Stabilization and structure calculations for noncovalent interactions in extended molecular systems based on wave function and density functional theories

Hydrogen-Bond-Dependent Conformational Switching: A Computational Challenge from Experimental Thermochemistry

We have compiled an experimental data set (SWITCH10) of equilibrium constants for a series of hydrogen-bond-dependent conformational switches. These organic molecules possess common functionalities and are representative in terms of size and composition of systems routinely studied computationally. They exist as two well-defined conformations which serve as a useful tool to benchmark computational estimates of experimental Gibbs energy differences. We examine the performance of HF theory and a variety of density functionals (B3LYP, B3LYP-D3, CAM-B3LYP, ωB97X-D, M06-2X) against these experimental benchmarks. Surprisingly, despite a strong similarity between the two switch conformations, the average errors (0.4-1.7 kcal·mol^-1) obtained across the data set for all methods are larger than obtained with HF calculations. B3LYP was found to outperform implicitly and explicitly dispersion-corrected functionals, with an average error smaller by 1 kcal·mol^-1. Unsystematic errors in the optimized structures were found to contribute to the relatively poor performance obtained, while quasi-rigid rotor harmonic oscillator thermal contributions are important in improving the accuracy of computed Gibbs energy differences. These results emphasize the challenge of quantitative accuracy in computing solution-phase thermochemistry for flexible systems and caution against the often used (but unstated) assumption of favorable error cancellation in comparing conformers or stereoisomers.

Which DFT levels of theory are appropriate in predicting the prolyl cis–trans isomerization in solution?

DFTs were assessed for the conformational preferences of the peptides containing Pro and its derivatives in chloroform and water.

Towards reliable ab initio sublimation pressures for organic molecular crystals - are we there yet?

Knowledge of molecular crystal sublimation equilibrium data is vital in many industrial processes, but this data can be difficult to measure experimentally for low-volatility species. Theoretical prediction of sublimation pressures could provide a useful supplement to experiment, but the exponential temperature dependence of sublimation (or any saturated vapor) pressure curve makes this challenging. An uncertainty of only a few percent in the sublimation enthalpy or entropy can propagate to an error in the sublimation pressure exceeding several orders of magnitude for a given temperature interval. Despite this fundamental difficulty, this paper performs some of the first ab initio predictions of sublimation pressure curves. Four simple molecular crystals (ethane, methanol, benzene, and imidazole) have been selected for a case study showing the currently achievable accuracy of quantum chemistry calculations. Fragment-based ab initio techniques and the quasi-harmonic approximation are used for calculations of cohesive and phonon properties of the crystals, while the vapor phase is treated by the ideal gas model. Ab initio sublimation pressure curves for model compounds are compared against their experimental counterparts. The computational uncertainties are estimated, weak points of the computational methodology are identified, and further improvements are proposed.

Does the stability of the stacking motif surpass the planar motif in 2-amino-4-nitrophenol? - a CCSD(T) analysis

In this work we analyzed O-H...O, O-H...N, and N-H...O contacts existing in the 2-amino-4-nitrophenol structure engaged in ANP molecules through quantum chemical methods. Furthermore, the above contacts were favored to comprehensively understand the stability of noncovalent interactions, π stacking and hydrogen bonding, surviving in 2-amino-4-nitrophenol. The geometries of π stacking and hydrogen bond interactions between two 2-amino-4-nitrophenols were optimized at BLYP-D3/def2-QZVP with dispersion 3 and MP2/cc-pVTZ levels of theory, and their stability was compared using the CCSD(T) interaction energies. The analyses predicted a particularly strong π stacking interaction of 2-amino-4-nitrophenol with hydrogen bond due to the narrow equivalent configuration of NO2 interactions with the other 2-amino-4-nitrophenols. Furthermore, this work focused on analyzing the stability of the individual hydrogen bonds existing in planar and stacked arrangements. Graphical abstract Stacked and planar motif in 2-amino-4-nitrophenol.

n→π* Interactions Modulate the Properties of Cysteine Residues and Disulfide Bonds in Proteins

Noncovalent interactions are ubiquitous in biology, taking on roles that include stabilizing the conformation of and assembling biomolecules, and providing an optimal environment for enzymatic catalysis. Here, we describe a noncovalent interaction that engages the sulfur atoms of cysteine residues and disulfide bonds in proteins-their donation of electron density into an antibonding orbital of proximal amide carbonyl groups. This n→ π* interaction tunes the reactivity of the CXXC motif, which is the critical feature of thioredoxin and other enzymes involved in redox homeostasis. In particular, an n→ π* interaction lowers the p K_a value of the N-terminal cysteine residue of the motif, which is the nucleophile that initiates catalysis. In addition, the interplay between disulfide n→ π* interactions and C5 hydrogen bonds leads to hyperstable β-strands. Finally, n→ π* interactions stabilize vicinal disulfide bonds, which are naturally diverse in function. These previously unappreciated n→ π* interactions are strong and underlie the ability of cysteine residues and disulfide bonds to engage in the structure and function of proteins.

Read between the Molecules: Computational Insights into Organic Semiconductors

The performance and key electronic properties of molecular organic semiconductors are dictated by the interplay between the chemistry of the molecular core and the intermolecular factors of which manipulation has inspired both experimentalists and theorists. This Perspective presents major computational challenges and modern methodological strategies to advance the field. The discussion ranges from insights and design principles at the quantum chemical level, in-depth atomistic modeling based on multiscale protocols, morphological prediction and characterization as well as energy-property maps involving data-driven analysis. A personal overview of the past achievements and future direction is also provided.

Phosphorescent Modulation of Metallophilic Clusters and Recognition of Solvents through a Flexible Host-Guest Assembly: A Theoretical Investigation

MP2 (Second order approximation of Møller–Plesset perturbation theory) and DFT/TD-DFT (Density functional theory/Time-dependent_density_functional_theory) investigations have been performed on metallophilic nanomaterials of host clusters [Au(NHC)₂]⁺⋅⋅⋅[M(CN)₂]⁻⋅⋅⋅[Au(NHC)₂]⁺ (NHC = N-heterocyclic carbene, M = Au, Ag) with high phosphorescence. The phosphorescence quantum yield order of clusters in the experiments was evidenced by their order of μ_S1/ΔE_S1−T1 values (μS1: S₀ → S₁ transition dipole, ∆ES1−T1: splitting energy between the lowest-lying singlet S₁ and the triplet excited state T₁ states). The systematic variation of the guest solvents (S1: CH₃OH, S2: CH₃CH₂OH, S3: H₂O) are employed not only to illuminate their effect on the metallophilic interaction and phosphorescence but also as the probes to investigate the recognized capacity of the hosts. The simulations revealed that the metallophilic interactions are mainly electrostatic and the guests can subtly modulate the geometries, especially metallophilic Au⋅⋅⋅M distances of the hosts through mutual hydrogen bond interactions. The phosphorescence spectra of hosts are predicted to be blue-shifted under polar solvent and the excitation from HOMO (highest occupied molecular orbital) to LUMO (lowest unoccupied molecular orbital) was found to be responsible for the ³MLCT (triplet metal-to-ligand charge transfer) characters in the hosts and host-guest complexes. The results of investigation can be introduced as the clues for the design of promising blue-emitting phosphorescent and functional materials.

On the Nature of σ-σ, σ-π, and π-π Stacking in Extended Systems

Stacking interactions have been evaluated, employing computational methods, in dimers formed by analogous aliphatic and aromatic species of increasing size. Changes in stability as the systems become larger are mostly controlled by the balance of increasing repulsion and dispersion contributions, while electrostatics plays a secondary but relevant role. The interaction energy increases as the size of the system grows, but it does much faster in π-π dimers than in σ-π complexes and more remarkably than in σ-σ dimers. The main factor behind the larger stability of aromatic dimers compared to complexes containing aliphatic molecules is related to changes in the properties of the aromatic systems due to electron delocalization leading to larger dispersion contributions. Besides, an extra stabilization in π-π complexes is due to the softening of the repulsive wall in aromatic species that allows the molecules to come closer.

Accurate Noncovalent Interactions via Dispersion-Corrected Second-Order Møller-Plesset Perturbation Theory

Noncovalent interactions govern many important areas of chemistry, ranging from biomolecules to molecular crystals. Here, an accurate and computationally inexpensive dispersion-corrected second-order Møller-Plesset perturbation theory model (MP2D) is presented. MP2D recasts the highly successful dispersion-corrected MP2C model in a framework based on Grimme's D3 dispersion correction, combining Grimme's D3 dispersion coefficients with new analogous uncoupled Hartree-Fock ones and five global empirical parameters. MP2D is faster than MP2C, and unlike MP2C, it is suitable for geometry optimizations and can describe both intra- and intermolecular noncovalent interactions with high accuracy. MP2D approaches the accuracy of higher-level ab initio wave function techniques and out-performs a widely used hybrid dispersion-corrected density functional on a range of intermolecular, intramolecular, and thermochemical benchmarks.

A quantification scheme for non-covalent interactions in the enantio-controlling transition states in asymmetric catalysis

The origin of enantioselectivity in asymmetric catalysis is attributed to the energy difference between lower and higher energy diastereomeric transition states, which are respectively responsible for the formation of major and minor enantiomers. Although the increase in the number of transition state models emphasizes the role of weak non-covalent interactions in asymmetric induction, the strength of such interactions is seldom quantified. Through this article, we propose a simple and effective method of quantifying the total non-covalent interaction in stereocontrolling transition states belonging to a group of three representative asymmetric catalytic reactions involving chiral phosphoric acids. Our method relies on rational partitioning of a given transition state into two (or three) sub-units, such that the complex network of intramolecular interactions can be ameliorated to a set of intermolecular interactions between two sub-units. The computed strength of interaction obtained using the counterpoise (CP) method on suitably partitioned transition states provides improved estimates of non-covalent interactions, which are also devoid of basis set superposition error (BSSE). It has been noted that catalysts decorated with larger aromatic arms provide cumulative non-covalent interactions (C-Hπ, N-Hπ and ππ) to the tune of 10 to 15 kcal mol-1. Fine-tuning of the magnitude and nature of these interactions can provide valuable avenues in the design of asymmetric catalysts.

GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction

We present the implementation of GAtor, a massively parallel, first-principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several subpopulations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low-energy minima, including those located in disconnected, hard to reach regions of the potential energy landscape. The best structures generated are re-relaxed and re-ranked using a hierarchy of increasingly accurate DFT functionals and dispersion methods. GAtor is applied to a chemically diverse set of four past blind test targets, characterized by different types of intermolecular interactions. The experimentally observed structures and other low-energy structures are found for all four targets. In particular, for Target II, 5-cyano-3-hydroxythiophene, the top ranked putative crystal structure is a Z' = 2 structure with P1̅ symmetry and a scaffold packing motif, which has not been reported previously.

Assessing Intermolecular Interactions in Guest-Free Clathrate Hydrate Systems

Recently, empty hydrate structures sI, sII, sH, and others have been proposed as low-density ice structures by both experimental observations and computer simulations. Some of them have been synthesized in the laboratory, which motivates further investigations on the stability of such guest-free clathrate structures. Using semiempirical and ab initio-based water models, as well as dispersion-corrected density functional theory approaches, we predict their stability, including cooperative many-body effects, in comparison with reference data from converged wave function-based DF-MP2 electronic structure calculations. We show that large basis sets and counterpoise corrections are required to improve convergence in the interaction/binding energies for such systems. Therefore, extrapolation schemes based on triple/quadruple and quadruple/quintuple ζ quality basis sets are used to reach high accuracy. Eleven different water structures corresponding to dodecahedron, edge sharing, face sharing, and fused cubes, as a part of the WATER27 database, as well as cavities from the sI, sII, and sH clathrate hydrates formed by 20, 24, 28, and 36 water molecules, are employed, and new benchmark energies are reported. Using these benchmark sets of interaction energies, we assess the performance of both analytical models and direct DFT calculations for such clathrate-like systems. In particular, seven popular water models (TIP4P/ice, TIP4P/2005, q-TIP4P/F, TTM2-F, TTM3-F, TTM4-F, and MB-pol) available in the literature, and nine density functional approximations (3 meta-GGAs, 3 hybrids, and 3 range separated functionals) are used to investigate their accuracy. By including dispersion corrections, our results show that errors in the interaction energies are reduced for most of the DFT functionals. Despite the difficulties faced by current water models and DFT functionals to accurately describe the interactions in such water systems, we found some general trends that could serve to extend their applicability to larger systems.

Cation-π interactions in protein-ligand binding: theory and data-mining reveal different roles for lysine and arginine

The interactions of neutral aromatic ligands with cationic arginine, histidine and lysine amino acid residues have been studied with ab initio calculations, symmetry adapted perturbation theory (SAPT), and a systematic meta-analysis of X-ray structures.

We have studied the cation–π interactions of neutral aromatic ligands with the cationic amino acid residues arginine, histidine and lysine using ab initio calculations, symmetry adapted perturbation theory (SAPT), and a systematic meta-analysis of all available Protein Data Bank (PDB) X-ray structures. Quantum chemical potential energy surfaces (PES) for these interactions were obtained at the DLPNO-CCSD(T) level of theory and compared against the empirical distribution of 2012 unique protein–ligand cation–π interactions found in X-ray crystal structures. We created a workflow to extract these structures from the PDB, filtering by interaction type and residue pK_a. The gas phase cation–π interaction of lysine is the strongest by more than 10 kcal mol^–1, but the empirical distribution of 582 X-ray structures lies away from the minimum on the interaction PES. In contrast, 1381 structures involving arginine match the underlying calculated PES with good agreement. SAPT analysis revealed that underlying differences in the balance of electrostatic and dispersion contributions are responsible for this behavior in the context of the protein environment. The lysine–arene interaction, dominated by electrostatics, is greatly weakened by a surrounding dielectric medium and causes it to become essentially negligible in strength and without a well-defined equilibrium separation. The arginine–arene interaction involves a near equal mix of dispersion and electrostatic attraction, which is weakened to a much smaller degree by the surrounding medium. Our results account for the paucity of cation–π interactions involving lysine, even though this is a more common residue than arginine. Aromatic ligands are most likely to interact with cationic arginine residues as this interaction is stronger than for lysine in higher polarity surroundings.

Dissecting the concave-convex π-π interaction in corannulene and sumanene dimers: SAPT(DFT) analysis and performance of DFT dispersion-corrected methods

The characteristics of the concave-convex π-π interactions are evaluated in 32 buckybowl dimers formed by corannulene, sumanene, and two substituted sumanenes (with S and CO groups), using symmetry-adapted perturbation theory [SAPT(DFT)] and density functional theory (DFT). According to our results, the main stabilizing contribution is dispersion, followed by electrostatics. Regarding the ability of DFT methods to reproduce the results obtained with the most expensive and rigorous methods, TPSS-D seems to be the best option overall, although its results slightly tend to underestimate the interaction energies and to overestimate the equilibrium distances. The other two tested DFT-D methods, B97-D2 and B3LYP-D, supply rather reasonable results as well. M06-2X, although it is a good option from a geometrical point of view, leads to too weak interactions, with differences with respect to the reference values amounting to about 4 kcal/mol (25% of the total interaction energy). © 2017 Wiley Periodicals, Inc.

Noncovalent interactions between cisplatin and graphene prototypes

Cisplatin (CP) has been widely used as an anticancer drug for more than 30 years despite severe side effects due to its low bioavailability and poor specificity. For this reason, it is paramount to study and design novel nanomaterials to be used as vectors capable to effectively deliver the drug to the biological target. The CP square-planar geometry, together with its low water solubility, suggests that it could be possibly easily adsorbed on 2D graphene nanostructures through the interaction with the related highly conjugated π-electron system. In this work, pyrene has been first selected as the minimum approximation to the graphene plane, which allows to properly study the noncovalent interactions determining the CP adsorption. In particular, electronic structure calculations at the MP2C and DFT-SAPT levels of theory have allowed to obtain benchmark interaction energies for some limiting configurations of the CP-pyrene complex, as well as to assess the role of the different contributions to the total interaction: it has been found that the parallel configurations of the aggregate are mainly stabilized around the minimum region by dispersion, in a similar way as for complexes bonded through π-π interactions. Then, the benchmark interaction energies have been used to test corresponding estimations obtained within the less expensive DFT to validate an optimal exchange-correlation functional which includes corrections to take properly into account for the dispersion contribution. Reliable DFT interaction energies have been therefore obtained for CP adsorbed on graphene prototypes of increasing size, ranging from coronene, ovalene, and up to C₁₅₀ H₃₀ . Finally, DFT geometry optimizations and frequency calculations have also allowed a reliable estimation of the adsorption enthalpy of CP on graphene, which is found particularly favorable (about -20 kcal/mol at 298 K and 1 bar) being twice that estimated for the corresponding benzene adsorption. © 2017 Wiley Periodicals, Inc.

Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning

By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher’s iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.

Close contacts involving germanium and tin in crystal structures: experimental evidence of tetrel bonds

Modeling indicates the presence of a region of low electronic density (a "σ-hole") on group 14 elements, and this offers an explanation for the ability of these elements to act as electrophilic sites and to form attractive interactions with nucleophiles. While many papers have described theoretical investigations of interactions involving carbon and silicon, such investigations of the heavier group 14 elements are relatively scarce. The purpose of this review is to rectify, to some extent, the current lack of experimental data on interactions formed by germanium and tin with nucleophiles. A survey of crystal structures in the Cambridge Structural Database is reported. This survey reveals that close contacts between Ge or Sn and lone-pair-possessing atoms are quite common, they can be either intra- or intermolecular contacts, and they are usually oriented along the extension of the covalent bond formed by the tetrel with the most electron-withdrawing substituent. Several examples are discussed in which germanium and tin atoms bear four carbon residues or in which halogen, oxygen, sulfur, or nitrogen substituents replace one, two, or three of those carbon residues. These close contacts are assumed to be the result of attractive interactions between the involved atoms and afford experimental evidence of the ability of germanium and tin to act as electrophilic sites, namely tetrel bond (TB) donors. This ability can govern the conformations and the packing of organic derivatives in the solid state. TBs can therefore be considered a promising and robust tool for crystal engineering. Graphical abstract Intra- and intermolecular tetrel bonds involving organogermanium and -tin derivatives in crystalline solids.

Quantum-Chemical Insights into the Self-Assembly of Carbon-Based Supramolecular Complexes

Understanding how molecular systems self-assemble to form well-organized superstructures governed by noncovalent interactions is essential in the field of supramolecular chemistry. In the nanoscience context, the self-assembly of different carbon-based nanoforms (fullerenes, carbon nanotubes and graphene) with, in general, electron-donor molecular systems, has received increasing attention as a means of generating potential candidates for technological applications. In these carbon-based systems, a deep characterization of the supramolecular organization is crucial to establish an intimate relation between supramolecular structure and functionality. Detailed structural information on the self-assembly of these carbon-based nanoforms is however not always accessible from experimental techniques. In this regard, quantum chemistry has demonstrated to be key to gain a deep insight into the supramolecular organization of molecular systems of high interest. In this review, we intend to highlight the fundamental role that quantum-chemical calculations can play to understand the supramolecular self-assembly of carbon-based nanoforms through a limited selection of supramolecular assemblies involving fullerene, fullerene fragments, nanotubes and graphene with several electron-rich π-conjugated systems.

Identifying pragmatic quasi-harmonic electronic structure approaches for modeling molecular crystal thermal expansion

Quasi-harmonic approaches provide an economical route to modeling the temperature dependence of molecular crystal structures and properties. Several studies have demonstrated good performance of these models, at least for rigid molecules, when using fragment-based approaches with correlated wavefunction techniques. Many others have found success employing dispersion-corrected density functional theory (DFT). Here, a hierarchy of models in which the energies, geometries, and phonons are computed either with correlated methods or DFT are examined to identify which combinations produce useful predictions for properties such as the molar volume, enthalpy, and entropy as a function of temperature. The results demonstrate that refining DFT geometries and phonons with single-point energies based on dispersion-corrected second-order Møller-Plesset perturbation theory can provide clear improvements in the molar volumes and enthalpies compared to those obtained from DFT alone. Predicted entropies, which are governed by vibrational contributions, benefit less clearly from the hybrid schemes. Using these hybrid techniques, the room-temperature thermochemistry of acetaminophen (paracetamol) is predicted to address the discrepancy between two experimental sublimation enthalpy measurements.

Chemical sensing with 2D materials

During the last decade, two-dimensional materials (2DMs) have attracted great attention due to their unique chemical and physical properties, which make them appealing platforms for diverse applications in sensing of gas, metal ions as well as relevant chemical entities.

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML) methods are emerging as a powerful approach to constructing various forms of transferable atomistic potentials. They have been successfully applied in a variety of applications in chemistry, biology, catalysis, and solid-state physics. However, these models are heavily dependent on the quality and quantity of data used in their fitting. Fitting highly flexible ML potentials, such as neural networks, comes at a cost: a vast amount of reference data is required to properly train these models. We address this need by providing access to a large computational DFT database, which consists of more than 20 M off equilibrium conformations for 57,462 small organic molecules. We believe it will become a new standard benchmark for comparison of current and future methods in the ML potential community.

High-affinity and selective detection of pyrophosphate in water by a resorcinarene salt receptor

Pyrophosphate (PPi) is a byproduct of DNA and RNA synthesis, and abnormal levels are indicative of disease. We report the high-affinity binding of PPi in water by N-alkyl ammonium resorcinarene chloride receptors. Experimental analysis using ¹H and ³¹P NMR, isothermal titration calorimetry, mass spectrometry, and UV-vis spectroscopy all support exceptional selectivity of these systems for PPi in water. The measured affinity of K₁ = 1.60 × 10⁷ M^-1 for PPi is three orders of magnitude larger than that observed for binding to another phosphate, ATP. This exceptional anion-binding affinity in water is explored through a detailed density functional theory computational study. These systems provide a promising avenue for the development of future innovative medical diagnostic tools.

Electrophilicity of oxalic acid monomer is enhanced in the dimer by intermolecular proton transfer

We have analyzed the effect of excess electron attachment on the network of hydrogen bonds in the oxalic acid dimer (OA)₂. The most stable anionic structures may be viewed as complexes of a neutral hydrogenated moiety HOA˙ coordinated to an anionic deprotonated moiety (OA-H)^-. HOA˙ acts as a double proton donor and (OA-H)^- as a double proton acceptor. Thus the excess electron attachment drives intermolecular proton transfer. We have identified several cyclic hydrogen bonded structures of (OA)₂^-. Their stability has been analyzed in terms of the stability of the involved conformers, the energetic penalty for deformation of these conformers to the geometry of the dimer, and the two-body interaction energy between the deformed HOA˙ and (OA-H)^-. There are at least seven isomers of (OA)₂^- with stabilization energies in the range of 1.26-1.39 eV. These energies are dominated by attractive two-body interaction energies. The anions are vertically bound electronically by 3.0-3.4 eV and adiabatically bound by at least 1.6 eV. The computational predictions are consistent with the anion photoelectron spectrum of (OA)₂^-. The spectrum consists of a broad feature, with an onset of 2.5 eV and spanning to 4.3 eV. The electron vertical detachment energy (VDE) is assigned to be 3.3 eV.

Comparison of Various Means of Evaluating Molecular Electrostatic Potentials for Noncovalent Interactions

The various heterodimers formed by a series of Lewis acids with NH₃ as Lewis base are identified. Lewis acids include those that can form chalcogen (HSF and HSBr), pnicogen (H₂ PF and H₂ PBr), and tetrel (H₃ SiF and H₃ SiBr) bonds, as well as H-bonds and halogen bonds. The molecular electrostatic potential (MEP) of each Lewis acid is considered in a number of ways. Pictorial versions show broad regions of positive and negative MEP, on surfaces that vary with respect to either the value of the chosen isopotential, or their distance from the nuclei. Specific points are identified where the MEP reaches a maximum on a particular isodensity surface (V_s,max ). The locations and values of V_s,max were evaluated on different isodensity surfaces, and compared to the stabilities of the various equilibrium geometries. As the chosen isodensity is decreased, and the MEP maxima drift away from the molecule, some points maintain their angular positions with respect to the molecule, whereas others undergo a reorientation. The lowering isodensity also causes some of the maxima to disappear. In general, there is a fairly good correlation between the energetic ordering of the equilibrium structures and the values of V_s,max . A number of possible Lewis acid sites on the heteroaromatic imidazole ring were also considered and presents some cautions about application of V_s,max as the principal criterion for predicting equilibrium geometries. © 2017 Wiley Periodicals, Inc.

Quantitative analysis of solid-state diversity in trifluoromethylated phenylhydrazones

The cooperative roles of various structural motifs associated with the presence of different intermolecular interactions in the formation of molecular crystals are investigated in a series of trifluoromethylated phenylhydrazones. Out of the six compounds analysed, two exhibit three-dimensional structural similarities with geometrically equivalent building blocks, while a third exists as two polymorphic forms crystallized from ethanol solutions at low temperature (277 K) and room temperature (298 K), respectively. The compounds were characterized via single-crystal and powder X-ray diffraction techniques and differential scanning calorimetry. In the absence of any strong hydrogen bonding, the supramolecular constructs are primarily stabilized via molecular pairs with a high dispersion-energy contribution, due to the presence of molecular stacking along the molecular backbone along with C-H...π interactions in the solid state, in preference to an electrostatic contribution. The interaction energies for the most stabilizing molecular building blocks are in the range -29 to -43 kJ mol^-1. In addition, weak N-H...F, C-H...F and N-H...C interactions and F...F, F...C, F...N and C...N contacts act as secondary motifs, providing additional stability to the crystal packing. The overall molecular arrangements are carefully analysed in terms of their nature and energetics, and the roles of different molecular pairs towards the crystal structure are delineated. A topological study using the quantum theory of atoms in molecules was used to characterize all the atomic interactions in the solid state. It established the presence of (3, -1) bond critical points and the closed-shell nature of all the interactions.

N-Protonated Isomers and Coulombic Barriers to Dissociation of Doubly Protonated Ala8Arg

Collision-induced dissociation (or tandem mass spectrometry, MS/MS) of a protonated peptide results in a spectrum of fragment ions that is useful for inferring amino acid sequence. This is now commonplace and a foundation of proteomics. The underlying chemical and physical processes are believed to be those familiar from physical organic chemistry and chemical kinetics. However, first-principles predictions remain intractable because of the conflicting necessities for high accuracy (to achieve qualitatively correct kinetics) and computational speed (to compensate for the high cost of reliable calculations on such large molecules). To make progress, shortcuts are needed. Inspired by the popular mobile proton model, we have previously proposed a simplified theoretical model in which the gas-phase fragmentation pattern of protonated peptides reflects the relative stabilities of N-protonated isomers, thus avoiding the need for transition-state information. For singly protonated Ala _n (n = 3-11), the resulting predictions were in qualitative agreement with the results from low-energy MS/MS experiments. Here, the comparison is extended to a model tryptic peptide, doubly protonated Ala₈Arg. This is of interest because doubly protonated tryptic peptides are the most important in proteomics. In comparison with experimental results, our model seriously overpredicts the degree of backbone fragmentation at N₉. We offer an improved model that corrects this deficiency. The principal change is to include Coulombic barriers, which hinder the separation of the product cations from each other. Coulombic barriers may be equally important in MS/MS of all multiply charged peptide ions. Graphical Abstract ᅟ.

Looking Back, Looking Forward at Halogen Bonding in Drug Discovery

Halogen bonding has emerged at the forefront of advances in improving ligand: receptor interactions. In particular the newfound ability of this extant non-covalent-bonding phenomena has revolutionized computational approaches to drug discovery while simultaneously reenergizing synthetic approaches to the field. Here we survey, via examples of classical applications involving halogen atoms in pharmaceutical compounds and their biological hosts, the unique advantages that halogen atoms offer as both Lewis acids and Lewis bases.