Monte Carlo docking with ubiquitin

Structure, dynamics and interactions of p47, a major adaptor of the AAA ATPase, p97

p47 is a major adaptor molecule of the cytosolic AAA ATPase p97. The principal role of the p97-p47 complex is in regulation of membrane fusion events. Mono-ubiquitin recognition by p47 has also been shown to be crucial in the p97-p47-mediated Golgi membrane fusion events. Here, we describe the high-resolution solution structures of the N-terminal UBA domain and the central domain (SEP) from p47. The p47 UBA domain has the characteristic three-helix bundle fold and forms a highly stable complex with ubiquitin. We report the interaction surfaces of the two proteins and present a structure for the p47 UBA-ubiquitin complex. The p47 SEP domain adopts a novel fold with a betabetabetaalphaalphabeta secondary structure arrangement, where beta4 pairs in a parallel fashion to beta1. Based on biophysical studies, we demonstrate a clear propensity for the self-association of p47. Furthermore, p97 N binding abolishes p47 self-association, revealing the potential interaction surfaces for recognition of other domains within p97 or the substrate.

Protein docking using a genetic algorithm

A genetic algorithm (GA) for protein-protein docking is described, in which the proteins are represented by dot surfaces calculated using the Connolly program. The GA is used to move the surface of one protein relative to the other to locate the area of greatest surface complementarity between the two. Surface dots are deemed complementary if their normals are opposed, their Connolly shape type is complementary, and their hydrogen bonding or hydrophobic potential is fulfilled. Overlap of the protein interiors is penalized. The GA is tested on 34 large protein-protein complexes where one or both proteins has been crystallized separately. Parameters are established for which 30 of the complexes have at least one near-native solution ranked in the top 100. We have also successfully reassembled a 1,400-residue heptamer based on the top-ranking GA solution obtained when docking two bound subunits.

Reaching the global minimum in docking simulations: a Monte Carlo energy minimization approach using Bezier splines

The docking problem faces two major challenges: the global optimization of a multivariable function, such as the energy, and the ability to discriminate between true and false positive results, i.e., native from nonnative structures based on the input energy function. Among all energy evaluation tools, only a local energy-minimization method using an accurate enough potential function is able to discriminate between native and nonnative structures. To meet these requirements, a Monte Carlo with energy-minimization method has been incorporated into a new ECEPP/3 docking program. The efficiency of the simulation results from the use of an energy-grid technique based on Bezier splines and from a simplification of the receptor by switching on the energy of only important residues of the active site. Simulations of a thrombin-inhibitor complex show that the global minimum of the energy function was reached in every independent run within less than 3 min of time on an IBM RX 6000 computer. For comparison, 10 standard independent Monte Carlo simulations with 10(6) steps in each were carried out. Only three of them led to a conformation close to the x-ray structure. The latter simulations required an average of 24 min and about 10 hr with and without the grid, respectively. Another important result is that the Bezier spline technique not only speeds up the calculation by reducing the number of operations during the energy evaluation but also helps in reaching the global minimum by smoothing out the potential energy surface.

Atomic solvation parameters in the analysis of protein-protein docking results

Several sets of amino acid surface areas and transfer free energies were used to derive a total of nine sets of atomic solvation parameters (ASPs). We tested the accuracy of each of these sets of parameters in predicting the experimentally determined transfer free energies of the amino acid derivatives from which the parameters were derived. In all cases, the calculated and experimental values correlated well. We then chose three parameter sets and examined the effect of adding an energetic correction for desolvation based on these three parameter sets to the simple potential function used in our multiple start Monte Carlo docking method. A variety of protein-protein interactions and docking results were examined. In the docking simulations studied, the desolvation correction was only applied during the final energy calculation of each simulation. For most of the docking results we analyzed, the use of an octanol-water-based ASP set marginally improved the energetic ranking of the low-energy dockings, whereas the other ASP sets we tested disturbed the ranking of the low-energy dockings in many of the same systems. We also examined the correlation between the experimental free energies of association and our calculated interaction energies for a series of proteinase-inhibitor complexes. Again, the octanol-water-based ASP set was compatible with our standard potential function, whereas ASP sets derived from other solvent systems were not.