Macromolecular docking is the computational modelling of the quaternary structure of complexes formed by two or more interacting biological macromolecules.
[1] The ultimate goal of docking is the prediction of the three-dimensional structure of the macromolecular complex of interest as it would occur in a living organism.
With further increases in computational power, it became possible to model changes in internal geometry of the interacting partners that may occur when a complex is formed.
[3] Computers discriminated between good and bad models using a scoring function which rewarded large interface area, and pairs of molecules in contact but not occupying the same space.
With the emergence of bioinformatics, the focus moved towards developing generalized techniques which could be applied to an arbitrary set of complexes at acceptable computational cost.
The new methods were envisaged to apply even in the absence of phylogenetic or experimental clues; any specific prior knowledge could still be introduced at the stage of choosing between the highest ranking output models, or be framed as input if the algorithm catered for it.
1992 saw the publication of the correlation method,[5] an algorithm which used the fast Fourier transform to give a vastly improved scalability for evaluating coarse shape complementarity on rigid-body models.
When substantial conformational change occurs within the components at the time of complex formation, rigid-body docking is inadequate.
After making exclusions based on prior knowledge or stereochemical clash, the remaining space of possible complexed structures must be sampled exhaustively, evenly and with a sufficient coverage to guarantee a near hit.
Monte Carlo methods are not guaranteed to search exhaustively, so that the best configuration may be missed even using a scoring function which would in theory identify it.
To find a score which forms a consistent basis for selecting the best configuration, studies are carried out on a standard benchmark (see below) of protein–protein interaction cases.
The ultimate goal in protein–protein docking is to select the ideal ranking solution according to a scoring scheme that would also give an insight into the affinity of the complex.
[8][9][10][11][12] However the correlation between experimentally determined binding affinities and the predictions of nine commonly used scoring functions have been found to be nearly orthogonal (R2 ~ 0).
Experimental methods for the determination of binding affinities are: surface plasmon resonance (SPR), Förster resonance energy transfer, radioligand-based techniques, isothermal titration calorimetry (ITC), microscale thermophoresis (MST) or spectroscopic measurements and other fluorescence techniques.
[15] The set is chosen to cover a wide range of interaction types, and to avoid repeated features, such as the profile of interactors' structural families according to the SCOP database.
[19] The protein-RNA benchmark has been updated to include more structures solved by X-ray crystallography and now it consists of 126 test cases.
Each entry of the benchmark includes several biochemical parameters associated with the experimental data, along with the method used to determine the affinity.
The set may be used to benchmark biophysical models aiming to relate affinity to structure in protein–protein interactions, taking into account the reactants and the conformation changes that accompany the association reaction, instead of just the final product.
[22] The Critical Assessment of PRediction of Interactions[23] is an ongoing series of events in which researchers throughout the community try to dock the same proteins, as provided by the assessors.