Similarity measures for protein ensembles

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Similarity measures for protein ensembles. / Lindorff-Larsen, Kresten; Ferkinghoff-Borg, Jesper.

In: PLoS ONE, Vol. 4, No. 1, e4203, 2009.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Lindorff-Larsen, K & Ferkinghoff-Borg, J 2009, 'Similarity measures for protein ensembles', PLoS ONE, vol. 4, no. 1, e4203. https://doi.org/10.1371/journal.pone.0004203

APA

Lindorff-Larsen, K., & Ferkinghoff-Borg, J. (2009). Similarity measures for protein ensembles. PLoS ONE, 4(1), [e4203]. https://doi.org/10.1371/journal.pone.0004203

Vancouver

Lindorff-Larsen K, Ferkinghoff-Borg J. Similarity measures for protein ensembles. PLoS ONE. 2009;4(1). e4203. https://doi.org/10.1371/journal.pone.0004203

Author

Lindorff-Larsen, Kresten ; Ferkinghoff-Borg, Jesper. / Similarity measures for protein ensembles. In: PLoS ONE. 2009 ; Vol. 4, No. 1.

Bibtex

@article{1d2e316eca5c4ea1970ea840a5befa11,
title = "Similarity measures for protein ensembles",
abstract = "Analyses of similarities and changes in protein conformation can provide important information regarding protein function and evolution. Many scores, including the commonly used root mean square deviation, have therefore been developed to quantify the similarities of different protein conformations. However, instead of examining individual conformations it is in many cases more relevant to analyse ensembles of conformations that have been obtained either through experiments or from methods such as molecular dynamics simulations. We here present three approaches that can be used to compare conformational ensembles in the same way as the root mean square deviation is used to compare individual pairs of structures. The methods are based on the estimation of the probability distributions underlying the ensembles and subsequent comparison of these distributions. We first validate the methods using a synthetic example from molecular dynamics simulations. We then apply the algorithms to revisit the problem of ensemble averaging during structure determination of proteins, and find that an ensemble refinement method is able to recover the correct distribution of conformations better than standard single-molecule refinement.",
keywords = "Algorithms, Computer Simulation, Methods, Probability, Protein Conformation, Proteins, Proteomics",
author = "Kresten Lindorff-Larsen and Jesper Ferkinghoff-Borg",
year = "2009",
doi = "10.1371/journal.pone.0004203",
language = "English",
volume = "4",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "1",

}

RIS

TY - JOUR

T1 - Similarity measures for protein ensembles

AU - Lindorff-Larsen, Kresten

AU - Ferkinghoff-Borg, Jesper

PY - 2009

Y1 - 2009

N2 - Analyses of similarities and changes in protein conformation can provide important information regarding protein function and evolution. Many scores, including the commonly used root mean square deviation, have therefore been developed to quantify the similarities of different protein conformations. However, instead of examining individual conformations it is in many cases more relevant to analyse ensembles of conformations that have been obtained either through experiments or from methods such as molecular dynamics simulations. We here present three approaches that can be used to compare conformational ensembles in the same way as the root mean square deviation is used to compare individual pairs of structures. The methods are based on the estimation of the probability distributions underlying the ensembles and subsequent comparison of these distributions. We first validate the methods using a synthetic example from molecular dynamics simulations. We then apply the algorithms to revisit the problem of ensemble averaging during structure determination of proteins, and find that an ensemble refinement method is able to recover the correct distribution of conformations better than standard single-molecule refinement.

AB - Analyses of similarities and changes in protein conformation can provide important information regarding protein function and evolution. Many scores, including the commonly used root mean square deviation, have therefore been developed to quantify the similarities of different protein conformations. However, instead of examining individual conformations it is in many cases more relevant to analyse ensembles of conformations that have been obtained either through experiments or from methods such as molecular dynamics simulations. We here present three approaches that can be used to compare conformational ensembles in the same way as the root mean square deviation is used to compare individual pairs of structures. The methods are based on the estimation of the probability distributions underlying the ensembles and subsequent comparison of these distributions. We first validate the methods using a synthetic example from molecular dynamics simulations. We then apply the algorithms to revisit the problem of ensemble averaging during structure determination of proteins, and find that an ensemble refinement method is able to recover the correct distribution of conformations better than standard single-molecule refinement.

KW - Algorithms

KW - Computer Simulation

KW - Methods

KW - Probability

KW - Protein Conformation

KW - Proteins

KW - Proteomics

U2 - 10.1371/journal.pone.0004203

DO - 10.1371/journal.pone.0004203

M3 - Journal article

C2 - 19145244

VL - 4

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 1

M1 - e4203

ER -

ID: 37812396