Correlation properties of a spatially quasi-incoherent imaging interferometer.

The depth-gating capacity of a spatially quasi-incoherent imaging interferometer is investigated in relation to the 3D correlation properties of diffraction field laser speckles. The system exploits a phase-stepped imaging Michelson-type interferometer in which spatially quasi-incoherent illumination is generated by passing an unexpanded laser beam through a rotating diffuser. Numerical simulations and optical experiments both verify that the depth-gating capacity of the imaging interferometer scales as λ/2NAp2, where λ is the wavelength of the laser and NAp is the numerical aperture of the illumination. For a set depth gate of 150 µm, the depth-gating capacity of the interferometer is demonstrated by scanning a standard USAF target through the measurement volume. The results obtained show that an imaging tool of this kind is expected to provide useful capabilities for imaging through disturbing media and where a single wavelength is required.

Fully Automated Creation of Virtual Chemical Fragment Spaces Using the Open-Source Library OpenChemLib.

Synthetically accessible chemical spaces provide a valuable source to search for small-molecule analogues or new starting points in drug discovery projects. Having a toolbox at hand that can automatically create searchable representations of such spaces using reaction definitions and building blocks as inputs is a prerequisite to put this approach into practice. Herein, we present a tool kit to create such virtual chemical spaces. It is part of the OpenChemLib, an open-source Cheminformatics tool kit. Furthermore, we demonstrate the creation of a several billion molecules large chemical space from commercial building blocks and a list of common organic chemistry reactions.

Impact of preprocessing methods on the Raman spectra of brain tissue.

Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.

A Microfluidic System for Simultaneous Raman Spectroscopy, Patch-Clamp Electrophysiology, and Live-Cell Imaging to Study Key Cellular Events of Single Living Cells in Response to Acute Hypoxia.

The ability to sense changes in oxygen availability is fundamentally important for the survival of all aerobic organisms. However, cellular oxygen sensing mechanisms and pathologies remain incompletely understood and studies of acute oxygen sensing, in particular, have produced inconsistent results. Current methods cannot simultaneously measure the key cellular events in acute hypoxia (i.e., changes in redox state, electrophysiological properties, and mechanical responses) at controlled partial pressures of oxygen (pO2 ). The lack of such a comprehensive method essentially contributes to the discrepancies in the field. A sealed microfluidic system that combines i) Raman spectroscopy, ii) patch-clamp electrophysiology, and iii) live-cell imaging under precisely controlled pO2 have therefore been developed. Merging these modalities allows label-free and simultaneous observation of oxygen-dependent alterations in multiple cellular redox couples, membrane potential, and cellular contraction. This technique is adaptable to any cell type and allows in-depth insight into acute oxygen sensing processes underlying various physiologic and pathologic conditions.

Bypassing mitochondrial complex III using alternative oxidase inhibits acute pulmonary oxygen sensing.

Mitochondria play an important role in sensing both acute and chronic hypoxia in the pulmonary vasculature, but their primary oxygen-sensing mechanism and contribution to stabilization of the hypoxia-inducible factor (HIF) remains elusive. Alteration of the mitochondrial electron flux and increased superoxide release from complex III has been proposed as an essential trigger for hypoxic pulmonary vasoconstriction (HPV). We used mice expressing a tunicate alternative oxidase, AOX, which maintains electron flux when respiratory complexes III and/or IV are inhibited. Respiratory restoration by AOX prevented acute HPV and hypoxic responses of pulmonary arterial smooth muscle cells (PASMC), acute hypoxia-induced redox changes of NADH and cytochrome c, and superoxide production. In contrast, AOX did not affect the development of chronic hypoxia-induced pulmonary hypertension and HIF-1α stabilization. These results indicate that distal inhibition of the mitochondrial electron transport chain in PASMC is an essential initial step for acute but not chronic oxygen sensing.

Single-Step Preprocessing of Raman Spectra Using Convolutional Neural Networks.

Preprocessing of Raman spectra is generally done in three separate steps: (1) cosmic ray removal, (2) signal smoothing, and (3) baseline subtraction. We show that a convolutional neural network (CNN) can be trained using simulated data to handle all steps in one operation. First, synthetic spectra are created by randomly adding peaks, baseline, mixing of peaks and baseline with background noise, and cosmic rays. Second, a CNN is trained on synthetic spectra and known peaks. The results from preprocessing were generally of higher quality than what was achieved using a reference based on standardized methods (second-difference, asymmetric least squares, cross-validation). From 105 simulated observations, 91.4% predictions had smaller absolute error (RMSE), 90.3% had improved quality (SSIM), and 94.5% had reduced signal-to-noise (SNR) power. The CNN preprocessing generated reliable results on measured Raman spectra from polyethylene, paraffin and ethanol with background contamination from polystyrene. The result shows a promising proof of concept for the automated preprocessing of Raman spectra.

Accuracy evaluation and addition of improved dihedral parameters for the MMFF94s.

The Platinum dataset of protein-bound ligand conformations was used to benchmark the ability of the MMFF94s force field to generate bioactive conformations by minimization of randomly generated conformers. Torsion angle parameters that generally caused wrong geometries were reparameterized by conducting dihedral scans using ab initio calculations at the MP2 level. This reparameterization resulted in a systematic improvement of generated conformations.

Assessing the Predictive Power of Relative Binding Free Energy Calculations for Test Cases Involving Displacement of Binding Site Water Molecules.

Improved sampling methodologies, more accurate force fields, and access to longer simulation time scales have led to an increased application of Relative Binding Free Energy (RBFE) calculations in drug discovery projects. In order to assess the strengths and limitations of such tools, adequate benchmark sets are required that challenge the methodology in certain well-defined aspects. We applied Free Energy Perturbation (FEP) calculations to six congeneric ligand pairs taken from the literature, in which addition of a functional group resulted in the displacement of buried binding site water molecules and compared the calculated relative binding free energies with the experimental ones. We started the perturbations from different initial solvation states and registered large inconsistencies (large hysteresis) between the calculated values. We furthermore applied a Grand Canonical Monte Carlo (GCMC) solvent sampling step prior to the FEP calculation that led to a smaller hysteresis for the simulations. By applying a hydration site analysis to the trajectories of the end-states of the perturbation, we could point out that the low accuracy of the predictions as well as the high dependence of the prediction on the chosen initial state is likely caused by the trapping of binding site water molecules and/or insufficient solvation of buried cavities that are formed upon completion of the perturbation. This work highlights that RBFE calculations can suffer from slow solvent exchange of buried parts of the binding sites with the bulk.

Development of a Gas-Tight Microfluidic System for Raman Sensing of Single Pulmonary Arterial Smooth Muscle Cells Under Normoxic/Hypoxic Conditions.

Acute hypoxia changes the redox-state of pulmonary arterial smooth muscle cells (PASMCs). This might influence the activity of redox-sensitive voltage-gated K⁺-channels (Kv-channels) whose inhibition initiates hypoxic pulmonary vasoconstriction (HPV). However, the molecular mechanism of how hypoxia-or the subsequent change in the cellular redox-state-inhibits Kv-channels remains elusive. For this purpose, a new multifunctional gas-tight microfluidic system was developed enabling simultaneous single-cell Raman spectroscopic studies (to sense the redox-state under normoxic/hypoxic conditions) and patch-clamp experiments (to study the Kv-channel activity). The performance of the system was tested by optically recording the O2-content and taking Raman spectra on murine PASMCs under normoxic/hypoxic conditions or in the presence of H2O2. Oxygen sensing showed that hypoxic levels in the gas-tight microfluidic system were achieved faster, more stable and significantly lower compared to a conventional open system (1.6 ± 0.2%, respectively 6.7 ± 0.7%, n = 6, p < 0.001). Raman spectra revealed that the redistribution of biomarkers (cytochromes, FeS, myoglobin and NADH) under hypoxic/normoxic conditions were improved in the gas-tight microfluidic system (p-values from 0.00% to 16.30%) compared to the open system (p-value from 0.01% to 98.42%). In conclusion, the new redox sensor holds promise for future experiments that may elucidate the role of Kv-channels during HPV.

Endocrine Disruption at the Androgen Receptor: Employing Molecular Dynamics and Docking for Improved Virtual Screening and Toxicity Prediction.

The androgen receptor (AR) is a key target for the development of drugs targeting hormone-dependent prostate cancer, but has also an important role in endocrine disruption. Reliable prediction of the binding of ligands towards the AR is therefore of great relevance. Molecular docking is a powerful computational method for exploring small-ligand binding to proteins. It can be applied for virtual screening experiments but also for predicting molecular initiating events in toxicology. However, in case of AR, there is no antagonist-bound crystal structure yet available. Our study demonstrates that molecular docking approaches are not able to satisfactorily screen for AR antagonists because of this reason. Therefore, we applied Molecular Dynamics simulations to generate antagonist AR structures and showed that this leads to a vast improvement for the docking of AR antagonists. We benchmarked the ability of these antagonist AR structures discriminate between AR antagonists and decoys using an ensemble docking approach and obtained promising results with good enrichment. However, distinguishing AR antagonists from agonists with high confidence is not possible with the current approach alone.

Thermodynamic Insight into the Effects of Water Displacement and Rearrangement upon Ligand Modifications using Molecular Dynamics Simulations.

Computational methods, namely molecular dynamics (MD) simulations in combination with inhomogeneous fluid solvation theory (IFST) were used to retrospectively investigate various cases of ligand structure modifications that led to the displacement of binding site water molecules. Our findings are that water displacement per se is energetically unfavorable in the discussed examples, and that it is merely the fine balance between change in protein-ligand interaction energy, ligand solvation free energies, and binding site solvation free energies that determine if water displacement is favorable or not. We furthermore evaluated if we can reproduce experimental binding affinities by a computational approach combining changes in solvation free energies with changes in protein-ligand interaction energies and entropies. In two of the seven cases, this estimation led to large errors, implying that accurate predictions of relative binding free energies based on solvent thermodynamics is challenging. Nevertheless, MD simulations can provide insight regarding which water molecules can be targeted for displacement.

Molecular mechanisms of endocrine and metabolic disruption: An in silico study on antitrypanosomal natural products and some derivatives.

The VirtualToxLab is an in silico technology for estimating the toxic potential - endocrine and metabolic disruption, as well as aspects of carcinogenicity and cardiotoxicity - of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects. The simulations are conducted at the atomic level and explicitly allow for a mechanistic interpretation of the results (in real-time 3D/4D), thereby complying with the Setubal principles put forward in 2002 for computational approaches to toxicology. Moreover, the underlying "ab-initio" protocol is independent from any training data and makes the approach universal with respect to the applicability domain. The VirtualToxLab runs in client-server mode and is freely available to academic and non-profit organizations. As the underlying technology yields a thermodynamic estimate of the binding affinity, the associated ligand-protein complexes have been challenged by molecular-dynamics simulations to probe their kinetic stability. Human African trypanosomiasis is a neglected tropical disease caused by two subspecies of Trypanosoma brucei. The control of this parasitic infection relies on a few chemotherapeutic agents, most of which were discovered decades ago and pose many challenges including adverse side effects, poor efficacy, and the occurrence of drug resistances. Natural products, on the other hand, offer a high potential for the discovery of new drug leads due to their chemical diversity. In this in silico study, we analyze a series of 89 natural products and derivatives displaying anti-trypanosomal activity for their potential to trigger adverse effects. Our results indicate a moderate potential for a number of those compounds to bind to nuclear receptors and thereby ease the development of endocrine disregulation. A few others would seem to inhibit enzymes of the cytochrome P450 family and, hence, sustain drug-drug interactions.