Dataset Information

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations.

ABSTRACT: Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the "unphysical" rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These "diastereomeric differentiabilities" then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data.

SUBMITTER: Immel S

PROVIDER: S-EPMC8781118 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations.

Immel Stefan S Köck Matthias M Reggelin Michael M

Marine drugs 20211222 1

Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the "unphysical" rDG pseudo energies defined from NMR viola ...[more]

PMID: 35049868

Similar Datasets

Project description:A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This analysis is complicated by various factors. First, one needs to take the phylogenetic relationship between the species into account in order to distinguish conservation that is due to the occurrence of functional sites from spurious conservation that is due to evolutionary proximity. Second, one has to deal with the complexities of multiple alignments of orthologous intergenic regions, and one has to consider the possibility that functional sites may occur outside of conserved segments. Here we present a new motif sampling algorithm, PhyloGibbs, that runs on arbitrary collections of multiple local sequence alignments of orthologous sequences. The algorithm searches over all ways in which an arbitrary number of binding sites for an arbitrary number of transcription factors (TFs) can be assigned to the multiple sequence alignments. These binding site configurations are scored by a Bayesian probabilistic model that treats aligned sequences by a model for the evolution of binding sites and "background" intergenic DNA. This model takes the phylogenetic relationship between the species in the alignment explicitly into account. The algorithm uses simulated annealing and Monte Carlo Markov-chain sampling to rigorously assign posterior probabilities to all the binding sites that it reports. In tests on synthetic data and real data from five Saccharomyces species our algorithm performs significantly better than four other motif-finding algorithms, including algorithms that also take phylogeny into account. Our results also show that, in contrast to the other algorithms, PhyloGibbs can make realistic estimates of the reliability of its predictions. Our tests suggest that, running on the five-species multiple alignment of a single gene's upstream region, PhyloGibbs on average recovers over 50% of all binding sites in S. cerevisiae at a specificity of about 50%, and 33% of all binding sites at a specificity of about 85%. We also tested PhyloGibbs on collections of multiple alignments of intergenic regions that were recently annotated, based on ChIP-on-chip data, to contain binding sites for the same TF. We compared PhyloGibbs's results with the previous analysis of these data using six other motif-finding algorithms. For 16 of 21 TFs for which all other motif-finding methods failed to find a significant motif, PhyloGibbs did recover a motif that matches the literature consensus. In 11 cases where there was disagreement in the results we compiled lists of known target genes from the literature, and found that running PhyloGibbs on their regulatory regions yielded a binding motif matching the literature consensus in all but one of the cases. Interestingly, these literature gene lists had little overlap with the targets annotated based on the ChIP-on-chip data. The PhyloGibbs code can be downloaded from http://www.biozentrum.unibas.ch/~nimwegen/cgi-bin/phylogibbs.cgi or http://www.imsc.res.in/~rsidd/phylogibbs. The full set of predicted sites from our tests on yeast are available at http://www.swissregulon.unibas.ch.

Project description:BackgroundEndangered plant species are an important resource for new chemistry. Lindera melissifolia is native to the Southeastern U.S. and scarcely populates the edges of lakes and ponds. Quantum mechanics (QM) used in combination with NMR/ECD is a powerful tool for the assignment of absolute configuration in lieu of X-ray crystallography.MethodsThe EtOAc extract of L. melissifolia was subject to chromatographic analysis by VLC and HPLC. Spin-spin coupling constant (SSCC) were calculated using DFT at the MPW1PW91/6-31G(d,p) level for all staggered rotamers. ECD calculations employed Amber* force fields followed by PM6 semi-empirical optimizations. Hetero- and homo-nuclear coupling constants were extracted from 1D (1)H, E.COSY and HETLOC experiments.ResultsTwo meroterpenoids, melissifolianes A (1) and B (2) were purified and their 2-D structures elucidated using NMR and HRESIMS. The relative configuration of 1 was established using the combination of NOE-based distance restraints and the comparisons of experimental and calculated SSCCs. The comparison of calculated and experimental ECD assigned the absolute configuration of 1. The relative configuration of a racemic mixture, melissifoliane B (2) was established utilizing J-based analysis combined with QM and NMR techniques.Conclusion Our study of the Lindera melissifolia metabolome exemplifies how new chemistry remains undiscovered among the numerous endangered plant species and demonstrates how analysis by ECD and NMR combined with various QM calculations is a sensible approach to support the stereochemical assignment of molecules with conformationally restricted conformations.General significanceQM-NMR/ECD combined approaches are of utility for unambiguous assignment of 3-D structures, especially with limited plant material and when a molecule is conformationally restricted. Conservation of an endangered plant species can be supported through identification of its new chemistry and utilization of that chemistry for commercial purposes.

Dataset Information

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations.

Publications

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets