Structure-based design of non-natural amino-acid inhibitors of amyloid fibril formation.

Many globular and natively disordered proteins can convert into amyloid fibrils. These fibrils are associated with numerous pathologies as well as with normal cellular functions, and frequently form during protein denaturation. Inhibitors of pathological amyloid fibril formation could be useful in the development of therapeutics, provided that the inhibitors were specific enough to avoid interfering with normal processes. Here we show that computer-aided, structure-based design can yield highly specific peptide inhibitors of amyloid formation. Using known atomic structures of segments of amyloid fibrils as templates, we have designed and characterized an all-D-amino-acid inhibitor of the fibril formation of the tau protein associated with Alzheimer's disease, and a non-natural L-amino-acid inhibitor of an amyloid fibril that enhances sexual transmission of human immunodeficiency virus. Our results indicate that peptides from structure-based designs can disrupt the fibril formation of full-length proteins, including those, such as tau protein, that lack fully ordered native structures. Because the inhibiting peptides have been designed on structures of dual-ß-sheet 'steric zippers', the successful inhibition of amyloid fibril formation strengthens the hypothesis that amyloid spines contain steric zippers.

Designing RNA-based genetic control systems for efficient production from engineered metabolic pathways.

Engineered metabolic pathways can be augmented with dynamic regulatory controllers to increase production titers by minimizing toxicity and helping cells maintain homeostasis. We investigated the potential for dynamic RNA-based genetic control systems to increase production through simulation analysis of an engineered p-aminostyrene (p-AS) pathway in E. coli. To map the entire design space, we formulated 729 unique mechanistic models corresponding to all of the possible control topologies and mechanistic implementations in the system under study. Two thousand sampled simulations were performed for each of the 729 system designs to relate the potential effects of dynamic control to increases in p-AS production (total of 3 × 10(6) simulations). Our analysis indicates that dynamic control strategies employing aptazyme-regulated expression devices (aREDs) can yield >10-fold improvements over static control. We uncovered generalizable trends in successful control architectures and found that highly performing RNA-based control systems are experimentally tractable. Analyzing the metabolic control state space to predict optimal genetic control strategies promises to enhance the design of metabolic pathways.

Dynamic modeling of cellular populations within iBioSim.

As the complexity of synthetic genetic circuits increases, modeling is becoming a necessary first step to inform subsequent experimental efforts. In recent years, the design automation community has developed a wealth of computational tools for assisting experimentalists in designing and analyzing new genetic circuits at several scales. However, existing software primarily caters to either the DNA- or single-cell level, with little support for the multicellular level. To address this need, the iBioSim software package has been enhanced to provide support for modeling, simulating, and visualizing dynamic cellular populations in a two-dimensional space. This capacity is fully integrated into the software, capitalizing on iBioSim's strengths in modeling, simulating, and analyzing single-celled systems.