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The general scheme of protein docking methodology development. The scan (global search for complementarity) is performed on a simplified/coarse-grained representation of proteins (e.g., digitized on a grid, or discretized/approximated in other ways). The scan can be explicit (free) or based on similarity to known cocrystallized complexes (comparative). The refinement is supposed to bring back all or some structural resolution lost in the coarse-graining (e.g., by gradual transition from s smoothed intermolecular energy landscape to the one based on a physical force field, while tracking the position of the global minimum). The validity of the approach is determined by systematic benchmarking on representative sets of structures. To see this figure in color, go online.

Bound docking is the easiest docking case, because by definition it does not involve conformational change. Thus, the structures match ideally at the interface and the rigid body approach is the only tool required to deliver the correct solution. The bound docking problem has been considered solved for a number of years, in the sense that the existing docking approaches reliably and routinely deliver the near-native structures of the complex among the top predictions.

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Still, the majority of protein complexes in the nonredundant benchmark sets have small C root mean-square deviation (RMSD) between bound and unbound structures. Indeed, 71% of the Dockground set (30,31) has RMSD between superimposed unbound and bound proteins

Because most docking cases can be resolved by accounting for the flexibility of the surface side chains, the statistics of side-chain conformational changes is important. The results of a systematic large-scale study indicate that short and long side chains have different propensities for the conformational changes (34). Long side chains with three or more dihedral angles are often subject to large conformational transition. Shorter residues with one or two dihedral angles typically undergo local conformational changes not leading to a conformational transition. Most side chains undergo larger changes in the dihedral angle most distant from the backbone. The binding increases both polar and nonpolar interface areas. However, the increase of the nonpolar area is larger, suggesting that the protein association perturbs the unbound interfaces to increase the hydrophobic contribution to the binding free energy (34). Analysis of ensembles of bound and unbound conformations points to conformational selection as the binding mechanism for proteins. The bound and the unbound spectra of conformers also significantly overlap (35). An elastic network model, accounting for the mass distribution, was used to compare the binding site residues fluctuations with other surface residues, showing that, on average, the interface is more rigid (36).

Another reason has been the relative success of the traditional template free (ab initio) docking, as opposed to the ab initio modeling of the individual proteins. The rigid-body docking (six degrees of freedom) is a meaningful, working approximation for many complexes, whereas any practical approximation in protein folding involves the conformational search space of far greater dimensionality.

Still, the main reason for the almost complete dominance of the ab initio docking arguably has been the presumed lack of protein-protein templates. Protein-protein complexes are generally more difficult to crystallize than single proteins, limiting the number of templates. Moreover, proteins potentially participate in multiple protein-protein interactions, making the number of protein-protein prediction targets larger than that of the individual proteins. The large-scale efforts to determine the structures of proteins, like the Protein Structure Initiative (60), which established a high-throughput structure determination pipeline, provided a substantial amount of structural information (and a significant portion of prediction targets for the community assessment of structure prediction CASP (45)). However, it has been much less instrumental in supplying the structures of protein-protein complexes (including the lack of targets to the community assessment of predicted interactions CAPRI (59)), presumably because of the relative difficulty of crystallizing protein complexes.

Thus, the general notion in the protein docking field has been that although the comparative docking may be more reliable and accurate than the traditional free docking, similar to the comparative modeling for individual protein modeling, the lack of the templates relegates the practical use of this approach to some future time. That was until the presumption of the lack of templates was actually checked on the existing PDB. The systematic study (61) showed that, surprisingly, docking templates are readily available for complexes representing almost all known protein-protein interactions, provided the components themselves have a known structure or can be homology built.

The study is based on 126,897 protein interactions involving pairs of proteins in 771 species. The structure alignment-based models of complexes were generated by TM-align (62). The structural similarity of two complexes was evaluated by the min TM-scores (the smallest of the receptor and the ligand TM-scores (62)). Fig. 3 shows how the interaction RMSD (47), a measure of the binding mode similarity, correlates with the min TM-score, a measure of the structural similarity between the complexes, in an all-to-all pairwise comparison of 989 cocrystallized complexes. The phase transition occurs near min TM-score = 0.4, with binding modes mostly similar above, and mostly different below.

The protein-docking community began to organize and actively develop such community-wide activities at the First Conference on Modeling of Protein Interactions (MPI) at Charleston, SC, 2001 (63). These activities were further developed at the subsequent MPI ( -conference) and CAPRI ( ) conferences and other meetings.

A protein docking study was performed for two classes of biomolecular complexes: six enzyme/inhibitor and four antibody/antigen. Biomolecular complexes for which crystal structures of both the complexed and uncomplexed proteins are available were used for eight of the ten test systems. Our docking experiments consist of a global search of translational and rotational space followed by refinement of the best predictions. Potential complexes are scored on the basis of shape complementarity and favourable electrostatic interactions using Fourier correlation theory. Since proteins undergo conformational changes upon binding, the scoring function must be sufficiently soft to dock unbound structures successfully. Some degree of surface overlap is tolerated to account for side-chain flexibility. Similarly for electrostatics, the interaction of the dispersed point charges of one protein with the Coulombic field of the other is measured rather than precise atomic interactions. We tested our docking protocol using the native rather than the complexed forms of the proteins to address the more scientifically interesting problem of predictive docking. In all but one of our test cases, correctly docked geometries (interface Calpha RMS deviation

In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when a ligand and a target are bound to each other to form a stable complex.[1] Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions.

The associations between biologically relevant molecules such as proteins, peptides, nucleic acids, carbohydrates, and lipids play a central role in signal transduction. Furthermore, the relative orientation of the two interacting partners may affect the type of signal produced (e.g., agonism vs antagonism). Therefore, docking is useful for predicting both the strength and type of signal produced.

Molecular docking is one of the most frequently used methods in structure-based drug design, due to its ability to predict the binding-conformation of small molecule ligands to the appropriate target binding site. Characterisation of the binding behaviour plays an important role in rational design of drugs as well as to elucidate fundamental biochemical processes.[2][3]

Molecular docking research focuses on computationally simulating the molecular recognition process. It aims to achieve an optimized conformation for both the protein and ligand and relative orientation between protein and ligand such that the free energy of the overall system is minimized.

Geometric matching/shape complementarity methods describe the protein and ligand as a set of features that make them dockable.[10] These features may include molecular surface/complementary surface descriptors. In this case, the receptor's molecular surface is described in terms of its solvent-accessible surface area and the ligand's molecular surface is described in terms of its matching surface description. The complementarity between the two surfaces amounts to the shape matching description that may help finding the complementary pose of docking the target and the ligand molecules. Another approach is to describe the hydrophobic features of the protein using turns in the main-chain atoms. Yet another approach is to use a Fourier shape descriptor technique.[11][12][13] Whereas the shape complementarity based approaches are typically fast and robust, they cannot usually model the movements or dynamic changes in the ligand/protein conformations accurately, although recent developments allow these methods to investigate ligand flexibility. Shape complementarity methods can quickly scan through several thousand ligands in a matter of seconds and actually figure out whether they can bind at the protein's active site, and are usually scalable to even protein-protein interactions. They are also much more amenable to pharmacophore based approaches, since they use geometric descriptions of the ligands to find optimal binding. e24fc04721