Abstract
The experimental determination of the structure of protein complexes cannot keep pace with the generation of interactomic data, hence resulting in an ever-expanding gap. As the structural details of protein complexes are central to a full understanding of the function and dynamics of the cell machinery, alternative strategies are needed to circumvent the bottleneck in structure determination. Computational protein docking is a valid and valuable approach to model the structure of protein complexes. In this work, we describe a novel computational strategy to predict the structure of protein complexes based on data-driven docking: VORFFIP-driven dock (V-D2OCK). This new approach makes use of our newly described method to predict functional sites in protein structures, VORFFIP, to define the region to be sampled during docking and structural clustering to reduce the number of models to be examined by users. V-D2OCK has been benchmarked using a validated and diverse set of protein complexes and compared to a state-of-art docking method. The speed and accuracy compared to contemporary tools justifies the potential use of VD2OCK for high-throughput, genome-wide, protein docking. Finally, we have developed a web interface that allows users to browser and visualize V-D2OCK predictions from the convenience of their web-browsers.
Highlights
One of the most prevalent challenges in the post-genomic era is the charting and description of the protein networks that underpin cellular functions
We present the development of a high-throughput computational docking strategy: V-D2OCK, which combines protein-binding site prediction and data-driven docking
The clustering step greatly reduces the number of docking poses with a limited impact on the quality of the models, facilitating the analysis and visualization of the docking solutions
Summary
One of the most prevalent challenges in the post-genomic era is the charting and description of the protein networks that underpin cellular functions. Large-scale interactomic experiments (e.g.[1,2]) sought to describe the protein interactions that occur in cells, and albeit valuable, most of the information derived from these experiments does not provide the underlying structural, atomic details of the interactions. These details are central in order to realize the full potential of interactomic data in rational approaches such as the development of novel drugs to target protein.
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