Markov model-based polymer assembly from force field-parameterized building blocks.

A conventional by hand construction and parameterization of a polymer model for the purpose of molecular simulations can quickly become very work-intensive and time-consuming. Using the example of polyglycerol, I present a polymer decomposition strategy yielding a set of five monomeric residues that are convenient for an instantaneous assembly and subsequent force field simulation of a polyglycerol polymer model. Force field parameters have been developed in accordance with the classical Amber force field. Partial charges of each unit were fitted to the electrostatic potential using quantum-chemical methods and slightly modified in order to guarantee a neutral total polymer charge. In contrast to similarly constructed models of amino acid and nucleotide sequences, the glycerol building blocks may yield an arbitrary degree of bifurcations depending on the underlying probabilistic model. The iterative development of the overall structure as well as the relation of linear to branching units is controlled by a simple Markov model which is presented with few algorithmic details. The resulting polymer is highly suitable for classical explicit water molecular dynamics simulations on the atomistic level after a structural relaxation step. Moreover, the decomposition strategy presented here can easily be adopted to many other (co)polymers.

Peptide-polymer ligands for a tandem WW-domain, an adaptive multivalent protein-protein interaction: lessons on the thermodynamic fitness of flexible ligands.

Three polymers, poly(N-(2-hydroxypropyl)methacrylamide) (pHPMA), hyperbranched polyglycerol (hPG), and dextran were investigated as carriers for multivalent ligands targeting the adaptive tandem WW-domain of formin-binding protein (FBP21). Polymer carriers were conjugated with 3-9 copies of the proline-rich decapeptide GPPPRGPPPR-NH2 (P1). Binding of the obtained peptide-polymer conjugates to the tandem WW-domain was investigated employing isothermal titration calorimetry (ITC) to determine the binding affinity, the enthalpic and entropic contributions to free binding energy, and the stoichiometry of binding for all peptide-polymer conjugates. Binding affinities of all multivalent ligands were in the µM range, strongly amplified compared to the monovalent ligand P1 with a K D > 1 mM. In addition, concise differences were observed, pHPMA and hPG carriers showed moderate affinity and bound 2.3-2.8 peptides per protein binding site resulting in the formation of aggregates. Dextran-based conjugates displayed affinities down to 1.2 µM, forming complexes with low stoichiometry, and no precipitation. Experimental results were compared with parameters obtained from molecular dynamics simulations in order to understand the observed differences between the three carrier materials. In summary, the more rigid and condensed peptide-polymer conjugates based on the dextran scaffold seem to be superior to induce multivalent binding and to increase affinity, while the more flexible and dendritic polymers, pHPMA and hPG are suitable to induce crosslinking upon binding.

Hands-off linear interaction energy approach to binding mode and affinity estimation of estrogens.

With this work we target the development of a predictictive model for the identification of small molecules which bind to the estrogen receptor alpha and, thus, may act as endocrine disruptors. We propose a combined thermodynamic approach for the estimation of preferential binding modes along with corresponding free energy differences using a linear interaction energy (LIE) ansatz. The LIE model is extended by a Monte Carlo approach for the computation of conformational entropies as recently developed by our group. Incorporating the entropy contribution substantially increased the correlation with experimental affinity values. Both squared coefficients for the fitted data as well as the more meaningful leave-one-out cross-validation of predicted energies were elevated up to r(Fit)² = 0.87 and q(LOO)² = 0.82, respectively. All calculations have been performed on a set of 31 highly diverse ligands regarding their structural properties and affinities to the estrogen receptor alpha. Comparison of predicted ligand orientations with crystallographic data retrieved from the Protein database pdb.org revealed remarkable binding mode predictions.

Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.

Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Structural bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild type reference.

To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread occurred in multiple pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here we report a sequence and structural-bioinformatics analysis to estimate the effects of amino acid substitutions on the affinity of the SARS-CoV-2 spike receptor binding domain (RBD) to the human receptor hACE2. This is done through qualitative electrostatics and hydrophobicity analysis as well as molecular dynamics simulations used to develop a high-precision empirical scoring function (ESF) closely related to the linear interaction energy method and calibrated on a large set of experimental binding energies. For the latest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to all other VOC. Moreover, according to our ESF model, Omicron achieves a much higher ACE2 binding affinity than the wild type and, in particular, the highest among all VOCs except Alpha and thus requires special attention and monitoring.

A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening.

The unparalleled global effort to combat the continuing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic over the last year has resulted in promising prophylactic measures. However, a need still exists for cheap, effective therapeutics, and targeting multiple points in the viral life cycle could help tackle the current, as well as future, coronaviruses. Here, we leverage our recently developed, ultra-large-scale in silico screening platform, VirtualFlow, to search for inhibitors that target SARS-CoV-2. In this unprecedented structure-based virtual campaign, we screened roughly 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets. In addition to targeting the active sites of viral enzymes, we also targeted critical auxiliary sites such as functionally important protein-protein interactions.

A Multi-Pronged Approach Targeting SARS-CoV-2 Proteins Using Ultra-Large Virtual Screening.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed in silico screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets in the cloud. In addition to targeting the active sites of viral enzymes, we also target critical auxiliary sites such as functionally important protein-protein interaction interfaces. This multi-target approach not only increases the likelihood of finding a potent inhibitor, but could also help identify a collection of anti-coronavirus drugs that would retain efficacy in the face of viral mutation. Drugs belonging to different regimen classes could be combined to develop possible combination therapies, and top hits that bind at highly conserved sites would be potential candidates for further development as coronavirus drugs. Here, we present the top 200 in silico hits for each target site. While in-house experimental validation of some of these compounds is currently underway, we want to make this array of potential inhibitor candidates available to researchers worldwide in consideration of the pressing need for fast-tracked drug development.

Effects of carbamazepine and two of its metabolites on the non-biting midge Chironomus riparius in a sediment full life cycle toxicity test.

The antiepileptic drug carbamazepine (CBZ) and its main metabolites carbamazepine-10,11-epoxide (EP-CBZ) and 10,11-dihydro-10,11-dihydroxy-carbamazepine (DiOH-CBZ) were chosen as test substances to assess chronic toxicity on the non-biting midge Chironomus riparius. All the three substances were tested in a 40-day sediment full life cycle test (according to OECD 233) in which mortality, emergence, fertility, and clutch size were evaluated. In addition, these parameters were considered to calculate the population growth rate which represents an integrated measure to assess population relevant effects. With an LC50 of 0.20 mg/kg (time-weighted mean), the metabolite EP-CBZ was significantly more toxic than the parent substance CBZ (LC50: 1.1 mg/kg). Especially mortality, emergence, and fertility showed to be sensitive parameters under the exposure to CBZ and EP-CBZ. By using classical molecular dynamics (MD) simulations, the binding of CBZ to the ecdysone receptor was investigated as one possible mode of action (MoA) but appeared to be unlikely. The second metabolite DiOH-CBZ did not cause any effects within the tested concentration rage (0.17-1.2 mg/kg). Even though CBZ was less toxic compared to EP-CBZ, CBZ is found in the environment at much higher concentrations and therefore causes a higher potential risk for sediment dwelling organisms compared to its metabolites. Nevertheless, the current study illustrates the importance of including commonly found metabolites into the risk assessment of parent substances.

How to simulate affinities for host-guest systems lacking binding mode information: application to the liquid chromatographic separation of hexabromocyclododecane stereoisomers.

A novel approach for the simulation of host-guest systems by systematically scanning the host molecule's orientations within the guest cavity is presented along with a thermodynamic strategy for determining preferential binding modes and corresponding optimal interaction energies between host and guest molecules. By way of example, the elution order of hexabromocyclododecane stereoisomers from high performance liquid chromatography separation on a permethylated ß-cyclcodextrin stationary phase has been computed using classical molecular dynamics simulations with the explicit solvents water and acetonitrile. Comparison of estimated with experimental separation data reveals remarkable squared coefficients of correlation with R(2) = 0.87 and a very high correlation R(LOO2) = 0.72 using the leave-one-out cross-validation method and water as solvent. In particular, the approach presented shapes up as very robust in terms of the evaluated time range under consideration, reflecting well thermodynamic equilibria. These and further observations correlating with experimental results suggest the suitability of the underlying force fields and our multi-mode approach for the estimation of relative binding affinities for host-guest systems with unknown binding modes.

Photochemical trans-/cis-isomerization and quantitation of zearalenone in edible oils.

The emphasis of the present work was to investigate the photochemical conversion of trans- to cis-zearalenone in edible oils under real-life conditions. For quantitation purposes a cis-zearalenone standard was synthesized and characterized for its identity and purity (≥95%) by (1)H NMR, X-ray crystallography, HPLC fluorescence and mass spectrometric detection. In a sample survey of 12 edible oils (9 corn oils, 3 hempseed oils) from local supermarkets all corn oils contained trans-zearalenone (median 194 µg/kg), but no cis-zearalenone was detected. For alteration studies trans-zearalenone contaminated corn oils were exposed to sunlight over 4 and 30 weeks, revealing an obvious shift toward cis-zearalenone up to a cis/trans ratio of 9:1 by storage in colorless glass bottles. Irradiation experiments of trans-zearalenone in different organic solvents confirmed the preferred formation of cis-zearalenone possibly caused by entropic effects rather than by enthalpic entities as investigated by quantum chemical and classical force field simulations.