Rosetta Commons Research Experience for Undergraduates
A Cyberlinked Program in Computational Biomolecular Structure & Design
Interns in this geographically-distributed REU program have the opportunity to participate in research using the Rosetta Commons software. The Rosetta Commons software suite includes algorithms for computational modeling and analysis of protein structures. It has enabled notable scientific advances in computational biology, including de novo protein design, enzyme design, ligand docking, and structure prediction of biological macromolecules and macromolecular complexes.
- One week of Rosetta Code School (June 1 through June 5) where you will learnthe inner details of the RosettaPython code and community coding environment, so you are fully prepared for the summer!
- 8 weeks of hands-on research in a molecular modeling and design laboratory, developing new algorithms and discovering new science.
- The summer will finish with a trip to the Rosetta Conference in the gorgeous Cascade Mountains of Washington State, where you will present your research in a poster and connect with Rosetta developers from around the world. The conference will be held from August 4 through August 7.
- This program is supported by NSF. Interns will receive housing, paid travel expenses, and a $5,500 stipend.
Include the following in the application:
- Personal statement - why this internship interests you - brief summary of research and computing experience - why you are an appropriate candidate for the internship (up to 4000 characters)
- Two references (complete the reference forms with contact information)
- Select top three labs and projects of interest from the list below.
- Deadline for receipt of applications is February 1, 2020.
- Deadline for receipt of recommendation letters is February 5, 2020.
- Program contact: Camille Mathis: firstname.lastname@example.org.
- U.S.citizens, permanent residents, and U.S. nationals are eligible.
- College Sophomores or Juniors preferred
- Major in computer science, engineering, mathematics, chemistry, biology, and/or biophysics
- Available for at least 10 weeks during the summer of 2020.
- Interest in graduate school
- While not required, we seek candidates with some combination of experiences in scientific or academic research, C++/Python/*nix/databases, software engineering, object-oriented programming, and/or collaborative development (git).
Available projects and locations:
Bahl Lab @ Institute for Protein Innovation, Boston Children's Hospital, Harvard Medical School in Boston, Massachusetts
"Engineering antibodies with enhanced properties"
Antibody reagents are the backbone of cell biology and immunology, they are important diagnostic tools, and therapeutic antibodies represent a rapidly growing class of medicines. Stability is often a liability for the production, storage, and application of antibody-based technologies. Thus, we seek to generate novel antibodies with enhanced the stability in vitro and in vivo.
Baker Lab @ University of Washington in Seattle, Washington
“Design of self-assembling protein nanomaterials: three-dimensional crystals”
Designed protein crystals have great potential as structure determination tools and as biomaterials for energy, environmental, and health-related applications. While many proteins will crystallize under artificial vapor diffusion conditions, no method currently exists for controlling the crystallization of proteins into specific lattice architectures. The general rules for protein crystallization can be broken down into chemical, physical and geometric constraints. Incorporating these parameters into tools for the accurate structural modeling of macromolecules allows for the design of proteins. The project will use Rosetta modeling to design self-assembling protein crystals constructed from complex protein scaffolds with atomic-level accuracy.
Cooper Lab @ Northeastern University in Boston, MA
“Citizen Science Games in Protein Folding and Design”
We are exploring how citizen science and crowdsourcing through video games can help biochemists with their work. To do this, we have developed the game Foldit, a multiplayer online game that allows players without previous experience in biochemistry to work on protein folding and design problems. This project will focus on development of game-related aspects to understand and improve the player experience.
Das Lab @ Stanford University in Stanford, California
“Modeling and designing RNA at high resolution”
The project seeks to understand a big question related to the most fundamental — but also most mysterious — machine in living systems, the protein making ribosomes. Student will predict the effects of mutations in the ribosome active site and compare to experimental measurements on ribosome structure and activity made by our lab and collaborators using high-throughput biochemistry.
Fomekong Group @ EMD Serono Research & Development Institute in Billerica, MA
“Using Rosetta to build better drugs”
The Drug Structure, Prediction, and Design (DSPD) group is an interdisciplinary team of scientists built around a core of experts to drive drug discovery of small molecules and biologics. We blend experimental science with quantitative and qualitative in silico approaches that span computational chemistry and biology, structural biology, mathematics and data science.
A Rosetta intern in DSPD will choose from one of two projects that will support the Discovery Technologies drug candidate portfolio by helping drive an understanding what makes a molecule a good drug candidate. The projects include 1) docking experiments to recapitulate or predict small molecule-protein ternary complexes for the immune modulatory (IMiD) class of drugs; 2) modeling of drug derived peptides binding to MHC class II molecules from different species to understand how immunogenicity and anti-drug antibodies might differ in pre-clinical (animal) and clinical (human) settings.
Gray Lab @ Johns Hopkins University in Baltimore, MD
“Antibody engineering by deep learning”
Antibodies are an excellent model system for loop structure prediction and design, a difficult problem in the field. High-resolution models of the loop structure are necessary for successful docking to antigens or for design for improved affinities, yet traditional loop prediction methods have been frustrated on antibody loops because of their extreme variability. In this project, the student will apply deep learning methods, including transfer learning and attention gating to leverage data from a large set of protein structure and focus predictions on the key loop.
Horowitz Lab@ University of Denver in Denver Colorado
“Biochemistry Puzzles for Science Education”
The biochemistry computer game Foldit has become a common teaching tool from middle school to graduate school. Recently, we developed a new feature in Foldit allowing educators to create their own biochemistry puzzles that will fit their curriculum. The REU student will make custom biochemistry puzzles for education at various levels and design courses using Foldit.
Karanicolas lab @ Fox Chase Cancer Center in Philadelphia, Pennsylvania
“Designing targeted protein degraders”
PROTACs (PROteolysis TArgeting Chimeras) are a new approach to eliminate activity of a given protein in cells. Rather than inhibiting the protein of interest, PROTACs completely eliminate the target protein by inducing its degradation. PROTACs are bi-functional small molecules that use a chemical linker to join a “warhead” directed against some target protein with a moiety that recruits an E3 ubiquitin ligase. In this project, the student will apply docking to build models of several PROTACs in complex with their target proteins and E3 ubiquitin ligases, then will use these as input to develop a machine learning approach for predicting the efficacy of or designing a given PROTAC.
Kellogg lab @ Cornell University in Ithaca, New York
“Combining CryoEM and Rosetta to inform protein structure and dynamics”
Cryo-EM shows great promise as a general strategy to obtain atomic level information about protein structure. However, most cryo-EM reconstructions are within the 3.5-5 Å resolution regime, considered a 'low' resolution structure for most crystallographic model-building tools. Structures of dynamic assemblies are limited to even lower resolution, making these structures nearly impossible to interpret structurally. Therefore, interpreting low-resolution cryo-EM structures remains one of the great hurdles in the cryo-EM methodology, limiting our ability to obtain meaningful structures. This REU project will use Rosetta to infer atomic level information from low-resolution cryo-EM density maps.
King lab @ University of Washington in Seattle, Washington
“Breaking symmetry in the computational design of self-assembling protein nanomaterials”
Designed protein nanomaterials have myriad potential applications in renewable energy, materials science, medicine, and basic research. However, the classes of materials accessible to current design protocols are limited in size and complexity due to a strict reliance on symmetry. In nature, many protein complexes break strict symmetry to form highly specialized structures. In this project, we will enable the design of quasi-equivalent and pseudo-symmetrical protein assemblies to generate new classes of designed protein nanomaterials that are larger and more sophisticated than current materials and that enable greater control over the placement of functional groups.
Kortemme Lab @ University of California, San Francisco, in San Francisco, California
“Design of protein-based small molecule sensor/actuators”
The ability to sense small molecule signals is fundamental for living systems to probe their environment and respond to it. Learning how nature senses, and engineering proteins de novo that can sense and respond to new signals has many applications in fundamental and synthetic biology. The REUproject will be to use Rosetta to address challenges in engineering proteins computationally to sense small molecule ligands with high affinity and specificity and to test the computational hypotheses experimentally.
Meiler Lab@ Vanderbilt University, in Nashville, Tenessee
“Developing the Foldit Drug Discovery game”
Join the Meiler laboratory to develop a drug development version of the popular Rosetta-based computer game Foldit. We currently integrate machine learning cheminformatics drug discovery technologies into Rosetta. in parallel we expand the Graphical User Interface (GUI) of Foldit to enable drug design. The specific project for the intern within this team effort will be chosen based on interest and skill set.
Rocklin Lab @ Northwestern University in Chicago, IL
"Design of ultra-stable protein scaffolds"
Small, de novo designed proteins have the potential for widespread use as therapeutis, vaccines, and diagnostics. Compared with other protein technologies, designed proteins can achieve extreme stability against denaturation, although we do not understand the rules for designing highly stable proteins. We are developing new high-throughput assays to measure resistance to unfolding, aggregation, and degradation for thousands of proteins in parallel. This project will apply these assays to discover new principles for protein stability, and apply these principles to design hyperstable proteins.
Slusky Lab @ University of Kansas in Lawrence, Kansas
“Enzyme design for environmental remediation”
The design of novel enzymes could transform environmental remediation. For example, enzymes that degrade pollutants would be presented on the surface of bacterial cells. The products of the degradation would then be metabolized as carbon sources for the same or other organism. The REU project will be to use Rosetta to design and test enzymes that initiate the breakdown of common hydrocarbons in oil spills.
Smith Lab @ Wesleyan University in Middletown, Connecticut
“Elucidation of protein dynamics from Nuclear Magnetic Resonance (NMR) structural data”
Protein motion is increasingly recognized as being critical for biological function, but traditional approaches for structure determination from either X-ray crystallography or NMR spectroscopy fail to adequately represent the dynamics inherent in that data. The Smith lab recently developed a method that takes molecular motion into account using a more realistic model of the physics underlying the Nuclear Overhauser Effect (NOE). In this project, the student will use different Rosetta protocols to produce ensembles of multiple structures that are collectively fit against NOE data. Thus, the student will both validate tools for producing significantly better structural models and address longstanding questions about similarities and differences between the crystal and solution states respectively probed by X-ray and NMR data, learning ensembles, spectroscopy and protein dynamics.
Stein Lab @ University of Copenhagen in København, Denmark
“Accurate prediction of protein stability changes on homology models”
Predicting changes in protein stability upon mutation is a key component of protein engineering. This is particularly challenging if no experimental high-resolution structure has been resolved, which is the case for 90% of all proteins. Highly accurate models are needed for successful application of stability calculations. In this project, the student will generate different models, predict stability changes upon mutation and compare these to experimental results to identify minimum model requirements for accurate prediction.
GlaxoSmithKline in Collegeville, Pennsylvania
“Designing better biomolecules faster”
Neoleukin in Seattle, Washington
“De Novo Protein Design for Immunotherapeutics”
Arzeda Corporation in Seattle, Washington
“Enzyme design for the production of performance improving sustainable chemicals”
Intern Research Posters: