![]() ![]() 1997), to find possible binding geometries. Other algorithms use matching of surface cubes (Jiang and Kim 1991) or geometric hashing, that is, the identification and matching of convex and concave protein surface regions (Helmer-Citterich and Tramontano 1994 Norel et al. 1997 Vakser 1997 Ritchie and Kemp 2000 Chen and Weng 2002) to identify an optimal overlap between complementary surface layers of the protein partners. 2000), or Fourier correlation techniques (Katchalski-Katzir et al. The search for optimal geometric complementarity can be performed by matching the position of surface spheres and surface normals (Shoichet and Kuntz 1991), application of real space (Palma et al. Several methods have been developed to efficiently search the entire six-dimensional rotational and translational space of one protein partner or reduce the task by precalculating putative binding regions on both protein partners before docking (for review, see Halperin et al. Most current protein–protein docking methods use rigid protein partners to identify protein-binding geometries with optimal surface and electrostatic complementarity. The possibility to model the structure of many proteins based on sequence similarity to a known protein structure and the long-term goal to use such models in protein–protein docking efforts requires algorithms that tolerate errors in loop and side-chain placements of modeled protein structures. The prediction of putative protein–protein interaction geometries by using computational docking methods is therefore of increasing importance to obtain accurate models of protein–protein complexes. This is further complicated by the fact that many proteins can interact with several partners, requiring the determination of possible combinations of these protein–protein interactions. Experimental determination of protein–protein complexes is in many cases more difficult than is determination of the isolated components. However, compared with the available experimental protein structures, the number of experimentally solved protein–protein complexes is still small. Furthermore, for many more known protein sequences, relatively accurate three-dimensional protein models can be built based on sequence similarity to a known protein structure. At the same time, the number of experimentally determined protein structures is rapidly increasing. A variety of experimental and computational techniques can be used to identify possible protein binding partners of a given protein. Many biological processes involve protein–protein interactions. The approach could be extended to include protein loop flexibility, and might also be useful for docking of modeled protein structures. For most docking test systems using unbound partners, and without accounting for any information about the known binding geometry, a solution within ∼2 to 3.5 Å RMSD of the full mobile partner from the experimental geometry was found among the 40 top-scoring complexes. The multicopy approach significantly improves the docking performance, using unbound (apo) binding partners without a significant increase in computer time. To account for side-chain conformational changes in case of using unbound protein conformations, a multicopy approach has been used to select the most favorable side-chain conformation during the docking process. For most test cases, the energy-minimized experimental structure scores among the top five energy minima in systematic docking studies when using both partners in their bound conformations. Energy minimization of protein test complexes in the reduced representation results in geometries close to experiment with backbone root mean square deviations (RMSDs) of ∼1 to 3 Å for the mobile protein partner from the experimental geometry. During docking, an effective energy function between pseudo atoms has been used based on amino acid size and physico-chemical character. The reduced protein representation allows an efficient search for docking minima on the protein surfaces within. Docking is performed by energy minimization in rotational and translational degrees of freedom. A protein–protein docking approach has been developed based on a reduced protein representation with up to three pseudo atoms per amino acid residue.
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