Structure of the Inhibited State of the Sec Translocon.

Protein secretion in eukaryotes and prokaryotes involves a universally conserved protein translocation channel formed by the Sec61 complex. Unrelated small-molecule natural products and synthetic compounds inhibit Sec61 with differential effects for different substrates or for Sec61 from different organisms, making this a promising target for therapeutic intervention. To understand the mode of inhibition and provide insight into the molecular mechanism of this dynamic translocon, we determined the structure of mammalian Sec61 inhibited by the Mycobacterium ulcerans exotoxin mycolactone via electron cryo-microscopy. Unexpectedly, the conformation of inhibited Sec61 is optimal for substrate engagement, with mycolactone wedging open the cytosolic side of the lateral gate. The inability of mycolactone-inhibited Sec61 to effectively transport substrate proteins implies that signal peptides and transmembrane domains pass through the site occupied by mycolactone. This provides a foundation for understanding the molecular mechanism of Sec61 inhibitors and reveals novel features of translocon function and dynamics.

Structural basis of antifolate recognition and transport by PCFT.

Folates (also known as vitamin B9) have a critical role in cellular metabolism as the starting point in the synthesis of nucleic acids, amino acids and the universal methylating agent S-adenylsmethionine1,2. Folate deficiency is associated with a number of developmental, immune and neurological disorders3-5. Mammals cannot synthesize folates de novo; several systems have therefore evolved to take up folates from the diet and distribute them within the body3,6. The proton-coupled folate transporter (PCFT) (also known as SLC46A1) mediates folate uptake across the intestinal brush border membrane and the choroid plexus4,7, and is an important route for the delivery of antifolate drugs in cancer chemotherapy8-10. How PCFT recognizes folates or antifolate agents is currently unclear. Here we present cryo-electron microscopy structures of PCFT in a substrate-free state and in complex with a new-generation antifolate drug (pemetrexed). Our results provide a structural basis for understanding antifolate recognition and provide insights into the pH-regulated mechanism of folate transport mediated by PCFT.

Plasticity of the selectivity filter is essential for permeation in lysosomal TPC2 channels.

Two-pore channels are pathophysiologically important Na+- and Ca2+-permeable channels expressed in lysosomes and other acidic organelles. Unlike most other ion channels, their permeability is malleable and ligand-tuned such that when gated by the signaling lipid PI(3,5)P2, they are more Na+-selective than when gated by the Ca2+ mobilizing messenger nicotinic acid adenine dinucleotide phosphate. However, the structural basis that underlies such plasticity and single-channel behavior more generally remains poorly understood. A recent Cryo-electron microscopy (cryo-EM) structure of TPC2 bound to PI(3,5)P2 in a proposed open-channel conformation provided an opportunity to address this via molecular dynamics (MD) simulation. To our surprise, simulations designed to compute conductance through this structure revealed almost no Na+ permeation events even at very high transmembrane voltages. However further MD simulations identified a spontaneous transition to a dramatically different conformation of the selectivity filter that involved expansion and a flip in the orientation of two core asparagine residues. This alternative filter conformation was remarkably stable and allowed Na+ to flow through the channel leading to a conductance estimate that was in very good agreement with direct single-channel measurements. Furthermore, this conformation was more permeable for Na+ over Ca2+. Our results have important ramifications not just for understanding the control of ion selectivity in TPC2 channels but also more broadly in terms of how ion channels discriminate ions.

Recovery from desensitization in GluA2 AMPA receptors is affected by a single mutation in the N-terminal domain interface.

AMPA-type ionotropic glutamate receptors (AMPARs) are central to various neurological processes, including memory and learning. They assemble as homo- or heterotetramers of GluA1, GluA2, GluA3, and GluA4 subunits, each consisting of an N-terminal domain (NTD), a ligand-binding domain, a transmembrane domain, and a C-terminal domain. While AMPAR gating is primarily controlled by reconfiguration in the ligand-binding domain layer, our study focuses on the NTDs, which also influence gating, yet the underlying mechanism remains enigmatic. In this investigation, we employ molecular dynamics simulations to evaluate the NTD interface strength in GluA1, GluA2, and NTD mutants GluA2-H229N and GluA1-N222H. Our findings reveal that GluA1 has a significantly weaker NTD interface than GluA2. The NTD interface of GluA2 can be weakened by a single point mutation in the NTD dimer-of-dimer interface, namely H229N, which renders GluA2 more GluA1-like. Electrophysiology recordings demonstrate that this mutation also leads to slower recovery from desensitization. Moreover, we observe that lowering the pH induces more splayed NTD states and enhances desensitization in GluA2. We hypothesized that H229 was responsible for this pH sensitivity; however, GluA2-H229N was also affected by pH, meaning that H229 is not solely responsible and that protons exert their effect across multiple domains of the AMPAR. In summary, our work unveils an allosteric connection between the NTD interface strength and AMPAR desensitization.

Exploring the influence of pore shape on conductance and permeation.

There are increasing numbers of ion channel structures featuring heteromeric subunit assembly, exemplified by synaptic α1ßB glycine and α4ß2 nicotinic receptors. These structures exhibit inherent pore asymmetry, but the relevance of this to function is unknown. Furthermore, molecular dynamics simulations performed on symmetrical homomeric channels often lead to thermal distortion whereby conformations of the resulting ensemble are also asymmetrical. When functionally annotating ion channels, researchers often rely on minimal constrictions determined via radius-profile calculations performed with computer programs, such as HOLE or CHAP, coupled with an assessment of pore hydrophobicity. However, such tools typically employ spherical probe particles, limiting their ability to accurately capture pore asymmetry. Here, we introduce an algorithm that employs ellipsoidal probe particles, enabling a more comprehensive representation of the pore geometry. Our analysis reveals that the use of nonspherical ellipsoids for pore characterization provides a more accurate and easily interpretable depiction of conductance. To quantify the implications of pore asymmetry on conductance, we systematically investigated carbon nanotubes with varying degrees of pore asymmetry as model systems. The conductance through these channels shows surprising effects that would otherwise not be predicted with spherical probes. The results have broad implications not only for the functional annotation of biological ion channels but also for the design of synthetic channel systems for use in areas such as water filtration. Furthermore, we make use of the more accurate characterization of channel pores to refine a physical conductance model to obtain a heuristic estimate for single-channel conductance. The code is freely available, obtainable as pip-installable python package and provided as a web service.

Are we fitting data or noise? Analysing the predictive power of commonly used datasets in drug-, materials-, and molecular-discovery.

Data-driven techniques for establishing quantitative structure property relations are a pillar of modern materials and molecular discovery. Fuelled by the recent progress in deep learning methodology and the abundance of new algorithms, it is tempting to chase benchmarks and incrementally build ever more capable machine learning (ML) models. While model evaluation has made significant progress, the intrinsic limitations arising from the underlying experimental data are often overlooked. In the chemical sciences data collection is costly, thus datasets are small and experimental errors can be significant. These limitations of such datasets affect their predictive power, a fact that is rarely considered in a quantitative way. In this study, we analyse commonly used ML datasets for regression and classification from drug discovery, molecular discovery, and materials discovery. We derived maximum and realistic performance bounds for nine such datasets by introducing noise based on estimated or actual experimental errors. We then compared the estimated performance bounds to the reported performance of leading ML models in the literature. Out of the nine datasets and corresponding ML models considered, four were identified to have reached or surpassed dataset performance limitations and thus, they may potentially be fitting noise. More generally, we systematically examine how data range, the magnitude of experimental error, and the number of data points influence dataset performance bounds. Alongside this paper, we release the Python package NoiseEstimator and provide a web-based application for computing realistic performance bounds. This study and the resulting tools will help practitioners in the field understand the limitations of datasets and set realistic expectations for ML model performance. This work stands as a reference point, offering analysis and tools to guide development of future ML models in the chemical sciences.

Automated Absolute Binding Free Energy Calculation Workflow for Drug Discovery.

Absolute binding free energies play a crucial role in drug development, particularly as part of the lead discovery process. In recent work, we showed how in silico predictions directly could support drug development by ranking and recommending favorable ideas over unfavorable ones. Here, we demonstrate a Python workflow that enables the calculation of ABFEs with minimal manual input effort, such as the receptor PDB and ligand SDF files, and outputs a .tsv file containing the ranked ligands and their corresponding binding free energies. The implementation uses Snakemake to structure and control the execution of tasks, allowing for dynamic control of parameters and execution patterns. We provide an example of a benchmark system that demonstrates the effectiveness of the automated workflow.

Analysis of Glycan Recognition by Concanavalin A Using Absolute Binding Free Energy Calculations.

Carbohydrates are key biological mediators of molecular recognition and signaling processes. In this case study, we explore the ability of absolute binding free energy (ABFE) calculations to predict the affinities of a set of five related carbohydrate ligands for the lectin protein, concanavalin A, ranging from 27-atom monosaccharides to a 120-atom complex-type N-linked glycan core pentasaccharide. ABFE calculations quantitatively rank and estimate the affinity of the ligands in relation to microcalorimetry, with a mean signed error in the binding free energy of -0.63 ± 0.04 kcal/mol. Consequently, the diminished binding efficiencies of the larger carbohydrate ligands are closely reproduced: the ligand efficiency values from isothermal titration calorimetry for the glycan core pentasaccharide and its constituent trisaccharide and monosaccharide compounds are respectively -0.14, -0.22, and -0.41 kcal/mol per heavy atom. ABFE calculations predict these ligand efficiencies to be -0.14 ± 0.02, -0.24 ± 0.03, and -0.46 ± 0.06 kcal/mol per heavy atom, respectively. Consequently, the ABFE method correctly identifies the high affinity of the key anchoring mannose residue and the negligible contribution to binding of both ß-GlcNAc arms of the pentasaccharide. While challenges remain in sampling the conformation and interactions of these polar, flexible, and weakly bound ligands, we nevertheless find that the ABFE method performs well for this lectin system. The approach shows promise as a quantitative tool for predicting and deconvoluting carbohydrate-protein interactions, with potential application to design of therapeutics, vaccines, and diagnostics.

Computed Protein-Protein Enthalpy Signatures as a Tool for Identifying Conformation Sampling Problems.

Understanding the thermodynamic signature of protein-peptide binding events is a major challenge in computational chemistry. The complexity generated by both components possessing many degrees of freedom poses a significant issue for methods that attempt to directly compute the enthalpic contribution to binding. Indeed, the prevailing assumption has been that the errors associated with such approaches would be too large for them to be meaningful. Nevertheless, we currently have no indication of how well the present methods would perform in terms of predicting the enthalpy of binding for protein-peptide complexes. To that end, we carefully assembled and curated a set of 11 protein-peptide complexes where there is structural and isothermal titration calorimetry data available and then computed the absolute enthalpy of binding. The initial "out of the box" calculations were, as expected, very modest in terms of agreement with the experiment. However, careful inspection of the outliers allows for the identification of key sampling problems such as distinct conformations of peptide termini not being sampled or suboptimal cofactor parameters. Additional simulations guided by these aspects can lead to a respectable correlation with isothermal titration calorimetry (ITC) experiments (R2 of 0.88 and an RMSE of 1.48 kcal/mol overall). Although one cannot know prospectively whether computed ITC values will be correct or not, this work shows that if experimental ITC data are available, then this in conjunction with computed ITC, can be used as a tool to know if the ensemble being simulated is representative of the true ensemble or not. That is important for allowing the correct interpretation of the detailed dynamics of the system with respect to the measured enthalpy. The results also suggest that computational calorimetry is becoming increasingly feasible. We provide the data set as a resource for the community, which could be used as a benchmark to help further progress in this area.

Correction: Limitations of non-polarizable force fields in describing anion binding poses in non-polar synthetic hosts.

Correction for 'Limitations of non-polarizable force fields in describing anion binding poses in non-polar synthetic hosts' by David Seiferth et al., Phys. Chem. Chem. Phys., 2023, 25, 17596-17608, https://doi.org/10.1039/D3CP00479A.

Limitations of non-polarizable force fields in describing anion binding poses in non-polar synthetic hosts.

Transmembrane anion transport by synthetic ionophores has received increasing interest not only because of its relevance for understanding endogenous anion transport, but also because of potential implications for therapeutic routes in disease states where chloride transport is impaired. Computational studies can shed light on the binding recognition process and can deepen our mechanistic understanding of them. However, the ability of molecular mechanics methods to properly capture solvation and binding properties of anions is known to be challenging. Consequently, polarizable models have been suggested to improve the accuracy of such calculations. In this study, we calculate binding free energies for different anions to the synthetic ionophore, biotin[6]uril hexamethyl ester in acetonitrile and to biotin[6]uril hexaacid in water by employing non-polarizable and polarizable force fields. Anion binding shows strong solvent dependency consistent with experimental studies. In water, the binding strengths are iodide > bromide > chloride, and reversed in acetonitrile. These trends are well captured by both classes of force fields. However, the free energy profiles obtained from potential of mean force calculations and preferred binding positions of anions depend on the treatment of electrostatics. Results from simulations using the AMOEBA force-field, which recapitulate the observed binding positions, suggest strong effects from multipoles dominate with a smaller contribution from polarization. The oxidation status of the macrocycle was also found to influence anion recognition in water. Overall, these results have implications for the understanding of anion host interactions not just in synthetic ionophores, but also in narrow cavities of biological ion channels.

State-dependent protein-lipid interactions of a pentameric ligand-gated ion channel in a neuronal membrane.

Pentameric ligand-gated ion channels (pLGICs) are receptor proteins that are sensitive to their membrane environment, but the mechanism for how lipids modulate function under physiological conditions in a state dependent manner is not known. The glycine receptor is a pLGIC whose structure has been resolved in different functional states. Using a realistic model of a neuronal membrane coupled with coarse-grained molecular dynamics simulations, we demonstrate that some key lipid-protein interactions are dependent on the receptor state, suggesting that lipids may regulate the receptor's conformational dynamics. Comparison with existing structural data confirms known lipid binding sites, but we also predict further protein-lipid interactions including a site at the communication interface between the extracellular and transmembrane domain. Moreover, in the active state, cholesterol can bind to the binding site of the positive allosteric modulator ivermectin. These protein-lipid interaction sites could in future be exploited for the rational design of lipid-like allosteric drugs.

SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction.

A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess the predictive power of methods involved in computer-aided drug design. Application of various methods to fragment molecules are now widely used in the search for new drugs. However, there is little in the way of systematic validation specifically for fragment-based approaches. We have performed a large crystallographic high-throughput fragment screen against the therapeutically relevant second bromodomain of the Pleckstrin-homology domain interacting protein (PHIP2) that revealed 52 different fragments bound across 4 distinct sites, 47 of which were bound to the pharmacologically relevant acetylated lysine (Kac) binding site. These data were used to assess computational screening, binding pose prediction and follow-up enumeration. All submissions performed randomly for screening. Pose prediction success rates (defined as less than 2 Å root mean squared deviation against heavy atom crystal positions) ranged between 0 and 25% and only a very few follow-up compounds were deemed viable candidates from a medicinal-chemistry perspective based on a common molecular descriptors analysis. The tight deadlines imposed during the challenge led to a small number of submissions suggesting that the accuracy of rapidly responsive workflows remains limited. In addition, the application of these methods to reproduce crystallographic fragment data still appears to be very challenging. The results show that there is room for improvement in the development of computational tools particularly when applied to fragment-based drug design.

Muscle acetylcholine receptor conversion into chloride conductance at positive potentials by a single mutation.

Charge selectivity forms the basis of cellular excitation or inhibition by Cys-loop ligand-gated ion channels (LGICs), and is essential for physiological receptor function. There are no reports of naturally occurring mutations in LGICs associated with the conversion of charge selectivity. Here, we report on a CHRNA1 mutation (α1Leu251Arg) in a patient with congenital myasthenic syndrome associated with transformation of the muscle acetylcholine receptor (AChR) into an inhibitory channel. Performing patch-clamp experiments, the AChR was found to be converted into chloride conductance at positive potentials, whereas whole-cell currents at negative potentials, although markedly reduced, were still carried by sodium. Umbrella sampling molecular dynamics simulations revealed constriction of the channel pore radius to 2.4 Å as a result of the mutation, which required partial desolvation of the ions in order to permeate the pore. Ion desolvation was associated with an energetic penalty that was compensated for by the favorable electrostatic interaction of the positively charged arginines with chloride. These findings reveal a mechanism for the transformation of the muscle AChR into an inhibitory channel in a clinical context.

Identification of compounds that bind the centriolar protein SAS-6 and inhibit its oligomerization.

Centrioles are key eukaryotic organelles that are responsible for the formation of cilia and flagella, and for organizing the microtubule network and the mitotic spindle in animals. Centriole assembly requires oligomerization of the essential protein spindle assembly abnormal 6 (SAS-6), which forms a structural scaffold templating the organization of further organelle components. A dimerization interaction between SAS-6 N-terminal "head" domains was previously shown to be essential for protein oligomerization in vitro and for function in centriole assembly. Here, we developed a pharmacophore model allowing us to assemble a library of low-molecular-weight ligands predicted to bind the SAS-6 head domain and inhibit protein oligomerization. We demonstrate using NMR spectroscopy that a ligand from this family binds at the head domain dimerization site of algae, nematode, and human SAS-6 variants, but also that another ligand specifically recognizes human SAS-6. Atomistic molecular dynamics simulations starting from SAS-6 head domain crystallographic structures, including that of the human head domain which we now resolve, suggest that ligand specificity derives from favorable Van der Waals interactions with a hydrophobic cavity at the dimerization site.

Substrate conformational dynamics facilitate structure-specific recognition of gapped DNA by DNA polymerase.

DNA-binding proteins utilise different recognition mechanisms to locate their DNA targets; some proteins recognise specific DNA sequences, while others interact with specific DNA structures. While sequence-specific DNA binding has been studied extensively, structure-specific recognition mechanisms remain unclear. Here, we study structure-specific DNA recognition by examining the structure and dynamics of DNA polymerase I Klenow Fragment (Pol) substrates both alone and in DNA-Pol complexes. Using a docking approach based on a network of 73 distances collected using single-molecule FRET, we determined a novel solution structure of the single-nucleotide-gapped DNA-Pol binary complex. The structure resembled existing crystal structures with regards to the downstream primer-template DNA substrate, and revealed a previously unobserved sharp bend (∼120°) in the DNA substrate; this pronounced bend was present in living cells. MD simulations and single-molecule assays also revealed that 4-5 nt of downstream gap-proximal DNA are unwound in the binary complex. Further, experiments and coarse-grained modelling showed the substrate alone frequently adopts bent conformations with 1-2 nt fraying around the gap, suggesting a mechanism wherein Pol recognises a pre-bent, partially-melted conformation of gapped DNA. We propose a general mechanism for substrate recognition by structure-specific enzymes driven by protein sensing of the conformational dynamics of their DNA substrates.

Proton Control of Transitions in an Amino Acid Transporter.

Amino acid transport into the cell is often coupled to the proton electrochemical gradient, as found in the solute carrier 36 family of proton-coupled amino acid transporters. Although no structure of a human proton-coupled amino acid transporter exists, the crystal structure of a related homolog from bacteria, GkApcT, has recently been solved in an inward-occluded state and allows an opportunity to examine how protons are coupled to amino acid transport. Our working hypothesis is that release of the amino acid substrate is facilitated by the deprotonation of a key glutamate residue (E115) located at the bottom of the binding pocket, which forms part of the intracellular gate, allowing the protein to transition from an inward-occluded to an inward-open conformation. During unbiased molecular dynamics simulations, we observed a transition from the inward-occluded state captured in the crystal structure to a much more open state, which we consider likely to be representative of the inward-open state associated with substrate release. To explore this and the role of protons in these transitions, we have used umbrella sampling to demonstrate that the transition from inward occluded to inward open is more energetically favorable when E115 is deprotonated. That E115 is likely to be protonated in the inward-occluded state and deprotonated in the inward-open state is further confirmed via the use of absolute binding free energies. Finally, we also show, via the use of absolute binding free energy calculations, that the affinity of the protein for alanine is very similar regardless of either the conformational state or the protonation of E115, presumably reflecting the fact that all the key interactions are deep within the binding cavity. Together, our results give a detailed picture of the role of protons in driving one of the major transitions in this transporter.

Mutational Analysis and Modeling of Negative Allosteric Modulator Binding Sites in AMPA Receptors.

The α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) constitute a subclass of the ionotropic glutamate receptor superfamily, which functions as glutamate-gated cation channels to mediate the majority of excitatory neurotransmission in the central nervous system. AMPARs are therapeutic targets in a range of brain disorders associated with abnormal glutamate hyperactivity. Multiple classes of AMPAR inhibitors have been developed during the past decades, including competitive antagonists, ion channel blockers, and negative allosteric modulators (NAMs). At present, the NAM is the only class of AMPAR ligands that have been developed into safe and useful drugs in humans in the form of perampanel (Fycompa), which was recently approved for treatment of epilepsy. Compared with the detailed understanding of other AMPAR ligand classes, surprisingly little information has been available regarding the molecular mechanism of perampanel and other classes of NAMs at AMPARs; including the location and structure of NAM binding pockets in the receptor complex. However, structures of the AMPAR GluA2 in complex with NAMs were recently reported that unambiguously identified the NAM binding sites. In parallel with this work, our aim with the present study was to identify specific residues involved in the formation of the NAM binding site for three prototypical AMPAR NAMs. Hence, we have performed a mutational analysis of the AMPAR region that links the four extracellular ligand-binding domains to the central ion channel in the transmembrane domain region. Furthermore, we perform computational ligand docking of the NAMs into structural models of the homomeric GluA2 receptor and optimize side chain conformations around the NAMs to model how NAMs bind in this specific site. The new insights provide potentially valuable input for structure-based drug design of new NAMs. SIGNIFICANCE STATEMENT: The α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors are glutamate-gated ion channels that mediate the majority of excitatory neurotransmission in the brain. Negative allosteric modulators of AMPA receptors are considered to have significant therapeutic potential in diseases linked to glutamate hyperactivity. The present work employs mutational analysis and molecular modeling of the binding site for prototypical NAMs to provide new molecular insight into how NAMs interact with the AMPA receptor, which is of potential use for future design of new types of NAMs.

Towards understanding promiscuity in multidrug efflux pumps.

Drug export from cells is a major factor in the acquisition of cellular resistance to antimicrobial and cancer chemotherapy, and poses a significant threat to future clinical management of disease. Many of the proteins that catalyse drug efflux do so with remarkably low substrate specificity, a phenomenon known as multidrug transport. For these reasons we need a greater understanding of drug recognition and transport in multidrug pumps to inform research that attempts to circumvent their action. Structural and computational studies have been heralded as being great strides towards a full elucidation of multidrug recognition and transport. In this review we summarise these advances and ask how close we are to a molecular understanding of this remarkable phenomenon.