Antibody-Antigen Docking¶

Antibody-antigen docking predicts the structure of an antibody-antigen complex based on their sequences, helping users understand how the antibody and antigen bind and the location of binding sites.

In practice, due to prediction errors, the top 5-10 predictions with the highest scores are returned, providing the most likely structures. Each model provides quality control metrics to help you understand the reliability of the prediction.

Challenges & Features¶

The large size of protein systems makes determining the optimal orientation of two proteins challenging. Additionally, flexible regions often undergo conformational changes during docking. GeoBiologics' Antibody-Antigen Docking module has the following advantages:

State-of-the-art Algorithm: Utilizing the latest geometric deep learning techniques and large-scale structural pretraining, GeoFlow demonstrates strong and robust performance in antibody-antigen docking, with a success rate 93% higher than AlphaFold2.
Flexible Docking Supported: GeoFlow considers conformational changes of flexible regions during docking. It supports generating multiple candidate poses and scoring them, making it more suitable for real-world applications compared to rigid docking.
Easy to Use: Only the antigen and antibody sequences are needed for docking. The predicted structures are automatically aligned and clustered to visualize the epitope distribution more clearly.

Inputs¶

To submit an Antibody-Antigen Docking job, open the Project Editor and select "Antibody-Antigen Docking" from the "Structure Modeling" dropdown menu. The following job submission form will appear:

Complex: The sequence of the antibody-antigen complex.
- Upload a FASTA file: Click the "" button to upload your own antibody-antigen complex (a .FASTA file). The file can only contain one antibody-antigen complex, and the FASTA labels of the chains must have the same prefix and end with _{chain_id}.
- Copy FASTA strings: Copy the FASTA string content directly into the sequence input box. The platform will automatically parse and create a multi-chain sequence. The FASTA string must meet the above requirements. You can input the following sequence to test this feature:
```
>6xc3_C
NLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIR
GDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVNYNYLYRLFRKSNLKPFERDISTEIYQA
GSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPK

>6xc3_H
QMQLVQSGTEVKKPGESLKISCKGSGYGFITYWIGWVRQMPGKGLEWMGIIYPGDSETRYSPSFQGQVTI
SADKSINTAYLQWSSLKASDTAIYYCAGGSGISTPMDVWGQGTTVTVSS

>6xc3_L
DIQLTQSPDSLAVSLGERATINCKSSQSVLYSSINKNYLAWYQQKPGQPPKLLIYWASTRESGVPDRFSG
SGSGTDFTLTISSLQAEDVAVYYCQQYYSTPYTFGQGTKVEIK
```
- Direct Input: Input the sequence of each chain in the sequence input box. Note that the chain IDs are fixed as ABCDEF... sequentially.
Antigen Chain: Select which chains belong to the antigen; the remaining chains will default to the antibody.

Chain Type Recognition

GeoBiologics will automatically recognize and label the V-domain structure in the structure, including the antibody heavy chain (heavy), antibody light chain (light), TCR alpha chain (alpha), and TCR beta chain (beta).
Job Name: The name of the job. Note that the job name must be unique within the project.

Models & Parameters¶

Click the Show Parameters button to expand the model and parameter settings.

You can use our proprietary GeoAbDockv4 model for this task. The parameters of this model are as follows:

Mode: You can choose between "fast" and "accurate". The former is faster, while the latter samples more docking poses and has a higher docking success rate.

Results¶

Click Job Results in the Files & Jobs panel to view the job results.

Clustermap of Predicted Structures¶

This section displays the clustering results of 10 predicted structures. Each square in the clustermap is annotated with the DockQ value (ranging from 0 to 1) between two predicted structures, where larger values indicates higher similarity. Through this clustermap, you can easily identify which structures are similar and which are different, without the need to manually check each structure.

Summary Table¶

The summary table shows the confidence and various metrics of the 10 predicted structures, including:

Rank: Rank of samples starting from 0, sorted in descending order by wpTM score.
Score: wpTM score of the predicted structure (ranging from 0 to 1), indicating confidence in the prediction. Generally, if wpTM > 0.8, the structure is considered quite reliable; if wpTM < 0.5, it is less reliable.
Efinal: Final energy predicted by the AMBER99 model, with lower values being better.
Buried ASA: Reduction in solvent-accessible surface area due to antigen-antibody binding. For reliable structures, this value is usually > 1200.
Clash: Number of amino acids in the antibody that clash with (are too close to) the antigen. Ideally, this value should be 0.
Paratope: Total number of amino acids in the antibody close to the antigen, usually > 10.

Results are stored in a CSV file, which can be downloaded by clicking the " Export CSV " button in the top-right corner of the results table.

In the Ops column on the right, you can click the " File " button to view the complex structure. This will automatically redirect to the Mol* Viewer to examine the specific structure and docking details of the antibody-antigen complex.

The antigens in all predicted structures are pre-aligned for you to compare the differences in antibody positions. If you need to align predicted and real complex structures, refer to the Structure Alignment documentation.

Next steps¶

Based on the predicted antibody-antigen complex structure, you might want to perform structure-based affinity maturation to optimize the binding affinity of your antibody-antigen complex.