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231224s2016 xx |||||o 00| ||eng c |
| 024 |
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|a 10.1002/jcc.24412
|2 doi
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|a pubmed24n0869.xml
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Kingsley, Laura J
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations
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|c 2016
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 14.09.2018
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|a Date Revised 13.11.2018
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2016 Wiley Periodicals, Inc.
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|a Crystallization of protein-protein complexes can often be problematic and therefore computational structural models are often relied on. Such models are often generated using protein-protein docking algorithms, where one of the main challenges is selecting which of several thousand potential predictions represents the most near-native complex. We have developed a novel technique that involves the use of steered molecular dynamics (sMD) and umbrella sampling to identify near-native complexes among protein-protein docking predictions. Using this technique, we have found a strong correlation between our predictions and the interface RMSD (iRMSD) in ten diverse test systems. On two of the systems, we investigated if the prediction results could be further improved using potential of mean force calculations. We demonstrated that a near-native (<2.0 Å iRMSD) structure could be identified in the top-1 ranked position for both systems. © 2016 Wiley Periodicals, Inc
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, N.I.H., Extramural
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 650 |
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4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
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| 650 |
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4 |
|a ZDOCK
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| 650 |
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4 |
|a potential of mean force
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| 650 |
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4 |
|a protein-protein interaction
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| 650 |
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4 |
|a steered molecular dynamics
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| 650 |
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4 |
|a umbrella sampling
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| 650 |
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|a Proteins
|2 NLM
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| 700 |
1 |
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|a Esquivel-Rodríguez, Juan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Ying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kihara, Daisuke
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lill, Markus A
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 37(2016), 20 vom: 20. Juli, Seite 1861-5
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
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|g volume:37
|g year:2016
|g number:20
|g day:20
|g month:07
|g pages:1861-5
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|u http://dx.doi.org/10.1002/jcc.24412
|3 Volltext
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