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Antibodies are essential proteins of the immune system that bind to potentially pathogenic molecules. Their ability to bind to a huge variety of substances with high specificity and affinity means that they are used as research tools and therapeutic agents. These important applications have motivated the need for computational tools, such as databases of structural information and predictive algorithms, to better understand their behaviour and assist in their discovery and development.

SAbDab is an online structural antibody database for easily searching and selecting antibodies or groups of antibodies and presenting structural data in a consistent manner. The SAbDab tools allow for selecting sets of antibody structures based on factors such as resolution, similarity to a given sequence, amino acid composition and antibody-antigen binding affinity.

To generate therapeutic antibodies some engineering is required. This is informed by structural properties such as which antibody residues contact an antigen. In the absence of experimental data, computational methods are required to predict such features. SAbPred is a structural antibody prediction web server – a single platform for structure-based tools useful for designing antibodies.

SAbDab and SAbPred are valuable resources for both computational and experimental antibody research and engineering.  Researchers at the University of Oxford have improved their usability by encompassing the SAbDab / SAbPred tools into a unified, environment-agonist platform. The platform is built in a vagrant VirtualBox – a virtualization that allows an operating system with all its software to run in a special environment on top of the existing operating system. This means that the SAbDab / SAbPred tools are available in a single place, available through a simple install, and can be easily managed and transferred between systems.

The tools included in the platform are as follows:

• SAbDab – the structural antibody database
• Thera-SAbDab – a database of therapeutic sequences linked to their structures where available
• SAbPred – the antibody prediction toolbox:
• ABodyBuilder-ML – a pipeline for modelling antibody variable regions
• ABodyBuilder2 – end-to-end deep learning antibody Fv modelling
• TCRBuilder2 – end-to-end deep learning TCR Fv modelling
• NanoBodyBuilder2 – end-to-end deep learning nanobody modelling
• PEARS – antibody-specific prediction of sidechain conformations
• ANARCI – antibody numbering software
• SCALOP – canonical form predictor
• TAP – developability prediction of potential therapeutics
• Hu-mAb – antibody humanisation and human-ness prediction