About

Srirupa Chakraborty is an Assistant Professor in the Departments of Chemical Engineering and Chemistry and Chemical Biology at Northeastern University. She obtained her PhD in Biophysics from the State University of New York at Buffalo in 2016, after earning a B.S. in Physics from Presidency College, University of Calcutta, and an M.S. in Physics from the Indian Institute of Technology – Guwahati (India). Her research focuses on the interface of biology, chemistry, and physics, utilizing computer-aided structural modeling and simulations of biomolecules. Her work aims to elucidate the conformational dynamics of viral envelope glycoproteins and other densely glycosylated systems, with the goal of designing knowledge-based therapeutic strategies and novel biomaterials. Prior to her current position, she was a visiting scientist at IBM Watson Research Center, where her research on cognitive learning platform design led to two patents. She has also been a postdoctoral researcher at Los Alamos National Laboratory, where she developed novel tools and techniques to bridge in silico results and experimental data through theoretical modeling. Her research has been recognized with awards such as the Wiley Computers in Chemistry Outstanding Research Award by the American Chemical Society in 2020 and an award from the Consortia for HIV/AIDS Vaccine Development in 2019. Currently, her research involves structure-dynamics modeling of complex biosystems, including the development of computational models for biomedical problems, such as mucosal glycopeptide mesh modeling for disease understanding and biomaterial design. She has been awarded the NIH Maximizing Investigator’s Research Award (MIRA) for Early Stage Investigators in 2023.

Research topics

• Biology
• Chemistry
• Computer Science
• Virology
• Biochemistry
• Internal medicine
• Cell biology
• Mathematics
• Computational biology
• Medicine
• Genetics

Selected publications

• Decoding epitope immunodominance in HIV Env using cryoEM and machine learning

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-11

  articleOpen access

  ABSTRACT Viral surface glycoproteins, such as the HIV envelope protein (Env), present numerous antibody (Ab) epitopes, yet immune responses consistently focus on only a subset, a phenomenon known as immunodominance. Although structural studies have provided insights into Env antigenicity, our understanding of the molecular features that govern efficient Ab engagement remains incomplete, thereby limiting the predictive and rational design of vaccines. Here, we characterized the structural determinants of epitope immunodominance in HIV Env by integrating high-resolution cryoEM-based polyclonal epitope mapping (cryoEMPEM) across different clades with quantitative analyses of epitope topology, accessibility, and physicochemical properties. More than 70 new structures were resolved to assemble a library of >100 Env-antibody complexes. These data informed the development of a surface-centric, machine-learning model to predict relative A ntigen S urface I mmunodominance (ASI model). Comparison of ASI-predicted epitope sites with the specificities of Env-induced antibodies showed that the model accurately identifies immunodominant regions and highlights the structural features driving immune bias. Notably, immunogens redesigned based on model predictions successfully redirected Ab responses toward a normally subdominant epitope, demonstrating the potential of strategies coupling targeted assembly of focused structural libraries with machine learning to uncover complex molecular patterns and enable design of more effective vaccine antigens.

  PublisherOA PDFDOI

• Interpretable Antibody–Antigen Structural Interface Prediction via Adaptive Graph Learning and Cyclic Transfer

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-12

  articleOpen accessSenior authorCorresponding

  Experimental structural methods can identify antibody-antigen interfaces with high precision, but they remain time-consuming and resource-intensive, limiting their application across the rapidly expanding space of antibody and antigen sequences. Computational models capable of predicting these interfaces could therefore accelerate antibody discovery and provide insight into the principles governing immune recognition. However, this problem remains challenging due to limited structural datasets, severe class imbalance, and the complex, non-local nature of biomolecular interactions. Here we present VASCIF (Variable-domain Antibody-antigen Structural Complex Interface Finder), a structure-aware framework built on a Masked Graph Attention (MGA) architecture that represents protein complexes as residue graphs and captures long-range structural dependencies through attention-based message passing. The framework is straightforward to implement and enables efficient inference, allowing substantially faster predictions than other existing structure-based approaches. Evaluated on curated structural complexes across multiple benchmark datasets using rigorous cross-validation, VASCIF achieves state-of-the-art performance for residue-level interface prediction. Interpretability analyses reveal that the model recovers biophysically meaningful interaction patterns consistent with known principles of antibody recognition, and redefining interfaces using larger residue distance thresholds (~10 Å) significantly improves predictive performance. Together, VASCIF provides a practical predictive framework and new insights into antibody-antigen molecular recognition.

  PublisherOA PDFDOI

• Unveiling Interaction Signatures Across Viral Pathogens through VASCO: Viral Antigen-Antibody Structural COmplex dataset

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-14

  preprintOpen accessSenior authorCorresponding

  ABSTRACT Viral antigen-antibody (Ag-Ab) interactions shape immune responses, drive pathogen neutralization, and inform vaccine strategies. Understanding their structural basis is crucial for predicting immune recognition, optimizing immunogen design to induce broadly neutralizing antibodies (bnAbs), and developing antiviral therapeutics. However, curated structural benchmarks for viral Ag-Ab interactions remain scarce. To address this, we present VASCO (Viral Antibody-antigen Structural COmplex dataset), a high-resolution, non-redundant collection of ∼1225 viral Ag-Ab complexes sourced from the Protein Data Bank (PDB) and refined via energy minimization. Spanning Coronaviruses, Influenza, Ebola, HIV, and others, VASCO provides a comprehensive structural reference for viral immune recognition. By comparing VASCO against general protein-protein interactions (GPPI), we identify distinct sequence and structural features that define viral Ag-Ab binding. While conventional descriptors show broad similarities across datasets, deeper analyses reveal key sequence-space interactions, secondary structure preferences, and manifold-derived latent features that distinguish viral complexes. These insights highlight the limitations of GPPI-trained predictive models and the need for specialized computational frameworks. VASCO serves as a critical resource for advancing viral immunology, improving predictive modeling, and guiding immunogen design to elicit protective antibody responses. By bridging sequence and structural immunological datasets, VASCO should enable better docking, affinity prediction, and antiviral therapeutic development—key to pandemic preparedness and emerging pathogen response.

  PublisherOA PDFDOI

• BPS2025 - Generating decoy interactions for explainable deep-learning antibody-antigen scoring and prediction

  Biophysical Journal · 2025-02-01

  articleSenior author
  PublisherDOI

• BPS2025 - Glycan-mediated regulation of IgG1 Fc-CD16a interactions: Insights from all-atom simulations

  Biophysical Journal · 2025-02-01

  articleSenior author
  PublisherDOI

• Structural and immunological characterization of the H3 influenza hemagglutinin during antigenic drift

  Nature Communications · 2025-12-11 · 2 citations

  articleOpen access

  The quest for a universal influenza vaccine holds great promise for mitigating the global burden of influenza-related morbidity and mortality. However, challenges persist in identifying conserved epitopes capable of eliciting robust and durable immune responses. In this study, we explore the influence of glycan evolution on H3 hemagglutinin from 1968 to present day and its impacts on protein structure, antigenicity and immunogenicity by using computational, biochemical and biophysical techniques. Structural characterization of HK/68 and Sing/16 by cryo-electron microscopy shows that while HK/68 is resistant to enzymatic deglycosylation, removal of glycans destabilizes the hyperglycosylated head and membrane-proximal region in Sing/16. Furthermore, the appearance of glycans in Sing/16 hemagglutinin head domain shifts the polyclonal immune response upon vaccination to target the esterase and stem. These insights expand our understanding of glycans beyond their role in protein folding and highlight the interplay among glycan integration and immune recognition to design a universal influenza vaccine.

  PublisherOA PDFDOI

• BPS2025 - Modeling the effects of mutational and glycoform perturbations in HIV-1 glycan shield dynamics

  Biophysical Journal · 2025-02-01

  articleSenior author
  PublisherDOI

• BPS2025 - A novel approach to glycan ensemble design through combinatorial optimization and structural characterization in silico

  Biophysical Journal · 2025-02-01

  articleSenior author
  PublisherDOI

• Spatial charge-hydrophobicity configuration modulates cationic peptide transport in cartilage

  Biophysical Journal · 2025-09-18 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• BPS2025 - Scaling the mucosal hydrogel: A tractable atomistic model to study MUC2 structure-dynamics

  Biophysical Journal · 2025-02-01

  articleSenior author
  PublisherDOI

Frequent coauthors

• S. Gnanakaran

  Los Alamos National Laboratory

  24 shared

• Bette Korber

  New Mexico Consortium

  16 shared

• Andrew B. Ward

  Scripps Research Institute

  15 shared

• Zachary T. Berndsen

  15 shared

• Rachael A. Mansbach

  Concordia University

  12 shared

• Kien Nguyen

  Los Alamos National Laboratory

  11 shared

• Wenjun Zheng

  Shandong First Medical University

  7 shared

• Anthony Auerbach

  University at Buffalo, State University of New York

  6 shared

Labs

• SimBioSys LabPI

Education

• PhD, Biophysics

  University at Buffalo

  2016

• Masters, Physics

  Indian Institute of Technology Guwahati

  2009

• B.Sc., Physics

  Presidency University Kolkata

  2007

Awards & honors

• Wiley Computers in Chemistry Outstanding Research Award by t…
• award by the Consortia for HIV/AIDS Vaccine Development (CHA…
• National Institutes of Health Maximizing Investigator’s Rese…
• travel award to attend the Biophysical Society 2025 Annual M…