Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables.

The joint analysis of two datasets [Formula: see text] and [Formula: see text] that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union [Formula: see text] in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables.

Tensor-SqRA: Modeling the transition rates of interacting molecular systems in terms of potential energies.

Estimating the rate of rare conformational changes in molecular systems is one of the goals of molecular dynamics simulations. In the past few decades, a lot of progress has been done in data-based approaches toward this problem. In contrast, model-based methods, such as the Square Root Approximation (SqRA), directly derive these quantities from the potential energy functions. In this article, we demonstrate how the SqRA formalism naturally blends with the tensor structure obtained by coupling multiple systems, resulting in the tensor-based Square Root Approximation (tSqRA). It enables efficient treatment of high-dimensional systems using the SqRA and provides an algebraic expression of the impact of coupling energies between molecular subsystems. Based on the tSqRA, we also develop the projected rate estimation, a hybrid data-model-based algorithm that efficiently estimates the slowest rates for coupled systems. In addition, we investigate the possibility of integrating low-rank approximations within this framework to maximize the potential of the tSqRA.

Novel multi-objective affinity approach allows to identify pH-specific µ-opioid receptor agonists.

Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the [Formula: see text]-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported [Formula: see text]-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale.

Guideline "Motor neuron diseases" of the German Society of Neurology (Deutsche Gesellschaft für Neurologie).

INTRODUCTION: In 2021, the Deutsche Gesellschaft für Neurology published a new guideline on diagnosis and therapy of motor neuron disorders. Motor neuron disorders affect upper motor neurons in the primary motor cortex and/or lower motor neurons in the brain stem and spinal cord. The most frequent motor neuron disease amyotrophic lateral sclerosis (ALS) is a rapidly progressive disease with an average life expectancy of 2-4 years with a yearly incidence of 3.1/100,000 in Central Europe (Rosenbohm et al. in J Neurol 264(4):749-757, 2017. https://doi.org/10.1007/s00415-017-8413-3 ). It is considered a rare disease mainly due to its low prevalence as a consequence of short disease duration. RECOMMENDATIONS: These guidelines comprise recommendations regarding differential diagnosis, neuroprotective therapies and multidisciplinary palliative care including management of respiration and nutrition as well as provision of assistive devices and end-of-life situations. CONCLUSION: Diagnostic and therapeutic guidelines are necessary due the comparatively high number of cases and the aggressive disease course. Given the low prevalence and the severe impairment of patients, it is often impossible to generate evidence-based data so that ALS guidelines are partially dependent on expert opinion.

Modelling altered signalling of G-protein coupled receptors in inflamed environment to advance drug design.

We previously reported the successful design, synthesis and testing of the prototype opioid painkiller NFEPP that does not elicit adverse side effects. The design process of NFEPP was based on mathematical modelling of extracellular interactions between G-protein coupled receptors (GPCRs) and ligands, recognizing that GPCRs function differently under pathological versus healthy conditions. We now present an additional and novel stochastic model of GPCR function that includes intracellular dissociation of G-protein subunits and modulation of plasma membrane calcium channels and their dependence on parameters of inflamed and healthy tissue (pH, radicals). The model is validated against in vitro experimental data for the ligands NFEPP and fentanyl at different pH values and radical concentrations. We observe markedly reduced binding affinity and calcium channel inhibition for NFEPP at normal pH compared to lower pH, in contrast to the effect of fentanyl. For increasing radical concentrations, we find enhanced constitutive G-protein activation but reduced ligand binding affinity. Assessing the different effects, the results suggest that, compared to radicals, low pH is a more important determinant of overall GPCR function in an inflamed environment. Future drug design efforts should take this into account.

Assessing transition rates as functions of environmental variables.

We present a method to estimate the transition rates of molecular systems under different environmental conditions that cause the formation or the breaking of bonds and require the sampling of the Grand Canonical Ensemble. For this purpose, we model the molecular system in terms of probable "scenarios," governed by different potential energy functions, which are separately sampled by classical MD simulations. Reweighting the canonical distribution of each scenario according to specific environmental variables, we estimate the grand canonical distribution, then use the Square Root Approximation method to discretize the Fokker-Planck operator into a rate matrix and the robust Perron Cluster Cluster Analysis method to coarse-grain the kinetic model. This permits efficiently estimating the transition rates of conformational states as functions of environmental variables, for example, the local pH at a cell membrane. In this work, we formalize the theoretical framework of the procedure, and we present a numerical experiment comparing the results with those provided by a constant-pH method based on non-equilibrium Molecular Dynamics Monte Carlo simulations. The method is relevant for the development of new drug design strategies that take into account how the cellular environment influences biochemical processes.

Cyanine Dye Coupling Mediates Self-assembly of a pH Sensitive Peptide into Novel 3D Architectures.

Synthetic multichromophore systems are of great importance in artificial light harvesting devices, organic optoelectronics, tumor imaging and therapy. Here, we introduce a promising strategy for the construction of self-assembled peptide templated dye stacks based on coupling of a de novo designed pH sensitive peptide with a cyanine dye Cy5 at its N-terminus. Microscopic techniques, in particular cryogenic TEM (cryo-TEM) and cryo-electron tomography technique (cryo-ET), reveal two types of highly ordered three-dimensional assembly structures on the micrometer scale. Unbranched compact layered rods are observed at pH 7.4 and two-dimensional membrane-like assemblies at pH 3.4, both species displaying spectral features of H-aggregates. Molecular dynamics simulations reveal that the coupling of Cy5 moieties promotes the formation of both ultrastructures, whereas the protonation states of acidic and basic amino acid side chains dictates their ultimate three-dimensional organization.

Parameter estimation on multivalent ITC data sets.

The Wiseman fitting can be used to extract binding parameters from ITC data sets, such as heat of binding, number of binding sites, and the overall dissociation rate. The classical Wiseman fitting assumes a direct binding process and neglects the possibility of intermediate binding steps. In principle, it only provides thermodynamic information and not the kinetics of the process. In this article we show that a concentration dependent dissociation constant could possibly stem from intermediate binding steps. The mathematical form of this dependency can be exploited with the aid of the Robust Perron Cluster Cluster Analysis method. Our proposed extension of the Wiseman fitting rationalizes the concentration dependency, and can probably also be used to determine the kinetic parameters of intermediate binding steps of a multivalent binding process. The novelty of this paper is to assume that the binding rate varies per titration step due to the change of the ligand concentration and to use this information in the Wiseman fitting. We do not claim to produce the most accurate values of the binding parameters, we rather present a novel method of how to approach multivalent bindings from a different angle.

Deposition of Nanosized Amino Acid Functionalized Bismuth Oxido Clusters on Gold Surfaces.

Bismuth compounds are of growing interest with regard to potential applications in catalysis, medicine, and electronics, for which their environmentally benign nature is one of the key factors. One thing that currently hampers the further development of bismuth oxido-based materials, however, is the often low solubility of the precursors, which makes targeted immobilisation on substrates challenging. We present an approach towards the solubilisation of bismuth oxido clusters by introducing an amino carboxylate as a functional group. For this purpose, the bismuth oxido cluster [Bi38O45(NO3)20(dmso)28](NO3)4·4dmso (dmso = dimethyl sulfoxide) was reacted with the sodium salt of tert-butyloxycabonyl (Boc)-protected phenylalanine (L-Phe) to obtain the soluble and chiral nanocluster [Bi38O45(Boc-Phe-O)24(dmso)9]. The exchange of the nitrates by the amino carboxylates was proven by nuclear magnetic resonance, Fourier-transform infrared spectroscopy, as well as elemental analysis and X-ray photoemission spectroscopy. The solubility of the bismuth oxido cluster in a protic as well as an aprotic polar organic solvent and the growth mode of the clusters upon spin, dip, and drop coating on gold surfaces were studied by a variety of microscopy, as well as spectroscopic techniques. In all cases, the bismuth oxido clusters form crystalline agglomerations with size, height, and distribution on the substrate that can be controlled by the choice of the solvent and of the deposition method.

Polymorphism and Visible-Light-Driven Photocatalysis of Doped Bi2O3:M (M = S, Se, and Re).

δ-Bi2O3:M (M = S, Se, and Re) with an oxygen-defective fluorite-type structure is obtained by a coprecipitation method starting from the bismuth oxido cluster [Bi38O45(OMc)24(dmso)9]·2dmso·7H2O (A) in the presence of additives such as Na2SO4, Na2SeO4, NH4ReO4, Na2SeO3·5H2O, and Na2SO3. The coprecipitation of the starting materials with aqueous NaOH results in the formation of alkaline reaction mixtures, and the cubic bismuth(III)-based oxides Bi14O20(SO4) (1c), Bi14O20(SeO4) (2c), Bi14O20(ReO4.5) (3c), Bi12.25O16.625(SeO3)1.75 (4c), and Bi10.51O14.765(SO3)0.49(SO4)0.51 (5c) are obtained after microwave-assisted heating; formation of compound 5c is the result of partial oxidation of sulfur. The compounds 1c, 2c, 4c, and 5c absorb UV light only, whereas compound 3c absorbs in the visible-light region of the solar spectrum. Thermal treatment of the as-prepared metastable bismuth(III) oxide chalcogenates 1c and 2c at T = 600 °C provides a monotropic phase transition into their tetragonal polymorphs Bi14O20(SO4) (1t) and Bi14O20(SeO4) (2t), while compound 3c is transformed into the tetragonal modification of Bi14O20(ReO4.5) (3t) after calcination at T = 700 °C. Compounds of the systems Bi2O3-SOx (x = 2 and 3) and Bi2O3-Re2O7 are thermally stable up to T = 800 °C, whereas compounds of the system Bi2O3-SeO3 completely lose SeO3. Thermal treatment of 4c and 5c in air results in the oxidation of the tetravalent to hexavalent sulfur and selenium, respectively, upon heating to T = 400-500 °C. The as-prepared cubic bismuth(III)-based oxides 1c-5c were studied with regard to the photocatalytic decomposition of rhodamine B under visible-light irradiation with compound 3c showing the highest turnover and efficiency.

Plasma Proteome Fingerprints Reveal Distinctiveness and Clinical Outcome of SARS-CoV-2 Infection.

BACKGROUND: We evaluated how plasma proteomic signatures in patients with suspected COVID-19 can unravel the pathophysiology, and determine kinetics and clinical outcome of the infection. METHODS: Plasma samples from patients presenting to the emergency department (ED) with symptoms of COVID-19 were stratified into: (1) patients with suspected COVID-19 that was not confirmed (n = 44); (2) non-hospitalized patients with confirmed COVID-19 (n = 44); (3) hospitalized patients with confirmed COVID-19 (n = 53) with variable outcome; and (4) patients presenting to the ED with minor diseases unrelated to SARS-CoV-2 infection (n = 20). Besides standard of care diagnostics, 177 circulating proteins related to inflammation and cardiovascular disease were analyzed using proximity extension assay (PEA, Olink) technology. RESULTS: Comparative proteome analysis revealed 14 distinct proteins as highly associated with SARS-CoV-2 infection and 12 proteins with subsequent hospitalization (p < 0.001). ADM, IL-6, MCP-3, TRAIL-R2, and PD-L1 were each predictive for death (AUROC curve 0.80-0.87). The consistent increase of these markers, from hospital admission to intensive care and fatality, supported the concept that these proteins are of major clinical relevance. CONCLUSIONS: We identified distinct plasma proteins linked to the presence and course of COVID-19. These plasma proteomic findings may translate to a protein fingerprint, helping to assist clinical management decisions.

Structural Changes during the Growth of Atomically Precise Metal Oxido Nanoclusters from Combined Pair Distribution Function and Small-Angle X-ray Scattering Analysis.

The combination of in situ pair distribution function (PDF) analysis and small-angle X-ray scattering (SAXS) enables analysis of the formation mechanism of metal oxido nanoclusters and cluster-solvent interactions as they take place. Herein, we demonstrate the method for the formation of clusters with a [Bi38 O45 ] core. Upon dissolution of crystalline [Bi6 O5 (OH)3 (NO3 )5 ]⋅3 H2 O in DMSO, an intermediate rapidly forms, which slowly grows to stable [Bi38 O45 ] clusters. To identify the intermediate, we developed an automated modeling method, where smaller [Bix Oy ] structures based on the [Bi38 O45 ] framework are tested against the data. [Bi22 O26 ] was identified as the main intermediate species, illustrating how combined PDF and SAXS analysis is a powerful tool to gain insight into nucleation on an atomic scale. PDF also provides information on the interaction between nanoclusters and solvent, which is shown to depend on the nature of the ligands on the cluster surface.

Markov models from the square root approximation of the Fokker-Planck equation: calculating the grid-dependent flux.

Molecular dynamics (MD) are extremely complex, yet understanding the slow components of their dynamics is essential to understanding their macroscopic properties. To achieve this, one models the MD as a stochastic process and analyses the dominant eigenfunctions of the associated Fokker-Planck operator, or of closely related transfer operators. So far, the calculation of the discretized operators requires extensive MD simulations. The square-root approximation of the Fokker-Planck equation is a method to calculate transition rates as a ratio of the Boltzmann densities of neighboring grid cells times a flux, and can in principle be calculated without a simulation. In a previous work we still used MD simulations to determine the flux. Here, we propose several methods to calculate the exact or approximate flux for various grid types, and thus estimate the rate matrix without a simulation. Using model potentials we test computational efficiency of the methods, and the accuracy with which they reproduce the dominant eigenfunctions and eigenvalues. For these model potentials, rate matrices with up to [Formula: see text] states can be obtained within seconds on a single high-performance compute server if regular grids are used.

ISOKANN: Invariant subspaces of Koopman operators learned by a neural network.

The problem of determining the rate of rare events in dynamical systems is quite well-known but still difficult to solve. Recent attempts to overcome this problem exploit the fact that dynamic systems can be represented by a linear operator, such as the Koopman operator. Mathematically, the rare event problem comes down to the difficulty in finding invariant subspaces of these Koopman operators K. In this article, we describe a method to learn basis functions of invariant subspaces using an artificial neural network.

Sub-ambient pressure IR-MALDI ion mobility spectrometer for the determination of low and high field mobilities.

A new ion mobility (IM) spectrometer, enabling mobility measurements in the pressure range between 5 and 500 mbar and in the reduced field strength range E/N of 5-90 Td, was developed and characterized. Reduced mobility (K0) values were studied under low E/N (constant value) as well as high E/N (deviation from low field K0) for a series of molecular ions in nitrogen. Infrared matrix-assisted laser desorption ionization (IR-MALDI) was used in two configurations: a source working at atmospheric pressure (AP) and, for the first time, an IR-MALDI source working with a liquid (aqueous) matrix at sub-ambient/reduced pressure (RP). The influence of RP on IR-MALDI was examined and new insights into the dispersion process were gained. This enabled the optimization of the IM spectrometer for best analytical performance. While ion desolvation is less efficient at RP, the transport of ions is more efficient, leading to intensity enhancement and an increased number of oligomer ions. When deciding between AP and RP IR-MALDI, a trade-off between intensity and resolving power has to be considered. Here, the low field mobility of peptide ions was first measured and compared with reference values from ESI-IM spectrometry (at AP) as well as collision cross sections obtained from molecular dynamics simulations. The second application was the determination of the reduced mobility of various substituted ammonium ions as a function of E/N in nitrogen. The mobility is constant up to a threshold at high E/N. Beyond this threshold, mobility increases were observed. This behavior can be explained by the loss of hydrated water molecules.

Author Correction: pKa of opioid ligands as a discriminating factor for side effects.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

From a Cerium-Doped Polynuclear Bismuth Oxido Cluster to ß-Bi2O3:Ce.

The simultaneous hydrolysis of Bi(NO3)3·5H2O and Ce(NO3)3·6H2O results in the formation of novel heterometallic bismuth oxido clusters with the general formula [Bi38O45(NO3)24(DMSO)28+δ]:Ce (DMSO = dimethyl sulfoxide; cerium content <1.50%), which is demonstrated by single-crystal X-ray diffraction analysis. The incorporation of cerium into the cluster core is a result of the interplay of hydrolysis and condensation of the metal nitrates in the presence of oxygen. Diffuse-reflectance UV-vis and X-ray photoelectron spectroscopy reveal the presence of CeIV in the final bismuth oxido clusters as a result of oxidation of the cerium source. The cerium atoms are statistically distributed mainly on the bismuth atom positions of the central [Bi6O9] motif of the [Bi38O45] cluster core. Hydrolysis and subsequent annealing of the bismuth oxido clusters in the temperature range of 300-400 °C provides ß-Bi2O3:Ce samples with slightly lowered band gaps of approximately 2.3 eV compared to the undoped ß-Bi2O3 (approximately 2.4 eV). The sintering behavior of ß-Bi2O3 is significantly affected by the cerium dopant. Finally, differences in the efficiency of the as-prepared ß-Bi2O3:Ce and undoped ß-Bi2O3 samples in the photocatalytic decomposition of the biocide triclosan in an aqueous solution under visible-light irradiation are demonstrated.

In Vivo Wireless Sensors for Gut Microbiome Redox Monitoring.

A perturbed gut microbiome has recently been linked with multiple disease processes, yet researchers currently lack tools that can provide in vivo, quantitative, and real-time insight into these processes and associated host-microbe interactions. We propose an in vivo wireless implant for monitoring gastrointestinal tract redox states using oxidation-reduction potentials (ORP). The implant is powered and conveniently interrogated via ultrasonic waves. We engineer the sensor electronics, electrodes, and encapsulation materials for robustness in vivo, and integrate them into an implant that endures autoclave sterilization and measures ORP for 12 days implanted in the cecum of a live rat. The presented implant platform paves the way for long-term experimental testing of biological hypotheses, offering new opportunities for understanding gut redox pathophysiology mechanisms, and facilitating translation to disease diagnosis and treatment applications.

pKa of opioid ligands as a discriminating factor for side effects.

The non-selective activation of central and peripheral opioid receptors is a major shortcoming of currently available opioids. Targeting peripheral opioid receptors is a promising strategy to preclude side effects. Recently, we showed that fentanyl-derived µ-opioid receptor (MOR) agonists with reduced acid dissociation constants (pKa) due to introducing single fluorine atoms produced injury-restricted antinociception in rat models of inflammatory, postoperative and neuropathic pain. Here, we report that a new double-fluorinated compound (FF6) and fentanyl show similar pKa, MOR affinity and [35S]-GTPγS binding at low and physiological pH values. In vivo, FF6 produced antinociception in injured and non-injured tissue, and induced sedation and constipation. The comparison of several fentanyl derivatives revealed a correlation between pKa values and pH-dependent MOR activation, antinociception and side effects. An opioid ligand's pKa value may be used as discriminating factor to design safer analgesics.

Generalized Markov modeling of nonreversible molecular kinetics.

Markov state models are to date the gold standard for modeling molecular kinetics since they enable the identification and analysis of metastable states and related kinetics in a very instructive manner. The state-of-the-art Markov state modeling methods and tools are very well developed for the modeling of reversible processes in closed equilibrium systems. On the contrary, they are largely not well suited to deal with nonreversible or even nonautonomous processes of nonequilibrium systems. Thus, we generalized the common Robust Perron Cluster Cluster Analysis (PCCA+) method to enable straightforward modeling of nonequilibrium systems as well. The resulting Generalized PCCA (G-PCCA) method readily handles equilibrium as well as nonequilibrium data by utilizing real Schur vectors instead of eigenvectors. This is implemented in the G-PCCA algorithm that enables the semiautomatic coarse graining of molecular kinetics. G-PCCA is not limited to the detection of metastable states but also enables the identification and modeling of cyclic processes. This is demonstrated by three typical examples of nonreversible systems.