Minimally modified human blood coagulation factor X to bypass direct factor Xa inhibitors.

BACKGROUND: Direct oral factor (F)Xa inhibitors are widely used as alternatives to conventional vitamin K antagonists in managing venous thromboembolism and nonvalvular atrial fibrillation. Unfortunately, bleeding-related adverse events remain a major concern in clinical practice. In case of bleeding or emergency surgery, rapid-onset reversal agents may be required to counteract the anticoagulant activity. OBJECTIVES: The ability of FXa variants to bypass the direct oral FXa inhibitors was assessed. METHODS: Human FXa variants were generated through substitution of phenylalanine 174 (F174) for either alanine, isoleucine, or serine. FXa variants were stably expressed in HEK293 cells and purified to homogeneity using ion-exchange chromatography. RESULTS: F174-substituted human FX variants demonstrated efficacy in restoring thrombin generation in plasma containing direct FXa inhibitors (apixaban, rivaroxaban, edoxaban). Their ability to bypass the anticoagulant effects stems from a significantly reduced sensitivity for the direct FXa inhibitors due to a decrease in binding affinity determined using molecular dynamics simulations and free energy computation. Furthermore, F174 modification resulted in a partial loss of inhibition by tissue factor pathway inhibitor, enhancing the procoagulant effect of F174-substituted FX. Consequently, the F174A- and F174S-substituted FX variants effectively counteracted the effects of 2 widely used anticoagulants, apixaban and rivaroxaban, in plasma of atrial fibrillation and venous thromboembolism patients. CONCLUSION: These human FX variants have the potential to serve as a rescue reversal strategy to overcome the effect of direct FXa inhibitors in case of life-threatening bleeding events or emergency surgical interventions.

Distant sequence regions of JBP1 contribute to J-DNA binding.

Base-J (ß-D-glucopyranosyloxymethyluracil) is a modified DNA nucleotide that replaces 1% of thymine in kinetoplastid flagellates. The biosynthesis and maintenance of base-J depends on the base-J-binding protein 1 (JBP1) that has a thymidine hydroxylase domain and a J-DNA-binding domain (JDBD). How the thymidine hydroxylase domain synergizes with the JDBD to hydroxylate thymine in specific genomic sites, maintaining base-J during semi-conservative DNA replication, remains unclear. Here, we present a crystal structure of the JDBD including a previously disordered DNA-contacting loop and use it as starting point for molecular dynamics simulations and computational docking studies to propose recognition models for JDBD binding to J-DNA. These models guided mutagenesis experiments, providing additional data for docking, which reveals a binding mode for JDBD onto J-DNA. This model, together with the crystallographic structure of the TET2 JBP1-homologue in complex with DNA and the AlphaFold model of full-length JBP1, allowed us to hypothesize that the flexible JBP1 N-terminus contributes to DNA-binding, which we confirmed experimentally. Α high-resolution JBP1:J-DNA complex, which must involve conformational changes, would however need to be determined experimentally to further understand this unique underlying molecular mechanism that ensures replication of epigenetic information.

OFraMP: a fragment-based tool to facilitate the parametrization of large molecules.

An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. OFraMP applied to paclitaxel (ATB ID 35922).

An Efficient, Site-Selective and Spontaneous Peptide Macrocyclisation During in†vitro Translation.

Macrocyclisation provides a means of stabilising the conformation of peptides, often resulting in improved stability, selectivity, affinity, and cell permeability. In this work, a new approach to peptide macrocyclisation is reported, using a cyanobenzothiazole-containing amino acid that can be incorporated into peptides by both in vitro translation and solid phase peptide synthesis, meaning it should be applicable to peptide discovery by mRNA display. This cyclisation proceeds rapidly, with minimal by-products, is selective over other amino acids including non N-terminal cysteines, and is compatible with further peptide elaboration exploiting such an additional cysteine in bicyclisation and derivatisation reactions. Molecular dynamics simulations show that the new cyclisation group is likely to influence the peptide conformation as compared to previous thioether-based approaches, through rigidity and intramolecular aromatic interactions, illustrating their complementarity.

Covalent Proteomimetic Inhibitor of the Bacterial FtsQB Divisome Complex.

The use of antibiotics is threatened by the emergence and spread of multidrug-resistant strains of bacteria. Thus, there is a need to develop antibiotics that address new targets. In this respect, the bacterial divisome, a multi-protein complex central to cell division, represents a potentially attractive target. Of particular interest is the FtsQB subcomplex that plays a decisive role in divisome assembly and peptidoglycan biogenesis in E. coli. Here, we report the structure-based design of a macrocyclic covalent inhibitor derived from a periplasmic region of FtsB that mediates its binding to FtsQ. The bioactive conformation of this motif was stabilized by a customized cross-link resulting in a tertiary structure mimetic with increased affinity for FtsQ. To increase activity, a covalent handle was incorporated, providing an inhibitor that impedes the interaction between FtsQ and FtsB irreversibly. The covalent inhibitor reduced the growth of an outer membrane-permeable E. coli strain, concurrent with the expected loss of FtsB localization, and also affected the infection of zebrafish larvae by a clinical E. coli strain. This first-in-class inhibitor of a divisome protein-protein interaction highlights the potential of proteomimetic molecules as inhibitors of challenging targets. In particular, the covalent mode-of-action can serve as an inspiration for future antibiotics that target protein-protein interactions.

C-X Bond Activation by Palladium: Steric Shielding versus Steric Attraction.

The C-X bond activation (X = H, C) of a series of substituted C(n°)-H and C(n°)-C(m°) bonds with C(n°) and C(m°) = H3 C- (methyl, 0°), CH3 H2 C- (primary, 1°), (CH3 )2 HC- (secondary, 2°), (CH3 )3 C- (tertiary, 3°) by palladium were investigated using relativistic dispersion-corrected density functional theory at ZORA-BLYP-D3(BJ)/TZ2P. The effect of the stepwise introduction of substituents was pinpointed at the C-X bond on the bond activation process. The C(n°)-X bonds become substantially weaker going from C(0°)-X, to C(1°)-X, to C(2°)-X, to C(3°)-X because of the increasing steric repulsion between the C(n°)- and X-group. Interestingly, this often does not lead to a lower barrier for the C(n°)-X bond activation. The C-H activation barrier, for example, decreases from C(0°)-X, to C(1°)-X, to C(2°)-X and then increases again for the very crowded C(3°)-X bond. For the more congested C-C bond, in contrast, the activation barrier always increases as the degree of substitution is increased. Our activation strain and matching energy decomposition analyses reveal that these differences in C-H and C-C bond activation can be traced back to the opposing interplay between steric repulsion across the C-X bond versus that between the catalyst and substrate.

An anti-tuberculosis compound screen using a zebrafish infection model identifies an aspartyl-tRNA synthetase inhibitor.

Finding new anti-tuberculosis compounds with convincing in vivo activity is an ongoing global challenge to fight the emergence of multidrug-resistant Mycobacterium tuberculosis isolates. In this study, we exploited the medium-throughput capabilities of the zebrafish embryo infection model with Mycobacterium marinum as a surrogate for M. tuberculosis. Using a representative set of clinically established drugs, we demonstrate that this model could be predictive and selective for antibiotics that can be administered orally. We further used the zebrafish infection model to screen 240 compounds from an anti-tuberculosis hit library for their in vivo activity and identified 14 highly active compounds. One of the most active compounds was the tetracyclic compound TBA161, which was studied in more detail. Analysis of resistant mutants revealed point mutations in aspS (rv2572c), encoding an aspartyl-tRNA synthetase. The target was genetically confirmed, and molecular docking studies propose the possible binding of TBA161 in a pocket adjacent to the catalytic site. This study shows that the zebrafish infection model is suitable for rapidly identifying promising scaffolds with in vivo activity.

ELIXIR and Toxicology: a community in development.

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Recent Developments in Linear Interaction Energy Based Binding Free Energy Calculations.

The linear interaction energy (LIE) approach is an end-point method to compute binding affinities. As such it combines explicit conformational sampling (of the protein-bound and unbound-ligand states) with efficiency in calculating values for the protein-ligand binding free energy ΔG bind . This perspective summarizes our recent efforts to use molecular simulation and empirically calibrated LIE models for accurate and efficient calculation of ΔG bind for diverse sets of compounds binding to flexible proteins (e.g., Cytochrome P450s and other proteins of direct pharmaceutical or biochemical interest). Such proteins pose challenges on ΔG bind computation, which we tackle using a previously introduced statistically weighted LIE scheme. Because calibrated LIE models require empirical fitting of scaling parameters, they need to be accompanied with an applicability domain (AD) definition to provide a measure of confidence for predictions for arbitrary query compounds within a reference frame defined by a collective chemical and interaction space. To enable AD assessment of LIE predictions (or other protein-structure and -dynamic based ΔG bind calculations) we recently introduced strategies for AD assignment of LIE models, based on simulation and training data only. These strategies are reviewed here as well, together with available tools to facilitate and/or automate LIE computation (including software for combined statistically-weighted LIE calculations and AD assessment).

Deriving a Polarizable Force Field for Biomolecular Building Blocks with Minimal Empirical Calibration.

Force field parametrization involves a complex set of linked optimization problems, with the goal of describing complex molecular interactions by using simple classical potential-energy functions that model Coulomb interactions, dispersion, and exchange repulsion. These functions comprise a set of atomic (and molecular) parameters and together with the bonded terms they constitute the molecular mechanics force field. Traditionally, many of these parameters have been fitted in a calibration approach in which experimental measures for thermodynamic and other relevant properties of small-molecule compounds are used for fitting and validation. As these approaches are laborious and time-consuming and represent an underdetermined optimization problem, we study methods to fit and derive an increasing number of parameters directly from electronic structure calculations, in order to greatly reduce possible parameter space for the remaining free parameters. In the current work we investigate a polarizable model with a higher order dispersion term for use in biomolecular simulation. Results for 49 biochemically relevant molecules are presented including updated parameters for hydrocarbon side chains. We show that our recently presented set of QM/MM derived atomic polarizabilities can be used in direct conjunction with partial charges and a higher order dispersion model that are quantum-mechanically determined, to freeze nearly all (i.e., 132 out of 138) nonbonded parameters to their quantum determined values.

Polarisable force fields: what do they add in biomolecular simulations?

The quality of biomolecular simulations critically depends on the accuracy of the force field used to calculate the potential energy of the molecular configurations. Currently, most simulations employ non-polarisable force fields, which describe electrostatic interactions as the sum of Coulombic interactions between fixed atomic charges. Polarisation of these charge distributions is incorporated only in a mean-field manner. In the past decade, extensive efforts have been devoted to developing simple, efficient, and yet generally applicable polarisable force fields for biomolecular simulations. In this review, we summarise the latest developments in accounting for key biomolecular interactions with polarisable force fields and applications to address challenging biological questions. In the end, we provide an outlook for future development in polarisable force fields.

Imidazo[2,1-b] [1,3,4]thiadiazoles with antiproliferative activity against primary and gemcitabine-resistant pancreatic cancer cells.

A new series of eighteen imidazo [2,1-b] [1,3,4]thiadiazole derivatives was efficiently synthesized and screened for antiproliferative activity against the National Cancer Institute (NCI-60) cell lines panel. Two out of eighteen derivatives, compounds 12a and 12h, showed remarkably cytotoxic activity with the half maximal inhibitory concentration values (IC50) ranging from 0.23 to 11.4 µM, and 0.29-12.2 µM, respectively. However, two additional compounds, 12b and 13g, displayed remarkable in vitro antiproliferative activity against pancreatic ductal adenocarcinoma (PDAC) cell lines, including immortalized (SUIT-2, Capan-1, Panc-1), primary (PDAC-3) and gemcitabine-resistant (Panc-1R), eliciting IC50 values ranging from micromolar to sub-micromolar level, associated with significant reduction of cell-migration and spheroid shrinkage. These remarkable results might be explained by modulation of key regulators of epithelial-to-mesenchymal transition (EMT), including E-cadherin and vimentin, and inhibition of metalloproteinase-2/-9. High-throughput arrays revealed a significant inhibition of the phosphorylation of 45 tyrosine kinases substrates, whose visualization on Cytoscape highlighted PTK2/FAK as an important hub. Inhibition of phosphorylation of PTK2/FAK was validated as one of the possible mechanisms of action, using a specific ELISA. In conclusion, novel imidazothiadiazoles show potent antiproliferative activity, mediated by modulation of EMT and PTK2/FAK.

A Modified Arrhenius Approach to Thermodynamically Study Regioselectivity in Cytochrome P450-Catalyzed Substrate Conversion.

The regio- (and stereo-)selectivity and specific activity of cytochrome P450s are determined by the accessibility of potential sites of metabolism (SOMs) of the bound substrate relative to the heme, and the activation barrier of the regioselective oxidation reaction(s). The accessibility of potential SOMs depends on the relative binding free energy (ΔΔGbind ) of the catalytically active substrate-binding poses, and the probability of the substrate to adopt a transition-state geometry. An established experimental method to measure activation energies of enzymatic reactions is the analysis of reaction rate constants at different temperatures and the construction of Arrhenius plots. This is a challenge for multistep P450-catalyzed processes that involve redox partners. We introduce a modified Arrhenius approach to overcome the limitations in studying P450 selectivity, which can be applied in multiproduct enzyme catalysis. Our approach gives combined information on relative activation energies, ΔΔGbind values, and collision entropies, yielding direct insight into the basis of selectivity in substrate conversion.

Combined Linear Interaction Energy and Alchemical Solvation Free-Energy Approach for Protein-Binding Affinity Computation.

Calculating free energies of binding (ΔGbind) between ligands and their target protein is of major interest to drug discovery and safety, yet it is still associated with several challenges and difficulties. Linear interaction energy (LIE) is an efficient in silico method for ΔGbind computation. LIE models can be trained and used to directly calculate binding affinities from interaction energies involving ligands in the bound and unbound states only, and LIE can be combined with statistical weighting to calculate ΔGbind for flexible proteins that may bind their ligands in multiple orientations. Here, we investigate if LIE predictions can be effectively improved by explicitly including the entropy of (de)solvation into our free-energy calculations. For that purpose, we combine LIE calculations for the protein-ligand-bound state with explicit free-energy perturbation to rigorously compute the unbound ligand's solvation free energy. We show that for 28 Cytochrome P450 2A6 (CYP2A6) ligands, coupling LIE with alchemical solvation free-energy calculation helps to improve obtained correlation between computed and reference (experimental) binding data.

The Role of c-Met as a Biomarker and Player in Innate and Acquired Resistance in Non-Small-Cell Lung Cancer: Two New Mutations Warrant Further Studies.

The c-Met receptor is a therapeutically actionable target in non-small-cell lung cancer (NSCLC), with one approved drug and several agents in development. Most suitable biomarkers for patient selection include c-Met amplification and exon-14 skipping. Our retrospective study focused on the frequency of different c-Met aberrations (overexpression, amplification and mutations) in 153 primary, therapy-naïve resection samples and their paired metastases, from Biobank@UZA. Furthermore, we determined the correlation of c-Met expression with clinicopathological factors, Epidermal Growth Factor Receptor (EGFR)-status and TP53 mutations. Our results showed that c-Met expression levels in primary tumors were comparable to their respective metastases. Five different mutations were detected by deep sequencing: three (E168D, S203T, N375S) previously described and two never reported (I333T, G783E). I333T, a new mutation in the Sema(phorin) domain of c-Met, might influence the binding of antibodies targeting the HGF-binding domain, potentially causing innate resistance. E168D and S203T mutations showed a trend towards a correlation with high c-Met expression (p = 0.058). We found a significant correlation between c-MET expression, EGFR expression (p = 0.010) and EGFR mutations (p = 0.013), as well as a trend (p = 0.057) with regards to TP53 mutant activity. In conclusion this study demonstrated a strong correlation between EGFR mutations, TP53 and c-Met expression in therapy-naïve primary resection samples. Moreover, we found two new c-Met mutations that warrant further studies.

A Comparative Linear Interaction Energy and MM/PBSA Study on SIRT1-Ligand Binding Free Energy Calculation.

Binding free energy (ΔGbind) computation can play an important role in prioritizing compounds to be evaluated experimentally on their affinity for target proteins, yet fast and accurate ΔGbind calculation remains an elusive task. In this study, we compare the performance of two popular end-point methods, i.e., linear interaction energy (LIE) and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), with respect to their ability to correlate calculated binding affinities of 27 thieno[3,2-d]pyrimidine-6-carboxamide-derived sirtuin 1 (SIRT1) inhibitors with experimental data. Compared with the standard single-trajectory setup of MM/PBSA, our study elucidates that LIE allows to obtain direct ("absolute") values for SIRT1 binding free energies with lower compute requirements, while the accuracy in calculating relative values for ΔGbind is comparable (Pearson's r = 0.72 and 0.64 for LIE and MM/PBSA, respectively). We also investigate the potential of combining multiple docking poses in iterative LIE models and find that Boltzmann-like weighting of outcomes of simulations starting from different poses can retrieve appropriate binding orientations. In addition, we find that in this particular case study the LIE and MM/PBSA models can be optimized by neglecting the contributions from electrostatic and polar interactions to the ΔGbind calculations.

Structural analysis of Cytochrome P450 BM3 mutant M11 in complex with dithiothreitol.

The bacterial Cytochrome P450 (CYP) BM3 (CYP102A1) is one of the most active CYP isoforms. BM3 mutants can serve as a model for human drug-metabolizing CYPs and/or as biocatalyst for selective formation of drug metabolites. Hence, molecular and computational biologists have in the last two decades shown strong interest in the discovery and design of novel BM3 variants with optimized activity and selectivity for substrate conversion. This led e.g. to the discovery of mutant M11 that is able to metabolize a variety of drugs and drug-like compounds with relatively high activity. In order to further improve our understanding of CYP binding and reactions, we performed a co-crystallization study of mutant M11 and report here the three-dimensional structure M11 in complex with dithiothreitol (DTT) at a resolution of 2.16 Å. The structure shows that DTT can coordinate to the Fe atom in the heme group. UV/Vis spectroscopy and molecular dynamics simulation studies underline this finding and as first structure of the CYP BM3 mutant M11 in complex with a ligand, it offers a basis for structure-based design of novel mutants.

Automated partial atomic charge assignment for drug-like molecules: a fast knapsack approach.

A key factor in computational drug design is the consistency and reliability with which intermolecular interactions between a wide variety of molecules can be described. Here we present a procedure to efficiently, reliably and automatically assign partial atomic charges to atoms based on known distributions. We formally introduce the molecular charge assignment problem, where the task is to select a charge from a set of candidate charges for every atom of a given query molecule. Charges are accompanied by a score that depends on their observed frequency in similar neighbourhoods (chemical environments) in a database of previously parameterised molecules. The aim is to assign the charges such that the total charge equals a known target charge within a margin of error while maximizing the sum of the charge scores. We show that the problem is a variant of the well-studied multiple-choice knapsack problem and thus weakly NP -complete. We propose solutions based on Integer Linear Programming and a pseudo-polynomial time Dynamic Programming algorithm. We demonstrate that the results obtained for novel molecules not included in the database are comparable to the ones obtained performing explicit charge calculations while decreasing the time to determine partial charges for a molecule from hours or even days to below a second. Our software is openly available.

Deriving Force-Field Parameters from First Principles Using a Polarizable and Higher Order Dispersion Model.

In this work we propose a strategy based on quantum mechanical (QM) calculations to parametrize a polarizable force field for use in molecular dynamics (MD) simulations. We investigate the use of multiple atoms-in-molecules (AIM) strategies to partition QM determined molecular electron densities into atomic subregions. The partitioned atomic densities are subsequently used to compute atomic dispersion coefficients from effective exchange-hole-dipole moment (XDM) calculations. In order to derive values for the repulsive van der Waals parameters from first principles, we use a simple volume relation to scale effective atomic radii. Explicit inclusion of higher order dispersion coefficients was tested for a series of alkanes, and we show that combining C6 and C8 attractive terms together with a C11 repulsive potential yields satisfying models when used in combination with our van der Waals parameters and electrostatic and bonded parameters as directly obtained from quantum calculations as well. This result highlights that explicit inclusion of higher order dispersion terms could be viable in simulation, and it suggests that currently available QM analysis methods allow for first-principles parametrization of molecular mechanics models.

A QM/MM Derived Polarizable Water Model for Molecular Simulation.

In this work, we propose an improved QM/MM-based strategy to determine condensed-phase polarizabilities and we use this approach to optimize a new and simple polarizable four-site water model for classical molecular simulation. For the determination of the model value for the polarizability from QM/MM, we show that our proposed consensus-fitting strategy significantly reduces the uncertainty in calculated polarizabilities in cases where the size of the local external electric field is small. By fitting electrostatic, polarization and dispersion properties of our water model based on quantum and/or combined QM/MM calculations, only a single model parameter (describing exchange repulsion) is left for empirical calibration. The resulting model performs well in describing relevant pure-liquid thermodynamic and transport properties, which illustrates the merit of our approach to minimize the number of free variables in our model.