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Abstract:
We introduce a new algorithm, IRECS (Iterative REduction of Conformational
Space), for identifying ensembles of most probable side-chain conformations for
homology modeling. On the basis of a given rotamer library, IRECS ranks all
side-chain rotamers of a protein according to the probability with which each
side chain adopts the respective rotamer conformation. This ranking enables the
user to select small rotamer sets that are most likely to contain a near-native
rotamer for each side chain. IRECS can therefore act as a fast heuristic
alternative to the Dead-End-Elimination algorithm (DEE). In contrast to DEE,
IRECS allows for the selection of rotamer subsets of arbitrary size, thus being
able to define structure ensembles for a protein. We show that the selection of
more than one rotamer per side chain is generally meaningful, since the
selected rotamers represent the conformational space of flexible side chains. A
knowledge-based statistical potential ROTA was constructed for the IRECS
algorithm. The potential was optimized to discriminate between side-chain
conformations of native and rotameric decoys of protein structures. By
restricting the number of rotamers per side chain to one, IRECS can optimize
side chains for a single conformation model. The average accuracy of IRECS for
the $\chi_1$ and $\chi_{1+2}$ dihedral angles amounts to 84.7\% and 71.6\%,
respectively, using a 40 degrees cutoff. When we compared IRECS with SCWRL and
SCAP, the performance of IRECS was comparable to that of both methods. IRECS
and the ROTA potential are available for download from the URL
http://irecs.bioinf.mpi-inf.mpg.de.
