Molecular interactions and the formation of molecular assemblies are underpinning most biological processes. Automated docking is an important tool for gaining a mechanistic understanding of these interactions and supporting biomedical applications ranging from drug design to the design of chemical probes used to investigate chemical pathways and identify therapeutic targets for diseases such as cancer and metabolic disorders. It is known that a variety of conformational changes ranging from amino side chain rearrangement to flexible loops and domain motions in macromolecules are often an essential and integral part of the interaction mechanism with a ligand. The representation of macromolecules as rigid molecules during the docking simulation is one of the most severe limitations of these techniques. We have developed a hierarchical data structure called the "Flexibility Tree" (FT) allowing the efficient representation and encoding of conformational subspaces of macromolecules and we have demonstrated using FTs for docking flexible ligand molecules into flexible receptors. We propose to incorporate the FT in the widely used docking program AutoDock in order to support docking flexible ligands against flexible receptors. Specifically, we will: 1) extend the AutoDock docking software suite with a new FT-based docking engine: AutoDock-FR that will support multi-resolution receptor flexibility, and pluggable search engines and scoring functions. We will also extend the Graphical User Interface AutoDockTools to support this new docking backend;2) extend the FT with the ability to better represent flexible loops and rotatmeric side chains, and interface protein flexibility prediction methods to support users in building FTs;and 3) create a dataset of molecular complexes in which macromolecular flexibility is known to be required for the success of automated docking procedures. This dataset will be used to test and validate the proposed software and will be made available to the community and provide a benchmark for evaluating docking methods. This Open-Source software development project will be based on best practices in software engineering and result in a modular, component-based software environment in which search techniques and scoring functions can be substituted and combined. The modular design we propose also defines a clear and clean mechanism for the addition of new algorithms as they become available, making AutoDock-FR evolvable and maintainable. This effort will greatly leverage methods developed by the community and provide unprecedented inter- operability. The fully fledged, user friendly, highly customizable, fully documented software we propose will be made available within an already widely used a popular docking program which will help its dissemination and adoption. AutoDock-FR will greatly extend the range of biological problems for which automated docking will be used successfully. It will impact the research of many chemists and biologist, extend the use of computational tools to a wider community of scientists, and greatly impact biomedical research.

Public Health Relevance

Automated docking has proven to be a useful tool for a variety of applications including: rational drug design, lead optimization and the design of chemical probes, however, the rigid models of macromolecules used by most docking programs severely limits the success rate of automated docking. We propose to extend the widely used and well established docking program AutoDock with a new docking engine AutoDock-FR that will allow for the representation of flexible receptor and the optimization of the receptor's conformation in the presence of the ligand during the docking simulation. This new capability in AutoDock will impact the research of many computational and medicinal chemists and biologist and contribute to our understanding of biological processes and thus significantly impact research in biomedicine.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Brazhnik, Paul
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Scripps Research Institute
La Jolla
United States
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Ravindranath, Pradeep Anand; Sanner, Michel F (2016) AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms. Bioinformatics 32:3142-3149
Forli, Stefano; Huey, Ruth; Pique, Michael E et al. (2016) Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 11:905-19
Ravindranath, Pradeep Anand; Forli, Stefano; Goodsell, David S et al. (2015) AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility. PLoS Comput Biol 11:e1004586