Modeling Errors in NOE Data with a Log-normal Distribution Improves the Quality of NMR Structures

Rieping, Wolfgang; Habeck, Michael; Nilges, Michaël

doi:10.1021/ja055092c

Cited by 41 publications

(31 citation statements)

References 13 publications

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“…We studied two error distributions: The first assumes a Gaussian shape with a flat plateau for distances between δ ± 0.2 × a in accordance with the approach by Nagano et al [11]. The second model is a lognormal distribution [32]. Both error models depend on an additional unknown error parameter σ , which reflects how well the experimental distance agrees with the model distance.…”

Section: Resultsmentioning

confidence: 99%

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Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data

2016

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Chromosome conformation capture (3C) techniques have revealed many fascinating insights into the spatial organization of genomes. 3C methods typically provide information about chromosomal contacts in a large population of cells, which makes it difficult to draw conclusions about the three-dimensional organization of genomes in individual cells. Recently it became possible to study single cells with Hi-C, a genome-wide 3C variant, demonstrating a high cell-to-cell variability of genome organization. In principle, restraint-based modeling should allow us to infer the 3D structure of chromosomes from single-cell contact data, but suffers from the sparsity and low resolution of chromosomal contacts. To address these challenges, we adapt the Bayesian Inferential Structure Determination (ISD) framework, originally developed for NMR structure determination of proteins, to infer statistical ensembles of chromosome structures from single-cell data. Using ISD, we are able to compute structural error bars and estimate model parameters, thereby eliminating potential bias imposed by ad hoc parameter choices. We apply and compare different models for representing the chromatin fiber and for incorporating singe-cell contact information. Finally, we extend our approach to the analysis of diploid chromosome data.

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Section: Resultsmentioning

confidence: 99%

“…The second error model is a lognormal distribution, which was introduced by Rieping et al [32] to describe errors of inherently positive quantities such as distance measurements: In both error models, σ quantifies the error of the Hi-C distance measurements.…”

Section: Methodsmentioning

confidence: 99%

Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data

2016

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“…The initial structures of the pilins were maintained in a flexible and adaptive way using log-harmonic distance restraints and automated weighting (Nilges et al, 2008) on the alpha carbons, apart from the two N-terminal residues (and the two C-terminal residues for the PulG pilin, for which no structural template was available). A log-harmonic distance restraint potential is derived from the log-normal distribution (Rieping et al, 2005) and depends on the square of the difference of the logarithms of the distances (Nilges et al, 2008):…”

Section: Use Of Conformational Restraintsmentioning

confidence: 99%

Modeling pilus structures from sparse data

Campos

Francetić

Nilges

2011

Journal of Structural Biology

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“…To evaluate a single restraint r i , we adopt the log-normal distribution advocated by Nilges and coworkers (Rieping et al, 2005a as a better representation of the errors in NOE distances and NMR data than the traditional flat-bottom harmonic well (FBHW). The FBHW suffers from problems including subjectiveness associated with fixing the bounds for the well ; the log-normal more gracefully degrades, and we integrate out its variance parameter s. Furthermore, the log-normal is non-negative and multiplicative.…”

Section: Inferential Frameworkmentioning

confidence: 99%

NMR Structural Inference of Symmetric Homo-Oligomers

Chandola

Yan

Potluri

et al. 2011

Journal of Computational Biology

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Symmetric homo-oligomers represent a majority of proteins, and determining their structures helps elucidate important biological processes, including ion transport, signal transduction, and transcriptional regulation. In order to account for the noise and sparsity in the distance restraints used in Nuclear Magnetic Resonance (NMR) structure determination of cyclic (C n ) symmetric homo-oligomers, and the resulting uncertainty in the determined structures, we develop a Bayesian structural inference approach. In contrast to traditional NMR structure determination methods, which identify a small set of low-energy conformations, the inferential approach characterizes the entire posterior distribution of conformations. Unfortunately, traditional stochastic techniques for inference may under-sample the rugged landscape of the posterior, missing important contributions from high-quality individual conformations and not accounting for the possible aggregate effects on inferred quantities from numerous unsampled conformations. However, by exploiting the geometry of symmetric homo-oligomers, we develop an algorithm that provides provable guarantees for the posterior distribution and the inferred mean atomic coordinates. Using experimental restraints for three proteins, we demonstrate that our approach is able to objectively characterize the structural diversity supported by the data. By simulating spurious and missing restraints, we further demonstrate that our approach is robust, degrading smoothly with noise and sparsity.

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Modeling Errors in NOE Data with a Log-normal Distribution Improves the Quality of NMR Structures

Cited by 41 publications

References 13 publications

Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data

Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data

Modeling pilus structures from sparse data

NMR Structural Inference of Symmetric Homo-Oligomers

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