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 3 through June 7) where you will learnthe inner details of the Rosetta C++ 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 5 through August 9.
- This program is supported by NSF. Interns will receive housing, paid travel expenses, and a $5,500 stipend.
- 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 2019.
- 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).
Complete and submit the application at the icon above.
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, 2019.
- Deadline for receipt of recommendation letters is February 5, 2019.
- For questions, please contact Camille Mathis at email@example.com.
Available projects and locations:
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.
Bystroff Lab @ Rensselaer Polytechnic Institute in Troy, New York
“Designing a fluorescent biosensor for DNA crossovers”
DNA duplexes can recognize each other by forming a tetraplex called a “paranemic crossover”, or PX for short. We have found that a Holliday junction cutting enzyme also cuts PX. We will use the enzyme as a template for the design of a polyvalent, fluorescent PX biosensor, using InterativeROSETTA. In the process, you will help us add functionality to this interactive tool.
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 be primarily focused on development of game-related aspects of the project and understanding and improving the player experience. C++ or other coding skills, game design/development/UX experience or interest will be useful. Specific potential projects include virtual reality and procedural content generation.
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
“Glycoengineering for immunotherapy and biofuels”
Glycosylation is a key marker of cancer cells, and biofuels are comprised of cellulosic material. Both require modeling of the structure and dynamics of the underlying sugars comprising the molecules. This project will apply new glycoprotein modeling tools for these critical applications.
Jiang Lab @ University of California, Los Angeles, in Los Angeles, California
"A hybrid, integrative approach to build high-resolution structural models of amyloid protein aggregates in neurodegenerative diseases"
Our limited knowledge of the atomic-resolution details of amyloid structures impedes the development of therapeutics to treat amyloid diseases, including Alzheimer’s disease. The goal is to enhance our structural knowledge through experimentally informed computational modeling. We plan to incorporate available structural data to guide and constrain the model construction of atomic structures of both amyloid protein fibrils and oligomers. These detailed models will make it possible to connect sequence data, including familial disease mutations, to function data, such as interaction partners and toxicity, and will thereby offer a better understanding of the role of amyloid aggregation in disease development. This structure-based analyses using Rosetta symmetry modelling will bridge the current gap between genetic discoveries from genomic research and molecular mechanisms leading to disease, revealing novel targets for therapeutic intervention. We will apply a hybrid, integrative approach to make testable structural models to integrate, explain, and guide experiments for α-synuclein fibrils in Parkinson's disease, Aβ fibrils and oligomers of Alzheimer’s disease, Tau fibrils associated with various neurodegenerative disorders and prion protein fibrils in prion transmission.
Jiang lab focuses on computational structural biology and drug design for Alzheimer's, Parkinson's, Lou Gehrig's disease and other degenerative disorders. Current research is driven by two key questions: How do unfolded or misfolded proteins self-associate into abnormal aggregates? How do these aggregates propagate and lead to disease? Ongoing research is to develop new therapeutic approach for neurodegenerative and other brain diseases, which includes: 1) design protein inhibitor that blocks the prion-like transmission of protein aggregates in neurodegenerative diseases; 2) design and test new protein that crosses the blood-brain barrier via carrier-mediated transport. The findings of the research will identify new drug targets, develop new therapeutics and design new therapeutic compounds or peptides for the treatment of neurodegenerative disorders.
Khare Lab @ Rutgers University in Piscataway, NJ
"Computational design of stimulus-responsive enzymes for therapeutics"
This project is aimed at developing “smart” enzymes that are controllably sensitive to stimuli and can become activated upon exposure to either an external stimulus such as light or an environmental stimulus such as a tumor environment-specific molecule. These designed enzymes, when activated, would then produce a toxic drug in a spatio-temporally controlled manner to better target chemotherapy and overcome its major limitations (e.g., side-effects). Python or other programming experience would be helpful.
Kortemme Lab @ University of California, San Francisco, in San Francisco, California
“Phenotypic prediction and design of allosteric small molecule sensors”
Allosteric proteins can switch their function in response to binding small molecules, which allows cells to sense signals in their environment. However, the small molecule binding pocket is often far away from the active site in the protein, and the signal transduction over this distance is not well understood. In this project, we will computationally model the effects of mutagenesis in an allosteric protein, compare these data to experimentally determined phenotypes, and test our computational hypotheses by experimental rewiring of an allosteric response.
Khulman Lab @ University of North Carolina, Chapel Hill, in Chapel Hill North Carolina
“Computer-based Design of Dengue Virus Vaccine Antigens”
Each year, ~390 million people are infected with dengue virus (DENV), presenting an urgent need for effective DENV vaccines. Despite the poor performance of DENV soluble recombinant envelope protein (sRecE) vaccines, a class of DENV broadly neutralizing Abs have been discovered to target epitopes present only on the DENV envelope protein dimer, suggesting that developing subunit vaccines based on sRecE dimers (sRecE) is a promising vaccine strategy. We seek to overcome the poor sRecE stability hindering its use as a subunit vaccine by using computational protein design to engineer stable DENV sRecE dimer vaccine antigens capable of eliciting broadly neutralizing DENV Abs.
Meiler Lab @ Vanderbilt University in Nashville, TN
“Rosetta Computer Aided Drug Design”
The Meiler laboratory is working on a new algorithm that allows the design of a small molecule to fit an protein binding pocket. We apply these algorithms to design and optimize small molecules as potential treatment for schizophrenia, Alzheimer’s disease, or Parkinson’s disease.
Siegel Lab @ University of California, Davis in Davis, California
Our long term goal is to develop a suite of tools that enable on-demand access to any physically possible biochemical transformation of interest. In order to achieve this goal, we must be able to rapidly and reliably identify enzymes with desired functions. The REU project in the Siegel lab will be focused on either developing methods to accurately model enzyme families and predict specificity profiles (dry lab focused). Alternatively, students can participate in our program of developing large data sets of engineered enzymes characterized in a self-consistent manner to determine expression, thermal stability, substrate recognition, and catalysis in order to develop novel energy functions that are predictive of enzyme properties (wet lab focused).
Sgourakis Lab @ University of California, Santa Cruz in Santa Cruz, California
“A structure-based pipeline for identification and ranking of tumor neoantigens”
We study proteins of the immune system that play important roles in human health. To perform their role, these proteins must interact with other biomolecules, such as other protein receptors and smaller peptides. Elucidating these molecular interactions at high resolution will help establish the biochemical basis of immune recognition. Besides obtaining an unprecedented basic science understanding of fundamental biological processes, the knowledge gained from our detailed molecular description will enable us to develop new therapeutic molecules for emerging immunotherapy applications to combat viral infections, autoimmune diseases and cancer. To achieve these goals, we employ a variety of state-of-the-art biophysical techniques, including X-ray crystallography, fluorescence spectroscopy, solution NMR, and computational modeling, followed upon by experiments using cell lines.
Wang Lab @ Peking University Beijing, China
“Computational Redesign of UDP-N-acetylhexosamine pyrophosphorylase for bioorthogonal labeling of protein glycosylation’
Description: Bioorthogonal labeling of glycan is a powerful strategy to study glycobiology, including O-GlcNAcylation. The incorporation of bioorthogonal reporters through GlcNAc salvage pathway is mediated by several enzymes, including UDP-N-acetylhexosamine pyrophosphorylase(AGX1). However, the tolerance of AGX1 on unnatural GlcNAc analogues is weak, leading to the low incorporation efficiency and hampering the application. Therefore, Rosetta will be used for rational design of new version AGX1 that can better tolerate GlcNAc analogues. The redesigned enzyme with switched specificity will enable bioorthogonal labeling of protein glycosylation more efficiently and can be a powerful tool for the field of glycobiology.
Whitehead Lab @ University of Colorado in Boulder, CO
“Predicting antibody evolutionary routes for universal Flu vaccine”
Vaccines work in large part by stimulating antibodies that can neutralize infection. These antibodies go through a selection process called affinity maturation that is akin to evolution. For many viruses that pose major threats to public health (e.g. Influenza, Dengue) antibodies that can effectively neutralize different subtypes of the virus are only weakly elicited, which allows for repeated annual viral infections. This project will focus on modeling antibody-virus interactions to predict potential evolutionary routes for antibodies that develop in response to potential vaccines for Influenza.
Intern Research Posters: