Decoding protein dynamicity in DNA ligase activity through deep learning-based structural ensembles.

Abstract

Numerous proteins perform their functions by transitioning between various structures. Understanding the conformational ensembles associated with these states is essential for uncovering crucial mechanistic aspects that regulate protein function. In this study, we utilized AlphaFold3 ( AF3 ) to investigate the structural dynamics and mechanisms of enzymes involved in DNA homeostasis, using NAD-dependent Taq ligases and human DNA Ligase 1 as a case example. Modifying the input parameters for AF3 yielded detailed conformational states of a DNA-binding enzyme, thereby offering enhanced mechanistic insights. We applied AF3 to model the various stages of thermophilic Taq DNA ligase activity, from its ground state to substrate-bound complexes, revealing significant mobility in the N-terminal adenylation and C-terminal BRCT domains. These detailed structural ensembles provided novel insights into the enzyme's behavior during DNA repair, underscoring the potential of AF3 in elucidating mechanistic details critical for therapeutic and biotechnological targeting. Extending this approach to human LIG1, we examined its end-joining activity on double-strand breaks ( DSBs ) with short 3' and 5' overhangs. In alignment with published experimental data, AF3 conformational ensembles indicated LIG1 has lower catalytic efficiency for 5' overhangs due to suboptimal DNA positioning within the catalytic site, demonstrating AF3's capability to capture subtle yet functionally significant conformational differences by generating conformational ensembles capturing greater structural variance.