Improved beta‐protein structure prediction by multilevel optimization of nonlocal strand pairings and local backbone conformation

Proteins with complex, nonlocal beta‐sheets are challenging for de novo structure prediction, due in part to the difficulty of efficiently sampling long‐range strand pairings. We present a new, multilevel approach to beta‐sheet structure prediction that circumvents this difficulty by reformulating structure generation in terms of a folding tree. Nonlocal connections in this tree allow us to explicitly sample alternative beta‐strand pairings while simultaneously exploring local conformational space using backbone torsion‐space moves. An iterative, energy‐biased resampling strategy is used to explore the space of beta‐strand pairings; we expect that such a strategy will be generally useful for searching large conformational spaces with a high degree of combinatorial complexity. Proteins 2006. © 2006 Wiley‐Liss, Inc.

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