Collaborative Research: ABI Development: The next stage in protein-protein docking

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Protein-protein interactions are integral to many mechanisms of cellular control, including protein localization, allosteric and gene regulation and signal transduction, and therefore their characterization is an important task for both experimental and computational approaches to systems biology. Genome-wide proteomics studies provide a growing list of putative protein-protein interactions, and demonstrate that most if not all proteins have interacting partners in the cell. However, these techniques can only identify whether two proteins possibly interact. A full comprehension of how proteins bind and form complexes can only come from high-resolution three-dimensional structures, since they provide the atomic details necessary to understand how the interactions occur and how the high degree of specificity can be achieved. While the most complete structural characterization is provided by X-ray crystallography, many biologically important interactions occur in weak, transient complexes that are not amenable to direct experimental analysis, even when both proteins can be isolated and their structures determined. The goal of this project is to further improve the protein-protein docking server ClusPro that, starting from the structures of component proteins, attempts to determine the structure of their complexes with accuracy close to that provided by X-ray crystallography. The server at https://cluspro.org already has close to 10,000 registered users, although it can also be used without registration. Docked structures generated by ClusPro have been reported in over 600 research papers. The public server is important for biological and chemical scientists who may not have extensive computational experience but still will be able to use state of the art docking methods. The increased availability of protein complex structures will have major impact in many areas of biology, biochemistry, and biotechnology. The project has six aims focusing on problems of increasing difficulty. First, a new opportunity to progress is provided by earlier development of the manifold fast Fourier transform (FFT) correlation algorithm, which increased the speed of sampling by orders of magnitude. An additional advantage of the method is that increasing the number of correlation function terms is computationally inexpensive. Therefore, Aim 1 is the development and implementation of complex energy functions, including distance-dependent and three-body potentials for scoring. Second, recently developed methods have shown considerable promise in predicting contacts between residues in proteins using evolutionary covariance information. The problem is that these methods require large numbers of evolutionarily related sequences of interacting proteins to robustly assess the extent of residue covariation. Aim 2 is exploring how sequence information can be used for extracting inter-protein contacts to improve docking results even with somewhat limited number of sequences. Third, with the increase in the number of protein complex structures, there is increasing need for integrating direct docking and template-based prediction methods, and several strategies will be explored to implement such integration. Fourth, the docking method will be modified to work optimally with homology models rather than X-ray structures of interacting proteins. The fifth aim is exploring new approaches to the docking of proteins with substantial backbone conformational changes upon binding. The sixth and final aim is implementing the newly developed algorithms in the ClusPro server.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.