All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks. Introduction Structural models of proteins and additional biomolecules help explain their functions and properties. internet 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 college students from different disciplines and encounter levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their technology and engineering study. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks. Intro Structural models of proteins and additional biomolecules help clarify their functions and properties. Methods for computational structure prediction (i.e. protein folding and docking, as well as relationships with nucleic acids, carbohydrates, and additional biomolecules) have been successful in many cases and certainly useful to travel structural and practical study hypotheses (1). Design of biomolecules (i.e. protein design, prediction of mutational effects, and molecular complex design) has also exhibited many successes, with potential effects in medicine, biology, biotechnology, materials, and chemistry (2). Therefore, there is a need to disseminate these interdisciplinary methods to a broader target audience. Here, we present a set of workshops for teaching or self-study of biomolecular structure prediction and design. Scientific and Pedagogical Background Computational methods are a relatively inexpensive way to forecast and manipulate biomolecular constructions, especially when experimental methods demonstrate hard. There is a long history in biophysics of using computational modeling to better understand structure, dynamics, and function. In fact, the 2013 Nobel Reward in Chemistry was granted for the pioneering contributions in quantum and molecular mechanics of complex chemical systems (3). There are now many available dynamic simulation tools DR4 for observing the behavior of biomolecules over time and predicting thermodynamic and kinetic properties from estimations of the systems partition function. Some of these tools include CHARMM, Schr?dinger software suite, MOE, NAMD, Amber, and Gromacs (4C9). A complementary approach to model biomolecules is with so-called approaches. Instead of looking for a full description of all the claims and kinetic rates of the system, these approaches seek the dominating, low-energy conformational state(s) that is (are) most relevant (4R,5S)-nutlin carboxylic acid at biological conditions (10). These methods often accelerate calculations with approximations, such as constant relationship lengths and perspectives, implicit solvent models, and empirically tuned energy functions. In exchange for these approximations, structure prediction methods can capture the structure of large biomolecules in equilibrium without necessitating (4R,5S)-nutlin carboxylic acid simulations over long timescales. These methods are fundamentally based on optimization of an energy function in a very large conformational space. The same algorithmic parts can then be applied in reverse to biomolecules by optimizing the energy function across different biomolecular sequences. One leading structure prediction and design software suite is definitely Rosetta, a collection of algorithms for protein structure prediction, docking, and design (10C13) as well as protein interactions with small molecules (14), nucleic acids (15), and carbohydrates in remedy or inside a lipid bilayer (16). Rosetta has been a medical leader in several blind structure prediction difficulties (17C21) and has shown proof-of-principle for many design goals, including folds (22C24), loop design, interface design (25C28), symmetric assembly (29, 30), and mineral binding (31, 32). In addition to its success in technology and executive, Rosetta is definitely suited for teaching structure prediction and design for a number of reasons. The Rosetta methods are available (4R,5S)-nutlin carboxylic acid like a Python library called PyRosetta (33), which makes them better to learn and combine with additional medical code libraries. PyRosetta allows access to low-level data and has a range of pre-built protocols for many jobs in biophysical study. College students can measure and manipulate protein conformations, dock proteins and small molecules, run.