Using deep learning methods like SGNet for predicting protein structures may introduce potential limitations or biases. One limitation is the reliance on training data, which may not fully capture all possible structural variations present in real-world scenarios. This could lead to inaccuracies or errors in predictions for novel protein complexes with unique characteristics not seen in the training set. Additionally, biases may arise from the design of the network architecture or loss functions used during training, impacting the model's ability to generalize across different types of proteins.