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Recently, we presented a method to assign atomic partial charges based on the DASH (dynamic attention-based substructure hierarchy) tree with high efficiency and quantum mechanical (QM)-like accuracy. In addition, the approach can be considered "rule based"—where the rules are derived from the attention values of a graph neural network—and thus, each assignment is fully explainable by visualizing the underlying molecular substructures. In this work, we demonstrate that these hierarchically sorted substructures capture the key features of the local environment of an atom and allow us to predict different atomic properties with high accuracy without building a new DASH tree for each property. The fast prediction of atomic properties in molecules with the DASH tree can, for example, be used as an efficient way to generate feature vectors for machine learning without the need for expensive QM calculations. The final DASH tree with the different atomic properties as well as the complete dataset with wave functions is made freely available. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) relaxation experiments shine light onto the dynamics of molecular systems in the picosecond to millisecond timescales. As these methods cannot provide an atomically resolved view of the motion of atoms, functional groups, or domains giving rise to such signals, relaxation techniques have been combined with molecular dynamics (MD) simulations to obtain mechanistic descriptions and gain insights into the functional role of side chain or domain motion. In this work, we present a comparison of five computational methods that permit the joint analysis of MD simulations and NMR relaxation experiments. We discuss their relative strengths and areas of applicability and demonstrate how they may be utilized to interpret the dynamics in MD simulations with the small protein ubiquitin as a test system. We focus on the aliphatic side chains given the rigidity of the backbone of this protein. We find encouraging agreement between experiment, Markov state models built in the χ1/χ2 rotamer space of isoleucine residues, explicit rotamer jump models, and a decomposition of the motion using ROMANCE. These methods allow us to ascribe the dynamics to specific rotamer jumps. Simulations with eight different combinations of force field and water model highlight how the different metrics may be employed to pinpoint force field deficiencies. Furthermore, the presented comparison offers a perspective on the utility of NMR relaxation to serve as validation data for the prediction of kinetics by state-of-the-art biomolecular force fields. [ABSTRACT FROM AUTHOR]

The Adaptive Solvent-Scaling (AdSoS) scheme [J. Chem. Phys. 155 (2021) 094107] is an adaptive-resolution approach for performing simulations of a solute embedded in a fine-grained (FG) solvent region surrounded by a coarse-grained (CG) solvent region, with a continuous FG ↔ CG switching of the solvent resolution across a buffer layer. Instead of relying on a distinct CG solvent model, AdSoS is based on CG models defined by a dimensional scaling of the FG solvent by a factor s, accompanied by the s-dependent modulation of its mass and interaction parameters. The latter changes are designed to achieve an isomorphism between the dynamics of the FG and CG models, and to preserve the dispersive and dielectric solvation properties of the solvent with respect to a solute at FG resolution. As a result, the AdSoS scheme minimizes the thermodynamic mismatch between different regions of the adaptive-resolution system. The present article generalizes the scheme initially introduced for a pure atomic liquid in slab geometry to more practically relevant situations involving (i) a molecular dipolar solvent (e.g., water); (ii) a radial geometry (i.e., spherical rather than planar layers); and (iii) the inclusion of a solute (e.g., water molecule, dipeptide, ion, or ion pair). [ABSTRACT FROM AUTHOR]

Clustering has become an indispensable tool in the presence of increasingly large and complex datasets. Most clustering algorithms depend, either explicitly or implicitly, on the sampled density. However, estimated densities are fragile due to the curse of dimensionality and finite sampling effects, for instance, in molecular dynamics simulations. To avoid the dependence on estimated densities, an energy-based clustering (EBC) algorithm based on the Metropolis acceptance criterion is developed in this work. In the proposed formulation, EBC can be considered a generalization of spectral clustering in the limit of large temperatures. Taking the potential energy of a sample explicitly into account alleviates requirements regarding the distribution of the data. In addition, it permits the subsampling of densely sampled regions, which can result in significant speed-ups and sublinear scaling. The algorithm is validated on a range of test systems including molecular dynamics trajectories of alanine dipeptide and the Trp-cage miniprotein. Our results show that including information about the potential-energy surface can largely decouple clustering from the sampled density. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations enable the study of the motion of small and large (bio)molecules and the estimation of their conformational ensembles. The description of the environment (solvent) has, therefore, a large impact. Implicit solvent representations are efficient but, in many cases, not accurate enough (especially for polar solvents, such as water). More accurate but also computationally more expensive is the explicit treatment of the solvent molecules. Recently, machine learning has been proposed to bridge the gap and simulate, in an implicit manner, explicit solvation effects. However, the current approaches rely on prior knowledge of the entire conformational space, limiting their application in practice. Here, we introduce a graph neural network based implicit solvent that is capable of describing explicit solvent effects for peptides with different compositions than those contained in the training set. [ABSTRACT FROM AUTHOR]

Replica-exchange enveloping distribution sampling (RE-EDS) is a pathway-independent multistate free-energy method currently implemented in the GROMOS software package for molecular dynamics (MD) simulations. It has a high intrinsic sampling efficiency as the interactions between the unperturbed particles have to be calculated only once for multiple end-states. As a result, RE-EDS is an attractive method for the calculation of relative solvation and binding free energies. An essential requirement for reaching this high efficiency is the separability of the nonbonded interactions into solute–solute, solute–environment, and environment–environment contributions. Such a partitioning is trivial when using a Coulomb term with a reaction-field (RF) correction to model the electrostatic interactions but not when using lattice-sum schemes. To avoid cutoff artifacts, the RF correction is typically used in combination with a charge-group-based cutoff, which is not supported by most small-molecule force fields as well as other MD engines. To address this issue, we investigate the combination of RE-EDS simulations with a recently introduced RF scheme including a shifting function that enables the rigorous calculation of RF electrostatics with atom-based cutoffs. The resulting approach is validated by calculating solvation free energies with the generalized AMBER force field in water and chloroform using both the GROMOS software package and a proof-of-concept implementation in OpenMM. [ABSTRACT FROM AUTHOR]

In the context of a project aiming at the replacement of the 3‐substituted β‐lactam ring in classical β‐lactam antibiotics by an N(3)‐acyl‐1,3‐diazetidinone moiety, we have investigated the reaction of isocyanates with imines derived from allyl glycinate and differently substituted propionaldehydes. Imines of aromatic aldehydes with anilines have been reported to react with acyl isocyanates to give 1,3‐diazetidinones or 2,3‐dihydro‐4H‐1,3,5‐oxadiazin‐4‐ones, via [2+2] or [4+2] cycloaddition, respectively. However, neither of these products was formed with imines derived from allyl glycinate and 2‐(mono)methyl propionaldehydes. α,α‐Dimethylation of the imine enabled the [4+2] cycloaddition pathway, but the desired 1,3‐diazetidinone products were not observed. Surprisingly, the imines obtained from thioesters of 2,2‐dimethyl 3‐oxo propionic acid reacted with aryl isocyanates or with benzyl isocyanate to give 5,5‐dimethyl‐2,4‐dioxo‐6‐(aryl‐/alkylthio)tetrahydropyrimidines, via thiol displacement and re‐addition to a putative six‐membered iminium intermediate. These experimental results obtained for the reactions could be rationalized by DFT calculations. In addition, we have shown that N(3)‐acyl‐1,3‐diazetidinone and 2,3‐dihydro‐4H‐1,3,5‐oxadiazin‐4‐one products can be distinguished based on experimental IR data in combination with theoretical reference spectra employing the IR spectra alignment (IRSA) algorithm. This discrimination was not possible by means of 1H, 13C, or 15N NMR spectroscopy [ABSTRACT FROM AUTHOR]

Time-split cross-validation is broadly recognized as the gold standard for validating predictive models intended for use in medicinal chemistry projects. Unfortunately this type of data is not broadly available outside of large pharmaceutical research organizations. Here we introduce the SIMPD (simulated medicinal chemistry project data) algorithm to split public data sets into training and test sets that mimic the differences observed in real-world medicinal chemistry project data sets. SIMPD uses a multi-objective genetic algorithm with objectives derived from an extensive analysis of the differences between early and late compounds in more than 130 lead-optimization projects run within the Novartis Institutes for BioMedical Research. Applying SIMPD to the real-world data sets produced training/test splits which more accurately reflect the differences in properties and machine-learning performance observed for temporal splits than other standard approaches like random or neighbor splits. We applied the SIMPD algorithm to bioactivity data extracted from ChEMBL and created 99 public data sets which can be used for validating machine-learning models intended for use in the setting of a medicinal chemistry project. The SIMPD code and simulated data sets are available under open-source/open-data licenses at github.com/rinikerlab/molecular_time_series. [ABSTRACT FROM AUTHOR]

A new approach termed Adaptive Solvent-Scaling (AdSoS) is introduced for performing simulations of a solute embedded in a fine-grained (FG) solvent region itself surrounded by a coarse-grained (CG) solvent region, with a continuous FG ↔ CG switching of the solvent resolution across a buffer layer. Instead of relying on a distinct CG solvent model, the AdSoS scheme is based on CG models defined by a dimensional scaling of the FG solvent by a factor s, accompanied by an s-dependent modulation of the atomic masses and interaction parameters. The latter changes are designed to achieve an isomorphism between the dynamics of the FG and CG models, and to preserve the dispersive and dielectric solvation properties of the solvent with respect to a solute at FG resolution. This scaling approach offers a number of advantages compared to traditional coarse-graining: (i) the CG parameters are immediately related to those of the FG model (no need to parameterize a distinct CG model); (ii) nearly ideal mixing is expected for CG variants with similar s-values (ideal mixing holding in the limit of identical s-values); (iii) the solvent relaxation timescales should be preserved (no dynamical acceleration typical for coarse-graining); (iv) the graining level NG (number of FG molecules represented by one CG molecule) can be chosen arbitrarily (in particular, NG = s3 is not necessarily an integer); and (v) in an adaptive-resolution scheme, this level can be varied continuously as a function of the position (without requiring a bundling mechanism), and this variation occurs at a constant number of particles per molecule (no occurrence of fractional degrees of freedom in the buffer layer). By construction, the AdSoS scheme minimizes the thermodynamic mismatch between the different regions of the adaptive-resolution system, leading to a nearly homogeneous scaled solvent density s3ρ. Residual density artifacts in and at the surface of the boundary layer can easily be corrected by means of a grid-based biasing potential constructed in a preliminary pure-solvent simulation. This article introduces the AdSoS scheme and provides an initial application to pure atomic liquids (no solute) with Lennard-Jones plus Coulomb interactions in a slab geometry. [ABSTRACT FROM AUTHOR]

The combination of Markov state modeling (MSM) and molecular dynamics (MD) simulations has been shown in recent years to be a valuable approach to unravel the slow processes of molecular systems with increasing complexity. While the algorithms for intermediate steps in the MSM workflow such as featurization and dimensionality reduction have been specifically adapted to MD datasets, conventional clustering methods are generally applied to the discretization step. This work adds to recent efforts to develop specialized density-based clustering algorithms for the Boltzmann-weighted data from MD simulations. We introduce the volume-scaled common nearest neighbor (vs-CNN) clustering that is an adapted version of the common nearest neighbor (CNN) algorithm. A major advantage of the proposed algorithm is that the introduced density-based criterion directly links to a free-energy notion via Boltzmann inversion. Such a free-energy perspective allows a straightforward hierarchical scheme to identify conformational clusters at different levels of a generally rugged free-energy landscape of complex molecular systems. [ABSTRACT FROM AUTHOR]

Thermally driven processes of molecular systems include transitions of energy barriers on the microsecond timescales and higher. Sufficient sampling of such processes with molecular dynamics simulations is challenging and often requires accelerating slow transitions using external biasing potentials. Different dynamic reweighting algorithms have been proposed in the past few years to recover the unbiased kinetics from biased systems. However, it remains an open question if and how these dynamic reweighting approaches are connected. In this work, we establish the link between the two main reweighting types, i.e., path-based and energy-based reweighting. We derive a path-based correction factor for the energy-based dynamic histogram analysis method, thus connecting the previously separate reweighting types. We show that the correction factor can be used to combine the advantages of path-based and energy-based reweighting algorithms: it is integrator independent, more robust, and at the same time able to reweight time-dependent biases. We can furthermore demonstrate the relationship between two independently derived path-based reweighting algorithms. Our theoretical findings are verified on a one-dimensional four-well system. By connecting different dynamic reweighting algorithms, this work helps to clarify the strengths and limitations of the different methods and enables a more robust usage of the combined types. [ABSTRACT FROM AUTHOR]

Mass spectrometry is a powerful technique for the structural and functional characterization of biomolecules. However, it remains challenging to accurately gauge the gas-phase structure of biomolecular ions and assess to what extent native-like structures are maintained. Here we propose a synergistic approach which utilizes Förster resonance energy transfer and two types of ion mobility spectrometry (i.e., traveling wave and differential) to provide multiple constraints (i.e., shape and intramolecular distance) for structure-refinement of gas-phase ions. We add microsolvation calculations to assess the interaction sites and energies between the biomolecular ions and gaseous additives. This combined strategy is employed to distinguish conformers and understand the gas-phase structures of two isomeric α-helical peptides that might differ in helicity. Our work allows more stringent structural characterization of biologically relevant molecules (e.g., peptide drugs) and large biomolecular ions than using only a single structural methodology in the gas phase. It remains challenging to gauge the gas-phase structure of biomolecular ions and assess to what extent native-like structures are maintained. Here, the authors utilize Förster resonance energy transfer and ion mobility spectrometry for more stringent structural characterization of biomolecular ions. [ABSTRACT FROM AUTHOR]

The relative stereochemistry of organic molecules can be determined by comparing theoretical and experimental infrared (IR) spectra of all isomers and assessing the best match. For this purpose, we have recently developed the IR spectra alignment (IRSA) algorithm for automated optimal alignment. IRSA provides a set of quantitative metrics to identify the candidate structure that agrees best with the experimental spectrum. While the correct diastereomer could be determined for the tested sets of rigid and flexible molecules, two issues were identified with more complex compounds that triggered further development. First, strongly overlapping peaks in the IR spectrum are not treated adequately in the current IRSA implementation. Second, the alignment of multiple spectra from different sources (e.g. IR and VCD or Raman) can be improved. In this study, we present an in-depth discussion of these points, followed by the description of modifications to the IRSA algorithm to address them. In particular, we introduce the concept of deconvolution of the experimental and theoretical spectra with a set of pseudo-Voigt bands. The pseudo-Voigt bands have a set of parameters, which can be employed in the alignment algorithm, leading to improved scoring functions. We test the modified algorithm on two data sets. The first set contains compounds with IR and Raman spectra measured in this study, and the second set contains compounds with IR and VCD spectra available in the literature. We show that the algorithm is able to determine the correct diastereomer in all cases. The results highlight that vibrational spectroscopy can be a valuable alternative or complementary method to inform about the stereochemistry of compounds, and the performance of the updated IRSA algorithm suggests that it is a powerful tool for quantitative-based spectral assignments in academia and industry. [ABSTRACT FROM AUTHOR]

This article documents enu, a freely-downloadable, open-source and stand-alone program written in C++ for the enumeration of the constitutional isomers and stereoisomers of a molecular formula. The program relies on graph theory to enumerate all the constitutional isomers of a given formula on the basis of their canonical adjacency matrix. The stereoisomers of a given constitutional isomer are enumerated as well, on the basis of the automorphism group of this matrix. The isomer list is then reported in the form of canonical SMILES strings within files in XML format. The specification of the molecule family of interest is very flexible and the code is optimized for computational efficiency. The algorithms and implementations underlying enu are described, and simple illustrative applications are presented. The enu code is freely available on GitHub at https://github.com/csms-ethz/CombiFF. [ABSTRACT FROM AUTHOR]

Collagen model peptides (CMPs) consisting of proline‐(2S,4R)‐hydroxyproline‐glycine (POG) repeats have provided a breadth of knowledge of the triple helical structure of collagen, the most abundant protein in mammals. Predictive tools for triple helix stability have, however, lagged behind since the effect of CMPs with different frames ([POG]n, [OGP]n, or [GPO]n) and capped or uncapped termini have so far been underestimated. Here, we elucidated the impact of the frame, terminal functional group and its charge on the stability of collagen triple helices. Combined experimental and theoretical studies with frame‐shifted, capped and uncapped CMPs revealed that electrostatic interactions, strand preorganization, interstrand H‐bonding, and steric repulsion at the termini contribute to triple helix stability. We show that these individual contributions are additive and allow for the prediction of the melting temperatures of CMP trimers. [ABSTRACT FROM AUTHOR]

Collagen model peptides (CMPs) consisting of proline‐(2S,4R)‐hydroxyproline‐glycine (POG) repeats have provided a breadth of knowledge of the triple helical structure of collagen, the most abundant protein in mammals. Predictive tools for triple helix stability have, however, lagged behind since the effect of CMPs with different frames ([POG]n, [OGP]n, or [GPO]n) and capped or uncapped termini have so far been underestimated. Here, we elucidated the impact of the frame, terminal functional group and its charge on the stability of collagen triple helices. Combined experimental and theoretical studies with frame‐shifted, capped and uncapped CMPs revealed that electrostatic interactions, strand preorganization, interstrand H‐bonding, and steric repulsion at the termini contribute to triple helix stability. We show that these individual contributions are additive and allow for the prediction of the melting temperatures of CMP trimers. [ABSTRACT FROM AUTHOR]

Reaction-field (RF) methods have been extensively used in molecular dynamics simulations to efficiently compute long-range electrostatic interactions. They assume a continuous dielectric medium outside a certain cutoff, which has shown to be a reasonable approximation in many cases. However, lattice sum or fast multipole methods are nowadays often used instead, which treat long-range interactions explicitly but may introduce different artefacts. In the following work, the major issue of RFs is addressed, i.e., their inability to account for inhomogeneity even in heterogenous environments (e.g., membranes or protein binding pockets). By using a first-order Laplace series expansion of the dielectric permittivity on the cutoff sphere, local anisotropic effects can be described in a simple form. It is shown that the resulting boundary-value problem cannot be solved analytically, but instead a well-behaved approximative anisotropic reaction field (ARF) is introduced, which preserves coordinate invariance and approaches the standard RF solution for homogeneous systems. The comparison of RF to the state-of-the-art particle-particle particle-mesh (P3M) method shows a difference in the orientation of molecules close to the interface between two different dielectrics (water/chloroform). It was found that the ARF leads to a correction of the orientational distribution toward the P3M reference for planar and spherical interfaces. [ABSTRACT FROM AUTHOR]

1H and 13C chemical shifts of 35 small, rigid molecules were measured under standardized conditions in chloroform-d and in tetrachloromethane. The solvent change mainly affects carbon shifts of polar functional groups. This difference due to specific interactions with CDCl3 cannot be adequately reproduced by DFT calculations in implicit solvent. The new dataset provides an accurate basis for the validation and calibration of shift calculations, especially with respect to improved solvent models. [ABSTRACT FROM AUTHOR]

To accurately study the chemical reactions in the condensed phase or within enzymes, both quantum-mechanical description and sufficient configurational sampling are required to reach converged estimates. Here, quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations play an important role, providing QM accuracy for the region of interest at a decreased computational cost. However, QM/MM simulations are still too expensive to study large systems on longer time scales. Recently, machine learning (ML) models have been proposed to replace the QM description. The main limitation of these models lies in the accurate description of long-range interactions present in condensed-phase systems. To overcome this issue, a recent workflow has been introduced combining a semi-empirical method (i.e. density functional tight binding (DFTB)) and a high-dimensional neural network potential (HDNNP) in a Δ-learning scheme. This approach has been shown to be capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. One of the promising alternative approaches to efficiently take long-range effects into account is the development of graph-convolutional neural networks (GCNNs) for the prediction of the potential-energy surface. In this work, we investigate the use of GCNN models – with and without a Δ-learning scheme – for (QM)ML/MM MD simulations. We show that the Δ-learning approach using a GCNN and DFTB as a baseline achieves competitive performance on our benchmarking set of solutes and chemical reactions in water. This method is additionally validated by performing prospective (QM)ML/MM MD simulations of retinoic acid in water and S-adenoslymethionine interacting with cytosine in water. The results indicate that the Δ-learning GCNN model is a valuable alternative for the (QM)ML/MM MD simulations of condensed-phase systems. [ABSTRACT FROM AUTHOR]

The calculation of relative binding free energies (RBFE) involves the choice of the end-state/system representation, of a sampling approach, and of a free-energy estimator. System representations are usually termed "single topology" or "dual topology". As the terminology is often used ambiguously in the literature, a systematic categorization of the system representations is proposed here. In the dual-topology approach, the molecules are simulated as separate molecules. Such an approach is relatively easy to automate for high-throughput RBFE calculations compared to the single-topology approach. Distance restraints are commonly applied to prevent the molecules from drifting apart, thereby improving the sampling efficiency. In this study, we introduce the program RestraintMaker, which relies on a greedy algorithm to find (locally) optimal distance restraints between pairs of atoms based on geometric measures. The algorithm is further extended for multi-state methods such as enveloping distribution sampling (EDS) or multi-site λ -dynamics. The performance of RestraintMaker is demonstrated for toy models and for the calculation of relative hydration free energies. The Python program can be used in script form or through an interactive GUI within PyMol. The selected distance restraints can be written out in GROMOS or GROMACS file formats. Additionally, the program provides a human-readable JSON format that can easily be parsed and processed further. The code of RestraintMaker is freely available on GitHub https://github.com/rinikerlab/restraintmaker. [ABSTRACT FROM AUTHOR]

The calculation of relative free-energy differences between different compounds plays an important role in drug design to identify potent binders for a given protein target. Most rigorous methods based on molecular dynamics simulations estimate the free-energy difference between pairs of ligands. Thus, the comparison of multiple ligands requires the construction of a "state graph", in which the compounds are connected by alchemical transformations. The computational cost can be optimized by reducing the state graph to a minimal set of transformations. However, this may require individual adaptation of the sampling strategy if a transformation process does not converge in a given simulation time. In contrast, path-free methods like replica-exchange enveloping distribution sampling (RE-EDS) allow the sampling of multiple states within a single simulation without the pre-definition of alchemical transition paths. To optimize sampling and convergence, a set of RE-EDS parameters needs to be estimated in a pre-processing step. Here, we present an automated procedure for this step that determines all required parameters, improving the robustness and ease of use of the methodology. To illustrate the performance, the relative binding free energies are calculated for a series of checkpoint kinase 1 inhibitors containing challenging transformations in ring size, opening/closing, and extension, which reflect changes observed in scaffold hopping. The simulation of such transformations with RE-EDS can be conducted with conventional force fields and, in particular, without soft bond-stretching terms. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) simulations are a powerful tool to follow the time evolution of biomolecular motions in atomistic resolution. However, the high computational demand of these simulations limits the timescales of motions that can be observed. To resolve this issue, so called enhanced sampling techniques are developed, which extend conventional MD algorithms to speed up the simulation process. Here, we focus on techniques that apply global biasing functions. We provide a broad overview of established enhanced sampling methods and promising new advances. As the ultimate goal is to retrieve unbiased information from biased ensembles, we also discuss benefits and limitations of common reweighting schemes. In addition to concisely summarizing critical assumptions and implications, we highlight the general application opportunities as well as uncertainties of global enhanced sampling. [ABSTRACT FROM AUTHOR]

In molecular dynamics (MD) simulations, free-energy differences are often calculated using free energy perturbation or thermodynamic integration (TI) methods. However, both techniques are only suited to calculate free-energy differences between two end states. Enveloping distribution sampling (EDS) presents an attractive alternative that allows to calculate multiple free-energy differences in a single simulation. In EDS, a reference state is simulated which "envelopes" the end states. The challenge of this methodology is the determination of optimal reference-state parameters to ensure equal sampling of all end states. Currently, the automatic determination of the reference-state parameters for multiple end states is an unsolved issue that limits the application of the methodology. To resolve this, we have generalised the replica-exchange EDS (RE-EDS) approach, introduced by Lee et al. [J. Chem. Theory Comput. 10, 2738 (2014)] for constant-pH MD simulations. By exchanging configurations between replicas with different reference-state parameters, the complexity of the parameter-choice problem can be substantially reduced. A new robust scheme to estimate the reference-state parameters from a short initial RE-EDS simulation with default parameters was developed, which allowed the calculation of 36 free-energy differences between nine small-molecule inhibitors of phenylethanolamine N-methyltransferase from a single simulation. The resulting freeenergy differences were in excellent agreement with values obtained previously by TI and two-state EDS simulations. [ABSTRACT FROM AUTHOR]

Only products resulting from [4+2] cycloaddition pathways were observed in the reactions of different isocyanates with aliphatic imines. The alternative [2+2] pathway is strongly disfavored, and the corresponding diazetidinone products were not observed. For imines incorporating a specifically located thioester group, acyl isocyanates and aryl isocyanates gave different final products. More information can be found in the Research Article by K.‐H. Altmann and co‐workers (DOI: 10.1002/chem.202304272)...By Etienne Cotter; Felix Pultar; Sereina Riniker and Karl‐Heinz AltmannReported by Author; Author; Author; Author [Extracted from the article]

In molecular dynamics (MD) simulations of condensed-phase systems, straight-cutoff truncation of the non-bonded interactions is well known to cause cutoff noise and serious artifacts in many simulated properties. These effects can be drastically reduced by applying the truncation based on distances between neutral charge groups (CG) rather than between individual atoms (AT). In addition, the mean effect of the omitted electrostatic interactions beyond the cutoff distance can be reintroduced using the reaction-field (RF) method, where the medium outside the cutoff sphere is approximated as a dielectric continuum of permittivity equal to that of the solvent. The RF scheme is generally applied with CG truncation. This is justified for low solvent permittivities, where the RF correction is small and an AT truncation would lead to severe issues, just as in the straight-cutoff case. However, it is less appropriate for solvents with high permittivities, where the RF correction acts as a physically motivated shifting function, and a CG truncation may in turn lead to artifacts and poorer energy conservation. In this study, we assess the impact of truncation artifacts considering the 57 organic liquids which were used in the calibration of the GROMOS-compatible 2016H66 force field. Combinations of shifting or switching schemes with RF-based electrostatic interactions as well as van der Waals (Lennard-Jones) interactions are then introduced to resolve the issues with AT truncation. These shifting and switching schemes have the following properties: (i) they bring the force but not the potential energy to zero at the cutoff; (ii) as a result, they lead to a modification of the interaction that is comparatively small; (iii) they permit to conduct rigorously conservative simulations; (iv) the energies can easily be corrected back to the unmodified form, either on the fly or in a post-processing step. The mathematical formalism of these schemes is presented in detail, and their validation is performed using the 57 organic liquids. [ABSTRACT FROM AUTHOR]

A new family of dienone musks was discovered by alkylation of different aldehydes with but-3-en-1-yn-1-yllithium and subsequent domino reaction of a Saucy–Marbet transfer vinylation–Claisen rearrangement with an intramolecular Diels–Alder reaction, and concluding Lewis acid catalyzed double-bond isomerization. The newly synthesized dienone structures possess pleasant musk odors displaying fatty, slightly fruity and green facets. Although the dienone musks were predicted in silico to bind to the OR5AN1 receptor based on QM/MM calculations, they were found to be inactive in the in vitro assay. The latter results suggest that the OR5AN1 receptor is not the prime musk receptor but primarily responsible for the animalic character of certain macrocyclic ketones and nitro musks. [ABSTRACT FROM AUTHOR]

The in silico prediction of partition coefficients is an important task in computer-aided drug discovery. In particular the octanol–water partition coefficient is used as surrogate for lipophilicity. Various computational approaches have been proposed, ranging from simple group-contribution techniques based on the 2D topology of a molecule to rigorous methods based molecular dynamics (MD) or quantum chemistry. In order to balance accuracy and computational cost, we recently developed the MD fingerprints (MDFPs), where the information in MD simulations is encoded in a floating-point vector, which can be used as input for machine learning (ML). The MDFP-ML approach was shown to perform similarly to rigorous methods while being substantially more efficient. Here, we present the application of MDFP-ML for the prediction of octanol–water partition coefficients in the SAMPL6 blind challenge. The underlying computational pipeline is made freely available in form of the MDFPtools package. [ABSTRACT FROM AUTHOR]

The time- and length-scale accessible to molecular dynamics simulations of biomolecular systems using atomic-level (AL) models is most limited by the calculation of the solvent-solvent interactions, which comprise the majority of the interactions and yet are seldom of specific interest. Coarse-graining (CG), in which multiple solvent molecules are subsumed into a single bead, provides a means of overcoming this limitation without resorting to implicit solvation models, which basically misrepresent the hydrophobic effect. Most existing CG models, however, do not explicitly include electrostatic interactions, and thus fail to reproduce important properties of the solvent such as dielectric screening. Moreover, CG models for one type of solvent molecule are seldom compatible with those for other solvents. Here, we develop polarizable CG models for the solvents dimethyl sulfoxide, chloroform, and methanol that are compatible with an existing CG model for water. The inclusion of polarizability greatly improves the reproduction of thermodynamic data measured experimentally and calculated from AL simulations for both the pure liquids and binary mixtures. [ABSTRACT FROM AUTHOR]

The relative binding free energy between two ligands to a specific protein can be obtained using various computational methods. The more accurate and also computationally more demanding techniques are the so-called free energy methods which use conformational sampling from molecular dynamics or Monte Carlo simulations to generate thermodynamic averages. Two such widely applied methods are the thermodynamic integration (TI) and the recently introduced enveloping distribution sampling (EDS) methods. In both cases relative binding free energies are obtained through the alchemical perturbations of one ligand into another in water and inside the binding pocket of the protein. TI requires many separate simulations and the specification of a pathway along which the system is perturbed from one ligand to another. Using the EDS approach, only a single automatically derived reference state enveloping both end states needs to be sampled. In addition, the choice of an optimal pathway in TI calculations is not trivial and a poor choice may lead to poor convergence along the pathway. Given this, EDS is expected to be a valuable and computationally efficient alternative to TI. In this study, the performances of these two methods are compared using the binding of ten tetrahydroisoquinoline derivatives to phenylethanolamine N-transferase as an example. The ligands involve a diverse set of functional groups leading to a wide range of free energy differences. In addition, two different schemes to determine automatically the EDS reference state parameters and two different topology approaches are compared. [ABSTRACT FROM AUTHOR]

The development of coarse-grained (CG) models that correctly represent the important features of compounds is essential to overcome the limitations in time scale and system size currently encountered in atomistic molecular dynamics simulations. Most approaches reported in the literature model one or several molecules into a single uncharged CG bead. For water, this implicit treatment of the electrostatic interactions, however, fails to mimic important properties, e.g., the dielectric screening. Therefore, a coarse-grained model for water is proposed which treats the electrostatic interactions between clusters of water molecules explicitly. Five water molecules are embedded in a spherical CG bead consisting of two oppositely charged particles which represent a dipole. The bond connecting the two particles in a bead is unconstrained, which makes the model polarizable. Experimental and all-atom simulated data of liquid water at room temperature are used for parametrization of the model. The experimental density and the relative static dielectric permittivity were chosen as primary target properties. The model properties are compared with those obtained from experiment, from clusters of simple-point-charge water molecules of appropriate size in the liquid phase, and for other CG water models if available. The comparison shows that not all atomistic properties can be reproduced by a CG model, so properties of key importance have to be selected when coarse graining is applied. Yet, the CG model reproduces the key characteristics of liquid water while being computationally 1-2 orders of magnitude more efficient than standard fine-grained atomistic water models. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) relaxation data and molecular dynamics (MD) simulations are combined to characterize the dynamics of the fungal prion HET‐s(218‐289) in its amyloid form. NMR data is analyzed with the dynamics detector method, which yields timescale‐specific information. An analogous analysis is performed on MD trajectories. Because specific MD predictions can be verified as agreeing with the NMR data, MD was used for further interpretation of NMR results: for the different timescales, cross‐correlation coefficients were derived to quantify the correlation of the motion between different residues. Short timescales are the result of very local motions, while longer timescales are found for longer‐range correlated motion. Similar trends on ns‐ and μs‐timescales suggest that μs motion in fibrils is the result of motion correlated over many fibril layers. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) relaxation data and molecular dynamics (MD) simulations are combined to characterize the dynamics of the fungal prion HET‐s(218‐289) in its amyloid form. NMR data is analyzed with the dynamics detector method, which yields timescale‐specific information. An analogous analysis is performed on MD trajectories. Because specific MD predictions can be verified as agreeing with the NMR data, MD was used for further interpretation of NMR results: for the different timescales, cross‐correlation coefficients were derived to quantify the correlation of the motion between different residues. Short timescales are the result of very local motions, while longer timescales are found for longer‐range correlated motion. Similar trends on ns‐ and μs‐timescales suggest that μs motion in fibrils is the result of motion correlated over many fibril layers. [ABSTRACT FROM AUTHOR]

An extension of the Nosé–Hoover thermostat equation for molecular dynamics (MD) simulation is introduced, which perturbs fast degrees of freedom out of canonical equilibrium, while preserving the average temperature of the system. Based on the generalised Liouville equation, it is shown that the newly introduced fast Nosé–Hoover (FNH) equations give rise to a dynamical system with a well-defined non-equilibrium probability distribution. Corresponding thermostat parameters are identified, which in principle allow to sample arbitrarily close to canonical equilibrium. Results show that the dynamic system properties (e.g. self-diffusion, shear viscosity, dielectric permittivity and rotational relaxation times) are only moderately perturbed for typical FNH setups. However, the non-equilibrium FNH equations modify the occurrence of rare events substantially and thus offer a novel approach for enhanced sampling in MD. In particular, it is shown that the FNH thermostat increased significantly the frequency of the folding and unfolding process of short b-peptides. The efficiency of the phase-space exploration solely depends on the additionally imposed quasi-equilibrium conditions, i.e. it does not rely on any modification of the potential-energy surface. [ABSTRACT FROM AUTHOR]

Abstract: Computer simulation of molecular systems enables structure–energy–function relationships of molecular processes to be described at the sub‐atomic, atomic, supra‐atomic, or supra‐molecular level. To interpret results of such simulations appropriately, the quality of the calculated properties must be evaluated. This depends on the way the simulations are performed and on the way they are validated by comparison to values Qexp of experimentally observable quantities Q. One must consider 1) the accuracy of Qexp, 2) the accuracy of the function Q(rN) used to calculate a Q‐value based on a molecular configuration rN of N particles, 3) the sensitivity of the function Q(rN) to the configuration rN, 4) the relative time scales of the simulation and experiment, 5) the degree to which the calculated and experimental properties are equivalent, and 6) the degree to which the system simulated matches the experimental conditions. Experimental data is limited in scope and generally corresponds to averages over both time and space. A critical analysis of the various factors influencing the apparent degree of (dis)agreement between simulations and experiment is presented and illustrated using examples from the literature. What can be done to enhance the validation of molecular simulation is also discussed. [ABSTRACT FROM AUTHOR]

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Polytheonamide B (pTB), a highly cytotoxic peptide produced by a symbiotic bacterium of the marine sponge Theonella swinhoei, forms a transmembrane pore consisting of 49 residues. More than half of its residues are posttranslationally modified. Epimerizations result in alternating L- and D-amino acids that allow the peptide to adopt a $$\beta ^{6.3}$$ -helical conformation. Unusually, the wide $$\beta ^{6.3}$$ -helix of pTB is stable in a polar environment, which is in contrast to gramicidin A, an antibiotic with similar function and structure. The role of the other posttranslational modifications (PTMs) such as side chain hydroxylations, C- and N-methylations is not well understood. In this study, the importance of these PTMs for the stability of $$\beta ^{6.3}$$ -helix is investigated using computational tools. By reverting the modified residues to their precursors and monitoring the effect on the dominant structure, we show that the N-methylations are crucial for the stability of the $$\beta ^{6.3}$$ -helix in a polar environment. They are the driving force for the formation of stable side chain hydrogen-bond chains that act as an 'exoskeleton.' Such exoskeletons could present a general design strategy for helical peptides. [ABSTRACT FROM AUTHOR]

Ribosomally synthesized and posttranslationally modified peptide natural products (RiPPs) exhibit diverse structures and bioactivities and are classified into distinct biosynthetic families. A recently reported family is the proteusins, with the prototype members polytheonamides being generated by almost 50 maturation steps, including introduction of d-residues at multiple positions by an unusual radical SAM epimerase. A region in the protein-like N-terminal leader of proteusin precursors is identified that is crucial for epimerization. It resembles a precursor motif previously shown to mediate interaction in thioether bridge-formation in class I lanthipeptide biosynthesis. Beyond this region, similarities were identified between proteusin and further RiPP families, including class I lanthipeptides. The data suggest that common leader features guide distinct maturation types and that nitrile hydratase-like enzymes are ancestors of several RiPP classes. [ABSTRACT FROM AUTHOR]

Ribosomally synthesized and posttranslationally modified peptide natural products (RiPPs) exhibit diverse structures and bioactivities and are classified into distinct biosynthetic families. A recently reported family is the proteusins, with the prototype members polytheonamides being generated by almost 50 maturation steps, including introduction of d-residues at multiple positions by an unusual radical SAM epimerase. A region in the protein-like N-terminal leader of proteusin precursors is identified that is crucial for epimerization. It resembles a precursor motif previously shown to mediate interaction in thioether bridge-formation in class I lanthipeptide biosynthesis. Beyond this region, similarities were identified between proteusin and further RiPP families, including class I lanthipeptides. The data suggest that common leader features guide distinct maturation types and that nitrile hydratase-like enzymes are ancestors of several RiPP classes. [ABSTRACT FROM AUTHOR]

Olfactory receptors (ORs) represent the largest subfamily of the superfamily G protein-coupled receptors (GPCRs). This family of membrane receptors functions as essential gateway for activation of many cellular signaling pathways. Finding universal principles underlying GPCR activation by studying ORs is important for the design of new therapeutics that target olfaction-related and other GPCR-malfunctioning diseases. In addition, gaining knowledge regarding the interactions between ORs and their cognate ligands (odorants) may contribute to solve the puzzle of how odor perception is encoded in humans. As no crystal structure of an OR is available yet, homology modeling can be applied to generate a three-dimensional OR model. Molecular docking, molecular dynamics simulations and qualitative structure-activity-relationship can further guide experimental research by investigating interactions at the atomic level. This article will review these computational techniques as well as present databases and popular software suites, which can support researchers in the OR research field. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Similarity-search methods using molecular fingerprints are an important tool for ligand-based virtual screening. A huge variety of fingerprints exist and their performance, usually assessed in retrospective benchmarking studies using data sets with known actives and known or assumed inactives, depends largely on the validation data sets used and the similarity measure used. Comparing new methods to existing ones in any systematic way is rather difficult due to the lack of standard data sets and evaluation procedures. Here, we present a standard platform for the benchmarking of 2D fingerprints. The open-source platform contains all source code, structural data for the actives and inactives used (drawn from three publicly available collections of data sets), and lists of randomly selected query molecules to be used for statistically valid comparisons of methods. This allows the exact reproduction and comparison of results for future studies. The results for 12 standard fingerprints together with two simple baseline fingerprints assessed by seven evaluation methods are shown together with the correlations between methods. High correlations were found between the 12 fingerprints and a careful statistical analysis showed that only the two baseline fingerprints were different from the others in a statistically significant way. High correlations were also found between six of the seven evaluation methods, indicating that despite their seeming differences, many of these methods are similar to each other. [ABSTRACT FROM AUTHOR]