Studying protein-protein interactions is crucial to gaining a better understanding of processes such as metabolic control, signal transduction, and gene regulation. Many biologically important interactions occur in weak, transient complexes that are not amenable to direct experimental analysis, even if both interacting proteins can be isolated and their structures determined. Thus it is important to develop computational docking methods that, starting from the structures of component proteins, can determine the structure of their complexes with accuracy close to that obtained by X-ray crystallography. Our multistage approach to docking starts with a rigid body search that samples billions of conformations on a grid using fast Fourier transform (FFT) correlation calculations, and includes post-processing to find near-native conformations among thousands of docked structures. Our methods were among the best in the latest CAPRI docking experiment, but results are still poor for antigen-antibody and some other types of complexes. The general goal of this proposal is the development of new algorithms that will improve the accuracy and reliability of multistage docking while retaining its computational efficiency, hence enabling the use of state-of- the-art methods for a web-based docking server. We will address three problems. First, Monte Carlo-based docking programs have demonstrated that searching for optimal side chain conformations in the interface can improve results. However, Monte Carlo is not a very efficient search, and such programs are computationally too demanding to globally explore the conformational space without a priori information on the structure of the complex. To remedy this we will develop efficient docking-specific side chain search algorithms within the framework of the multistage approach. This will include the identification of """"""""key"""""""" side chains that are most important for recognition, thereby reducing the side chains'degrees of freedom. Second, accounting for electrostatics, desolvation, hydrogen bonding, and possibly experimental constraints improves docking results, but the use of such complex scoring functions reduces the numerical advantage provided by the FFT correlation approach. We will regain this advantage by developing a multi-property 5-dimensional FFT-based algorithm that can be used with scoring functions of arbitrary complexity without added computational costs. Third, docking results are particularly poor for antibody-antigen pairs. Our preliminary data indicate that results can be substantially improved by adopting asymmetric hydrophobicity potentials that account for the biophysics of interactions in particular classes of protein-protein complexes. In the first six months of the project we set up a new version of our ClusPro server, heavily used by experimentalists, which will include recent developments. The server will be continuously updated as our new algorithms become available. In addition to providing the server, we have already started development on a modular program library specific to the docking problem that will be freely accessible.
Studying protein-protein interactions is crucial to gaining a better understanding of processes such as metabolic control, signal transduction and gene regulation. Since many biologically important interactions occur in weak, transient complexes that are not amenable to direct experimental analysis, it is important to develop computational docking methods that, starting from the structures of component proteins, can determine the structure of their complexes with accuracy close to that obtained by X-ray crystallography. The general goal of this proposal is to further improve the accuracy of multistage docking while retaining its computational efficiency, thereby enabling the use of state-of-the-art methods in protein docking servers available to the scientific community.
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