Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments

The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.

Similarity scores of vibrational spectra reveal the atomistic structure of pentapeptides in multiple basins

Vibrational spectroscopy combined with theoretical calculations is a powerful tool for analyzing the interaction and conformation of peptides at the atomistic level. Nonetheless, identifying the structure becomes increasingly difficult as the peptide size grows large. One example is acetyl-SIVSF-N-methylamide, a capped pentapeptide, whose atomistic structure has remained unknown since its first observation [T. Sekiguchi, M. Tamura, H. Oba, P. Çarçarbal, R. R. Lozada-Garcia, A. Zehnacker-Rentien, G. Grégoire, S. Ishiuchi and M. Fujii, Angew. Chem., Int. Ed., 2018, 57, 5626-5629]. Here, we propose a novel conformational search method, which exploits the structure-spectrum correlation using a similarity score that measures the agreement of theoretical and experimental spectra. Surprisingly, the two conformers have distinctly different energy and geometry. The second conformer is 25 kJ mol-1 higher in energy than the other, lowest-energy conformer. The result implies that there are multiple pathways in the early stage of the folding process: one to the global minimum and the other to a different basin. Once such a structure is established, the second conformer is unlikely to overcome the barrier to produce the most stable structure due to a vastly different hydrogen bond network of the backbone. Our proposed method can characterize the lowest-energy conformer and kinetically trapped, high-energy conformers of complex biomolecules.

Diserinol Isophthalamide: A Novel Reagent for Complexation with Biomolecular Anions in Electrospray Ionization Mass Spectrometry

Transferring biomolecules from solution to vacuum facilitates a detailed analysis of molecular structure and dynamics by isolating molecules of interest from a complex environment. However, inherent in the ion desolvation process is the loss of solvent hydrogen bonding partners, which are critical for the stability of a condensed-phase structure. Thus, transfer of ions to vacuum can favor structural rearrangement, especially near solvent-accessible charge sites, which tend to adopt intramolecular hydrogen bonding motifs in the absence of solvent. Complexation of monoalkylammonium moieties (e.g., lysine side chains) with crown ethers such as 18-crown-6 can disfavor structural rearrangement of protonated sites, but no equivalent ligand has been investigated for deprotonated groups. Herein we describe diserinol isophthalamide (DIP), a novel reagent for the gas-phase complexation of anionic moieties within biomolecules. Complexation is observed to the C-terminus or side chains of the small model peptides GD, GE, GG, DF-OMe, VYV, YGGFL, and EYMPME in electrospray ionization mass spectrometry (ESI-MS) studies. In addition, complexation is observed with the phosphate and carboxylate moieities of phosphoserine and phosphotyrosine. DIP performs favorably in comparison to an existing anion recognition reagent, 1,1'-(1,2-phenylene)bis(3-phenylurea), that exhibits moderate carboxylate binding in organic solvent. This improved performance in ESI-MS experiments is attributed to reduced steric constraints to complexation with carboxylate groups of larger molecules. Overall, diserinol isophthalamide is an effective complexation reagent that can be applied in future work to study retention of solution-phase structure, investigate intrinsic molecular properties, and examine solvation effects.

Reconstructing the infrared spectrum of a peptide from representative conformers of the full canonical ensemble

Leucine enkephalin (LeuEnk), a biologically active endogenous opioid pentapeptide, has been under intense investigation because it is small enough to allow efficient use of sophisticated computational methods and large enough to provide insights into low-lying minima of its conformational space. Here, we reproduce and interpret experimental infrared (IR) spectra of this model peptide in gas phase using a combination of replica-exchange molecular dynamics simulations, machine learning, and ab initio calculations. In particular, we evaluate the possibility of averaging representative structural contributions to obtain an accurate computed spectrum that accounts for the corresponding canonical ensemble of the real experimental situation. Representative conformers are identified by partitioning the conformational phase space into subensembles of similar conformers. The IR contribution of each representative conformer is calculated from ab initio and weighted according to the population of each cluster. Convergence of the averaged IR signal is rationalized by merging contributions in a hierarchical clustering and the comparison to IR multiple photon dissociation experiments. The improvements achieved by decomposing clusters containing similar conformations into even smaller subensembles is strong evidence that a thorough assessment of the conformational landscape and the associated hydrogen bonding is a prerequisite for deciphering important fingerprints in experimental spectroscopic data.

Surrogate Based Genetic Algorithm Method for Efficient Identification of Low-Energy Peptide Structures

Identification of the most stable structure(s) of a system is a prerequisite for the calculation of any of its properties from first-principles. However, even for relatively small molecules, exhaustive explorations of the potential energy surface (PES) are severely hampered by the dimensionality bottleneck. In this work, we address the challenging task of efficiently sampling realistic low-lying peptide coordinates by resorting to a surrogate based genetic algorithm (GA)/density functional theory (DFT) approach (sGADFT) in which promising candidates provided by the GA are ultimately optimized with DFT. We provide a benchmark of several computational methods (GAFF, AMOEBApro13, PM6, PM7, DFTB3-D3(BJ)) as possible prescanning surrogates and apply sGADFT to two test case systems that are (i) two isomer families of the protonated Gly-Pro-Gly-Gly tetrapeptide (Masson, A.; J. Am. Soc. Mass Spectrom.2015, 26, 1444-1454) and (ii) the doubly protonated cyclic decapeptide gramicidin S (Nagornova, N. S.; J. Am. Chem. Soc.2010, 132, 4040-4041). We show that our GA procedure can correctly identify low-energy minima in as little as a few hours. Subsequent refinement of surrogate low-energy structures within a given energy threshold (≤10 kcal/mol (i), ≤5 kcal/mol (ii)) via DFT relaxation invariably led to the identification of the most stable structures as determined from high-resolution infrared (IR) spectroscopy at low temperature. The sGADFT method therefore constitutes a highly efficient route for the screening of realistic low-lying peptide structures in the gas phase as needed for instance for the interpretation and assignment of experimental IR spectra.

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.

PSIQUE: Protein Secondary Structure Identification on the Basis of Quaternions and Electronic Structure Calculations

The secondary structure is important in protein structure analysis, classification, and modeling. We have developed a novel method for secondary structure assignment, termed PSIQUE, based on the potential energy surface (PES) of polyalanine obtained using an infinitely long chain model and density functional theory calculations. First, uniform protein segments are determined in terms of a difference of quaternions between neighboring amino acids along the protein backbone. Then, the identification of the secondary structure motifs is carried out based on the minima found in the PES. PSIQUE shows good agreement with other secondary structure assignment methods. However, it provides better discrimination of subtle secondary structures (e.g., helix types) and termini and produces more uniform segments while also accounting for local distortions. Overall, PSIQUE provides a precise and reliable assignment of secondary structures, so it should be helpful for the detailed characterization of the protein structure.

Rational ligand choice extends the SABRE substrate scope

Here we report on chelating ligands for Signal Amplification By Reversible Exchange (SABRE) catalysts that permit hyperpolarisation on otherwise sterically hindered substrates. We demonstrate 1H enhancements of ∼100-fold over 8.5 T thermal for 2-substituted pyridines, and smaller, yet significant enhancements for provitamin B6 and caffeine. We also show 15N-enhancements of ∼1000-fold and 19F-enhancements of 30-fold.

Probing the conformational landscape and thermochemistry of DNA dinucleotide anions via helium nanodroplet infrared action spectroscopy

Isolation of biomolecules in vacuum facilitates characterization of the intramolecular interactions that determine three-dimensional structure, but experimental quantification of conformer thermochemistry remains challenging. Infrared spectroscopy of molecules trapped in helium nanodroplets is a promising methodology for the measurement of thermochemical parameters. When molecules are captured in a helium nanodroplet, the rate of cooling to an equilibrium temperature of ca. 0.4 K is generally faster than the rate of isomerization, resulting in "shock-freezing" that kinetically traps molecules in local conformational minima. This unique property enables the study of temperature-dependent conformational equilibria via infrared spectroscopy at 0.4 K, thereby avoiding the deleterious effects of spectral broadening at higher temperatures. Herein, we demonstrate the first application of this approach to ionic species by coupling electrospray ionization mass spectrometry (ESI-MS) with helium nanodroplet infrared action spectroscopy to probe the structure and thermochemistry of deprotonated DNA dinucleotides. Dinucleotide anions were generated by ESI, confined in an ion trap at temperatures between 90 and 350 K, and entrained in traversing helium nanodroplets. The infrared action spectra of the entrained ions show a strong dependence on pre-pickup ion temperature, consistent with the preservation of conformer population upon cooling to 0.4 K. Non-negative matrix factorization was utilized to identify component conformer infrared spectra and determine temperature-dependent conformer populations. Relative enthalpies and entropies of conformers were subsequently obtained from a van't Hoff analysis. IR spectra and conformer thermochemistry are compared to results from ion mobility spectrometry (IMS) and electronic structure methods. The implementation of ESI-MS as a source of dopant molecules expands the diversity of molecules accessible for thermochemical measurements, enabling the study of larger, non-volatile species.

Combining ultra-high resolution ion mobility spectrometry with cryogenic IR spectroscopy for the study of biomolecular ions

We explore the capability of SLIM-based IMS for isomer selectivity in combination with cryogenic, messenger-tagging IR spectroscopy.

Double-resonance spectroscopic schemes in combination with cryogenic ion traps are the go-to techniques when isomer-specific high-resolution spectra are required for analysis of molecular ions. Their limitation lies in the requirement for well-resolved, isomer-specific absorption bands as well as in the potentially time-consuming steps to identify each isomer. We present an alternative approach where isomeric species are readily separated using ion mobility spectrometry (IMS) and selected prior to cryogenic spectroscopic analysis. To date, most IMS approaches suffer from relatively low resolution, however, recent technological developments in the field of travelling-wave ion mobility using structures for lossless ion manipulation (SLIM) permit the use of extremely long drift paths, which greatly enhances the resolution. We demonstrate the power of combining this type of ultra-high resolution IMS with cryogenic vibrational spectroscopy by comparing mobility-resolved IR spectra of a disaccharide to those acquired using IR–IR double resonance. This new approach is especially promising for the investigation of larger molecules where spectral congestion interferes with double resonance techniques.

Frenkel-Holstein Hamiltonian applied to absorption spectra of quaterthiophene-based 2D hybrid organic-inorganic perovskites

For the prototypical two-dimensional hybrid organic-inorganic perovskites (2D HOIPs) (AE4T)PbX₄ (X = Cl, Br, and I), we demonstrate that the Frenkel-Holstein Hamiltonian (FHH) can be applied to describe the absorption spectrum arising from the organic component. We first model the spectra using only the four nearest neighbor couplings between translationally inequivalent molecules in the organic herringbone lattice as fitting parameters in the FHH. We next use linear-response time-dependent density functional theory (LR-TDDFT) to calculate molecular transition densities, from which extended excitonic couplings are evaluated based on the atomic positions within the 2D HOIPs. We find that both approaches reproduce the experimentally observed spectra, including changes in their shape and peak positions. The spectral changes are correlated with a decrease in excitonic coupling from X = Cl to X = I. Importantly, the LR-TDDFT-based approach with extended excitonic couplings not only gives better agreement with the experimental absorption line shape than the approach using a restricted set of fitted parameters but also allows us to relate the changes in excitonic coupling to the underlying geometry. We accordingly find that the decrease in excitonic coupling from X = Cl to Br to I is due to an increase in molecular separation, which in turn can be related to the increasing Pb-X bond length from Cl to I. Our research opens up a potential pathway to predicting optoelectronic properties of new 2D HOIPs from ab initio calculations and to gain insight into structural relations from 2D HOIP absorption spectra.

Automated exploration of the low-energy chemical space with fast quantum chemical methods

We propose and discuss an efficient scheme for the in silico sampling for parts of the molecular chemical space by semiempirical tight-binding methods combined with a meta-dynamics driven search algorithm. The focus of this work is set on the generation of proper thermodynamic ensembles at a quantum chemical level for conformers, but similar procedures for protonation states, tautomerism and non-covalent complex geometries are also discussed. The conformational ensembles consisting of all significantly populated minimum energy structures normally form the basis of further, mostly DFT computational work, such as the calculation of spectra or macroscopic properties. By using basic quantum chemical methods, electronic effects or possible bond breaking/formation are accounted for and a very reasonable initial energetic ranking of the candidate structures is obtained. Due to the huge computational speedup gained by the fast low-cost quantum chemical methods, overall short computation times even for systems with hundreds of atoms (typically drug-sized molecules) are achieved. Furthermore, specialized applications, such as sampling with implicit solvation models or constrained conformational sampling for transition-states, metal-, surface-, or noncovalently bound complexes are discussed, opening many possible applications in modern computational chemistry and drug discovery. The procedures have been implemented in a freely available computer code called CREST, that makes use of the fast and reliable GFNn-xTB methods.

Conformational Searching with Quantum Mechanics

Estimating the range of three-dimensional structures (conformations) that are available to a molecule is a key component of computer-aided drug design. Quantum mechanical simulation offers improved accuracy over forcefield methods, but at a high computational cost. The question is whether this increased cost can be justified in a context in which high-throughput analysis of large numbers of molecules is often key. This chapter discusses the application of quantum mechanics to conformational searching, with a focus on three key challenges: (1) the generation of ensembles that include a good approximation to a molecule's bioactive conformation at as prominent a ranking as possible; (2) rational analysis and modification of a pre-established bioactive conformation in terms of its energetics; and (3) approximation of real solution-phase conformational ensembles in tandem with NMR data. The impact of QM on the high-throughput application (1) is debatable, meaning that for the moment its primary application is still lower-throughput applications such as (2) and (3). The optimal choice of QM method is also discussed. Rigorous benchmarking suggests that DFT methods are only acceptable when used with large basis sets, but a trickle of papers continue to obtain useful results with relatively low-cost methods, leading to a dilemma that the literature has yet to fully resolve.

Quantum mechanics of proteins in explicit water: The role of plasmon-like solute-solvent interactions

Quantum-mechanical van der Waals dispersion interactions play an essential role in intraprotein and protein-water interactions-the two main factors affecting the structure and dynamics of proteins in water. Typically, these interactions are only treated phenomenologically, via pairwise potential terms in classical force fields. Here, we use an explicit quantum-mechanical approach of density-functional tight-binding combined with the many-body dispersion formalism and demonstrate the relevance of many-body van der Waals forces both to protein energetics and to protein-water interactions. In contrast to commonly used pairwise approaches, many-body effects substantially decrease the relative stability of native states in the absence of water. Upon solvation, the protein-water dispersion interaction counteracts this effect and stabilizes native conformations and transition states. These observations arise from the highly delocalized and collective character of the interactions, suggesting a remarkable persistence of electron correlation through aqueous environments and providing the basis for long-range interaction mechanisms in biomolecular systems.

Theory and practice of modeling van der Waals interactions in electronic-structure calculations

The accurate description of long-range electron correlation, most prominently including van der Waals (vdW) dispersion interactions, represents a particularly challenging task in the modeling of molecules and materials. vdW forces arise from the interaction of quantum-mechanical fluctuations in the electronic charge density. Within (semi-)local density functional approximations or Hartree-Fock theory such interactions are neglected altogether. Non-covalent vdW interactions, however, are ubiquitous in nature and play a key role for the understanding and accurate description of the stability, dynamics, structure, and response properties in a plethora of systems. During the last decade, many promising methods have been developed for modeling vdW interactions in electronic-structure calculations. These methods include vdW-inclusive Density Functional Theory and correlated post-Hartree-Fock approaches. Here, we focus on the methods within the framework of Density Functional Theory, including non-local van der Waals density functionals, interatomic dispersion models within many-body and pairwise formulation, and random phase approximation-based approaches. This review aims to guide the reader through the theoretical foundations of these methods in a tutorial-style manner and, in particular, highlight practical aspects such as the applicability and the advantages and shortcomings of current vdW-inclusive approaches. In addition, we give an overview of complementary experimental approaches, and discuss tools for the qualitative understanding of non-covalent interactions as well as energy decomposition techniques. Besides representing a reference for the current state-of-the-art, this work is thus also designed as a concise and detailed introduction to vdW-inclusive electronic structure calculations for a general and broad audience.

Assessing the stability of alanine-based helices by conformer-selective IR spectroscopy

Polyalanine based peptides that carry a lysine at the C-terminus ([Ac-AlanLys + H](+)) are known to form α-helices in the gas phase. Three factors contribute to the stability of these helices: (i) the interaction between the helix macro dipole and the charge, (ii) the capping of dangling C[double bond, length as m-dash]O groups by lysine and (iii) the cooperative hydrogen bond network. In previous studies, the influence of the interaction between the helix dipole and the charge as well as the impact of the capping was studied intensively. Here, we complement these findings by systematically assessing the third parameter, the H-bond network. In order to selectively remove one H-bond along the backbone, we use amide-to-ester substitutions. The resulting depsi peptides were analyzed by ion-mobility and m/z-selective infrared spectroscopy as well as theoretical calculations. Our results indicate that peptides which contain only one ester bond still maintain the helical conformation. We conclude that the interaction between the charge and the helix macro-dipole is most crucial for the formation of the α-helical conformation and a single backbone H-bond has only little influence on the overall stability.

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.

About Underappreciated Yet Active Conformations of Thiourea Organocatalysts

Conformational dynamics can define the function of organocatalysts. While the accepted mechanism of Schreiner's catalyst features a double hydrogen bond to the substrate that only forms with the anti-anti conformation of its central thiourea group, our electronic-structure theory study reveals that binding of the model substrate methyl vinyl ketone prefers syn-anti conformations. We find a new mechanism featuring π stacking interactions and highlight the need for extensive structure searches for flexible molecules, especially when aiming for structure-based design of catalytic activity.

First-Principles Models for van der Waals Interactions in Molecules and Materials: Concepts, Theory, and Applications

Noncovalent van der Waals (vdW) or dispersion forces are ubiquitous in nature and influence the structure, stability, dynamics, and function of molecules and materials throughout chemistry, biology, physics, and materials science. These forces are quantum mechanical in origin and arise from electrostatic interactions between fluctuations in the electronic charge density. Here, we explore the conceptual and mathematical ingredients required for an exact treatment of vdW interactions, and present a systematic and unified framework for classifying the current first-principles vdW methods based on the adiabatic-connection fluctuation-dissipation (ACFD) theorem (namely the Rutgers-Chalmers vdW-DF, Vydrov-Van Voorhis (VV), exchange-hole dipole moment (XDM), Tkatchenko-Scheffler (TS), many-body dispersion (MBD), and random-phase approximation (RPA) approaches). Particular attention is paid to the intriguing nature of many-body vdW interactions, whose fundamental relevance has recently been highlighted in several landmark experiments. The performance of these models in predicting binding energetics as well as structural, electronic, and thermodynamic properties is connected with the theoretical concepts and provides a numerical summary of the state-of-the-art in the field. We conclude with a roadmap of the conceptual, methodological, practical, and numerical challenges that remain in obtaining a universally applicable and truly predictive vdW method for realistic molecular systems and materials.

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.

Direct and cost-efficient hyperpolarization of long-lived nuclear spin states on universal (15)N2-diazirine molecular tags

Conventional magnetic resonance (MR) faces serious sensitivity limitations which can be overcome by hyperpolarization methods, but the most common method (dynamic nuclear polarization) is complex and expensive, and applications are limited by short spin lifetimes (typically seconds) of biologically relevant molecules. We use a recently developed method, SABRE-SHEATH, to directly hyperpolarize (15)N2 magnetization and long-lived (15)N2 singlet spin order, with signal decay time constants of 5.8 and 23 minutes, respectively. We find >10,000-fold enhancements generating detectable nuclear MR signals that last for over an hour. (15)N2-diazirines represent a class of particularly promising and versatile molecular tags, and can be incorporated into a wide range of biomolecules without significantly altering molecular function.

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

We present a structural data set of the 20 proteinogenic amino acids and their amino-methylated and acetylated (capped) dipeptides. Different protonation states of the backbone (uncharged and zwitterionic) were considered for the amino acids as well as varied side chain protonation states. Furthermore, we studied amino acids and dipeptides in complex with divalent cations (Ca²⁺, Ba²⁺, Sr²⁺, Cd²⁺, Pb²⁺, and Hg²⁺). The database covers the conformational hierarchies of 280 systems in a wide relative energy range of up to 4 eV (390 kJ/mol), summing up to a total of 45,892 stationary points on the respective potential-energy surfaces. All systems were calculated on equal first-principles footing, applying density-functional theory in the generalized gradient approximation corrected for long-range van der Waals interactions. We show good agreement to available experimental data for gas-phase ion affinities. Our curated data can be utilized, for example, for a wide comparison across chemical space of the building blocks of life, for the parametrization of protein force fields, and for the calculation of reference spectra for biophysical applications.

Probing the role of electrostatics of polypeptide main-chain in protein folding by perturbing N-terminal residue stereochemistry: DFT study with oligoalanine models

Energetics of folding (ΔH_E→F, in kcal mol⁻¹) from the extended (E) structure to the folded (F) structure for Ia and Ib critically depend on the geometrical relationship between the backbone peptide units of the polypeptide structure.

Going clean: structure and dynamics of peptides in the gas phase and paths to solvation

The gas phase is an artificial environment for biomolecules that has gained much attention both experimentally and theoretically due to its unique characteristic of providing a clean room environment for the comparison between theory and experiment. In this review we give an overview mainly on first-principles simulations of isolated peptides and the initial steps of their interactions with ions and solvent molecules: a bottom up approach to the complexity of biological environments. We focus on the accuracy of different methods to explore the conformational space, the connections between theory and experiment regarding collision cross section evaluations and (anharmonic) vibrational spectra, and the challenges faced in this field.