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Replica Exchange Improves Sampling in Low-Resolution Docking Stage of RosettaDock

Overview of attention for article published in PLOS ONE, August 2013
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Title
Replica Exchange Improves Sampling in Low-Resolution Docking Stage of RosettaDock
Published in
PLOS ONE, August 2013
DOI 10.1371/journal.pone.0072096
Pubmed ID
Authors

Zhe Zhang, Oliver F. Lange

Abstract

Many protein-protein docking protocols are based on a shotgun approach, in which thousands of independent random-start trajectories minimize the rigid-body degrees of freedom. Another strategy is enumerative sampling as used in ZDOCK. Here, we introduce an alternative strategy, ReplicaDock, using a small number of long trajectories of temperature replica exchange. We compare replica exchange sampling as low-resolution stage of RosettaDock with RosettaDock's original shotgun sampling as well as with ZDOCK. A benchmark of 30 complexes starting from structures of the unbound binding partners shows improved performance for ReplicaDock and ZDOCK when compared to shotgun sampling at equal or less computational expense. ReplicaDock and ZDOCK consistently reach lower energies and generate significantly more near-native conformations than shotgun sampling. Accordingly, they both improve typical metrics of prediction quality of complex structures after refinement. Additionally, the refined ReplicaDock ensembles reach significantly lower interface energies and many previously hidden features of the docking energy landscape become visible when ReplicaDock is applied.

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Geographical breakdown

Country Count As %
United States 2 6%
Japan 1 3%
Argentina 1 3%
Unknown 31 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 29%
Researcher 8 23%
Student > Master 4 11%
Student > Bachelor 3 9%
Professor 3 9%
Other 6 17%
Unknown 1 3%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 37%
Agricultural and Biological Sciences 7 20%
Computer Science 3 9%
Chemical Engineering 2 6%
Chemistry 2 6%
Other 6 17%
Unknown 2 6%