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Computational Structure Prediction and Design of Biomolecular Structures

Scientists in this geographically-distributed post-baccalaureate program have the opportunity to participate in research using and developing the Rosetta Commons software. The Rosetta Commons software suite includes algorithms for computational modeling and design of proteins and other biomolecules. 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.  

This 1 year post-baccalaureate program is aimed at preparing underrepresented minority and/or disadvantaged students to succeed in PhD programs. 




  • One week of Rosetta Code School (June 1 through June 5) where you will learn the inner details of the Rosetta Python code and community coding environment, so you are fully prepared to research using the software.
  • Assignment to a Rosetta lab where you will be mentored by a graduate student and faculty member who will guide and foster your research.
  • Participation in the Summer Rosetta Conference in the gorgeous Cascade Mountains of Washington State (August 10 through August 13) and the Winter Rosetta Conference (location TBD in February 2022), where you will connect with Rosetta developers from around the world.
  • Salary, health benefits, and funding for conference travel are included.
  • Integration into the host institution’s NIH PREP program.



PREP Programs Provides:

  • Research experience: Scholars conduct hypothesis-driven research in their Mentor’s lab, with day-to-day guidance by an experienced PhD student or postdoc. Scholars participate fully in weekly lab meetings, attend weekly research seminars in their department, attend a vibrant PhD program retreat and a national conference of their choice.
  • Community: Scholars come together each month for two-hour ‘Journal Club’ events to present and discuss their research with Peer-Mentors (PhD students, postdocs) and faculty. These meetings include professional development mini-lessons on topics like the NSF-GRFP, graduate school applications, research posters, and more.
  • Project (‘mini-thesis’) meetings: Scholars gain confidence by organizing, preparing for, and convening three one-hour ‘mini-thesis’ meetings with two subject-expert faculty, plus their research mentor and the PREP Director. Scholars benefit both scientifically and professionally by building strong working relationships with multiple faculty members at Johns Hopkins who are experts in their field of interest.
  • Professional training and custom mentoring: Scholars participate in workshops designed to improve their scientific writing skills, and understand ethics in science, and can choose from many other workshops including communication and improvisation. Each Scholar charts an individual development plan with the PREP Director, with custom mentoring both formal (monthly one-hour meetings) and informally as needed.
  • Preparation for GRE or MCAT exam, graduate school applications and interviews.
  • Annual salary plus health, retirement, tuition and other benefits.



  • Individuals from racial and ethnic groups that have been shown by government studies, to be underrepresented in health-related sciences on a national basis.
  • Individuals with disabilities, who are defined as those with a physical or mental impairment that substantially limits one or more major life activities, as described in the Americans with Disabilities Act of 1990, as amended.
  • Individuals from disadvantaged backgrounds
  • U.S.citizens, permanent residents, and U.S. nationals are eligible.
  • Undergraduate major in computer science, engineering, mathematics, chemistry, biology, and/or biophysics
  • 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.




  • Resume
  • Unofficial transcript
  • Personal statement that summarizes why you are an appropriate candidate (up to 2000 characters) including:
    • Why this program interests you
    • Brief summary of research and computing experience
    • Research career goals
  • Two recommendation letters, completed recommendations can be sent to
  • Select top three labs and projects of interest from the list below.
  • Deadline for receipt of applications is February 1, 2021.
  • Deadline for receipt of recommendation letters is February 5, 2021.
  • Program contact: Camille Mathis:



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. The PREP trainee will learn antibody engineering, homology modeling and docking, and machine learning. 

Horowitz Lab @ University of Denver in Denver, CO
"Chaperone Nucleic Acids"
It has long been known that nucleic acids carry the genetic information necessary for life. Nucleic acids also play vital structural, catalytic, and regulatory roles in the cell. Very recently, we discovered that nucleic acids perform an additional unsuspected but crucial task—preventing protein aggregation as molecular chaperones. Molecular chaperones are critical for maintaining the health of the proteome (termed proteostasis), which is of prime importance to human health. Defects in proteostasis are linked to many crippling diseases, including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and ALS. The work in the Horowitz lab is focused on understanding how nucleic acids act as chaperones, and discovering which nucleic acids are important for these functions in the cell, and which can be developed for treating disease, with research spanning biochemistry, genetics, molecular biology, and biophysics.

Lindert lab @ Ohio State University in Columbus, OH
"Structure Modeling using Mass Spec Data"
Knowledge of protein structure is paramount to the understanding of biological function and for developing new therapeutics. Mass spectrometry experiments which provide some structural information, but not enough to unambiguously assign atomic positions have been developed recently. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. We are developing integrative modeling techniques, computational modeling with mass spec data, that enable prediction of protein complex structure from the experimental data.  

Rocklin Lab @ Northwestern University in Chicago, IL
"Applying high-throughput experimental data to guide computational protein design"
Today, most computational protein design tools like Rosetta use the features of natural proteins structures (which amino acids like to be near each other, what types of structures are very common, etc) to guide the design of new proteins. However, for many applications, we want to design proteins with properties far beyond what already exists in nature. To achieve this, we need new sources of data - not just natural protein structures - that can guide design into new territory. Our lab develops new experimental methods to measure properties like folding stability, binding affinity, and dynamics for tens to hundreds of thousands of designed or natural proteins at the same time. We then use these new large datasets to guide protein design proteins. We have a range of different focused on basic science, therapeutic development, and tools for synthetic biology. Each person's project is described on our website ( We will work with an intern or post-bac to find which project in our lab is best for their interests.