The circuitry of the brain is characterized by cell heterogeneity, sprawling cellular anatomy, and astonishingly complex patterns of connectivity. Determining how complex neural circuits control behavior is a major challenge that is often approached using surgical, chemical, or transgenic approaches to ablate neurons. However, all these approaches suffer from a lack of precise spatial and temporal control. This drawback would be overcome if cellular ablation could be controlled with light. Cells are naturally and cleanly ablated through apoptosis due to the terminal activation of caspases. Here, we describe the engineering of a light-activated human caspase-3 (Caspase-LOV) by exploiting its natural spring-loaded activation mechanism through rational insertion of the light-sensitive LOV2 domain that expands upon illumination. We apply the light-activated caspase (Caspase-LOV) to study neurodegeneration in larval and adult Drosophila Using the tissue-specific expression system (UAS)-GAL4, we express Caspase-LOV specifically in three neuronal cell types: retinal, sensory, and motor neurons. Illumination of whole flies or specific tissues containing Caspase-LOV-induced cell death and allowed us to follow the time course and sequence of neurodegenerative events. For example, we find that global synchronous activation of caspase-3 drives degeneration with a different time-course and extent in sensory versus motor neurons. We believe the Caspase-LOV tool we engineered will have many other uses for neurobiologists and others for specific temporal and spatial ablation of cells in complex organisms.

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.

Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site closely matches the design model. Our computational design strategy opens broad avenues to link biological outputs to new signals.

Accurate energy functions are critical to macromolecular modeling and design. We describe new tools for identifying inaccuracies in energy functions and guiding their improvement, and illustrate the application of these tools to the improvement of the Rosetta energy function. The feature analysis tool identifies discrepancies between structures deposited in the PDB and low-energy structures generated by Rosetta; these likely arise from inaccuracies in the energy function. The optE tool optimizes the weights on the different components of the energy function by maximizing the recapitulation of a wide range of experimental observations. We use the tools to examine three proposed modifications to the Rosetta energy function: improving the unfolded state energy model (reference energies), using bicubic spline interpolation to generate knowledge-based torisonal potentials, and incorporating the recently developed Dunbrack 2010 rotamer library (Shapovalov & Dunbrack, 2011).

Accurate energy functions are critical to macromolecular modeling and design. We describe new tools for identifying inaccuracies in energy functions and guiding their improvement, and illustrate the application of these tools to the improvement of the Rosetta energy function. The feature analysis tool identifies discrepancies between structures deposited in the PDB and low-energy structures generated by Rosetta; these likely arise from inaccuracies in the energy function. The optE tool optimizes the weights on the different components of the energy function by maximizing the recapitulation of a wide range of experimental observations. We use the tools to examine three proposed modifications to the Rosetta energy function: improving the unfolded state energy model (reference energies), using bicubic spline interpolation to generate knowledge-based torisonal potentials, and incorporating the recently developed Dunbrack 2010 rotamer library (Shapovalov & Dunbrack, 2011).