Daisuke Mitomo

Visualization of conformational distribution of short to medium size segments in globular proteins and identification of local structural motifs

Analysis of the conformational distribution of polypeptide segments in a conformational space is the first step for understanding a principle of structural diversity of proteins. Here, we present a statistical analysis of protein local structures based on interatomic Cα distances. Using principal component analysis (PCA) on the intrasegment Cα–Cα atomic distances, the conformational space of protein segments, which we call the protein segment universe, has been visualized, and three essential coordinate axes, suitable for describing the universe, have been identified. Three essential axes specified radius of gyration, structural symmetry, and separation of hairpin structures from other structures. Among the segments of arbitrary length, 6–22 residues long, the conservation of those axes was uncovered. Further application of PCA to the two largest clusters in the universe revealed local structural motifs. Although some of motifs have already been reported, we identified a possibly novel strand motif. We also showed that a capping box, which is one of the helix capping motifs, was separated into independent subclusters based on the Cα geometry. Implications of the strand motif, which may play a role for protein–protein interaction, are discussed. The currently proposed method is useful for not only mapping the immense universe of protein structures but also identification of structural motifs.

Transition state of a SH3 domain detected with principle component analysis and a charge‐neutralized all‐atom protein model

The src SH3 domain has been known to be a two‐state folder near room temperature. However, in a previous study with an all‐atom model simulation near room temperature, the transition state of this protein was not successfully detected on a free‐energy profile using two axes: the radius of gyration (Rg) and native contact reproduction ratio (Q value). In this study, we focused on an atom packing effect to characterize the transition state and tried another analysis to detect it. To explore the atom packing effect more efficiently, we introduced a charge‐neutralized all‐atom model, where all of the atoms in the protein and water molecules were treated explicitly, but their partial atomic charges were set to zero. Ten molecular dynamics simulations were performed starting from the native structure at 300 K, where the simulation length of each run was 90 ns, and the protein unfolded in all runs. The integrated trajectories (10 × 90 = 900 ns) were analyzed by a principal component analysis (PCA) and showed a clear free‐energy barrier between folded‐ and unfolded‐state conformational clusters in a conformational space generated by PCA. There were segments that largely deformed when the conformation passed through the free‐energy barrier. These segments correlated well with the structural core regions characterized by large ϕ‐values, and the atom‐packing changes correlated with the conformational deformations. Interestingly, using the same simulation data, no significant barrier was found in a free‐energy profile using the Rg and Q values for the coordinate axes. These results suggest that the atom packing effect may be one of the most important determinants of the transition state. Proteins 2006.

CALCULATION OF PROTEIN–LIGAND BINDING FREE ENERGY USING SMOOTH REACTION PATH GENERATION (SRPG) METHOD: A COMPARISON OF THE EXPLICIT WATER MODEL, GB/SA MODEL AND DOCKING SCORE FUNCTION

We compared the protein-ligand binding free energies (G) obtained by the explicit water model, the MM-GB/SA (molecular-mechanics generalized Born surface area) model, and the docking scoring function. The free energies by the explicit water model and the MM-GB/SA model were calculated by the previously developed Smooth Reaction Path Generation (SRPG) method. In the SRPG method, a smooth reaction path was generated by linking two coordinates, one a bound state and the other an unbound state. The free energy surface along the path was calculated by a molecular dynamics (MD) simulation, and the binding free energy was estimated from the free energy surface. We applied these methods to the streptavidin-and-biotin system. The G value by the explicit water model was close to the experimental value. The G value by the MM-GB/SA model was overestimated and that by the scoring function was underestimated. The free energy surface by the explicit water model was close to that by the GB/SA model around the bound state (distances of < 6 A), but the discrepancy appears at distances of > 6 A. Thus, the difference in long-range Coulomb interaction should cause the error in G. The scoring function cannot take into account the entropy change of the protein. Thus, the error of G could depend on the target protein.