X-ray crystallography is a critical tool in the study of biological systems. It is able to provide atomic resolution information that has been a prerequisite to understanding the fundamentals of life, from the structure of the double helix to the structure of the intact 70S ribosome. It is also a method that is central to the development of new therapeutics for human disease, both in commercial and academic settings. Our goal is to enable researchers to generate better atomic models than is possible with current methods. This will lead to more insightful biological interpretation and better drugs against human diseases. The development of automated methods will free researchers from time consuming manual interpretation of data and models. Collectively, these advancements will save thousands of researcher hours and make the crystallographic technique more accessible to biologists with less specialized expertise. The final stages of a crystallographic structure solution require the optimization of an atomic model with respect to the experimental diffraction data. The goal is to generate a model that best describes the contents of the crystal, principally modeled by the identity, position and mean square displacement of atoms. It is these atomic models that are interpreted to understand biological function, and are the basis for the design of novel therapeutics. The development of methods that generate improved models will positively impact both of these important research tasks. By introducing new ways to parameterize models it will be possible to generate robust and accurate models at data resolution limits that thwart current methods. By using additional information in model optimization, including energy functions from the Rosetta structure-modeling system, it will be possible to generate more accurate models that provide a better basis for subsequent interpretation. All of these algorithms will be developed in the context of the Phenix system for integrated and automated crystallography. The foundational components of Phenix will be extended to support new approaches to structure solution, refinement and validation. Working with the other researchers in the Phenix team is crucial to the success of the entire project. Collaboration, enabled by a modern software development environment, fosters a tight integration of algorithms, which can then be applied to solve a common problem. In addition, the availability of a wide range of methods in a consistent environment promotes their reuse to solve new problems. As a result the Phenix team is collectively able to very rapidly prototype and make available new methods to researchers.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Lawrence Berkeley National Laboratory
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