Role of conformational sampling in computing mutation-induced changes in protein structure and stability

Abstract

The prediction of changes in protein stability and structure resulting from single amino acid substitutions is both a fundamental test of macromolecular modeling methodology and an important current problem as high throughput sequencing reveals sequence polymorphisms at an increasing rate. In principle, given the structure of a wild-type protein and a point mutation whose effects are to be predicted, an accurate method should recapitulate both the structural changes and the change in the folding-free energy. Here, we explore the performance of protocols which sample an increasing diversity of conformations. We find that surprisingly similar performances in predicting changes in stability are achieved using protocols that involve very different amounts of conformational sampling, provided that the resolution of the force field is matched to the resolution of the sampling method. Methods involving backbone sampling can in some cases closely recapitulate the structural changes accompanying mutations but not surprisingly tend to do more harm than good in cases where structural changes are negligible. Analysis of the outliers in the stability change calculations suggests areas needing particular improvement; these include the balance between desolvation and the formation of favorable buried polar interactions, and unfolded state modeling.

Figure 1

xtent of conformational sampling in the ΔΔG prediction protocols. Protocols considered here are on the right, and previously described methods (Refs. 1–5) on the left.

Figure 2

omparison of predicted to experimentally observed ΔΔGs. Method 16 (Table I) which employs backbone minimization after repacking all sidechains was used in this calculation. The correlation is 0.69 on the full set of 1210 mutations. Predicted values along the x-axis versus experimental values (kcal/mol) on the y-axis. The equation of the best-fit line is y = 0.57x. All results are for the 1,210 mutation test set except for those in the last row, which are on a reduced set of 771 mutations, due to the computational cost of the ensemble method.

Figure 3

xamples for which modeling backbone flexibility improves structural recapitulation. (A) T4-lysozyme mutant (1qtb), V 42 A; (B) T4-lysozyme mutant (241l) A 29 I; (C) FK506 binding protein (1fkj) W 59 L; and (D) T4-lysozyme (2lzm) I 3 V. Pink, starting wild-type crystal structure; blue, mutant crystal structure; gray, structural prediction with limited backbone minimization; and green, structure produced with less stringent constraints around the site of mutation and uniform harmonic constraints outside this region (row 18, Table I). In (D), green is the structure produced from perturbed backbone protocol (row 20, Table I).