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S-nitrosylation, the covalent addition of NO to the thiol side chain of cysteine, is an important post-transitional modification (PTM) that can affect the function of proteins. As such, PTMs extend and diversify protein functions and thus characterizing consequences of PTM at a molecular level is of great interest. Although PTMs can be detected through various direct/indirect methods, they lack the capabilities to investigate the modifications at the molecular level. In the present work local and global structural dynamics, their correlation, the hydration structure, and the infrared spectroscopy for WT and S-nitrosylated Kirsten rat sarcoma virus (KRAS) and Hemoglobin (Hb) are characterized from molecular dynamics simulations. It is found that for KRAS attaching NO to Cys118 rigidifies the protein in the Switch-I region which has functional implications, whereas for Hb nitrosylation at Cys93 at the $\beta_1$ chain increases the flexibility of secondary structural motives for Hb in its T$_{0}$ and R$_{4}$ conformational substates. Solvent water access decreased by 40\% after nitrosylation in KRAS, similar to Hb for which, however, local hydration of the R$_4$NO state is yet lower than for T$_0$NO. Finally, S-nitrosylation leads to detectable peaks for NO stretch, however, congested IR region will make experimental detection of these bands difficult.

The reaction N(4S) + NO -> O(3P) + N2 plays a pivotal role in the conversion of atomic to molecular nitrogen in dense interstellar clouds and in the atmosphere. Here we report a joint experimental and computational investigation of the N + NO reaction with the aim of providing improved constraints on its low temperature reactivity. Thermal rates were measured over the 50 to 296 K range in a continuous supersonic flow reactor coupled with pulsed laser photolysis and laser induced fluorescence for the production and detection of N(4S) atoms, respectively. With decreasing temperature, the experimentally measured reaction rate was found to monotonously increase up to a value of (6.6 +- 1.3) x 10-11 cm3 s-1 at 50 K. To confirm this finding, quasi-classical trajectory simulations were carried out on a previously validated, full-dimensional potential energy surface (PES). However, around 50 K the computed rates decreased which required re-evaluation of the reactive PES in the long-range part due to a small spurious barrier with height 40 K in the entrance channel. By exploring different correction schemes the measured thermal rates can be adequately reproduced, displaying a clear negative temperature dependence over the entire temperature range. The possible astrochemical implications of an increased reaction rate at low temperature are also discussed., 28 pages, 6 figures and 2 tables in the main article. 3 figures and 1 table in the supplementary information. Accepted for publication in PCCP

Vibrational spectroscopy in supersonic jet expansions is a powerful tool to assess molecular aggregates in close to ideal conditions for the benchmarking of quantum chemical approaches. The low temperatures achieved as well as the absence of environment effects allow for a direct comparison between computed and experimental spectra. This provides potential benchmarking data which can be revisited to hone different computational techniques, and it allows for the critical analysis of procedures under the setting of a blind challenge. In the latter case, the final result is unknown to modellers, providing an unbiased testing opportunity for quantum chemical models. In this work, we present the spectroscopic and computational results for the first HyDRA blind challenge. The latter deals with the prediction of water donor stretching vibrations in monohydrates of organic molecules. This edition features a test set of 10 systems. Experimental water donor OH vibrational wavenumbers for the vacuum-isolated monohydrates of formaldehyde, tetrahydrofuran, pyridine, tetrahydrothiophene, trifluoroethanol, methyl lactate, dimethylimidazolidinone, cyclooctanone, trifluoroacetophenone and 1-phenylcyclohexane-cis-1,2-diol are provided. The results of the challenge show promising predictive properties in both purely quantum mechanical approaches as well as regression and other machine learning strategies.

Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas and in the condensed phase. This Perspective delineates the present status of the field from the efforts of others and some of our own work and discusses open questions and future prospects. The combination of physics-based long-range representations using multipolar charge distributions and kernel representations for the bonded interactions is shown to provide realistic models for the exploration of the infrared spectroscopy of molecules in solution. For reactions, empirical models connecting dedicated energy functions for the reactant and product states allow statistically meaningful sampling of conformational space whereas machine-learned energy functions are superior in accuracy. The future combination of physics-based models with machine-learning techniques and integration into all-purpose molecular simulation software provides a unique opportunity to bring such dynamics simulations closer to reality.

Feshbach resonances are fundamental to interparticle interactions and become particularly important in cold collisions with atoms, ions, and molecules. In this work, we present the detection of Feshbach resonances in a benchmark system for strongly interacting and highly anisotropic collisions: molecular hydrogen ions colliding with noble gas atoms. The collisions are launched by cold Penning ionization, which exclusively populates Feshbach resonances that span both short- and long-range parts of the interaction potential. We resolved all final molecular channels in a tomographic manner using ion-electron coincidence detection. We demonstrate the nonstatistical nature of the final-state distribution. By performing quantum scattering calculations on ab initio potential energy surfaces, we show that the isolation of the Feshbach resonance pathways reveals their distinctive fingerprints in the collision outcome.

Physical chemistry, chemical physics 25(20), 13933 - 13945 (2023). doi:10.1039/D3CP01416A, Recent advances in experimental methodology enabled studies of the quantum-state- and conforma- tional dependence of chemical reactions under precisely controlled conditions in the gas phase. Here, we generated samples of selected gauche and s-trans 2,3-dibromobutadiene (DBB) by electrostatic deflection in a molecular beam and studied their reaction with Coulomb crystals of laser-cooled Ca$^+$ ions in an ion trap. The rate coefficients for the total reaction were found to strongly depend on both the conformation of DBB and the electronic state of Ca$^+$. In the (4p) $^2P_{1/2}$ and (3d) $^2D_{3/2}$ excited states of Ca$^+$, the reaction is capture-limited and faster for the gauche conformer due to long-range ion-dipole interactions. In the (4s) $^2S_{1/2}$ ground state of Ca$^+$, the reaction rate for s-trans DBB still conforms with the capture limit, while that for gauche DBB is strongly suppressed. The exper- imental observations were analysed with the help of adiabatic capture theory, ab-initio calculations, and reactive molecular dynamics simulations on a machine-learned full-dimensional potential en- ergy surface of the system. The theory yields near-quantitative agreement for s-trans-DBB, but overestimates the reactivity of the gauche- conformer compared to the experiment. The present study points to the important role of molecular geometry even in strongly reactive exothermic systems and illustrates striking differences in the reactivity of individual conformers in gas-phase ion-molecule reactions., Published by RSC Publ., Cambridge

The dynamics and spectroscopy of N-methyl-acetamide (NMA) and trialanine in solution are characterized from molecular dynamics simulations using different energy functions, including a conventional point charge (PC)-based force field, one based on a multipolar (MTP) representation of the electrostatics, and a semiempirical DFT method. For the 1D infrared spectra, the frequency splitting between the two amide-I groups is 10 cm–1 from the PC, 13 cm–1 from the MTP, and 47 cm–1 from self-consistent charge density functional tight-binding (SCC-DFTB) simulations, compared with 25 cm–1 from experiment. The frequency trajectory required for the frequency fluctuation correlation function (FFCF) is determined from individual normal mode (INM) and full normal mode (FNM) analyses of the amide-I vibrations. The spectroscopy, time-zero magnitude of the FFCF C(t = 0), and the static component Δ02 from simulations using MTP and analysis based on FNM are all consistent with experiments for (Ala)3. Contrary to this, for the analysis excluding mode–mode coupling (INM), the FFCF decays to zero too rapidly and for simulations with a PC-based force field, the Δ02 is too small by a factor of two compared with experiments. Simulations with SCC-DFTB agree better with experiment for these observables than those from PC-based simulations. The conformational ensemble sampled from simulations using PCs is consistent with the literature (including PII, β, αR, and αL), whereas that covered by the MTP-based simulations is dominated by PII with some contributions from β and αR. This agrees with and confirms recently reported Bayesian-refined populations based on 1D infrared experiments. FNM analysis together with a MTP representation provides a meaningful model to correctly describe the dynamics of hydrated trialanine.

The dynamics of the C(

The prediction of product translational, vibrational, and rotational energy distributions for arbitrary initial conditions for reactive atom+diatom collisions is of considerable practical interest in atmospheric re-entry. Due to the large number of accessible states, determination of the necessary information from explicit (quasi-classical or quantum) dynamics studies is impractical. Here, a machine-learned (ML) model based on translational energy and product vibrational states assigned from a spectroscopic, ro-vibrational coupled energy expression based on the Dunham expansion is developed and tested quantitatively. All models considered in this work reproduce final state distributions determined from quasi-classical trajectory (QCT) simulations with $R^2 \sim 0.98$. As a further validation, thermal rates determined from the machine-learned models agree with those from explicit QCT simulations and demonstrate that the atomistic details are retained by the machine learning which makes them suitable for applications in more coarse-grained simulations. More generally, it is found that ML is suitable for designing robust and accurate models from mixed computational/experimental data which may also be of interest in other areas of the physical sciences.

An essential aspect for adequate predictions of chemical properties by machine learning models is the database used for training them. However, studies that analyze how the content and structure of the databases used for training impact the prediction quality are scarce. In this work, we analyze and quantify the relationships learned by a machine learning model (Neural Network) trained on five different reference databases (QM9, PC9, ANI-1E, ANI-1, and ANI-1x) to predict tautomerization energies from molecules in Tautobase. For this, characteristics such as the number of heavy atoms in a molecule, number of atoms of a given element, bond composition, or initial geometry on the quality of the predictions are considered. The results indicate that training on a chemically diverse database is crucial for obtaining good results and also that conformational sampling can partly compensate for limited coverage of chemical diversity. The overall best-performing reference database (ANI-1x) performs on average by 1 kcal/mol better than PC9, which, however, contains about 2 orders of magnitude fewer reference structures. On the other hand, PC9 is chemically more diverse by a factor of ∼5 as quantified by the number of atom-in-molecule-based fragments (amons) it contains compared with the ANI family of databases. A quantitative measure for deficiencies is the Kullback-Leibler divergence between reference and target distributions. It is explicitly demonstrated that when certain types of bonds need to be covered in the target database (Tautobase) but are undersampled in the reference databases, the resulting predictions are poor. Examples of this include the poor performance of all databases analyzed to predict C(sp2)-C(sp2) double bonds close to heteroatoms and azoles containing N-N and N-O bonds. Analysis of the results with a Tree MAP algorithm provides deeper understanding of specific deficiencies in predicting tautomerization energies by the reference datasets due to inadequate coverage of chemical space. Capitalizing on this information can be used to either improve existing databases or generate new databases of sufficient diversity for a range of machine learning (ML) applications in chemistry.

Understanding the formation of molecules under conditions relevant to interstellar chemistry is fundamental to characterize the chemical evolution of the universe. Using reactive molecular dynamics simulations with model-based or high-quality potential energy surfaces provides a means to specifically and quantitatively probe individual reaction channels at a molecular level. The formation of CO2 from collision of CO(1Σ) and O(1D) is characterized on amorphous solid water (ASW) under conditions typical in cold molecular clouds. Recombination takes place on the subnanosecond time scale and internal energy redistribution leads to stabilization of the product with CO2 remaining adsorbed on the ASW on extended time scales. Using a high-level, reproducing kernel-based potential energy surface for CO2, formation into and stabilization of CO2 and COO are observed.

The utility of halogenation in protein design is investigated by a combination of quantitative atomistic simulations and experiment. The approach is applied to insulin, a small, therapeutically relevant domain amenable to simulation and semi-synthesis. In a singly halogenated aromatic ring, the simulations predicted regiospecific inductive effects to modulate multiple surrounding electrostatic (weakly polar) interactions, thereby amplifying changes in thermodynamic stability. In accordance with the simulations, stabilization of insulin is demonstrated by single halogen atoms at the ortho position of an invariant phenylalanine (2-F-PheB24, 2-Cl-PheB24 and 2-Br-PheB24; ΔΔGu= -0.5 to -1.0 kcal/mol) located at the edge of a protein crevice. Corresponding meta and para substitutions have negligible effects. The ortho-modified insulin analogs exhibit enhanced resistance to fibrillation above room temperature and retain biological activity in mammalian cells and in a rat model of diabetes mellitus. Consequently, halogen-based stabilization of insulin and other therapeutic proteins may provide a biophysical strategy to circumvent the requirement for a distribution “cold chain” in the developing world and enhance the shelf life of pharmaceutical formulations.

Accounting for geometry-induced changes in the electronic distribution in molecular simulation is important for capturing effects such as charge flow, charge anisotropy and polarization. Multipolar force fields have demonstrated their ability to qualitatively and correctly represent chemically significant features such as sigma holes. It has also been shown that off-center point charges offer a compact alternative with similar accuracy. Here it is demonstrated that allowing relocation of charges within a minimally distributed charge model (MDCM) with respect to their reference atoms is a viable route to capture changes in the molecular charge distribution depending on geometry. The approach, referred to as ``flexible MDCM'' (fMDCM) is validated on a number of small molecules and provides accuracies in the electrostatic potential (ESP) of 0.5 kcal/mol on average compared with reference data from electronic structure calculations whereas MDCM and point charges have root mean squared errors of a factor of 2 to 5 higher. In addition, MD simulations in the $NVE$ ensemble using fMDCM for a box of flexible water molecules with periodic boundary conditions show a width of 0.1 kcal/mol for the fluctuation around the mean at 300 K on the 10 ns time scale. The accuracy in capturing the geometry dependence of the ESP together with the long-time stability in energy conserving simulations makes fMDCM a promising tool to introduce advanced electrostatics into atomistic simulations.

S-nitrosylation, the covalent addition of NO to the thiol side chain of cysteine, is an important post-transitional modification that can alter the function of various proteins. The structural dynamics and vibrational spectroscopy of S-nitrosylation in the condensed phase is investigated for the methyl-capped cysteine model system and for myoglobin. Using conventional point charge and physically more realistic multipolar force fields for the -SNO group it is found that the SN- and NO-stretch and the SNO-bend vibrations can be located and distinguished from the other protein modes for simulations of MbSNO at 50 K. The finding of stable cis- and trans-MbSNO is consistent with experiments on other proteins as is the observation of buried -SNO. For MbSNO the observed relocation of the EF loop in the simulations by $\sim 3$ \AA\/ is consistent with the available X-ray structure and the conformations adopted by the -SNO label are in good overall agreement with the X-ray structure. Despite the larger size of the -SNO group, MbSNO is found to recruit more water molecules within 10 \AA\/ of the modification site than WT Mb due to the stronger electrostatics. Similarly, when comparing the hydration between the A- and H-helices they differ by up to 30 \% between WT and MbSNO. This suggests that local hydration can also be significantly modulated through nitrosylation.

Thermal rates for the C(3P) + O2(3Σg−) ↔ CO(1Σ+)+ O(1D)/O(3P) reaction are investigated over a wide temperature range based on quasi classical trajectory (QCT) simulations on 3-dimensional, reactive potential energy surfaces (PESs) for the 1A′, (2)1A′, 1A′′, 3A′ and 3A′′ states. These five states are the energetically low-lying states of CO2 and their PESs are computed at the MRCISD+Q/aug-cc-pVTZ level of theory using a state-average CASSCF reference wave function. Analysis of the different electronic states for the CO2 → CO + O dissociation channel rationalizes the topography of this region of the PESs. The forward rates from QCT simulations match measurements between 15 K and 295 K whereas the equilibrium constant determined from the forward and reverse rates is consistent with that derived from statistical mechanics at high temperature. Vibrational relaxation, O + CO(ν = 1,2) → O + CO(ν = 0), is found to involve both, non-reactive and reactive processes. The contact time required for vibrational relaxation to take place is τ ≥ 150 fs for non-reacting and τ ≥ 330 fs for reacting (oxygen atom exchange) trajectories and the two processes are shown to probe different parts of the global potential energy surface. In agreement with experiments, low collision energy reactions for the C(3P) + O2(3Σg−, ν = 0) → CO(1Σ+) + O(1D) lead to CO(1Σ+, ν′ = 17) with an onset at Ec ∼ 0.15 eV, dominated by the 1A′ surface with contributions from the 3A′ surface. Finally, the barrier for the COA(1Σ+) + OB(3P) → COB(1Σ+) + OA(3P) atom exchange reaction on the 3A′ PES yields a barrier of ∼7 kcal mol−1 (0.300 eV), consistent with an experimentally reported value of 6.9 kcal mol−1 (0.299 eV).

The double proton transfer (DPT) reaction in hydrated formic acid dimer (FAD) is investigated at molecular-level detail. For this, a global and reactive machine learned (ML) potential energy surface (PES) is developed to run extensive (more than 100 ns) mixed ML/MM molecular dynamics (MD) simulations in explicit molecular mechanics (MM) solvent at MP2-quality for the solute. Simulations with fixed - as in a conventional empirical force field - and conformationally fluctuating - as available from the ML-based PES - charge models for FAD shows significant impact on the competition between DPT and dissociation of FAD into two formic acid monomers. With increasing temperature the barrier height for DPT in solution changes by about 10% ($\sim 1$ kcal/mol) between 300 K and 600 K. The rate for DPT is largest, $\sim 1$ ns$^{-1}$, at 350 K and decreases for higher temperatures due to destabilisation and increased probability for dissociation of FAD. The water solvent is found to promote the first proton transfer by exerting a favourable solvent-induced Coulomb force along the O-H$\cdots$O hydrogen bond whereas the second proton transfer is significantly controlled by the O-O separation and other conformational degrees of freedom. Double proton transfer in hydrated FAD is found to involve a subtle interplay and balance between structural and electrostatic factors.

The photodissociation dynamics of N

Distributed point charge models (DCM) and their minimal variants (MDCM) have been integrated with tools widely used for condensed-phase simulations, including a virial-based barostat and a slow-growth algorithm for thermodynamic integration. Minimal DCM is further developed in a systematic fashion to reduce fitting errors in the electrostatic interaction energy, and a new fragment-based approach offers considerable speedup of the MDCM fitting process for larger molecules with increased numbers of off-centered charged sites. Finally, polarizable (M)DCM is also introduced in the present work. The developments are used in condensed-phase simulations of popular force fields with commonly applied simulation conditions. (M)DCM equivalents for a range of widely used water force fields and for fluorobenzene (PhF) are developed and applied along with the original models to evaluate the impact of reformulating the electrostatic term. Comparisons of the molecular electrostatic potential (MEP), electrostatic interaction energies, and bulk properties from molecular dynamics simulations for a range of models from simple TIPnP (n = 3-5) to the polarizable, multipolar iAMOEBA models for water and an existing quadrupolar model for PhF confirm that DCMs retain the accuracy of the original models, providing a homogeneous, efficient, and generic point charge alternative to a multipolar electrostatic model for force field development and multilevel simulations.

Machine learning (ML) has become a promising tool for improving the quality of atomistic simulations. Using formaldehyde as a benchmark system for intramolecular interactions, a comparative assessm...

Constructing accurate, high dimensional molecular potential energy surfaces (PESs) for polyatomic molecules is challenging. Reproducing Kernel Hilbert space (RKHS) interpolation is an efficient way to construct such PESs. However, the scheme is most effective when the input energies are available on a regular grid. Thus the number of reference energies required can become very large even for penta-atomic systems making such an approach computationally prohibitive when using high-level electronic structure calculations. Here an efficient and robust scheme is presented to overcome these limitations and is applied to constructing high dimensional PESs for systems with up to 10 atoms. Using energies as well as gradients reduces the number of input data required and thus keeps the number of coefficients at a manageable size. Correct implementation of permutational symmetry in the kernel products is tested and explicitly demonstrated for the highly symmetric CH$_4$ molecule., Comment: 40 pages, 13 Figures

The determination of thermal and vibrational relaxation rates of triatomic systems suitable for application in hypersonic model calculations is discussed. For this, potential energy surfaces for ground and electronically excited state species need to be computed and represented with high accuracy and quasiclassical or quantum nuclear dynamics simulations provide the basis for determining the relevant rates. These include thermal reaction rates, state-to-state cross-sections, or vibrational relaxation rates. For exemplary systems - [NNO], [NOO], and [CNO] - all individual steps are described and a literature overview for them is provided. Finally, as some of these quantities involve considerable computational expense, for the example of state-to-state cross sections the construction of an efficient model based on neural networks is discussed. All such data is required and being used in more coarse-grained computational fluid dynamics simulations., Review article, 46 pages, 8 figures

The recombination dynamics of $^3$P oxygen atoms on cold amorphous solid water to form triplet and singlet molecular oxygen (O$_2$) is followed under conditions representative for cold clouds. It is found that both, formation of ground state ($X ^3 \Sigma_{g}^{-}$) O$_2$ and molecular oxygen in the two lowest singlet states ($a ^1\Delta_g$ and $b ^1\Sigma_g^+$) is possible and that the species can stabilize. The relative proportions of the species is approximately 1:1:1. These results also agree qualitatively with a kinetic model based on simplified wavepacket simulations. As the chemical reactivity of triplet and singlet O$_2$ is different it is likely that substantial amounts of $a ^1\Delta_g$ and $b ^1\Sigma_g^+$ oxygen influences the chemical evolution of cold clouds.

Accurate potential energy surfaces (PESs) have been determined for the 3A' and 3A'' states of N2O using electronic structure calculations at the multireference configuration interaction level with Davidson correction (MRCI+Q) and the augmented Dunning-type correlation consistent polarized triple zeta (aug-cc-pVTZ) basis set. More than 20 000 MRCI+Q/aug-cc-pVTZ energies are represented using a reproducing kernel Hilbert space (RKHS) scheme. The RKHS PESs successfully describe all reactant channels with high accuracy and all minima and transition states connecting them are determined. Quasiclassical trajectory (QCT) simulations are then used to determine reaction rates for N + NO and O + N2 collisions. Vibrational relaxation N2(ν = 1) → N2(ν = 0) and dissociation of N2 → 2N for O + N2 collisions are also investigated using QCT. The agreement between results obtained from the QCT simulations and from available experiments is favourable for reaction and vibrational relaxation rates, which provides a test for the accuracy of the PESs. The PESs can be used to calculate more detailed state-to-state observables relevant for applications to hypersonic reentry.

The photodissociation dynamics of [Formula: see text] excited from its (linear) 3[Formula: see text]/(bent) 3A″ ground to the first excited singlet and triplet states is investigated. Three-dimensional potential energy surfaces for the 1A′, 1A″, and 3A′ electronic states, correlating with the 1Δg and 3Πu states in linear geometry, for [Formula: see text] are constructed using high-level electronic structure calculations and represented as reproducing kernels. The reference ab initio energies are calculated at the MRCI+Q/aug-cc-pVTZ level of theory. For following the photodissociation dynamics in the excited states, rotational and vibrational distributions P( v′) and P( j′) for the N2 product are determined from vertically excited ground state distributions. Due to the different shapes of the ground state 3A″ potential energy surface and the excited states, appreciable angular momentum j′ ∼ 60 is generated in diatomic fragments. The lifetimes in the excited states extend to at least 50 ps. Notably, results from sampling initial conditions from a thermal ensemble and from the Wigner distribution of the ground state wavefunction are comparable.

The value of uncertainty quantification on predictions for trained neural networks (NNs) on quantum chemical reference data is quantitatively explored. For this, the architecture of the PhysNet NN was suitably modified and the resulting model (PhysNet-DER) was evaluated with different metrics to quantify its calibration, the quality of its predictions, and whether prediction error and the predicted uncertainty can be correlated. Training on the QM9 database and evaluating data in the test set within and outside the distribution indicate that error and uncertainty are not linearly related. However, the observed variance provides insight into the quality of the data used for training. Additionally, the influence of the chemical space covered by the training data set was studied by using a biased database. The results clarify that noise and redundancy complicate property prediction for molecules even in cases for which changes – such as double bond migration in two otherwise identical molecules – are small. The model was also applied to a real database of tautomerization reactions. Analysis of the distance between members in feature space in combination with other parameters shows that redundant information in the training dataset can lead to large variances and small errors whereas the presence of similar but unspecific information returns large errors but small variances. This was, e.g., observed for nitro-containing aliphatic chains for which predictions were difficult although the training set contained several examples for nitro groups bound to aromatic molecules. The finding underlines the importance of the composition of the training data and provides chemical insight into how this affects the prediction capabilities of a ML model. Finally, the presented method can be used for information-based improvement of chemical databases for target applications through active learning optimization.