Macrocyclization of bis-indole quinolines for selective stabilization of G-quadruplex DNA structures

The recognition of G-quadruplex (G4) DNA structures as important regulatory elements in biological mechanisms, and the connection between G4s and the evolvement of different diseases, has sparked interest in developing small organic molecules targeting G4s. However, such compounds often lack drug-like properties and selectivity. Here, we describe the design and synthesis of a novel class of macrocyclic bis-indole quinolines based on their non-macrocyclic lead compounds. The effects of the macrocyclization on the ability to interact with G4 DNA structures were investigated using biophysical assays and molecular dynamic simulations. Overall, this revealed compounds with potent abilities to interact with and stabilize G4 structures and a clear selectivity for both G4 DNA over dsDNA and for parallel/hybrid G4 topologies, which could be attributed to the macrocyclic structure. Moreover, we obtained knowledge about the structure–activity relationship of importance for the macrocyclic design and how structural modifications could be made to construct improved macrocyclic compounds. Thus, the macrocyclization of G4 ligands can serve as a basis for the optimization of research tools to study G4 biology and potential therapeutics targeting G4-related diseases.

Macrocyclization improves the selectivity, affinity, and ability to stabilize G4 DNA structures.     

Thermodynamics of ion binding and occupancy in potassium channels

Potassium channels modulate various cellular functions through efficient and selective conduction of K⁺ ions. The mechanism of ion conduction in potassium channels has recently emerged as a topic of debate. Crystal structures of potassium channels show four K⁺ ions bound to adjacent binding sites in the selectivity filter, while chemical intuition and molecular modeling suggest that the direct ion contacts are unstable. Molecular dynamics (MD) simulations have been instrumental in the study of conduction and gating mechanisms of ion channels. Based on MD simulations, two hypotheses have been proposed, in which the four-ion configuration is an artifact due to either averaged structures or low temperature in crystallographic experiments. The two hypotheses have been supported or challenged by different experiments. Here, MD simulations with polarizable force fields validated by ab initio calculations were used to investigate the ion binding thermodynamics. Contrary to previous beliefs, the four-ion configuration was predicted to be thermodynamically stable after accounting for the complex electrostatic interactions and dielectric screening. Polarization plays a critical role in the thermodynamic stabilities. As a result, the ion conduction likely operates through a simple single-vacancy and water-free mechanism. The simulations explained crystal structures, ion binding experiments and recent controversial mutagenesis experiments. This work provides a clear view of the mechanism underlying the efficient ion conduction and demonstrates the importance of polarization in ion channel simulations.

Polarization shapes the energy landscape of ion conduction in potassium channels.     

Precipitation-free high-affinity multivalent binding by inline lectin ligands

Multivalent ligand–protein interactions are a key concept in biology mediating, for example, signalling and adhesion. Multivalent ligands often have tremendously increased binding affinities. However, they also can cause crosslinking of receptor molecules leading to precipitation of ligand–receptor complexes. Plaque formation due to precipitation is a known characteristic of numerous fatal diseases limiting a potential medical application of multivalent ligands with a precipitating binding mode. Here, we present a new design of high-potency multivalent ligands featuring an inline arrangement of ligand epitopes with exceptionally high binding affinities in the low nanomolar range. At the same time, we show with a multi-methodological approach that precipitation of the receptor is prevented. We distinguish distinct binding modes of the ligands, in particular we elucidate a unique chelating binding mode, where four receptor binding sites are simultaneously bridged by one multivalent ligand molecule. The new design concept of inline multivalent ligands, which we established for the well-investigated model lectin wheat germ agglutinin, has great potential for the development of high-potency multivalent inhibitors as future therapeutics.

Integration of sugar epitopes into a backbone structure generates multivalent lectin ligands with a defined binding mode and high affinity without precipitating the protein.     

Targeting structural features of viral genomes with a nano-sized supramolecular drug

RNA targeting is an exciting frontier for drug design. Intriguing targets include functional RNA structures in structurally-conserved untranslated regions (UTRs) of many lethal viruses. However, computational docking screens, valuable in protein structure targeting, fail for inherently flexible RNA. Herein we harness MD simulations with Markov state modeling to enable nanosize metallo-supramolecular cylinders to explore the dynamic RNA conformational landscape of HIV-1 TAR untranslated region RNA (representative for many viruses) replicating experimental observations. These cylinders are exciting as they have unprecedented nucleic acid binding and are the first supramolecular helicates shown to have anti-viral activity in cellulo: the approach developed in this study provides additional new insight about how such viral UTR structures might be targeted with the cylinder binding into the heart of an RNA-bulge cavity, how that reduces the conformational flexibility of the RNA and molecular details of the insertion mechanism. The approach and understanding developed represents a new roadmap for design of supramolecular drugs to target RNA structural motifs across biology and nucleic acid nanoscience.

MD simulations and Markov state modeling explore induced fit binding of metallo-helicates to bulges in dynamic TAR RNA, reproduce experimental data, show how RNA conformational flexibility is reduced, and give mechanistic insight into insertion.     

Thermodynamics and kinetics of the amyloid-β peptide revealed by Markov state models based on MD data in agreement with experiment

The amlyoid-β peptide (Aβ) is closely linked to the development of Alzheimer''s disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the atomistic level. General key aspects of MD simulations are the force field used for modeling the peptide and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-μs MD simulations acquired for Aβ40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for the different MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-β peptide. This further allows us to provide answers for pertinent questions, like: which force fields are suitable for modeling Aβ? (a99SB-UCB and a99SB-ILDN/TIP4P-D); what does Aβ peptide really look like? (mostly extended and disordered) and; how long does it take MD simulations of Aβ to attain equilibrium? (at least 20–30 μs). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of Aβ in particular, and by extension other similar disordered proteins.

The convergence of MD simulations is tested using varying measures for the intrinsically disordered amyloid-β peptide (Aβ). Markov state models show that 20–30 μs of MD is needed to reliably reproduce the thermodynamics and kinetics of Aβ.     

Hierarchical dynamics in allostery following ATP hydrolysis monitored by single molecule FRET measurements and MD simulations

We report on a study that combines advanced fluorescence methods with molecular dynamics (MD) simulations to cover timescales from nanoseconds to milliseconds for a large protein. This allows us to delineate how ATP hydrolysis in a protein causes allosteric changes at a distant protein binding site, using the chaperone Hsp90 as test system. The allosteric process occurs via hierarchical dynamics involving timescales from nano- to milliseconds and length scales from Ångstroms to several nanometers. We find that hydrolysis of one ATP is coupled to a conformational change of Arg380, which in turn passes structural information via the large M-domain α-helix to the whole protein. The resulting structural asymmetry in Hsp90 leads to the collapse of a central folding substrate binding site, causing the formation of a novel collapsed state (closed state B) that we characterise structurally. We presume that similar hierarchical mechanisms are fundamental for information transfer induced by ATP hydrolysis through many other proteins.

We report on a study that combines advanced fluorescence methods with molecular dynamics simulations to cover timescales from nanoseconds to milliseconds for a large protein, the chaperone Hsp90.     

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein–ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol⁻¹, equivalent to Schrödinger''s FEP+ AUE of 3.66 ± 0.14 kJ mol⁻¹. For the first time, a setup is presented for overall high precision and high accuracy relative protein–ligand alchemical free energy calculations based on open-source software.

Relative ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design.     

Mechanochemical synthesis of glycine oligomers in a virtual rotational diamond anvil cell

Mechanochemistry of glycine under compression and shear at room temperature is predicted using quantum-based molecular dynamics (QMD) and a simulation design based on rotational diamond anvil cell (RDAC) experiments. Ensembles of high throughput semiempirical density functional tight binding (DFTB) simulations are used to identify chemical trends and bounds for glycine chemistry during rapid shear under compressive loads of up to 15.6 GPa. Significant chemistry is found to occur during compressive shear above 10 GPa. Recovered products consist of small molecules such as water, structural analogs to glycine, heterocyclic molecules, large oligomers, and polypeptides including the simplest polypeptide glycylglycine at up to 4% mass fraction. The population and size of oligomers generally increases with pressure. A number of oligomeric polypeptide precursors and intermediates are also identified that consist of two or three glycine monomers linked together through C–C, C–N, and/or C–O bridges. Even larger oligomers also form that contain peptide C–N bonds and exhibit branched structures. Many of the product molecules exhibit one or more chiral centers. Our simulations demonstrate that athermal mechanical compressive shearing of glycine is a plausible prebiotic route to forming polypeptides.

Compressive shearing forces can induce mechanochemical oligomerization reactions in glycine.     

Graph neural network based coarse-grained mapping prediction

The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice and there is a need for theory. The current state-of-the art method is mapping operators manually selected by experts. In this work, we demonstrate an automated approach by viewing this problem as supervised learning where we seek to reproduce the mapping operators produced by experts. We present a graph neural network based CG mapping predictor called Deep Supervised Graph Partitioning Model (DSGPM) that treats mapping operators as a graph segmentation problem. DSGPM is trained on a novel dataset, Human-annotated Mappings (HAM), consisting of 1180 molecules with expert annotated mapping operators. HAM can be used to facilitate further research in this area. Our model uses a novel metric learning objective to produce high-quality atomic features that are used in spectral clustering. The results show that the DSGPM outperforms state-of-the-art methods in the field of graph segmentation. Finally, we find that predicted CG mapping operators indeed result in good CG MD models when used in simulation.

We propose a scalable graph neural network-based method for automating coarse-grained mapping prediction for molecules.     

Reversible metathesis of ammonia in an acyclic germylene–Ni0 complex

Carbenes, a class of low-valent group 14 ligand, have shifted the paradigm in our understanding of the effects of supporting ligands in transition-metal reactivity and catalysis. We now seek to move towards utilizing the heavier group 14 elements in effective ligand systems, which can potentially surpass carbon in their ability to operate via ‘non-innocent’ bond activation processes. Herein we describe our initial results towards the development of scalable acyclic chelating germylene ligands (viz.1a/b), and their utilization in the stabilization of Ni⁰ complexes (viz.4a/b), which can readily and reversibly undergo metathesis with ammonia with no net change of oxidation state at the Ge^II and Ni⁰ centres, through ammonia bonding at the germylene ligand as opposed to the Ni⁰ centre. The DFT-derived metathesis mechanism, which surprisingly demonstrates the need for three molecules of ammonia to achieve N–H bond activation, supports reversible ammonia binding at Ge^II, as well as the observed reversibility in the overall reaction.

Chelating single-centre ambiphile ligands based upon low-coordinate, acyclic germylenes have been developed, remaining highly Lewis acidic even when bound to Ni⁰, remarkably allowing for the reversible metathesis of the N–H bonds in ammonia at Ge^II.     

Oxidation triggers guest dissociation during reorganization of an FeII4L6 twisted parallelogram

A three-dimensional Fe^II₄L₆ parallelogram was prepared from ferrocene-containing ditopic ligands. The steric preference of the bulky ferrocene cores towards meridional vertex coordination brought about this new structure type, in which the ferrocene units adopt three distinct conformations. The structure possesses two distinct, bowl-like cavities that host anionic guests. Oxidation of the ferrocene Fe^II to ferrocenium Fe^III causes rotation of the ferrocene hinges, converting the structure to an Fe^II₁L₁⁺ species with release of anionic guests, even though the average charge per iron increases in a way that would ordinarily increase guest binding strength. The degrees of freedom exhibited by these new structures – derived from the different configurations of the three ligands surrounding a meridional Fe^II center and the rotation of ferrocene cores – thus underpin their ability to reconfigure and eject guests upon oxidation.

An oxidation-triggered twist in its ferrocene ligands causes an Fe₄L₆ parallelogram to release its guests and collapse into a high spin Fe₁L₁ structure.     

Ligand design by targeting a binding site water

Solvent reorganization is a major driving force of protein–ligand association, but the contribution of binding site waters to ligand affinity is poorly understood. We investigated how altered interactions with a water network can influence ligand binding to a receptor. A series of ligands of the A_2A adenosine receptor, which either interacted with or displaced an ordered binding site water, were studied experimentally and by molecular dynamics simulations. An analog of the endogenous ligand that was unable to hydrogen bond to the ordered water lost affinity and this activity cliff was captured by molecular dynamics simulations. Two compounds designed to displace the ordered water from the binding site were then synthesized and evaluated experimentally, leading to the discovery of an A_2A agonist with nanomolar activity. Calculation of the thermodynamic profiles resulting from introducing substituents that interacted with or displaced the ordered water showed that the gain of binding affinity was enthalpy driven. Detailed analysis of the energetics and binding site hydration networks revealed that the enthalpy change was governed by contributions that are commonly neglected in structure-based drug optimization. In particular, simulations suggested that displacement of water from a binding site to the bulk solvent can lead to large energy contributions. Our findings provide insights into the molecular driving forces of protein–ligand binding and strategies for rational drug design.

Solvent reorganization is a major driving force of protein–ligand association, but the contribution of binding site waters to ligand affinity is poorly understood.     

Interpretable molecular models for molybdenum disulfide and insight into selective peptide recognition

Molybdenum disulfide (MoS₂) is a layered material with outstanding electrical and optical properties. Numerous studies evaluate the performance in sensors, catalysts, batteries, and composites that can benefit from guidance by simulations in all-atom resolution. However, molecular simulations remain difficult due to lack of reliable models. We introduce an interpretable force field for MoS₂ with record performance that reproduces structural, interfacial, and mechanical properties in 0.1% to 5% agreement with experiments. The model overcomes structural instability, deviations in interfacial and mechanical properties by several 100%, and empirical fitting protocols in earlier models. It is compatible with several force fields for molecular dynamics simulation, including the interface force field (IFF), CVFF, DREIDING, PCFF, COMPASS, CHARMM, AMBER, and OPLS-AA. The parameters capture polar covalent bonding, X-ray structure, cleavage energy, infrared spectra, bending stability, bulk modulus, Young''s modulus, and contact angles with polar and nonpolar solvents. We utilized the models to uncover the binding mechanism of peptides to the MoS₂ basal plane. The binding strength of several 7mer and 8mer peptides scales linearly with surface contact and replacement of surface-bound water molecules, and is tunable in a wide range from −86 to −6 kcal mol⁻¹. The binding selectivity is multifactorial, including major contributions by van-der-Waals coordination and charge matching of certain side groups, orientation of hydrophilic side chains towards water, and conformation flexibility. We explain the relative attraction and role of the 20 amino acids using computational and experimental data. The force field can be used to screen and interpret the assembly of MoS₂-based nanomaterials and electrolyte interfaces up to a billion atoms with high accuracy, including multiscale simulations from the quantum scale to the microscale.

We introduce a cutting-edge force field for molybdenum disulfide and use it to uncover mechanisms of peptide recognition and design.     

Accelerating high-throughput virtual screening through molecular pool-based active learning

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 10⁸ molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure–property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.

Bayesian optimization can accelerate structure-based virtual screening campaigns by minimizing the total number of simulations performed while still identifying the vast majority of computational hits.     

Combining free energy calculations with tailored enzyme activity assays to elucidate substrate binding of a phospho-lysine phosphatase

Studying enzymes that are involved in the regulation of dynamic post-translational modifications (PTMs) is of key importance in proteomics research. Such investigations can be particularly challenging when the modification itself is intrinsically labile. In this article, we elucidate the enzymatic activity of Phospholysine Phosphohistidine Inorganic Pyrophosphate Phosphatase (LHPP) towards different O- and N-phosphorylated peptides by a combined experimental and computational approach. LHPP has been previously described to hydrolyze the phosphoramidate bonds in different small molecule substrates, including phosphorylated lysine (pLys). Taking the instability of the phosphoramidate bond into account, we conducted a carefully adjusted enzymatic assay with various pLys pentapeptides to confirm enzymatic phosphatase activity with LHPP. Molecular docking was employed to explore possible binding poses of the substrates in complex with the enzyme. Molecular dynamics based free energy calculations, which are unique in their accuracy and solid theoretical basis, were further applied to predict relative binding affinity of different substrates. Comparison of simulations with experiments clearly suggested a distinct binding motif of pLys peptides as well as a very narrow promiscuity of LHPP. We believe this integrated approach can be widely adopted to study the structure and interaction of poorly characterized enzyme–substrate complexes, in particular with synthetically challenging or labile substrates.

Combining phosphatase activity assays with molecular docking and free energy calculations reveals low promiscuity and substrate binding of intrinsically labile phospho-lysine peptides to the enzyme LHPP.     

Metal–ligand bond strength determines the fate of organic ligands on the catalyst surface during the electrochemical CO2 reduction reaction

Colloidally synthesised nanocrystals (NCs) are increasingly utilised as catalysts to drive both thermal and electrocatalytic reactions. Their well-defined size and shape, controlled by organic ligands, are ideal to identify the parameters relevant to the activity, selectivity and stability in catalysis. However, the impact of the native surface ligands during catalysis still remains poorly understood, as does their fate. CuNCs are among the state-of-the-art catalysts for the electrochemical CO₂ reduction reaction (CO₂RR). In this work, we study CuNCs that are capped by different organic ligands to investigate their impact on the catalytic properties. We show that the latter desorb from the surface at a cathodic potential that depends on their binding strength with the metal surface, rather than their own electroreduction potentials. By monitoring the evolving surface chemistry in situ, we find that weakly bound ligands desorb very rapidly while strongly bound ligands impact the catalytic performance. This work provides a criterion to select labile ligands versus ligands that will persist on the surface, thus offering opportunity for interface design.

The metal–ligand binding strength is a key parameter in determining the role and fate of the surface ligands on nanoparticle catalysts during the electrochemical CO₂ reduction reaction.     

Mapping the binding site topology of amyloid protein aggregates using multivalent ligands

A key process in the development of neurodegenerative diseases such as Alzheimer''s and Parkinson''s diseases is the aggregation of proteins to produce fibrillary aggregates with a cross β-sheet structure, amyloid. The development of reagents that can bind these aggregates with high affinity and selectivity has potential for early disease diagnosis. By linking two benzothiazole aniline (BTA) head groups with different length polyethylene glycol (PEG) spacers, fluorescent probes that bind amyloid fibrils with low nanomolar affinity have been obtained. Dissociation constants measured for interaction with Aβ, α-synuclein and tau fibrils show that the length of the linker determines binding affinity and selectivity. These compounds were successfully used to image α-synuclein aggregates in vitro and in the post-mortem brain tissue of patients with Parkinson''s disease. The results demonstrate that multivalent ligands offer a powerful approach to obtain high affinity, selective reagents to bind the fibrillary aggregates that form in neurodegenerative disease.

Multivalent ligands offer a powerful approach to obtain high affinity reagents to bind the aggregates that form in neurodegenerative disease. Selectivity for different proteins was achieved by using different linkers to connect the head groups.     

Structural basis for recognition of bacterial cell wall teichoic acid by pseudo-symmetric SH3b-like repeats of a viral peptidoglycan hydrolase

Endolysins are bacteriophage-encoded peptidoglycan hydrolases targeting the cell wall of host bacteria via their cell wall-binding domains (CBDs). The molecular basis for selective recognition of surface carbohydrate ligands by CBDs remains elusive. Here, we describe, in atomic detail, the interaction between the Listeria phage endolysin domain CBD500 and its cell wall teichoic acid (WTA) ligands. We show that 3′O-acetylated GlcNAc residues integrated into the WTA polymer chain are the key epitope recognized by a CBD binding cavity located at the interface of tandem copies of beta-barrel, pseudo-symmetric SH3b-like repeats. This cavity consists of multiple aromatic residues making extensive interactions with two GlcNAc acetyl groups via hydrogen bonds and van der Waals contacts, while permitting the docking of the diastereomorphic ligands. Our multidisciplinary approach tackled an extremely challenging protein–glycopolymer complex and delineated a previously unknown recognition mechanism by which a phage endolysin specifically recognizes and targets WTA, suggesting an adaptable model for regulation of endolysin specificity.

Combining genetic, biochemical and computational approaches, we elucidated the molecular mechanisms underlying the recognition of Listeria wall teichoic acid by bacteriophage-encoded SH3b repeats.     

17O Hyperfine Spectroscopy Reveals Hydration Structure of Nitroxide Radicals in Aqueous Solutions

The hydration structure of nitroxide radicals in aqueous solutions is elucidated by advanced ¹⁷O hyperfine (hf) spectroscopy with support of quantum chemical calculations and MD simulations. A piperidine and a pyrrolidine-based nitroxide radical are compared and show clear differences in the preferred directionality of H-bond formation. We demonstrate that these scenarios are best represented in ¹⁷O hf spectra, where in-plane coordination over -type H-bonding leads to little spin density transfer on the water oxygen and small hf couplings, whereas -type perpendicular coordination generates much larger hf couplings. Quantitative analysis of the spectra based on MD simulations and DFT predicted hf parameters is consistent with a distribution of close solvating water molecules, in which directionality is influenced by subtle steric effects of the ring and the methyl group substituents.