically assumed to occur at individual molecules neglecting intermolecular vibrational coupling. Here, we show instead how collective vibrations from infrared (IR) coupled dipoles are seen in SERS from molecular monolayers. Mixing IR-active molecules with IR-inactive spacer molecules controls the intermolecular separation. Intermolecular coupling leads to vibrational frequency upshifts up to 8 cm-1, tuning with the mixing fraction and IR dipole strength, in excellent agreement with microscopic models and density functional theory. These cooperative frequency shifts can be used as a ruler to measure intermolecular distance and disorder with angstrom resolution. We demonstrate this for photochemical reactions of 4-nitrothiophenol, which depletes the number of neighboring IR-active molecules and breaks the collective vibration, enabling direct tracking of the reaction. Collective molecular vibrations reshape SERS spectra and need to be considered in the analysis of vibrational spectra throughout analytical chemistry and sensing.

Molecular Vibration Explorer: an Online Database and Toolbox for Surface-Enhanced Frequency Conversion and Infrared and Raman Spectroscopy.

We present Molecular Vibration Explorer, a freely accessible online database and interactive tool for exploring vibrational spectra and tensorial light-vibration coupling strengths of a large collection of thiolated molecules. The "Gold" version of the database gathers the results from density functional theory calculations on 2800 commercially available thiol compounds linked to a gold atom, with the main motivation to screen the best molecules for THz and mid-infrared to visible upconversion. Additionally, the "Thiol" version of the database contains results for 1900 unbound thiolated compounds. They both provide access to a comprehensive set of computed spectroscopic parameters for all vibrational modes of all molecules in the database. The user can simultaneously investigate infrared absorption, Raman scattering, and vibrational sum- and difference-frequency generation cross sections. Molecules can be screened for various parameters in custom frequency ranges, such as a large Raman cross-section under a specific molecular orientation, or a large orientation-averaged sum-frequency generation (SFG) efficiency. The user can select polarization vectors for the electromagnetic fields, set the orientation of the molecule, and customize parameters for plotting the corresponding IR, Raman, and sum-frequency spectra. We illustrate the capabilities of this tool with selected applications in the field of surface-enhanced spectroscopy.

Optical suppression of energy barriers in single molecule-metal binding.

Transient bonds between molecules and metal surfaces underpin catalysis, bio/molecular sensing, molecular electronics, and electrochemistry. Techniques aiming to characterize these bonds often yield conflicting conclusions, while single-molecule probes are scarce. A promising prospect confines light inside metal nanogaps to elicit in operando vibrational signatures through surface-enhanced Raman scattering. Here, we show through analysis of more than a million spectra that light irradiation of only a few microwatts on molecules at gold facets is sufficient to overcome the metallic bonds between individual gold atoms and pull them out to form coordination complexes. Depending on the molecule, these light-extracted adatoms persist for minutes under ambient conditions. Tracking their power-dependent formation and decay suggests that tightly trapped light transiently reduces energy barriers at the metal surface. This opens intriguing prospects for photocatalysis and controllable low-energy quantum devices such as single-atom optical switches.

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.

Detecting mid-infrared light by molecular frequency upconversion in dual-wavelength nanoantennas.

Coherent interconversion of signals between optical and mechanical domains is enabled by optomechanical interactions. Extreme light-matter coupling produced by confining light to nanoscale mode volumes can then access single mid-infrared (MIR) photon sensitivity. Here, we used the infrared absorption and Raman activity of molecular vibrations in plasmonic nanocavities to demonstrate frequency upconversion. We converted approximately 10-micrometer-wavelength incoming light to visible light by surface-enhanced Raman scattering (SERS) in doubly resonant antennas that enhanced upconversion by more than 1010. We showed 140% amplification of the SERS anti-Stokes emission when an MIR pump was tuned to a molecular vibrational frequency, obtaining lowest detectable powers of 1 to 10 microwatts per square micrometer at room temperature. These results have potential for low-cost and large-scale infrared detectors and spectroscopic techniques.

Modelling the active SARS-CoV-2 helicase complex as a basis for structure-based inhibitor design.

The RNA helicase (non-structural protein 13, NSP13) of SARS-CoV-2 is essential for viral replication, and it is highly conserved among the coronaviridae family, thus a prominent drug target to treat COVID-19. We present here structural models and dynamics of the helicase in complex with its native substrates based on thorough analysis of homologous sequences and existing experimental structures. We performed and analysed microseconds of molecular dynamics (MD) simulations, and our model provides valuable insights to the binding of the ATP and ssRNA at the atomic level. We identify the principal motions characterising the enzyme and highlight the effect of the natural substrates on this dynamics. Furthermore, allosteric binding sites are suggested by our pocket analysis. Our obtained structural and dynamical insights are important for subsequent studies of the catalytic function and for the development of specific inhibitors at our characterised binding pockets for this promising COVID-19 drug target.

Resolving sub-angstrom ambient motion through reconstruction from vibrational spectra.

Metal/organic-molecule interactions underpin many key chemistries but occur on sub-nm scales where nanoscale visualisation techniques tend to average over heterogeneous distributions. Single molecule imaging techniques at the atomic scale have found it challenging to track chemical behaviour under ambient conditions. Surface-enhanced Raman spectroscopy can optically monitor the vibrations of single molecules but understanding is limited by the complexity of spectra and mismatch between theory and experiment. We demonstrate that spectra from an optically generated metallic adatom near a molecule of interest can be inverted into dynamic sub-Å metal-molecule interactions using a comprehensive model, revealing anomalous diffusion of a single atom. Transient metal-organic coordination bonds chemically perturb molecular functional groups > 10 bonds away. With continuous improvements in computational methods for modelling large and complex molecular systems, this technique will become increasingly applicable to accurately tracking more complex chemistries.

Structural Dynamics and Catalytic Mechanism of ATP13A2 (PARK9) from Simulations.

ATP13A2 is a gene encoding a protein of the P5B subfamily of ATPases and is a PARK gene. Molecular defects of the gene are mainly associated with variations of Parkinson's disease (PD). Despite the established importance of the protein in regulating neuronal integrity, the three-dimensional structure of the protein currently remains unresolved crystallographically. We have modeled the structure and reactivity of the full-length protein in its E1-ATP state. Using molecular dynamics (MD), quantum cluster, and quantum mechanical/molecular mechanical (QM/MM) methods, we aimed at describing the main catalytic reaction, leading to the phosphorylation of Asp513. Our MD simulations suggest that two positively charged Mg2+ cations are present at the active site during the catalytic reaction, stabilizing a specific triphosphate binding mode. Using QM/MM calculations, we subsequently calculated the reaction profiles for the phosphoryl transfer step in the presence of one and two Mg2+ cations. The calculated barrier heights in both cases are found to be ∼12.5 and 7.5 kcal mol-1, respectively. We elucidated details of the catalytically competent ATP conformation and the binding mode of the second Mg2+ cofactor. We also examined the role of the conserved Arg686 and Lys859 catalytic residues. We observed that by significantly lowering the barrier height of the ATP cleavage reaction, Arg686 had major effect on the reaction. The removal of Arg686 increased the barrier height for the ATP cleavage by more than 5.0 kcal mol-1 while the removal of key electrostatic interactions created by Lys859 to the γ-phosphate and Asp513 destabilizes the reactant state. When missense mutations occur in close proximity to an active site residue, they can interfere with the barrier height of the reaction, which can halt the normal enzymatic rate of the protein. We also found large binding pockets in the full-length structure, including a transmembrane domain pocket, which is likely where the ATP13A2 cargo binds.

Nanoparticle surfactants for kinetically arrested photoactive assemblies to track light-induced electron transfer.

Nature controls the assembly of complex architectures through self-limiting processes; however, few artificial strategies to mimic these processes have been reported to date. Here we demonstrate a system comprising two types of nanocrystal (NC), where the self-limiting assembly of one NC component controls the aggregation of the other. Our strategy uses semiconducting InP/ZnS core-shell NCs (3 nm) as effective assembly modulators and functional nanoparticle surfactants in cucurbit[n]uril-triggered aggregation of AuNCs (5-60 nm), allowing the rapid formation (within seconds) of colloidally stable hybrid aggregates. The resultant assemblies efficiently harvest light within the semiconductor substructures, inducing out-of-equilibrium electron transfer processes, which can now be simultaneously monitored through the incorporated surface-enhanced Raman spectroscopy-active plasmonic compartments. Spatial confinement of electron mediators (for example, methyl viologen (MV2+)) within the hybrids enables the direct observation of photogenerated radical species as well as molecular recognition in real time, providing experimental evidence for the formation of elusive σ-(MV+)2 dimeric species. This approach paves the way for widespread use of analogous hybrids for the long-term real-time tracking of interfacial charge transfer processes, such as the light-driven generation of radicals and catalysis with operando spectroscopies under irreversible conditions.

Position-Dependent Diffusion from Biased Simulations and Markov State Model Analysis.

A variety of enhanced statistical and numerical methods are now routinely used to extract important thermodynamic and kinetic information from the vast amount of complex, high-dimensional data obtained from molecular simulations. For the characterization of kinetic properties, Markov state models, in which the long-time statistical dynamics of a system is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years. However, obtaining kinetic properties for molecular systems with high energy barriers remains challenging as often enhanced sampling techniques are required with biased simulations to observe the relevant rare events. Particularly, the calculation of diffusion coefficients remains elusive from biased molecular simulation data. Here, we propose a novel method that can calculate multidimensional position-dependent diffusion coefficients equally from either biased or unbiased simulations using the same formalism. Our method builds on Markov state model analysis and the Kramers-Moyal expansion. We demonstrate the validity of our formalism using one- and two-dimensional analytic potentials and also apply it to data from explicit solvent molecular dynamics simulations, including the water-mediated conformations of alanine dipeptide and umbrella sampling simulations of drug transport across a lipid bilayer. Importantly, the developed algorithm presents significant improvement compared to standard methods when the transport of solute across three-dimensional heterogeneous porous media is studied, for example, the prediction of membrane permeation of drug molecules.

Joint neutron/X-ray crystal structure of a mechanistically relevant complex of perdeuterated urate oxidase and simulations provide insight into the hydration step of catalysis.

Cofactor-independent urate oxidase (UOX) is an ∼137 kDa tetrameric enzyme essential for uric acid (UA) catabolism in many organisms. UA is first oxidized by O2 to de-hydro-isourate (DHU) via a peroxo intermediate. DHU then undergoes hydration to 5-hy-droxy-isourate (5HIU). At different stages of the reaction both catalytic O2 and water occupy the 'peroxo hole' above the organic substrate. Here, high-resolution neutron/X-ray crystallographic analysis at room temperature has been integrated with molecular dynamics simulations to investigate the hydration step of the reaction. The joint neutron/X-ray structure of perdeuterated Aspergillus flavus UOX in complex with its 8-azaxanthine (8AZA) inhibitor shows that the catalytic water molecule (W1) is present in the peroxo hole as neutral H2O, oriented at 45° with respect to the ligand. It is stabilized by Thr57 and Asn254 on different UOX protomers as well as by an O-H⋯π interaction with 8AZA. The active site Lys10-Thr57 dyad features a charged Lys10-NH3 + side chain engaged in a strong hydrogen bond with Thr57OG1, while the Thr57OG1-HG1 bond is rotationally dynamic and oriented toward the π system of the ligand, on average. Our analysis offers support for a mechanism in which W1 performs a nucleophilic attack on DHUC5 with Thr57HG1 central to a Lys10-assisted proton-relay system. Room-temperature crystallography and simulations also reveal conformational heterogeneity for Asn254 that modulates W1 stability in the peroxo hole. This is proposed to be an active mechanism to facilitate W1/O2 exchange during catalysis.