About

Stephen K. Burley is a University Professor and Henry Rutgers Chair at Rutgers University, with a research focus on structural biology, drug discovery, clinical medicine, and oncology. He is the Director of the Center for Integrative Proteomics Research (CIPR), the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), and the founding director of the Institute for Quantitative Biomedicine at Rutgers (iQB@R). His educational background includes a B.S. from the University of Western Ontario, a D. Phil. from the University of Oxford, and an M.D. from Harvard Medical School. Dr. Burley's work involves advancing understanding in structural biology and applying this knowledge to biomedical research and drug development, contributing significantly to the fields of biochemistry and molecular biology.

Research topics

• Computational biology
• Computer Science
• Biology
• Virology
• Medicine
• Computer Security
• Bioinformatics
• Artificial Intelligence
• Genetics
• Immunology
• Data science
• Biochemistry

Selected publications

• The NMR Exchange Format (NEF): Specification and Applications

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-24

  articleOpen access

  The NMR Exchange Format (NEF) is a community-driven standard for representing NMR experimental data in a consistent, interoperable, and machine-readable form. Built on the STAR syntax, NEF provides a structured framework for storing and exchanging chemical shifts, peak lists, various types of structural restraints, and related metadata, thus allowing for data exchange across software platforms. By enabling direct, lossless transfer of information, NEF simplifies multi-software workflows, improves reproducibility, and supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles. We describe the NEF specification, its current implementation across commonly used NMR software packages, and its application in areas including biomolecular structure determination, metabolomics, and ligand screening. Testing demonstrates that NEF can be used to exchange complete datasets between programs without loss of information or functionality. We also outline recent developments and future directions, such as inclusion of NMR relaxation data and support for non-standard residue topologies. NEFs growing adoption highlights its potential as a unifying standard for NMR data, enabling more efficient, transparent and collaborative research.

  PublisherDOI

• PDX1 phosphorylation at S61 by mTORC1 links nutrient signaling to β cell function and metabolic disease

  Cell Reports · 2026-01-01 · 25 citations

  articleOpen access

  PDX1 is a key transcription factor regulating insulin expression in response to glucose. Our previous work showed that PDX1 is also stimulated by amino acids (aa). Here, we demonstrate that PDX1 broadly mediates aa-regulated transcriptional programs in β cells, especially those controlling β cell proliferation and function. Mechanistically, mTORC1 phosphorylates PDX1 at serine 61 (S61), enhancing its protein stability and transcriptional activity. A certain monogenic diabetes mutation disrupts this phosphorylation and impairs PDX1 function. To investigate its physiological role, we generated mice carrying S61A and S61E mutations, mimicking unphosphorylated and phosphorylated states. S61 phosphorylation promoted insulin expression and β cell proliferation, leading to Western diet-induced hyperinsulinemia, obesity, and hepatic steatosis. These findings reveal the central role of aa-mTORC1-PDX1 signaling in coordinating β cell proliferation and function under both physiological and pathological conditions.

  PublisherDOI

• Multi-scale structural similarity embedding search across entire proteomes

  Bioinformatics · 2026-01-30 · 1 citations

  articleOpen access

  MOTIVATION: The rapid expansion of three-dimensional (3D) biomolecular structure information, driven by breakthroughs in artificial intelligence/deep learning (AI/DL)-based structure predictions, has created an urgent need for scalable and efficient structure similarity search methods. Traditional alignment-based approaches, such as structural superposition tools, are computationally expensive and challenging to scale with the vast number of available macromolecular structures. RESULTS: Herein, we present a scalable structure similarity search strategy designed to navigate extensive repositories of experimentally determined structures and computed structure models predicted using AI/DL methods. Our approach leverages protein language models and a deep neural network architecture to transform 3D structures into fixed-length vectors, enabling efficient large-scale comparisons. Although trained to predict TM-scores between single-domain structures, our model generalizes beyond the domain level, accurately identifying 3D similarity for full-length polypeptide chains and multimeric assemblies. By integrating vector databases, our method facilitates efficient large-scale structure retrieval, addressing the growing challenges posed by the expanding volume of 3D biostructure information. AVAILABILITY AND IMPLEMENTATION: Source code available at https://github.com/bioinsilico/rcsb-embedding-search. Source code DOI: https://doi.org/10.6084/m9.figshare.30546698.v1. Benchmark datasets DOI: https://doi.org/10.6084/m9.figshare.30546650.v1. Web server prototype available at: http://embedding-search.rcsb.org/.

  PublisherDOI

• OSTM1 is a ubiquitin E3 ligase that suppresses B-cell malignancy by activating the cAMP/PKA/CREB pathway

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-26

  article

  Abstract Osteoclastogenesis-associated transmembrane protein 1 (OSTM1) is a membrane-integral glycosylated protein known for regulating lysosomal homeostasis, with loss-of-function mutations causing autosomal recessive osteopetrosis. Through a whole-genome CRISPR/Cas9 screen, we identified OSTM1 as a critical tumor suppressor in B-cell malignancies. In humans, OSTM1 is frequently deleted or downregulated across a wide range of B-cell malignancies. In mice, B-cell-specific monoallelic or biallelic Ostm1 ablation cooperates with Cdkn2a loss to drive lymphomagenesis with near 100% penetrance. Mechanistically, we reveal that a cytosolic, non-glycosylated fraction of OSTM1 functions as an E3 ligase that targets phosphodiesterase 3B (PDE3B) for proteasomal degradation. Because PDE3B catalyzes the conversion of cAMP to AMP and thereby negatively regulating the cAMP-dependent PKA/CREB/CREBBP tumor suppressive pathway, the loss of OSTM1 leads to PDE3B stabilization and enhanced cell transformation. Our findings establish OSTM1 as a pivotal E3 ligase that prevents B-cell lymphomagenesis through the regulation of the cAMP/PKA/CREB pathway.

  PublisherDOI

• <scp>MolViewStories</scp> : Interactive molecular storytelling

  Protein Science · 2026-03-19

  articleOpen access

  Effectively communicating knowledge related to molecular structures and their associated data remains a challenge, as traditional static figures limit interactivity and professional visualization tools often require substantial expertise. MolViewStories addresses these limitations by providing an open-source, web-based platform for creating and sharing interactive, narrative-driven molecular visualizations. The platform uses the MolViewSpec standard for reproducible scene specification, extended to support animations, interactive descriptions, and synchronized audio commentary. Visualization is powered by the Mol* Viewer, which leverages Web Graphics Library (WebGL) for efficient 3D rendering and WebXR for immersive virtual and augmented reality experiences. Users can construct molecular narratives through an intuitive graphical interface or a command-line workflow, enabling both exploratory and automated use. Completed stories can be shared online, exported locally, or distributed as self-contained packages that remain functional indefinitely. Each story is assigned a persistent uniform resource locator (URL) and can be modified or reused as a template, promoting collaboration and community-driven content creation. We demonstrate the capabilities of MolViewStories through a diverse set of narratives illustrating its broad applicability to research communication, education, and public outreach. Together, these examples highlight how interactive, web-based storytelling can make molecular data more accessible, reproducible, and engaging. MolViewStories is freely available at https://molstar.org/mol-view-stories with open-source code accessible at https://github.com/molstar/mol-view-stories.

  PublisherDOI

• RCSB PDB AI Help Desk

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-13

  otherOpen accessSenior author

  Retrieval-Augmented Generation (RAG) chatbot built on LangChain with a PostgreSQL + pgvector vector store and GPT-4.1-mini that answers questions about protein-structure deposition, validation, biocuration, and wwPDB policies. Uses pymupdf4llm for Markdown-preserving PDF extraction, two-stage document chunking, Maximal Marginal Relevance retrieval, a topical guardrail, and a dual-LLM architecture with separate question-condensing and response-generation models. Deployed at https://rcsb-deposit-help.rcsb.org.

  PublisherDOI

• RCSB PDB AI Help Desk

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-13

  otherOpen accessSenior author

  Retrieval-Augmented Generation (RAG) chatbot built on LangChain with a PostgreSQL + pgvector vector store and GPT-4.1-mini that answers questions about protein-structure deposition, validation, biocuration, and wwPDB policies. Uses pymupdf4llm for Markdown-preserving PDF extraction, two-stage document chunking, Maximal Marginal Relevance retrieval, a topical guardrail, and a dual-LLM architecture with separate question-condensing and response-generation models. Deployed at https://rcsb-deposit-help.rcsb.org.

  PublisherDOI

• ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models

  Journal of Molecular Biology · 2026-01-01

  articleOpen access

  The recent development of highly accurate protein structure prediction tools has led to a rapid expansion in the scope of computational structural biology, enabling a much wider range of modelling studies than ever before. These new in silico opportunities help life science researchers understand how proteins interact with their environment and support design of new molecules with desired properties. Ultimately, they have broad applications, e.g. in medicine, drug discovery or engineering. To ensure reproducibility and to facilitate data exchange and reuse, predicted structures or computed structure models can be stored using ModelCIF, a rich data representation designed to include the atomic coordinates/metadata. The previously published version of ModelCIF (1.4.4; 2022-12-21) mainly covered protein structure predictions generated by homology and ab initio modelling. In this work, we present an extension of the ModelCIF (https://github.com/ihmwg/ModelCIF) data standard and its associated tools. This extension supports important new use cases, including modelling protein-ligand and protein-protein interactions, sampling multiple conformational states and designing proteins de novo. We define guidelines for storage and validation of modelling results for those use cases by applying new and existing ModelCIF categories to capture protocols, inputs and outputs. Additionally, we outline updates to the software tools and resources that implement these new standards and provide functionality for model generation, validation, archiving, and visualisation. By enabling consistent metadata capture across different modelling workflows, this framework aims to support the FAIR dissemination of computational models, thereby promoting reproducibility and reusability in downstream applications.

  PublisherDOI

• Abstract 617: OSTM1 is a ubiquitin E3 ligase that suppresses B-cell malignancy by activating the cAMP/PKA pathway.

  Cancer Research · 2026-04-03

  article

  Abstract Osteoclastogenesis-associated transmembrane protein 1 (OSTM1) is a membrane-integral glycosylated protein that regulates lysosomal homeostasis and osteoclast maturation. Its loss-of-function mutations cause autosomal recessive osteopetrosis (ARO). In addition, OSTM1 was described as a putative ubiquitin E3 ligase yet with ill-defined substrates and biological functions. Using a whole-genome CRISPR/Cas9 screening in the interleukin-3 (IL3)-dependent Ba/F3 murine pro-B cell line, we identified OSTM1 whose silencing confers IL3-independent growth and in vivo transformation of Ba/F3 cells. In humans, OSTM1 is frequently deleted or downregulated across a wide range of B cell malignancies. In mice, B cell-specific monoallelic and biallelic ablations of Ostm1 cooperates with Cdkn2a ablation to drive lymphomagenesis with a near 100% penetrance. Mechanistically, a fraction of OSTM1, non-glycosylated and cytosol located, acts as an E3 ligase and interacts with phosphodiesterase 3B (PDE3B) to promote its proteasomal degradation. As PDE3B catalyzes the conversion of cAMP to AMP hence negatively regulates the cAMP-dependent PKA/CREB/CREBBP tumor suppressive pathway, loss of OSTM1 leads to PDE3B stabilization and increased cell growth. Together, our findings uncover OSTM1 as a tumor-suppressive E3 ligase by promoting the proteasomal degradation of PDE3B and activating the cAMP-dependent PKA pathway. Citation Format: Muhammad Usama Tariq, Namratha Sheshadri, Julia Shen, Jaeyong Jung, Rongrong Li, Kevin Lu, Junrong Yan, Mark Koch, Hassan Sajjad, Barbara Rosati, Giuseppe Caso, Richard LIN, Brinda Vallat, Stephen Burley, Tong Liu, Hong Li, Christian Hinrichs, Jun Wang, Lynn Wang, Jean Vacher, Ping Xie, Wei-Xing Zong. OSTM1 is a ubiquitin E3 ligase that suppresses B-cell malignancy by activating the cAMP/PKA pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 617.

  PublisherDOI

• MolViewSpec: a Mol* extension for describing and sharing molecular visualizations

  Nucleic Acids Research · 2025-05-06 · 3 citations

  articleOpen access

  Data visualization is a pivotal component of a structural biologist's arsenal. The Mol* Viewer makes molecular visualizations available to broader audiences via most web browsers. While Mol* provides a wide range of functionality, it has a steep learning curve and is only available via a JavaScript interface. To enhance the accessibility and usability of web-based molecular visualization, we introduce MolViewSpec (molstar.org/mol-view-spec), a standardized approach for defining molecular visualizations that decouples the definition of complex molecular scenes from their rendering. Scene definition can include references to commonly used structural, volumetric, and annotation data formats together with a description of how the data should be visualized and paired with optional annotations specifying colors, labels, measurements, and custom 3D geometries. Developed as an open standard, this solution paves the way for broader interoperability and support across different programming languages and molecular viewers, enabling more streamlined, standardized, and reproducible visual molecular analyses. MolViewSpec is freely available as a Mol* extension and a standalone Python package.

  PublisherOA PDFDOI

Recent grants

• PDB MANAGEMENT BY THE RESEARCH COLLABORATORY FOR STRUCTURAL BIOINFORMATICS

  NIH · $19.3M · 2019–2024

• PDB Management by The Research Collaboratory for Structural Bioinformatics

  NSF · $22.0M · 2024–2029

• NIH Grant P50GM062529

  NIH · $33.8M · 2007

• NIH Grant R01GM061262

  NIH · $1.6M · 2005

• CIBR: BBSRC-NSF/BIO: Next generation Protein Data Bank - FACT infrastructure with value added FAIR data supporting diverse research and education user communities.

  NSF · $1.6M · 2020–2023

Frequent coauthors

• David S. Goodsell

  Rutgers, The State University of New Jersey

  169 shared

• John Westbrook

  Rutgers, The State University of New Jersey

  167 shared

• Christine Zardecki

  Rutgers, The State University of New Jersey

  163 shared

• José M. Duarte

  139 shared

• Andrej Šali

  University of California, San Francisco

  121 shared

• Jasmine Young

  Rutgers, The State University of New Jersey

  118 shared

• Helen Berman

  Rutgers, The State University of New Jersey

  117 shared

• Shuchismita Dutta

  Indian Institute of Engineering Science and Technology, Shibpur

  106 shared

Education

• Intern/Resident Internal Medicine, Medicine

  Brigham and Women's Hospital

  1990

• M.D., Medicine

  Harvard Medical School

  1987

• D.Phil., Laboratory of Molecular Biophysics

  University of Oxford

  1983

Awards & honors

• Doctor of Science (Honoris causa) from the University of Wes…