Loop modeling: Sampling, filtering, and scoring.
Bottom Line: DFIRE is found to be a particularly effective statistical potential that can bias conformation space toward conformations that are close to the native structure.Its application as a filter prior to a full molecular mechanics energy minimization both improves prediction accuracy and offers a significant savings in computer time.The approach is also shown to be quite successful in predicting loop conformations for cases where the native side chain conformations are assumed to be unknown, suggesting that it will prove effective in real homology modeling applications.
Affiliation: Howard Hughes Medical Institute, Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, USA.Show MeSH
Mentions: The dashed line in Figure 1 shows the average value of RMSDmin for each loop set. As can be seen, most RMSDmin values are below 1.5 Å whereas the majority of the RMSD values for conformations selected by the scoring functions are above this value, even when DFIRE is used. Thus, there is significant room for improvement in terms of the consistent selection of low RMSD conformations. One approach is to use more accurate scoring functions, for example from atomic level force fields that include solvation effects. However, these tend to be too slow and too sensitive to small structural variations to apply to a large ensemble of conformations. Figure 2 contains a plot of RMSDBest, the average value of the lowest RMSD conformation among the N top scoring loops ranked by DFIRE, as a function of N. For all loop lengths, the plots appear to level off at about 50–100 low-energy loops. This suggests that it might be productive to apply a detailed potential function to a subset of loops that have been selected by a more simplified scoring function. This approach is the basis of the hybrid loop prediction protocol that is described in the next section.Figure 2
Affiliation: Howard Hughes Medical Institute, Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, USA.