First-principles data set of 45,892 isolated and cation-coordinated conformers of 20 proteinogenic amino acids

Application of modern artificial intelligence techniques in the development of organic molecular force fields

The molecular force field (FF) determines the accuracy of molecular dynamics (MD) and is one of the major bottlenecks that limits the application of MD in molecular design. Recently, artificial intelligence (AI) techniques, such as machine-learning potentials (MLPs), have been rapidly reshaping the landscape of MD. Meanwhile, organic molecular systems feature unique characteristics, and require more careful treatment in both model construction, optimization, and validation. While an accurate and generic organic molecular force field is still missing, significant progress has been made with the facilitation of AI, warranting a promising future. In this review, we provide an overview of the various types of AI techniques used in molecular FF development and discuss both the advantages and weaknesses of these methodologies. We show how AI methods provide unprecedented capabilities in many tasks such as potential fitting, atom typification, and automatic optimization. Meanwhile, it is also worth noting that more efforts are needed to improve the transferability of the model, develop a more comprehensive database, and establish more standardized validation procedures. With these discussions, we hope to inspire more efforts to solve the existing problems, eventually leading to the birth of next-generation generic organic FFs.

Hybrid quantum-classical polarizability model for single molecule biosensing

Optical whispering gallery mode biosensors are able to detect single molecules through effects of their polarizability. We address the factors that affect the polarizability of amino acids, which are the building blocks of life, via electronic structure theory. Amino acids are detected in aqueous environments, where their polarizability is different compared to the gasphase due to solvent effects. Solvent effects include structural changes, protonation and the local field enhancement through the solvent (water). We analyse the impact of these effects and find that all contribute to an increased effective polarizability in the solvent. We also address the excess polarizability relative to the displaced water cavity and develop a hybrid quantum-classical model that is in good agreement with self-consistent calculations. We apply our model to calculate the excess polarizability of 20 proteinogenic amino acids and determine the minimum resolution required to distinguish the different molecules and their ionised conformers based on their polarizability.

System-Specific Parameter Optimization for Nonpolarizable and Polarizable Force Fields

The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer (CT) and polarization (POL) effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding CT and POL. Furthermore, we present an open-source parametrization tool, called FFAFFURR, that enables the (system-specific) parametrization of OPLS-AA and CTPOL models. The performance of our workflow was evaluated by its ability to reproduce quantum chemistry energies and by molecular dynamics simulations of a zinc-finger protein.

Constructing Potential Energy Surface with Correlated Theory for Dipeptides Using Molecular Tailoring Approach

We demonstrate a cost-effective alternative employing the fragment-based molecular tailoring approach (MTA) for building the potential energy surface (PES) for two dipeptides viz. alanine-alanine and alanine-proline employing correlated theory, with augmented Dunning basis sets. About 1369 geometries are generated for each test dipeptide by systematically varying the dihedral angles Φ ${{\rm{\Phi }}}$ and Ψ ${{{\Psi }}}$ . These conformational geometries are partially optimized by relaxing all the other Z-matrix parameters, fixing the values of Φ ${{\rm{\Phi }}}$ and Ψ ${{{\Psi }}}$ . The MP2 level PES is constructed from the MTA-energies of chemically intact geometries using minimal hardware. The fidelity of MP2/aug-cc-pVDZ level PES is brought out by comparing it with its full calculation counterpart. Further, we bring out the power of the method by reporting the MTA-based CCSD/aug-cc-pVDZ level PES for these two dipeptides containing 498 and 562 basis functions respectively.

Benchmark Ab Initio Determination of the Conformers, Proton Affinities, and Gas-Phase Basicities of Cysteine

A systematic conformational mapping combined with literature data leads to 85 stable neutral cysteine conformers. The implementation of the same mapping process for the protonated counterparts reveals 21 N-(amino-), 64 O-(carbonyl-), and 37 S-(thiol-)protonated cysteine conformers. Their relative energies and harmonic vibrational frequencies are given at the MP2/aug-cc-pVDZ level of theory. Further benchmark ab initio computations are performed for the 10 lowest-lying neutral and protonated amino acid conformers (for each type) such as CCSD(T)-F12a/cc-pVDZ-F12 geometry optimizations (and frequency computations for cysteine) as well as auxiliary correction computations of the basis set effects up to CCSD(T)-F12b/cc-pVQZ-F12, electron correlation effects up to CCSDT(Q), core correlation effects, second-order Douglass-Kroll relativistic effects, and zero-point energy contributions. Boltzmann-averaged 0 (298.15) K proton affinity and [298.15 K gas-phase basicity] values of cysteine are predicted to be 214.96 (216.39) [208.21], 201.83 (203.55) [194.16], and 193.31 (194.74) [186.40] kcal/mol for N-, O-, and S-protonation, respectively, also considering the previously described auxiliary corrections.

Impact of anion polarizability on ion pairing in microhydrated salt clusters

Despite longstanding interest in the mechanism of salt dissolution in aqueous media, a molecular level understanding remains incomplete. Here, cryogenic ion trap vibrational action spectroscopy is combined with electronic structure calculations to track salt hydration in a gas phase model system one water molecule at a time. The infrared photodissociation spectra of microhydrated lithium dihalide anions [LiXX'(H2O) n ]- (XX' = I2, ClI and Cl2; n = 1-3) in the OH stretching region (3800-2800 cm-1) provide a detailed picture of how anion polarizability influences the competition among ion-ion, ion-water and water-water interactions. While exclusively contact ion pairs are observed for n = 1, the formation of solvent-shared ion pairs, identified by markedly red-shifted OH stretching bands (<3200 cm-1), originating from the bridging water molecules, is favored already for n = 2. For n = 3, Li+ reaches its maximum coordination number of four only in [LiI2(H2O)3]-, in accordance with the hard and soft Lewis acid and base principle. Water-water hydrogen bond formation leads to a different solvent-shared ion pair motif in [LiI2(H2O)3]- and network formation even restabilizes the contact ion pair motif in [LiCl2(H2O)3]-. Structural assignments are exclusively possible after the consideration of anharmonic effects. Molecular dynamics simulations confirm that the significance of large amplitude motion (of the water molecules) increases with increasing anion polarizability and that needs to be considered already at cryogenic temperatures.

Exhaustive Mapping of the Conformational Space of Natural Dipeptides by the DFT-D3//COSMO-RS Method

We extensively mapped energy landscapes and conformations of 22 (including three His protonation states) proteinogenic α-amino acids in trans configuration and the corresponding 484 (22²) dipeptides. To mimic the environment in a protein chain, the N- and C-termini of the studied systems were capped with acetyl and N-methylamide groups, respectively. We systematically varied the main chain dihedral angles (ϕ, ψ) by 40° steps and all side chain angles by 90° or 120° steps. We optimized the molecular geometries with the GFN2-xTB semiempirical (SQM) method and performed single point density functional theory calculations at the BP86-D3/DGauss-DZVP//COSMO-RS level in water, 1-octanol, N,N-dimethylformamide, and n-hexane. For each restrained (nonequilibrium) structure, we also calculated energy gradients (in water) and natural atomic charges. The exhaustive and unprecedented QM-based sampling enabled us to construct Ramachandran plots of quantum mechanical (QM(BP86-D3)//COSMO-RS) energies calculated on SQM structures, for all 506 (484 dipeptides and 22 amino acids) studied systems. We showed how the character of an amino acid side chain influences the conformational space of single amino acids and dipeptides. With clustering techniques, we were able to identify unique minima of amino acids and dipeptides (i.e., minima on the GFN2-xTB potential energy surfaces) and analyze the distribution of their BP86-D3//COSMO-RS conformational energies in all four solvents. We also derived an empirical formula for the number of unique minima based on the overall number of rotatable bonds within each peptide. The final peptide conformer data set (PeptideCs) comprises over 400 million structures, all of them annotated with QM(BP86-D3)//COSMO-RS energies. Thanks to its completeness and unbiased nature, the PeptideCs can serve, inter alia, as a data set for the validation of new methods for predicting the energy landscapes of protein structures. This data set may also prove to be useful in the development and reparameterization of biomolecular force fields. The data set is deposited at Figshare (10.25452/figshare.plus.19607172) and can be accessed using a simple web interface at http://peptidecs.uochb.cas.cz.

Better force fields start with better data: A data set of cation dipeptide interactions

We present a data set from a first-principles study of amino-methylated and acetylated (capped) dipeptides of the 20 proteinogenic amino acids - including alternative possible side chain protonation states and their interactions with selected divalent cations (Ca2+, Mg2+ and Ba2+). The data covers 21,909 stationary points on the respective potential-energy surfaces in a wide relative energy range of up to 4 eV (390 kJ/mol). Relevant properties of interest, like partial charges, were derived for the conformers. The motivation was to provide a solid data basis for force field parameterization and further applications like machine learning or benchmarking. In particular the process of creating all this data on the same first-principles footing, i.e. density-functional theory calculations employing the generalized gradient approximation with a van der Waals correction, makes this data suitable for first principles data-driven force field development. To make the data accessible across domain borders and to machines, we formalized the metadata in an ontology.

AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been proposed, ranging from simple linear models to kernel regression and highly nonlinear neural networks. Here we apply two widely different approaches to the same, challenging problem: the sampling of the conformational landscape of polypeptides at finite temperature. We develop a local kernel regression (LKR) coupled with a supervised sparsity method and compare it with a more established approach based on Behler-Parrinello type neural networks. In the context of the LKR, we discuss how the supervised selection of the reference pool of environments is crucial to achieve accurate potential energy surfaces at a competitive computational cost and leverage the locality of the model to infer which chemical environments are poorly described by the DFTB baseline. We then discuss the relative merits of the two frameworks and perform Hamiltonian-reservoir replica-exchange Monte Carlo sampling and metadynamics simulations, respectively, to demonstrate that both frameworks can achieve converged and transferable sampling of the conformational landscape of complex and flexible biomolecules with comparable accuracy and computational cost.

TorsiFlex: an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids

In this work, we introduce TorsiFlex, a user-friendly software written in Python 3 and designed to find all the torsional conformers of flexible acyclic molecules in an automatic fashion. For the mapping of the torsional potential energy surface, the algorithm implemented in TorsiFlex combines two searching strategies: preconditioned and stochastic. The former is a type of systematic search based on chemical knowledge and should be carried out before the stochastic (random) search. The algorithm applies several validation tests to accelerate the exploration of the torsional space. For instance, the optimized structures are stored and this information is used to prevent revisiting these points and their surroundings in future iterations. TorsiFlex operates with a dual-level strategy by which the initial search is carried out at an inexpensive electronic structure level of theory and the located conformers are reoptimized at a higher level. Additionally, the program takes advantage of conformational enantiomerism, when possible. As a case study, and in order to exemplify the effectiveness and capabilities of this program, we have employed TorsiFlex to locate the conformers of the twenty proteinogenic amino acids in their neutral canonical form. TorsiFlex has produced a number of conformers that roughly doubles the amount of the most complete work to date.

Quantum Deep Descriptor: Physically Informed Transfer Learning from Small Molecules to Polymers

In this study, we propose a physically informed transfer learning approach for materials informatics (MI) using a quantum deep descriptor (QDD) obtained from the quantum deep field (QDF). The QDF is a machine learning model based on density functional theory (DFT) and can be trained with a large database of molecular properties. The pre-trained QDF model can provide an effective molecular descriptor that encodes the fundamental quantum-chemical characteristics (i.e., the wave function or orbital, electron density, and energies of a molecule) learned from the large database; we refer to this descriptor as a QDD. We show that a QDD pre-trained with certain properties of small molecules can predict different properties (e.g., the band gap and dielectric constant) of polymers compared with some existing descriptors. We believe that our DFT-based, physically informed transfer learning approach will not only be useful for practical applications in MI but will also provide quantum-chemical insights into materials in the future. All codes used in this study are available at https://github.com/masashitsubaki.

Density Functional Theory Study of Reaction Equilibria in Signal Amplification by Reversible Exchange

The front cover artwork is provided by the groups of Prof. Thomas Theis (North Carolina State University) Prof. Volker Blum (Duke University). The image shows the reaction network of Signal Amplification by Reversible Exchange (SABRE), elucidated by density functional theory (DFT). Read the full text of the Review at 10.1002/cphc.202100204.

Density Functional Theory Study of Reaction Equilibria in Signal Amplification by Reversible Exchange

An in-depth theoretical analysis of key chemical equilibria in Signal Amplification by Reversible Exchange (SABRE) is provided, employing density functional theory calculations to characterize the likely reaction network. For all reactions in the network, the potential energy surface is probed to identify minimum energy pathways. Energy barriers and transition states are calculated, and harmonic transition state theory is applied to calculate exchange rates that approximate experimental values. The reaction network energy surface can be modulated by chemical potentials that account for the dependence on concentration, temperature, and partial pressure of molecular constituents (hydrogen, methanol, pyridine) supplied to the experiment under equilibrium conditions. We show that, under typical experimental conditions, the Gibbs free energies of the two key states involved in pyridine-hydrogen exchange at the common Ir-IMes catalyst system in methanol are essentially the same, i. e., nearly optimal for SABRE. We also show that a methanol-containing intermediate is plausible as a transient species in the process.

Physics-Inspired Structural Representations for Molecules and Materials

The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.

Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization

In order to accurately understand and estimate molecular properties, finding energetically favorable molecular conformations is the most fundamental task for atomistic computational research on molecules and materials. Geometry optimization based on quantum chemical calculations has enabled the conformation prediction of arbitrary molecules, including de novo ones. However, it is computationally expensive to perform geometry optimizations for enormous conformers. In this study, we introduce the gray-box optimization (GBO) framework, which enables optimal control over the entire geometry optimization process, among multiple conformers. Algorithms designed for GBO roughly estimate energetically preferable conformers during their geometry optimization iterations. They then preferentially compute promising conformers. To evaluate the performance of the GBO framework, we applied it to a test set consisting of seven dipeptides and mycophenolic acid to determine their stable conformations at the density functional theory level. We thus preferentially obtained energetically favorable conformations. Furthermore, the computational costs required to find the most stable conformation were significantly reduced (approximately 1% on average, compared to the naive approach for the dipeptides).

The Rise of Neural Networks for Materials and Chemical Dynamics

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.

Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization

Machine learning methods usually depend on internal parameters—so called hyperparameters—that need to be optimized for best performance. Such optimization poses a burden on machine learning practitioners, requiring expert knowledge, intuition or computationally demanding brute-force parameter searches. We here assess three different hyperparameter selection methods: grid search, random search and an efficient automated optimization technique based on Bayesian optimization (BO). We apply these methods to a machine learning problem based on kernel ridge regression in computational chemistry. Two different descriptors are employed to represent the atomic structure of organic molecules, one of which introduces its own set of hyperparameters to the method. We identify optimal hyperparameter configurations and infer entire prediction error landscapes in hyperparameter space that serve as visual guides for the hyperparameter performance. We further demonstrate that for an increasing number of hyperparameters, BO and random search become significantly more efficient in computational time than an exhaustive grid search, while delivering an equivalent or even better accuracy.

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules

Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. We here supply a diverse benchmark spectroscopy dataset of 61,489 molecules extracted from organic crystals in the Cambridge Structural Database (CSD), denoted OE62. Molecular equilibrium geometries are reported at the Perdew-Burke-Ernzerhof (PBE) level of density functional theory (DFT) including van der Waals corrections for all 62 k molecules. For these geometries, OE62 supplies total energies and orbital eigenvalues at the PBE and the PBE hybrid (PBE0) functional level of DFT for all 62 k molecules in vacuum as well as at the PBE0 level for a subset of 30,876 molecules in (implicit) water. For 5,239 molecules in vacuum, the dataset provides quasiparticle energies computed with many-body perturbation theory in the G0W0 approximation with a PBE0 starting point (denoted GW5000 in analogy to the GW100 benchmark set (M. van Setten et al. J. Chem. Theory Comput. 12, 5076 (2016))).

Ultrafast Quantum Interference in the Charge Migration of Tryptophan

Extreme-ultraviolet-induced charge migration in biorelevant molecules is a fundamental step in the complex path leading to photodamage. In this work we propose a simple interpretation of the charge migration recently observed in an attosecond pump-probe experiment on the amino acid tryptophan. We find that the decay of the prominent low-frequency spectral structure with increasing pump-probe delay is due to a quantum beating between two geometrically distinct, almost degenerate charge oscillations. Quantum beating is ubiquitous in these systems, and at least on the few-to-tens of femtosecond time scales, it may dominate over decoherence the line intensities of time-resolved spectra. We also address the experimentally observed phase shift in the charge oscillations of two different amino acids, tryptophan and phenylalanine. Our results indicate that a beyond mean-field treatment of the electron dynamics is necessary to reproduce the correct behavior.

Ideal-gas thermodynamic properties of proteinogenic aliphatic amino acids calculated by R1SM approach

In this work, a R1SM approach was applied for the calculation of ideal-gas thermodynamic properties of five amino acids with aliphatic side chains: glycine, alanine, valine, leucine, and isoleucine. The first step of the calculation was an extensive conformational analysis that located several conformers not reported previously. A new systematic and user-friendly nomenclature of the conformers was introduced, and the stable conformers were clearly assigned with the previously used labeling where possible. Stability and calculated relative energies of the conformers were compared between various levels of theory and with several experimental studies, demonstrating a good performance of the selected B3LYP-D3/6-311+G(2df,p) level of theory. As a second step, the theoretically calculated vibrational frequencies were compared to the previously reported experimental spectra to verify the performance of the applied double-linear scaling factor. Finally, ideal-gas heat capacities, enthalpies, and absolute entropies were calculated, accounting for all stable conformers using the R1SM model. The resulting thermodynamic data are presented for the first time, since they cannot be determined experimentally and their rigorous calculation requires a complex thermodynamic model.

Cohesive properties of the crystalline phases of twenty proteinogenic α-aminoacids from first-principles calculations

Cohesive properties (lattice and cohesive energy of the crystal and corresponding sublimation enthalpy) of the complete set of twenty enantiopure anhydrous proteinogenic amino acids are investigated using first-principles calculations. In contrast to neutral amino acid molecules in the vapor phase, all amino acids form crystals in their zwitterionic form. Therefore, reliable ab initio calculations of the proton transfer energy are an indispensable step of such calculations. Simplifying procedures, designed to rationalize the computational cost of the quasi-harmonic approximation, which proves too demanding if performed fully at the given quantum level of theory, are presented and tested. For this purpose, atomic multipoles (up to the quadrupoles) for the amoeba force field are parametrized for all amino acid zwitterions. While the calculated lattice energies of the amino acids range from 235-458 kJ mol-1 in absolute value, the proton transfer energies typically amount to 100-220 kJ mol-1, which translates to sublimation enthalpies ranging from 117-202 kJ mol-1, appreciably exceeding the sublimation enthalpy values common for nonionic molecular crystals. Critically assessed experimental data on sublimation enthalpies are used as a benchmark for comparison of the data calculated in this work. Cohesive properties of most amino acids calculated in this work, combining the PBE-D3(BJ)/PAW and CCSD(T)-F12/aug-cc-pVDZ levels of theory used for predictions of the lattice energies and of the proton transfer energies, respectively, exhibit a reasonable agreement with the experiment. At the same time, this work contains the first published data on cohesive properties for several enantiopure amino acids.

Energy refinement and analysis of structures in the QM9 database via a highly accurate quantum chemical method

A wide variety of data-driven approaches have been introduced in the field of quantum chemistry. To extend the applicable range and improve the prediction power of those approaches, highly accurate quantum chemical benchmarks that cover extremely large chemical spaces are required. Here, we report ~134 k quantum chemical calculations performed with G4MP2, the fourth generation of the G-n series in which second-order perturbation theory is employed. A single composite method calculation executes several low-level calculations to reproduce the results of high-level ab initio calculations with the aim of saving computational costs. Therefore, our database reports the results of the various methods (e.g., density functional theory, Hartree-Fock, Møller-Plesset perturbation theory, and coupled-cluster theory). Additionally, we examined the structure information of both the QM9 and the revised databases via chemical graph analysis. Our database can be applied to refine and improve the quality of data-driven quantum chemical prediction. Furthermore, we reported the raw outputs of all calculations performed in this work for other potential applications.

In-depth structural analysis of glycans in the gas phase

Although there have been substantial improvements in glycan analysis over the past decade, the lack of both high-resolution and high-throughput methods hampers progress in glycomics. This perspective article highlights the current developments of liquid chromatography, mass spectrometry, ion-mobility spectrometry and cryogenic IR spectroscopy for glycan analysis and gives a critical insight to their individual strengths and limitations. Moreover, we discuss a novel concept in which ion mobility-mass spectrometry and cryogenic IR spectroscopy is combined in a single instrument such that datasets consisting of m/z, collision cross sections and IR fingerprints can be obtained. This multidimensional data will then be compared to a comprehensive reference library of intact glycans and their fragments to accurately identify unknown glycans on a high-throughput scale with minimal sample requirements. Due to the complementarity of the obtained information, this novel approach is highly diagnostic and also suitable for the identification of larger glycans; however, the workflow and instrumentation is straightforward enough to be implemented into a user-friendly setup.

Microhydration of protonated 5-hydroxyindole revealed by infrared spectroscopy

Controlled microsolvation of protonated aromatic biomolecules with water is fundamental to understand proton transfer reactions in aqueous environments. We measured infrared photodissociation (IRPD) spectra of mass-selected microhydrates of protonated 5-hydroxyindole (5HIH+-Wn, W = H2O, n = 1-3) in the OH and NH stretch ranges (2700-3800 cm-1), which are sensitive to the spectroscopic characteristics of interior solvation, water network formation, and proton transfer to solvent. Analysis of the IRPD spectra by dispersion-corrected density functional theory calculations (B3LYP-D3/aug-cc-pVTZ) reveals the coexistence of C3- and C4-protonated carbenium ions, 5HIH+(C3) and 5HIH+(C4), as well as the O-protonated oxonium ion, 5HIH+(O). Monohydrated 5HIH+-W clusters are formed by hydrogen-bonding (H-bonding) of the first water to the most acidic functional group, namely, the NH group in the case of 5HIH+(C3), the OH group for 5HIH+(C4), and the OH2 group for 5HIH+(O). The latter benefits from its twofold degeneracy and the outstandingly high binding energy of D0 ∼ 100 kJ mol-1. Larger 5HIH+-W2/3 clusters preferably grow (i) by H-bonding of the second water to the remaining vacant functional group and and/or (ii) by formation of W2 water chains at the respective most acidic functional group. Our IRPD spectra of 5HIH+-Wn do not indicate any proton transfer to the solvent up to n = 3, in line with the proton affinities of 5HI and Wn. Comparison of 5HIH+-Wn to neutral 5HI-W and cationic 5HI+-Wn clusters elucidates the impact of different charge states on the topology of the initial solvation shell. Furthermore, to access the influence of the size of the arene ion and a second functional group, we draw a comparison to microhydration of protonated phenol.

PEPCONF, a diverse data set of peptide conformational energies

We present an extensive and diverse database of peptide conformational energies. Our database contains five different classes of model geometries: dipeptides, tripeptides, and disulfide-bridged, bioactive, and cyclic peptides. In total, the database consists of 3775 conformational energy data points and 4530 conformer geometries. All the reference energies have been calculated at the LC-ωPBE-XDM/aug-cc-pVTZ level of theory, which is shown to yield conformational energies with an accuracy in the order of tenths of a kcal/mol when compared to complete-basis-set coupled-cluster reference data. The peptide conformational data set (PEPCONF) is presented as a high-quality reference set for the development and benchmarking of molecular-mechanics and semi-empirical electronic structure methods, which are the most commonly used techniques in the modeling of medium to large proteins.

Probing chirality recognition of protonated glutamic acid dimers by gas-phase vibrational spectroscopy and first-principles simulations

We characterize stereospecific aspects of homochiral and heterochiral dimers of glutamic acid by infrared spectroscopy and first-principles molecular dynamics simulations.

Machine learning unifies the modeling of materials and molecules

Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provide new insight into the potential energy surface of materials and molecules.

Mapping and classifying molecules from a high-throughput structural database

High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational searches, as well as the agglomeration of data of heterogeneous provenance leads to considerable challenges when it comes to navigating the database, representing its structure at a glance, understanding structure-property relations, eliminating duplicates and identifying inconsistencies. Here we present a case study, based on a data set of conformers of amino acids and dipeptides, of how machine-learning techniques can help addressing these issues. We will exploit a recently-developed strategy to define a metric between structures, and use it as the basis of both clustering and dimensionality reduction techniques-showing how these can help reveal structure-property relations, identify outliers and inconsistent structures, and rationalise how perturbations (e.g. binding of ions to the molecule) affect the stability of different conformers.

Trends for isolated amino acids and dipeptides: Conformation, divalent ion binding, and remarkable similarity of binding to calcium and lead

We derive structural and binding energy trends for twenty amino acids, their dipeptides, and their interactions with the divalent cations Ca2+, Ba2+, Sr2+, Cd2+, Pb2+, and Hg2+. The underlying data set consists of more than 45,000 first-principles predicted conformers with relative energies up to ~4 eV (~400 kJ/mol). We show that only very few distinct backbone structures of isolated amino acids and their dipeptides emerge as lowest-energy conformers. The isolated amino acids predominantly adopt structures that involve an acidic proton shared between the carboxy and amino function. Dipeptides adopt one of two intramolecular-hydrogen bonded conformations C5 or . Upon complexation with a divalent cation, the accessible conformational space shrinks and intramolecular hydrogen bonding is prevented due to strong electrostatic interaction of backbone and side chain functional groups with cations. Clear correlations emerge from the binding energies of the six divalent ions with amino acids and dipeptides. Cd2+ and Hg2+ show the largest binding energies-a potential correlation with their known high acute toxicities. Ca2+ and Pb2+ reveal almost identical binding energies across the entire series of amino acids and dipeptides. This observation validates past indications that ion-mimicry of calcium and lead should play an important role in a toxicological context.

Comparing molecules and solids across structural and alchemical space

A general procedure to compare molecules and materials powers insightful representations of energy landscapes and precise machine-learning predictions of properties.