2024 Volume 17 Pages 40-47
Molecular docking simulations utilizing scoring functions are pivotal for assessing the stability of complex formations. The unique biochemical characteristics of antibody-antigen interfaces, however, present challenges in applying general parameter sets of scoring functions to these molecules, necessitating the customization of the scoring function to enhance prediction accuracy for structural configurations and binding affinities. In response to this, we have developed models within the Rosetta software framework, widely recognized for its utility in predicting antigen-antibody docking, to optimize the parameters of its scoring function. Through a quantitative evaluation of the shape of decoy distribution generated by Rosetta, we have been able to refine the parameters for each antibody-antigen complex, yielding a notable improvement in the prediction accuracy of the software for a given dataset. Furthermore, we have identified a distinct parameter set that is effective for the majority of complexes in our dataset, though not universally applicable. This study introduces a novel approach to customizing scoring functions, potentially contributing to advancements in drug discovery and deepening our understanding of the complexities inherent in antibody-antigen interactions at a molecular level.
