The University of California San Francisco is awarded grant advance approaches that can capture and engineer structural changes in proteins. This is important both to make designs more accurate and to engineer functions that require structural changes. Computational protein design has enormous potential to transform many areas of science and engineering by creating proteins with useful functions: new enzymes to build biological synthesis pathways that can produce important chemicals and fuels; selective signaling molecules that can process biological information; and new protein materials with sophisticated functions mimicking their biological counterparts. This research seeks to develop computational methods to ultimately make such complex protein functions designable. Specifically, project will produce (i) methods to model changes in enzymatic specificity and improve the design of active sites; (ii) methods to model and design signaling proteins that act as switches; and (iii) benchmarked and documented protocols available to biologists and biological engineers.

Recent design successes that created novel enzymes, protein-protein interactions, and nanomaterials are spurring both academic and industrial interest in proteins with modified, new, and useful functions. Combined with a considerable expansion of technological capabilities (such as large-scale de novo construction and characterization of biological "parts"), there will be an increasing need for more predictive design methods, such as the ones developed here, to create proteins with new and more complex activities. Applications span broad areas including computational and structural biology, metabolic engineering, and synthetic and cellular biology. The new methods developed under this grant will be used in graduate courses and team projects in molecular engineering designed to foster collaboration between students from the biological and physical/engineering sciences, as well as in undergraduate and high-school research activities at UCSF. Improved computational methods will be disseminated broadly as source code via the Rosetta suite of computational tools ( and via easily accessible web applications.

National Science Foundation (NSF)
Division of Biological Infrastructure (DBI)
Standard Grant (Standard)
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Peter H. McCartney
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University of California San Francisco
San Francisco
United States
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