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dc.contributor.authorShen, Yangen_US
dc.contributor.authorPaschalidis, Ioannis Ch.en_US
dc.contributor.authorVakili, Piroozen_US
dc.contributor.authorVajda, Sandoren_US
dc.date.accessioned2009-04-13T23:04:01Z
dc.date.available2009-04-13T23:04:01Z
dc.date.issued2008-10-01
dc.identifier.citation2008. "Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes," PLoS Computational Biology. vol. 4 issue. 10 .
dc.identifier.otherPMC2538569
dc.identifier.uri10.1371/journal.pcbi.1000191
dc.identifier.urihttps://hdl.handle.net/2144/992
dc.description.abstractSimilarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 � ligand interface Ca root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods.en_US
dc.relation.ispartofPLoS Computational Biology
dc.relation.ispartofseriesvol. 4 issue. 10
dc.titleProtein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexesen_US
dc.typeArticleen_US


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