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. Author manuscript; available in PMC: 2010 Aug 1.
Published in final edited form as: Science. 2010 Feb 4;327(5968):1014–1018. doi: 10.1126/science.1183649

Figure 4.

Figure 4

Effect of incorporation of experimental data on energy minimization. (a) The Rosetta all atom energy (black line) has many local minima making minimization difficult, but the global minimum is generally close to the native structure (N). The experimental bias (red line), while smoother, has degeneracies and lacks resolution because the data are sparse. Local minima of the all-atom energy and the experimental bias are uncorrelated far away from the native structure but coincide close to the native structure. Accordingly, far from the global minimum, including the experimental data during optimization usually results in higher energies (arrow 1), while close to the native structure (N), including the data results in lower energies(arrow 2). (b) Lines represent the lowest energies sampled by structures at various RMSDs after optimization in the absence (black line) or presence (red line) of experimental data. Generally, the all-atom energy and experimental data are in concordance for conformations close to the native protein structure but not for conformations far from the native structure. If this concordance condition is met, close to the native structure the experimental data can guide sampling towards the global minimum (arrow 2) and thus constrained optimization can result in lower energy conformations than unconstrained optimization, while biased optimization is less effective than unconstrained optimization distant from the native structure leading to higher energies(arrow 1). In contrast, (c–d) All-atom energy and RMSD of final Rosetta ensemble from iterative refinement with and without experimental data. Lines represent the median of the 10 lowest energy models per RMSD-bin. (c) 1f21 – an unsuccessful calculation; biased optimization with RDC data(red) yields similar energies as unbiased optimization (black); there is a large remaining energy gap to the native structure (blue dots). (d) Alg13 – a successful calculation; biased optimization with the experimental data (red) results in lower energies than unbiased optimization (black).