Enhancing antibody-antigen interaction prediction with atomic flexibility.

ABSTRACT: Antibodies are indispensable components of the immune system, known for their specific binding to antigens. Beyond their natural immunological functions, they are fundamental in developing vaccines and therapeutic interventions for infectious diseases. The complex architecture of antibodies, particularly their variable regions responsible for antigen recognition, presents significant challenges for computational modeling. Recent advancements in deep learning have markedly improved protein structure prediction; however, accurately modeling antibody-antigen (Ab-Ag) interactions remains challenging due to the inherent flexibility of antibodies and the dynamic nature of binding processes. In this study, we examine the use of predicted Local Distance Difference Test (pLDDT) scores as indicators

SUBMITTER: Joubbi S 

PROVIDER: S-EPMC12530544 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature