Generating Dynamic Structures Through Physics‐Based Sampling of Predicted Inter‐Residue Geometries

Deep learning-based methods, such as AlphaFold2, have revolutionized the prediction of static protein structures. However, modeling alternative conformations and dynamic structures remains an unsolved problem. Here, we present trRosettaX2-Dynamics (trX2-D), an innovative solution building on our CASP15 and CASP16 winning method, trRosettaX2. trX2-D tackles this challenge by employing physics-based iterative sampling of trRosettaX2's predicted inter-residue geometric distributions. The model underwent pre-training on high-resolution X-ray structures, followed by fine-tuning on approximately 7000 dynamic NMR structures. This dual training regime significantly bolsters its capacity to predict alternative conformations and dynamic structures. At its core, trX2-D employs a Transformer-based neural network to initially predict a set of inter-residue geometric constraints. These constraints are then iteratively sampled to generate dynamic structures, entirely circumventing the need for prior knowledge of native structural states. Extensive benchmarking across three distinct datasets-two focused on alternative conformations and one on dynamic structures-demonstrates trX2-D's promising ability to predict alternative conformations and accurately capture structural dynamics. This work highlights the potential of integrating deep learning predictions with physics-based sampling to advance the field of protein dynamic structure prediction.