The AutoDock suite of programs are widely distributed community codes, used by a broad base of biomedical researchers for the prediction of biomolecular interactions and the screening of chemical compound libraries in the process of drug discovery. AutoDock was initially developed 21 years ago, to solve the flexible ligand- protein docking problem. In the intervening years both the user community and the AutoDock suite have significantly expanded. AutoDock and AutoDock Vina are now distributed under open source licenses;AutoDock is currently the world's most widely used and cited molecular docking program. Critical funding is sought to support the growing user community and to further develop AutoDock software capabilities. We are proposing to extend the usefulness, usability and maintainability of the AutoDock software, and to enhance the interactions between the users and developers by further development of the codes, utilizing contemporary software engineering practices. Our experience with the object-oriented high-level language Python has shown us that an agile, component-based, approach to software development can produce code that is easily extensible, inter-operable, maintainable, and platform independent. Computationally intensive components will be written in C or C++ and integrated with Python wrappers. Our goals for the continued development and maintenance of the AutoDock suite are to expand its usefulness, its usability and its maintainability, within an evolvable software platform, for the broad diversity of its users. In order to achieve these goals, we propose the following three specific aims that will target utility, usability and community support: 1. Utility Expand the capabilities of the AutoDock software and associated methods, to include: methods for enhanced performance;scoring function enhancements;and improved methods for pre-processing and analysis through new software development and integration of external methods 2. Usability. Develop integration-layer architecture and an AutoDock Applications Manager (ADAM) software to enhance interoperability with external methods and to facilitate the design, implementation, and archiving and reproducibility of docking and virtual screening computations. We will also develop a unified ADAM Graphical User Interface and visualization environment for docking and virtual screening, with turnkey, wizard-like interfaces for novice users and interactive tools for problem specification and analysis of virtual screening results 3. Community Support. Continue and enhance AutoDock community interaction and Internet content to strengthen user support, contributed open-source development, software dissemination, tutorials, and documentation.

Public Health Relevance

Computer software has become a critical tool in contemporary biomedical research. AutoDock is the most widely used computer program for predicting the interaction of chemical compounds with biological molecules and is currently in use by over 30,000 researchers worldwide. The goal of this proposal is to support new advances and more effective use of this program to aid scientists in their discovery and design of new pharmaceuticals and other chemical compounds to aid in medical research.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM069832-09A1
Application #
8371936
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Preusch, Peter C
Project Start
2004-01-01
Project End
2016-07-31
Budget Start
2012-09-01
Budget End
2013-07-31
Support Year
9
Fiscal Year
2012
Total Cost
$509,643
Indirect Cost
$240,702
Name
Scripps Research Institute
Department
Type
DUNS #
781613492
City
La Jolla
State
CA
Country
United States
Zip Code
92037
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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
Bianco, Giulia; Forli, Stefano; Goodsell, David S et al. (2016) Covalent docking using autodock: Two-point attractor and flexible side chain methods. Protein Sci 25:295-301
Fiore, Mario; Forli, Stefano; Manetti, Fabrizio (2016) Targeting Mitogen-Activated Protein Kinase-Activated Protein Kinase 2 (MAPKAPK2, MK2): Medicinal Chemistry Efforts To Lead Small Molecule Inhibitors to Clinical Trials. J Med Chem 59:3609-34
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Perryman, Alexander L; Yu, Weixuan; Wang, Xin et al. (2015) A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA. J Chem Inf Model 55:645-59
Deng, Nanjie; Forli, Stefano; He, Peng et al. (2015) Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease. J Phys Chem B 119:976-88
Forli, Stefano; Olson, Arthur J (2015) Computational challenges of structure-based approaches applied to HIV. Curr Top Microbiol Immunol 389:31-51
Forli, Stefano (2015) Charting a Path to Success in Virtual Screening. Molecules 20:18732-58

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