Evidence map›Paper›PMID 28762605›Full record

ArticleProtein science : a publication of the Protein Society2018

Ensemblator v3: Robust atom-level comparative analyses and classification of protein structure ensembles.

Andrew E Brereton, P Andrew Karplus

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Andrew E BreretonDepartment of Biochemistry and Biophysics, Oregon State University, Corvallis, Oregon, 97331.
P Andrew KarplusDepartment of Biochemistry and Biophysics, Oregon State University, Corvallis, Oregon, 97331.

Funding

Improving Modeling by Learning from Details of High Accuracy Protein StructuresR01GM083136 · NIGMS · OREGON STATE UNIVERSITY · PI KARPLUS, PAUL ANDREW · 2008 to 2015
$1.7M
NIGMS NIH HHS R01 GM083136
6 · The paper itself

Abstract

Ensembles of protein structures are increasingly used to represent the conformational variation of a protein as determined by experiment and/or by molecular simulations, as well as uncertainties that may be associated with structure determinations or predictions. Making the best use of such information requires the ability to quantitatively compare entire ensembles. For this reason, we recently introduced the Ensemblator (Clark et al., Protein Sci 2015; 24:1528), a novel approach to compare user-defined groups of models, in residue level detail. Here we describe Ensemblator v3, an open-source program that employs the same basic ensemble comparison strategy but includes major advances that make it more robust, powerful, and user-friendly. Ensemblator v3 carries out multiple sequence alignments to facilitate the generation of ensembles from non-identical input structures, automatically optimizes the key global overlay parameter, optionally performs "ensemble clustering" to classify the models into subgroups, and calculates a novel "discrimination index" that quantifies similarities and differences, at residue or atom level, between each pair of subgroups. The clustering and automatic options mean that no pre-knowledge about an ensemble is required for its analysis. After describing the novel features of Ensemblator v3, we demonstrate its utility using three case studies that illustrate the ease with which complex analyses are accomplished, and the kinds of insights derived from clustering into subgroups and from the detailed information that locates significant differences. The Ensemblator v3 enhances the structural biology toolbox by greatly expanding the kinds of problems to which this ensemble comparison strategy can be applied.

Indexed as

Databases, ProteinSoftwareProtein ConformationProteinsProteinsclusteringensemble clusteringNMR ensembleprotein structure comparisonpythonRosettastructure predictionsuperpositiontemplate-based modeling

Identifiers

PMID28762605
PMCPMC5734391

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.