You are here

Rosetta Commons Summer Internships

Rosetta Commons Summer Internships

The Rosetta Commons is pleased to be offering undergraduate student internships for the summer of 2018.

The program:

  • One week of Rosetta Code School (June 4 through June 8) where you will learn the 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 6 through August 10. 
  • This program is supported by NSF. Interns will receive housing, paid travel expenses, and a $5,500 stipend. 

 

Eligibility:

  • 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 2018. 
  • 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).

 

To apply:

       https://app.lc.applyyourself.com/AYApplicantLogin/fl_ApplicantLogin.asp?id=jhu-si

  • Complete and submit the application at the icon above. 

  • Include the following in the application:
    • Resume
    • Transcript
    • 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, 2018.
  • Deadline for receipt of recommendation letters is February 6, 2018. 
  • For questions, please contact Camille Mathis at cmat...@jhu.edu.

 

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.

 

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.

 

 

Dunbrack Lab @ Fox Chase Cancer Center in Philadelphia, PA

“Structural bioinformatics of protein kinases”

Kinases are key proteins in almost all cellular processes. They are often dysregulated in cancer and are a major target in cancer drug discovery. Kinases adopt many conformations, both active and inactive, that allow them to perform their function in a regulated manner. We have completed the development of a new classification system for these conformations, but there are many issues left to investigate including 1) the role of N and C-terminal tails on the regulation of kinases; 2) the shape, volume, and biophysical properties in each conformational state of each kinase; 3) how the dynamics of the kinase domain are affected by mutation and how these changes affect kinase function and regulation. A project within any of these areas would be suitable for a summer research project utilizing Rosetta.

 

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.

 

Furman Lab @ Hebrew University in Jerusalum, Israel

“How do post-translational modifications change the communication of a protein with its partners?”

In this project, we will use the solved structure of a peptide motif-binding domain (e.g. a post-translational modification enzyme bound to a substrate) to generate a structure-based protocol that can model known and subsequently detect new substrates. Identification of key interactions in the complex structure will provide us insights into the mechanism of the enzyme, as well as on its specificity. Analysis of the new substrate proteins will help us characterize the functional impact.

 

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 Angelos, 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

“Design of enzymes for biodegradation and 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.

ADEPT.png

 

Lindert Lab @ Ohio State University in Columbus, OH

“Protein Structure Prediction from Mass Spectrometry”

Over the last two decades, mass spectrometry (MS) has emerged as a key approach for addressing challenging problems in structural biochemistry. MS techniques have been demonstrated as an efficient analytical tool to yield three-dimensional structural information on proteins and their molecular complexes. For this, MS is usually used in conjunction with covalent labeling techniques. While the labeling approaches frequently yield important structural information, these MS labeling techniques are not generally sufficient to allow unambiguous determination of protein structure. The Lindert lab is developing tools in Rosetta that can use the covalent labeling MS data to guide protein structure prediction. This project will be primarily focused on developing score functions that can assess the agreement of protein models with the experimental MS data.

 

Meiler @ 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.

 

Mills Lab @ Arizona State University in Tempe, Arizona

“Design of fluorescent protein sensors”

The ability to study biological systems in great detail was revolutionized by the development of fluorescent proteins like GFP. Despite the wealth of fluorescence-based assays available to the modern biochemist, new tools are still needed. Research in the Mills lab seeks to develop new fluorescent proteins with the ability to sense dynamic biological processes including protein-protein, protein-ligand, and protein-metal interactions. To facilitate this, we use Rosetta to design atomically precise protein environments that respond to the aforementioned signals in predictable ways. Our research program will therefore provide an opportunity to learn about Rosetta and the computational protein design process, but also to produce the designed proteins and experimentally characterize them in the laboratory.

 

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).

 

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.

The Intern Experience:

 

Intern Research Posters:

           

 

 

 

 

 

 

Award Number: 1659649

 

AttachmentSize
tinyStanleyposter.jpg731.48 KB