Dataset Information

MESMER: minimal ensemble solutions to multiple experimental restraints.

ABSTRACT:

Motivation

Macromolecular structures and interactions are intrinsically heterogeneous, temporally adopting a range of configurations that can confound the analysis of data from bulk experiments. To obtain quantitative insights into heterogeneous systems, an ensemble-based approach can be employed, in which predicted data computed from a collection of models is compared to the observed experimental results. By simultaneously fitting orthogonal structural data (e.g. small-angle X-ray scattering, nuclear magnetic resonance residual dipolar couplings, dipolar electron-electron resonance spectra), the range and population of accessible macromolecule structures can be probed.

Results

We have developed MESMER, software that enables the user to identify ensembles that can recapitulate experimental data by refining thousands of component collections selected from an input pool of potential structures. The MESMER suite includes a powerful graphical user interface (GUI) to streamline usage of the command-line tools, calculate data from structure libraries and perform analyses of conformational and structural heterogeneity. To allow for incorporation of other data types, modular Python plugins enable users to compute and fit data from nearly any type of quantitative experimental data.

Results

Conformational heterogeneity in three macromolecular systems was analyzed with MESMER, demonstrating the utility of the streamlined, user-friendly software.

Availability and implementation

https://code.google.com/p/mesmer/

SUBMITTER: Ihms EC

PROVIDER: S-EPMC4542774 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Publications

MESMER: minimal ensemble solutions to multiple experimental restraints.

Ihms Elihu C EC Foster Mark P MP

Bioinformatics (Oxford, England) 20150210 12

<h4>Motivation</h4>Macromolecular structures and interactions are intrinsically heterogeneous, temporally adopting a range of configurations that can confound the analysis of data from bulk experiments. To obtain quantitative insights into heterogeneous systems, an ensemble-based approach can be employed, in which predicted data computed from a collection of models is compared to the observed experimental results. By simultaneously fitting orthogonal structural data (e.g. small-angle X-ray scatt ...[more]

PMID: 25673340

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Project description:Introduction and motivationMany health studies measure a continuous outcome and compare means between groups. Since means for biological data are often difficult to interpret clinically, it is common to dichotomise using a cut-point and present the 'percentage abnormal' alongside or in place of means. Examples include birthweight where 'abnormal' is defined as <2500 g (low birthweight), systolic blood pressure with abnormal defined as >140 mm Hg (high blood pressure) and lung function with varying definitions of the 'limit of normal'. In vulnerable populations with low means, for example, birthweight in a population of preterm babies, a given difference in means between two groups will represent a larger difference in the percentage with low birthweight than in a general population of babies where most will be full term. Thus, in general, the difference in percentage of patients with abnormal values for a given difference in means varies according to the reference population's mean value. This phenomenon leads to challenges in interpreting differences in means in vulnerable populations and in defining an outcome-specific minimal clinically important difference (MCID) in means since the proportion abnormal, which is useful in interpreting means, is not constant-it varies with the population mean. This has relevance for study power calculations and data analyses in vulnerable populations where a small observed difference in means may be difficult to interpret clinically and may be disregarded, even if associated with a relatively large difference in percentage abnormal which is clinically relevant.MethodsTo address these issues, we suggest both difference in means and difference in percentage (proportion) abnormal are considered when choosing the MCID, and that both means and percentages abnormal are reported when analysing the data.ConclusionsWe describe a distributional approach to analyse proportions classified as abnormal that avoids the usual loss of precision and power associated with dichotomisation.

Dataset Information

MESMER: minimal ensemble solutions to multiple experimental restraints.

Motivation

Results

Results

Availability and implementation

Publications

MESMER: minimal ensemble solutions to multiple experimental restraints.

Similar Datasets

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