Protein-ligand binding free energy calculation by the Smooth Reaction Path Generation (SRPG) Method.

We developed a new molecular dynamics simulation method for protein-ligand binding free energy calculation in an explicit water model. This method consists of three steps: (1) generation of a compound dissociation path starting from a stable protein-compound complex structure, (2) calculation of the free energy surface along the dissociation path, and (3) calculation of the free energy surface of a small area around the protein-compound complex structure, which is a free energy minimum. The protein-compound binding free energy is estimated from the information obtained by the above three steps. This method was applied to a small system, a 18-crown-6 ether with its ligand ion, and a realistic system consisting of a target protein with its inhibitor. This approximation worked well; the protein-inhibitor dissociation was successfully performed, and the binding free energies were calculated.

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.

A method to enhance the hit ratio by a combination of structure-based drug screening and ligand-based screening.

We examined the procedures to combine two different in silico drug-screening results to achieve a high hit ratio. When the 3D structure of the target protein and some active compounds are known, both structure-based and ligand-based in silico screening methods can be applied. In the present study, the machine-learning score modification multiple target screening (MSM-MTS) method was adopted as a structure-based screening method, and the machine-learning docking score index (ML-DSI) method was adopted as a ligand-based screening method. To combine the predicted compound's sets by these two screening methods, we examined the product of the sets (consensus set) and the sum of the sets. As a result, the consensus set achieved a higher hit ratio than the sum of the sets and than either individual predicted set. In addition, the current combination was shown to be robust enough for the structural diversities both in different crystal structure and in snapshot structures during molecular dynamics simulations.

Folding of the 25 residue Abeta(12-36) peptide in TFE/water: temperature-dependent transition from a funneled free-energy landscape to a rugged one.

The free-energy landscape of the Alzheimer beta-amyloid peptide Abeta(12-36) in a 40% (v/v) 2,2,2-trifluoroethanol (TFE)/water solution was determined by using multicanonical molecular dynamics simulations. Simulations using this enhanced conformational sampling technique were initiated from a random unfolded polypeptide conformation. Our simulations reliably folded the peptide to the experimental NMR structure, which consists of two linked helices. The shape of the free energy landscape for folding was found to be strongly dependent on temperature: Above 325 K, the overall shape was funnel-like, with the bottom of the funnel coinciding exactly with the NMR structure. Below 325 K, on the other hand, the landscape became increasingly rugged, with the emergence of new conformational clusters connected by low free-energy pathways. Finally, our simulations reveal that water and TFE solvate the polypeptide in different ways: The hydrogen bond formation between TFE and Abeta was enhanced with decreasing temperature, while that between water and Abeta was depressed.

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 (R(g)) 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 x 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 phi-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 R(g) 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.

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 C(alpha) distances. Using principal component analysis (PCA) on the intrasegment C(alpha)-C(alpha) 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 C(alpha) 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.

General dynamic properties of Abeta12-36 amyloid peptide involved in Alzheimer's disease from unfolding simulation.

To study the folding/unfolding properties of a beta-amyloid peptide Abeta(12-36) of Alzheimer's disease, five molecular dynamics simulations of Abeta(12-36) in explicit water were done at 450 K starting from a structure that is stable in trifluoroethanol/water at room temperature with two alpha-helices. Due to high temperature, the initial helical structure unfolded during the simulation. The observed aspects of the unfolding were as follows. 1) One helix (helix 1) had a longer life than the other (helix 2), which correlates well with the theoretically computed Phi values. 2) Temporal prolongation of helix 1 was found before unfolding. 3) Hydrophobic cores formed frequently with rearrangement of amino-acid residues in the hydrophobic cores. The formation and rearrangement of the hydrophobic cores may be a general aspect of this peptide in the unfolded state, and the structural changes accompanied by the hydrophobic-core rearrangement may lead the peptide to the most stable structure. 4) Concerted motions (collective modes) appeared to unfold helix 1. The collective modes were similar with those observed in another simulation at 300 K. The analysis implies that the conformation moves according to the collective modes when the peptide is in the initial stage of protein unfolding and in the final stage of protein folding.