DeepAAAssembly is a computational protocol specifically designed for antibody-antigen complex structure modeling. This protocol integrates deep learning-based inter-chain residue distance prediction with a Monte Carlo conformational sampling method to generate accurate binding conformations. Specifically, the predicted distance maps are employed as knowledge-based energy constraints to guide sampling toward physically plausible configurations. To efficiently explore the conformational space, we designed a two-stage exploration-exploitation strategy. In the exploration stage, diverse binding orientations are extensively sampled under the guidance of the energy landscape. In the subsequent exploitation stage, refinement is performed on the complementarity-determining region loops to enhance geometric and energetic complementarity at the interface. Finally, the sampled conformations are evaluated through an energy-guided confidence selection mechanism, from which the optimal conformation is identified as the final output. (More about DeepAAAssembly...)

• 2025/11/05: We developed DeepAAAssembly for antibody-antigen complex structure modeling.

• Suhui Wang^✝, Jianan Zhuang^✝, Xinyue Cui, Zexin Lv, Dongliang Hou, Guijun Zhang*. High-accuracy structure modeling for antibody-antigen complexes. bioRxiv, DOI: https://doi.org/10.1101/2025.11.03.686275