The goal of this project is to develop computational methods, algorithms and software for predicting the three dimensional structures of protein-protein complexes. Such methods are extremely valuable for providing insights into the biological functions of the interacting proteins and can guide the design of novel pharmaceuticals. This project builds on the existing strengths in our lab. We have built several protein docking algorithms (ZDOCK, RDOCK and ZRANK), a docking benchmark and a docking server, which are widely used by other researchers. We have recently developed a new energy function, IFACE, which improves the performance of ZDOCK. We have made significant progress on combining ZRANK with side-chain conformation search. The first two Aims of the project regard algorithm development.
In Aim 1, we propose to further develop the IFACE potential by using different types of protein-protein complexes for training, and allowing the potential to be distance-dependent. We also plan to develop a five-dimensional fast Fourier transform algorithm ZDOCK5D for efficiently sampling the six rigid-body degrees of freedom between two rigid proteins. This effort will take advantage of the scoring expertise we have developed for ZDOCK, and will yield significant speed improvements. The development of a general method for focused searching is also planned, which will improve both accuracy and speed.
In Aim 2, we will develop methods to explicitly explore side chain flexibility. We will predict which residues are likely to undergo side chain conformational change upon complex formation. We plan to combine our ZRANK with RosettaDock (a docking program developed by the labs of Gray and Baker) to perform side chain searches. We will further develop ZRANK so that it can effectively rerank the structures for which side-chains and rotational and translational placements have been refined by RosettaDock.
Aim 3 is focused on developing a pipeline for updating our docking benchmark and producing two updates during the 4-year course of this project. We will also further develop classifiers based on atomic contact vectors for distinguishing transient complexes from obligate complexes, an important step for both updating the benchmark and for developing target functions of docking algorithms.
In Aim 4, we describe plans to improve the software engineering of our existing computational suite. This undertaking will benefit future algorithmic and software development, in line with the proposals outlined in Aims 1-3. We will further develop our docking server. The software available on the server will be extended to include many new features planned in this application and the server hardware will be allotted more computing power. Finally, we will strengthen our user support to maximize the impact of our suite of programs on the user community.

Public Health Relevance

Knowledge of the 3-dimensional (3D) structures of protein complexes provides insights into the biological functions of the component proteins and can aid the design of protein drugs. Our lab has developed a suite of widely used protein-protein docking algorithms and a widely used docking benchmark. The goal of this project is to continue to develop these computational methods, algorithms, and software for studying protein-protein interactions, in order to foster broader use in the protein docking community.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-Q (01))
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Wehrle, Janna P
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University of Massachusetts Medical School Worcester
Biostatistics & Other Math Sci
Schools of Medicine
United States
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