Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: "The ability to accurately predict antibody-antigen complex structures from thei r sequences could greatly advance our understanding of the immune system and wou ld aid in the development of novel antibody therapeutics. "There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). "To understand the current state of the field, we compare six representative met hods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer, Ros eTTAFold), two composite methods that initially predict antibody and antigen str uctures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline com bining homology modelling with rigid-body docking informed by ML-based epitope a nd paratope prediction (AbAdapt).
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