Jeffrey Gray

Professor

Johns Hopkins University

United States

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Professor Jeffrey Gray is a faculty member at Johns Hopkins University in the United States. His research focuses on protein design, antibody engineering, and the interactions of gangliosides in live cells. Recent publications reflect his expertise in deep learning applications for antibody structure prediction and computational methods for enhancing protein stability and binding specificity.

Articles (20)

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2020

Collaborators (9)

Courtney J. Murren

UNITED STATES

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Jane Disney

Associate Professor of Environmental Health

Mount Desert Island Biological Laboratory

UNITED STATES

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Benjamin Orsburn

Johns Hopkins University

UNITED STATES

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Matthew P. DeLisa

Cornell University

UNITED STATES

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C. Sarah Cohen

Stanford University

UNITED STATES

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Ronald Schnaar

Professor

Johns Hopkins University School of Medicine

UNITED STATES

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Marc Ostermeier

Professor

Johns Hopkins University

UNITED STATES

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Lee-Shin Chu

Johns Hopkins University

UNITED STATES

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James Fraser

Professor

University of California, San Francisco

UNITED STATES

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Jeffrey Gray

Research Interests

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About

Articles (20)

The Human Ganglioside Interactome in Live Cells Revealed Using Clickable Photoaffinity Ganglioside Probes

Thermostability Enhancement of GH 62 α-l-Arabinofuranosidase by Directed Evolution and Rational Design

Flexible protein–protein docking with a multitrack iterative transformer

AI models for protein design are driving antibody engineering

IgLM: Infilling language modeling for antibody sequence design

Fate and Efficacy of Engineered Allogeneic Stem Cells Targeting Cell Death and Proliferation Pathways in Primary and Brain Metastatic Lung Cancer

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies

Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus

Antibody structure prediction using interpretable deep learning

“How Do We Do This at a Distance?!” A Descriptive Study of Remote Undergraduate Research Programs during COVID-19

Accurate positioning of functional residues with robotics-inspired computational protein design

Simultaneous prediction of antibody backbone and side-chain conformations with deep learning

Diverse Scientific Benchmarks for Implicit Membrane Energy Functions

Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta

An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants

Development of a Broadly Accessible, Computationally Guided Biochemistry Course-Based Undergraduate Research Experience

Structural Basis for Peptide Substrate Specificities of Glycosyltransferase GalNAc-T2

Robustification of RosettaAntibody and Rosetta SnugDock

Deep Learning in Protein Structural Modeling and Design

Novel sampling strategies and a coarse‐grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37–45

Collaborators (9)

Courtney J. Murren

Jane Disney

Benjamin Orsburn

Matthew P. DeLisa

C. Sarah Cohen

Ronald Schnaar

Marc Ostermeier

Lee-Shin Chu

James Fraser

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