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Fluorescent biosensors are a valuable means to report the spatiotemporal dynamics of protein activities in live cells and animals. However, biosensors affect the activities they are reporting. This can be ameliorated by increasing sensitivity, to use lower biosensor concentrations, or by choosing designs that minimize undesirable interactions. For biosensors in which fluorescent components interact to produce Forster Resonance Energy Transfer (FRET), perturbation is often due to interaction of biosensor components with nonfluorescent, endogenous proteins, rather than productive interactions that lead to FRET. Here we engineer the interface between biosensor components using charge swap and ‘knob into hole’ mutations to reduce all but desired interactions. Novel biosensors for Rac1 and Cdc42 showed reduced interactions with endogenous GTPases and effectors, normal activation by guanine nucleotide exchange factors (GEFs), and correctly reproduced previous reports of GTPase activation dynamics. Assaying concentration-dependent effects on cell motility showed substantially reduced perturbation of normal cell behavior. Computational models indicated that minimal perturbation could be achieved over a broader range of concentrations using the new ‘orthogonal’ biosensors. 

ABSTRACT Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of 16 modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications, such as protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a Web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels, as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks. 

Abstract Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.