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Organization Card

Oxford Protein Informatics Group (OPIG)

Computational research at the interface of protein science, immunology, and drug discovery

We are a research group led by Charlotte Deane in the Department of Statistics, University of Oxford. Our work combines machine learning, structural biology, and cheminformatics to tackle fundamental problems in protein structure, antibody engineering, and small molecule drug design.


Research Areas

Immunoinformatics — Antibody and T-cell receptor structure prediction, developability assessment, repertoire analysis, and antigen binding.

Protein Structure — Structure prediction, conformational dynamics, protein folding, and language models for genomic and proteomic sequences.

Small Molecules — Structure-based drug design, molecular docking, generative models for drug-like molecules, and binding affinity prediction.


Key Open-Source Tools & Databases

Tool Description
SAbDab Structural Antibody Database — all antibody structures from the PDB, updated weekly
OAS Observed Antibody Space — billions of antibody sequences from immune repertoire sequencing studies
STCRDab Structural T-Cell Receptor Database
CoV-AbDab Coronavirus Antibody Database — antibodies targeting SARS-CoV-2 and related coronaviruses
AbLang Antibody language model for sequence infilling and representation
ANARCI Antibody and TCR sequence numbering and annotation
ABlooper Fast, accurate CDR loop structure prediction
Paragraph Graph neural network-based antibody paratope prediction
MolSnapper Conditioning diffusion models for structure-based drug design
STRIFE Fragment elaboration using pharmacophoric constraints
CaLM Codon-aware language model for protein engineering

All of our open-source code lives at github.com/oxpig.


Get Involved

Interested in a DPhil or postdoc? See our current research and get in touch.