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 journal › Journal article › Research › peer-review
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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