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RaMP: Post-Baccalaureate Training Program in Biomolecular Structure Prediction and Design

RaMP: Post-Baccalaureate Training Program in Biomolecular Structure Prediction and Design

The Rosetta Commons Research and Mentoring for Postbaccalureates (RaMP) is a one-year fellowship program intended for students from groups underrepresented in STEM, first generation college students, and students at under-resourced institutions to gain research experience to succeed in PhD programs.

Trainees in this geographically distributed RaMP program participate in research using the Rosetta Commons software. The Rosetta Commons software library includes physics-based and deep learning algorithms for biomolecular modeling and design. 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.

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The RaMP Provides:

  • Rosetta Code School: where trainees will learn the inner details of the Rosetta Python code and community coding environment, so you are fully prepared to research using the software.
  • Research experience: Trainees 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.
  • Participation: in the Winter Rosetta Conference, where you will connect with Rosetta developers from around the world.
  • Compensation: Salary, health benefits, and funding for conference travel are included.
  • Preparation: for graduate school applications and interviews.
  • Community: Trainees come together each month for ‘Journal Club’ events to present and discuss their research with peers and faculty mentors. These meetings include professional development mini-lessons on topics like the NSF-GRFP, graduate school applications, research posters, and more.
  • Project meetings: Trainees gain confidence by organizing, preparing for, and convening monthly project meetings with the program's PI, Dr. Jeff Gray and Co-PI Dr. Matthew O'Meara. Scholars benefit both scientifically and professionally by building strong working relationships with multiple faculty members who are experts in their field of interest.
  • Specialized mentoring: Mentors will participate in a four-part Culturally Responsive Mentoring Workshop series that will guide them in increasing their capacity for and self-awareness of culturally responsive mentoring best practices. This series will be facilitated by Steven ThomasThe mentor, co-mentor, and participant will form a “mentorship triad,” a tight interpersonal structure functioning to enhance a student’s potential


  • Individuals from groups underrepresented in STEM, first generation college students, and students at under-resourced institutions. 
  • U.S. citizens, U.S. nationals, or permanent residents of the United States are eligible. 
  • Participants must have a baccalaureate college degree before participating in the program (applicants must apply to the program before or within four years of graduation, with extensions allowed for family, medical leave, or military service). Individuals currently enrolled or accepted into a graduate program are not 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 5000 characters) including:
    • Why this program interests you
    • Brief summary of research and computing experience
    • Research career goals
  • Two letters of recommendation (complete the reference forms in the application with contact information)
  • Select top three labs and projects of interest from the list below.
  • Deadline for receipt of applications is February 19, 2024.
  • Deadline for receipt of recommendation letters is February 22, 2024.
  • Program contact: Camille Mathis,



Glasgow Lab @ Columbia University in New York, NY

"Designing proteins with customized residue-level protection factors."

The problem of designing proteins with specific structures has been partially solved with the advances the physics or/and deep learning-based methods, but it is still challenging to design proteins with customized conformational ensemble features. Protection factors (PFs, equivalent to the folding free energy ΔG) represent all the features of a protein’s conformational ensemble. In this project, we will forward-design proteins with customized protection factors by combining biophysical models with ML-based structure prediction and design. We will use molecular dynamics (MD) simulations to test the designed proteins. As a model, we will use Trp-cage: a 20-residue fast-folding miniprotein. Our goal is to control how Trp-cage folds into its native structure. First, we will write a method to extract PFs from a given protein structure using an established prediction model, and we will implement a custom loss function for AFdesign based on this method. In the second stage, we will design new proteins with defined structures and diverse PF patterns using the method developed in the first stage. In the third stage, we will explore the free energy landscapes of the designed proteins using MD simulations, experimentally test selected designs, and use the results to make improvements to the model.


Khare Lab @ Rutgers University in New Brunswick, NJ

"Towards targeted protein editing"

The ability to precisely edit genomes has transformed modern biotechnology and medicine; however, technology for the in-situ precision editing of proteins has been lacking. Targeting functionally-important but structurally-disordered segments would be particularly useful but it is challenging. We are developing new and applying existing machine learning algorithms to the design of editor proteins that contain a binding domain and an enzymatic domain to ensure the selective recognition and modification, respectively, of a chosen target protein in the presence of thousands other proteins present in the cell. Iterative Design-Build-Test-Learn cycles involve a close interplay of biochemical experiments and model development to obtain robust, generalizable and interpretable ML models that predictively enable selective protein editing. You will learn how to use and develop ML methods in protein design and engineering, curate, generate and analyze large sequence datasets, and work closely with experimentalists.


King Lab @ University of Washington in Seattle, WA

"mRNA-launched nanoparticle vaccines"

Over the last several years, we have established computationally designed protein nanoparticles as robust and versatile scaffolds for making more potent vaccines through multivalent antigen display. In 2022, the first such vaccine was licensed for use in humans, which clinically de-risked the platform. We have recently designed sets of new homomeric self-assembling proteins displaying viral glycoprotein antigens that can be "launched" from a standard mRNA-LNP vaccine. That is, instead of the mRNA vaccine encoding membrane-anchored antigen, it encodes a secreted nanoparticle immunogen. We have found that these mRNA-launched nanoparticle vaccines elicit neutralizing antibody responses in mice that are up to 10 times more potent than membrane-anchored antigen. In this project, the trainee will use powerful machine learning (ML)-based methods for protein design such as RFdiffusion and ProteinMPNN to design additional mRNA-launched nanoparticle vaccines in which the spacing and orientation of target antigens is precisely and systematically varied so that we can optimize these proteins to obtain the best possible antibody response.


Kuhlman Lab @ University of North Carolina, Chapel Hill in Chapel Hill, NC

"Applying machine learning to protein design"

Advances in machine learning are revolutionizing the fields of protein structure prediction and design. The participant will help create and test protocols that make use of Rosetta in combination with machine learning to design new protein structures and complexes.  


Lindert Lab @ The Ohio State University in Columbus, OH

"Machine-learning based structure modeling using mass spec data"

Knowledge of protein structure is paramount to our 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 deep-learning based modeling techniques, that enable prediction of protein complex structure from the mass spec data.


O'Meara Lab @ University of Michigan, Ann Arbor in Ann Arbor, MI

“Bayesian methods for modeling function in structure to function studies”

 The O’Meara Lab develops methods for simulation-based inference. The aim of this project is to develop Bayesian statistical methods to better capture complex experimental designs typical in biochemistry and biophysics. The participant will develop Bayesian workflows in R and Stan for foundational pharmacology models including Hill-equation dose-response, enzyme kinetic models, etc., and apply them to benchmark Rosetta simulations and computational biophysics modeling more broadly. 


Siegel Lab @ University of California Davis in Davis, CA

"Computational enzyme design and modeling"

The Siegel Lab engineers enzymes to address human-centered challenges in health, food, and environmental systems; the group is primarily focused on work with direct applications in these spaces. The postbac will use Rosetta to model and design enzymes that catalyze novel biochemical reactions in projects that align with the mission of the lab. Using insights from in-silico experiments, he/she will characterize and evaluate their designs in the wet-lab, learning both computational and benchwork skills. 


Smith Lab @ Wesleyan University in Middletown, CT

" Reshaping protein energy landscapes to optimize dynamics/function"

The Smith lab is currently developing and applying methods for combining Rosetta design calculations with molecular dynamics simulations to reshape the energy landscapes of proteins. We are applying these techniques to several unique systems: 1) mini Flurorescence Activating Proteins (mFAPs) are beta barrels de novo designed in Rosetta to bind and activate the fluorescence of small molecule chromophores. Our lab developed an algorithm that can for the first time predict how mutations to the protein affect chromophore dynamics and thus brightness. We are reshaping the energy landscape of these proteins to favor macrostates with high protein function. 2) Rosetta-designed miniprotein binders potentailly represent a new class of protein drugs that can be readily created using the structure of the target protein alone. However, we have found that model miniproteins exhibit unexpected and undesired conformational heterogeneity and spontaneous postranslational modifications. We are redesigning these proteins to avoid such deleterious outcomes. You will learn to use Rosetta with free energy data analysis techniques, optimize algorithm parameters with existing datasets, and apply these techniques to improve protein function.


Computationally-stabilized-SARS-COV2-RBD-construct Design-of-proteins-by-sewing-to-recomine-sructures  Antibody-structures-presidicted-de-novo-wuth-subangstrom-loop






Award Number: 2216011