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Background: The problem of determining the physical conformation of a protein dimer, given the structures of the two interacting proteins in their unbound state, is a difficult one. The location of the docking interface is determined largely by geometric complementarity, but finding complementary geometry is complicated by the flexibility of the backbone and side-chains of both proteins. We seek to generate candidates for docking that approximate the bound state well, even in cases where there is backbone and/or side-chain difference from unbound to bound states.

Results: We divide the surfaces of each protein into local patches and describe the effect of side-chain flexibility on each patch by sampling the space of conformations of its side-chains. Likely positions of individual side-chains are given by a rotamer library; this library is used to derive a sample of possible mutual conformations within the patch. We enforce broad coverage of torsion space. We control the size of the sample by using energy criteria to eliminate unlikely configurations, and by clustering similar configurations, resulting in 50 candidates for a patch, a manageable number for docking.

Conclusions: Using a database of protein dimers for which the bound and unbound structures of the monomers are known, we show that from the unbound patch we are able to generate candidates for docking that approximate the bound structure. In patches where backbone change is small (within 1 Å RMSD of bound), we are able to account for flexibility and generate candidates that are good approximations of the bound state (82% are within 1 Å and 98% are within 1.4 Å RMSD of the bound conformation). We also find that even in cases of moderate backbone flexibility our candidates are able to capture some of the overall shape change. Overall, in 650 of 700 test patches we produce a candidate that is either within 1 Å RMSD of the bound conformation or is closer to the bound state than the unbound is.


© 2010 Francis-Lyon et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.