Rapid prediction of NMR spectral properties with quantified uncertainty.
Ontology highlight
ABSTRACT:
SUBMITTER: Jonas E
PROVIDER: S-EPMC6684566 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Most of the existing research in assembly pathway prediction/analysis of viral capsids makes the simplifying assumption that the configuration of the intermediate states can be extracted directly from the final configuration of the entire capsid. This assumption does not take into account the conformational changes of the constituent proteins as well as minor changes to the binding interfaces that continue throughout the assembly process until stabilization. This article presents a statistical-ensemble-based approach that samples the configurational space for each monomer with the relative local orientation between monomers, to capture the uncertainties in binding and conformations. Further, instead of using larger capsomers (trimers, pentamers) as building blocks, we allow all possible subassemblies to bind in all possible combinations. We represent the resulting assembly graph in two different ways: First, we use the Wilcoxon signed-rank measure to compare the distributions of binding free energy computed on the sampled conformations to predict likely pathways. Second, we represent chemical equilibrium aspects of the transitions as a Bayesian Factor graph where both associations and dissociations are modeled based on concentrations and the binding free energies. We applied these protocols on the feline panleukopenia virus and the Nudaurelia capensis virus. Results from these experiments showed a significant departure from those that one would obtain if only the static configurations of the proteins were considered. Hence, we establish the importance of an uncertainty-aware protocol for pathway analysis, and we provide a statistical framework as an important first step toward assembly pathway prediction with high statistical confidence.
| S-EPMC5757074 | biostudies-literature
Project description:Uncertainty quantification in artificial intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in materials science. While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i.e., the evaluation of the uncertainty on each prediction, are not as frequently available. In this work, we compare three different approaches to obtain such individual uncertainty, testing them on 12 ML-physical properties. Specifically, we investigated using the quantile loss function, machine learning the prediction intervals directly, and using Gaussian processes. We identify each approach's advantages and disadvantages and end up slightly favoring the modeling of the individual uncertainties directly, as it is the easiest to fit and, in most of the cases, minimizes over- and underestimation of the predicted errors. All data for training and testing were taken from the publicly available JARVIS-DFT database, and the codes developed for computing the prediction intervals are available through the JARVIS-tools package.
| S-EPMC8655759 | biostudies-literature
Project description:Transmembrane flux of Cs+ (a K+ congener) was measured in human red blood cells (RBCs; erythrocytes) on the 10-s time scale. This is the first report on dissolution dynamic nuclear polarization (dDNP) nuclear magnetic resonance (NMR) spectroscopy with this nuclide in mammalian cells. Four technical developments regularized sample delivery and led to high quality NMR spectra. Cation-free media with the Piezo1 (mechanosensitive cation channel) activator yoda1 maximized the extent of membrane transport. First-order rate constants describing the fluxes were estimated using a combination of statistical methods in Mathematica, including the Markov chain Monte Carlo (MCMC) algorithm. Fluxes were in the range 4-70??mol Cs+ (L RBC)-1?s-1; these are smaller than for urea, but comparable to glucose. Methodology and analytical procedures developed will be applicable to transmembrane cation transport studies in the presence of additional Piezo1 effectors, to other cellular systems, and potentially in vivo.
| S-EPMC6928147 | biostudies-literature
Project description:Here we introduce the fully quantified spectral imaging (FSI) method as a new tool to probe the stoichiometry and stability of protein complexes in biological membranes. The FSI method yields two dimensional membrane concentrations and FRET efficiencies in native plasma membranes. It can be used to characterize the association of membrane proteins: to differentiate between monomers, dimers, or oligomers, to produce binding (association) curves, and to measure the free energies of association in the membrane. We use the FSI method to study the lateral interactions of Vascular Endothelial Growth Factor Receptor 2 (VEGFR2), a member of the receptor tyrosine kinase (RTK) superfamily, in plasma membranes, in vivo. The knowledge gained through the use of the new method challenges the current understanding of VEGFR2 signaling.
| S-EPMC4755892 | biostudies-literature
Project description:Most of the existing research in assembly pathway prediction/analysis of viral capsids makes the simplifying assumption that the configuration of the intermediate states can be extracted directly from the final configuration of the entire capsid. This assumption does not take into account the conformational changes of the constituent proteins as well as minor changes to the binding interfaces that continue throughout the assembly process until stabilization. This paper presents a statistical-ensemble based approach which samples the configurational space for each monomer with the relative local orientation between monomers, to capture the uncertainties in binding and conformations. Furthermore, instead of using larger capsomers (trimers, pentamers) as building blocks, we allow all possible subassemblies to bind in all possible combinations. We represent the resulting assembly graph in two different ways: First, we use the Wilcoxon signed rank measure to compare the distributions of binding free energy computed on the sampled conformations to predict likely pathways. Second, we represent chemical equilibrium aspects of the transitions as a Bayesian Factor graph where both associations and dissociations are modeled based on concentrations and the binding free energies. We applied these protocols on the feline panleukopenia virus and the Nudaurelia capensis virus. Results from these experiments showed significant departure from those one would obtain if only the static configurations of the proteins were considered. Hence, we establish the importance of an uncertainty-aware protocol for pathway analysis, and provide a statistical framework as an important first step towards assembly pathway prediction with high statistical confidence.
| S-EPMC5604467 | biostudies-literature
Project description:Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in the observed data at different spatial and temporal scales, generate estimates of key kinetic parameters, assess the impact of interventions, optimize the impact of control strategies, and generate forecasts. We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models to time series data describing the temporal progression of case counts relating to population growth or infectious disease transmission dynamics. In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters, this frequentist approach relies on modeling the error structure in the data. We discuss issues related to parameter identifiability, uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.
| S-EPMC5726591 | biostudies-literature
Project description:Deep neural networks have been increasingly used in various chemical fields. In the nature of a data-driven approach, their performance strongly depends on data used in training. Therefore, models developed in data-deficient situations can cause highly uncertain predictions, leading to vulnerable decision making. Here, we show that Bayesian inference enables more reliable prediction with quantitative uncertainty analysis. Decomposition of the predictive uncertainty into model- and data-driven uncertainties allows us to elucidate the source of errors for further improvements. For molecular applications, we devised a Bayesian graph convolutional network (GCN) and evaluated its performance for molecular property predictions. Our study on the classification problem of bio-activity and toxicity shows that the confidence of prediction can be quantified in terms of the predictive uncertainty, leading to more accurate virtual screening of drug candidates than standard GCNs. The result of log?P prediction illustrates that data noise affects the data-driven uncertainty more significantly than the model-driven one. Based on this finding, we could identify artefacts that arose from quantum mechanical calculations in the Harvard Clean Energy Project dataset. Consequently, the Bayesian GCN is critical for molecular applications under data-deficient conditions.
| S-EPMC6839511 | biostudies-literature
Project description:Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.
| S-EPMC4446368 | biostudies-literature
Project description:MOTIVATION: To further our understanding of the mechanisms underlying biochemical pathways mathematical modelling is used. Since many parameter values are unknown they need to be estimated using experimental observations. The complexity of models necessary to describe biological pathways in combination with the limited amount of quantitative data results in large parameter uncertainty which propagates into model predictions. Therefore prediction uncertainty analysis is an important topic that needs to be addressed in Systems Biology modelling. RESULTS: We propose a strategy for model prediction uncertainty analysis by integrating profile likelihood analysis with Bayesian estimation. Our method is illustrated with an application to a model of the JAK-STAT signalling pathway. The analysis identified predictions on unobserved variables that could be made with a high level of confidence, despite that some parameters were non-identifiable. AVAILABILITY AND IMPLEMENTATION: Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html.
| S-EPMC3324512 | biostudies-other
Project description:A recent advance in construction technology is the use of self-healing cementitious materials containing synthetic microfibers and superabsorbent polymers. By stimulating autogenous healing by means of superabsorbent polymers, cracks are closed and this will cause an increase in durability and service life. However, this improved healing capacity has not been quantified yet in terms of increased further hydration and volume of healing products. This is needed to model the material and to stimulate the practical application in constructions. This paper provides quantitative data, obtained by an NMR study. Addition of 1?m% of selected superabsorbent polymer versus cement to a cementitious material, stimulated further hydration with nearly 40% in comparison with a traditional cementitious material, if 1?h water contact per day was allowed. At 90% relative humidity, no healing was observed in reference samples. While the further hydration around a crack in specimens with superabsorbent polymers was still 68% of that of a reference system with cyclic water contact, due to the uptake of moisture by the superabsorbent polymers. As such, NMR results quantify the positive influence of superabsorbent polymers in terms of stimulated autogenous healing and substantiate their benefits for application in the construction area.
| S-EPMC6971030 | biostudies-literature