I am a research fellow in Eric Fischer's group at Dana-Farber Cancer Institute and Harvard Medical School. The overarching goal of my research is to predict and control biomolecular assembly with a view to develop novel therapeutics and molecular biology tools.

As a graduate student in Jeffrey Gray’s group at Johns Hopkins Univeristy, I created computational tools in the Rosetta Molecular Modeling Suite to predict the structures of protein complexes. I am now using these docking and design tools to create artificial dimerization systems that can be triggered with compound treatment. Through targeted binding to E3 ubiquitin ligases, I am creating degradation systems that can be used to specifically and reversibly modulate protein levels in cells.

My hometown is New Delhi, India.


  • Targeted Protein Degradation
  • Small Molecule-Mediated Protein Dimerization
  • Biomolecular Docking
  • Computational Protein Design


  • Ph.D. in Chemical & Biomolecular Engineering, 2018

    Johns Hopkins University, MD, USA

  • B.Tech. in Biological Sciences & Bioengineering, 2012

    Indian Institute of Technology Kanpur, UP, India

Targeted Protein Degradation

My current research focuses on repurposing the waste disposal system of the cell, the ubiquitin–proteasome system to selectively degrade target proteins. The three aspects of the area that I am focusing on are:

  • Designing proteins to bind E3 ubiquitin ligases in a compound-dependent manner. These proteins have applications in engineered-cell therapies and molecular biology assays. I was awarded the CRI Irvington Postdoctoral Fellowship to pursue this strategy to improve the safety and efficacy of cancer immunotherapy.
  • Modeling and designing heterobifunctional molecules called proteolysis-targeting chimeras (PROTACs) to selectively degrade target proteins. These PROTACs target proteins previously considered undruggable.
  • Modeling small molecule-mediated dimerization pathways that can accelarate degradation.

Modeling of Biomolecular Complexes

As a graduate student in Jeffrey Gray’s group, I addressed one of the principal challenges in computational protein docking: predicting binding-induced conformational changes.

Protein flexibility can be incorporated into modeling by simulating conformational selection from a pre-generated set of backbone conformations. I devised a new algorithm called Adaptive Conformer Sampling (ACS) to efficiently sample hundreds of protein backbone conformations, which when combined with a new scoring scheme developed in the lab called Motif Dock Score (MDS), allowed us to accurately discriminate native-like binding states from others. Combining ACS and MDS in RosettaDock, we were able to obtain—for the first time—successful docking of nearly 50% of flexible protein complexes with backbone conformational change of 1.5–2.2 Å RMSD. Read article.

While the above protocol deals with hetero-dimers, a majority of proteins are thought to exist as homo- oligomers. Rosetta’s symmetric docking protocol performed poorly on higher order complexes. While investigating the source of this error, I found that the binding energy landscapes of homomeric proteins differed greatly from those of heterodimeric proteins. I used the insight to simulate induced fit in tightly-packed interfaces. Combined with the discriminatory ability of MDS, the new protocol, Rosetta SymDock2 outperformed all existing algorithms by accurately predicting 61% of cyclic complexes and 42% of dihedral complexes in a diverse benchmark of various point symmetries. Read article.

To test the real-world performance of our algorithms, from 2015 to 2018, I led the lab in a community-wide blind prediction challenge called Critical Assessment of PRedicted Interactions (CAPRI) rounds 37–45. We were able to successfully model 14 out of 23 heteromer, homomer, and oligosaccharide–protein complexes with previously undisclosed experimental structures. Ex post facto analysis revealed that the new algorithms that we had developed during this cycle would have further improved our performance. Read article.

The versatility of the docking methods allowed us to predict energetically plausible models of the CoREST complex with dual inhibitor of HDAC1/2 and LSD1 developed by Philip Cole’s lab. Read article.

Recent Publications

Key: * these authors contributed equally; † co-corresponding authors

Quickly discover relevant content by filtering publications.

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational …

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades …

Targeting the CoREST complex with dual histone deacetylase and demethylase inhibitors

Here we report corin, a synthetic hybrid agent derived from the class I HDAC inhibitor (entinostat) and an LSD1 inhibitor …



Organizer & Instructor

PyRosetta Code School

2019 – 2019 Johns Hopkins University, MD, USA
Designed, organized, and co-taught a week-long workshop to train scientists from experimental backgrounds or with limited coding experience to write computational protocols in Rosetta. Participants had hands-on programming experience to write custom PyRosetta protocols and use a wide range of Rosetta objects.


Protein Misfolding Diseases: A Molecular Perspective [AS.020.231]

2015 – 2015 Johns Hopkins University, MD, USA
Conceptualized and co-taught a one credit undergraduate course on molecular mechanisms of Alzheimer’s, Huntington’s and prion diseases. Students were encouraged to read, discuss and critique recent scientific literature in the field, and were evaluated based on it.


Preparing Furutre Faculty Teaching Academy

2014 – 2015 Johns Hopkins University, MD, USA
Participated in a professional development program to learn pedagogical theory and teach practice modules with feedback from the instructors. The program was designed to introduce course design, pedagogical models and methods, and develop evaluation skills.

Teaching Assistant

Computational Protein Structure Prediction and Design [EN.545.614]

2014 – 2014 Johns Hopkins University, MD, USA
Instructor: Dr. Jeffrey J. Gray
Assisted the students during weekly lab sessions. Designed some exam questions and graded assignments and examinations.

Teaching Assistant

Introduction to Chemical and Biological Process Engineering [EN.540.202]

2013 – 2013 Johns Hopkins University, MD, USA
Instructor: Dr. Lise Dahuron
Conducted peer-led recitations sections, taught as a substitute instructor and graded examinations.



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