The Antibody Prediction Toolbox

Welcome to SAbPred

SAbPred is a collection of computational tools that make predictions about the properties of antibodies, focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules.

Our webapps can be used to perform a variety of tasks:

The SAbPred tools were developed by the Oxford Protein Informatics Group (OPIG).

Other tools are available from our GitHub page.

Our structural antibody database, which collates and consistently annotates all antibody structures found in the PDB, can be found at our SAbDab site.

Web Apps


Generate Fv models with machine learning loop modelling.


Deep learning antibody Fv modelling.


Deep learning TCR Fv modelling.


Deep learning nanobody Fv modelling.


Antibody-specific prediction of side chain conformations.


Classify and number antibody and T-cell receptor amino-acid variable domain sequences.


Sequence-based antibody canonical loop structure annotation.


The Therapeutic Antibody Profiler - compare your antibody to known therapeutics to predict its developability.


Use Hu-mAb to propose mutations that will decrease the immunogenicity of your therapeutic.

SAbPred paper: Dunbar, J. et al (2016). Nucleic Acids Res. 44. W474-W478 [link]