Rapid and Reliable Structural Modeling of Adaptive Immune Receptors Using an Optimized AlphaFold3 workflow

AlphaFold (AF), a deep-learning based protein modelling approach, has revolutionized structural biology by generally achieving near-experimental accuracy in protein structure prediction. An impactful application of AF is the modelling of antibodies (Abs) and T-cell receptors (TCRs), key mediators of cellular immunity, whose structural specificity underlies responses in cancer, infection, and autoimmune diseases. In this work, we analyse AF3 performance by systematically examining how MSA composition, number of inference phases and inference parameters affect prediction accuracy and computational efficiency. We present an acceleration of [~]45-fold of the AF3 MSA phase using reduced UniRef90 subsets, combined with up to a 3.6-fold increase in AF3 inference speed through optimized parameters. We provide a highly accurate variant of the AF3 workflow specifically optimized for the modelling of the Abs and TCRs receptor domains, enabling rapid, reliable structural predictions at a scale suitable for high-throughput immunological studies. Our findings provide a foundation for faster therapeutic discovery and deeper molecular mechanism understanding of immune recognition. TeaserBy improving key steps in the process, we made AlphaFold3 about 40 times faster at modeling specific immune proteins