Proteins are essential constituents of biological systems. A profound understanding of protein structure is significant for unraveling the intricate mechanisms of biological processes. The recent development of computational methods using AI technology is revolutionizing the structural biology field. Accurate predictions of three-dimentional protein structures can be generated from protein sequences, enabling rapid and accurate insights into protein interactions and functions. This thesis aims to investigate the applications of various cutting-edge methods in protein complex structure prediction. We first explore using trRosetta for dimeric protein complexes, and the study shows that the single-chain protein structure predictor is feasible for protein complexes. In light of the success of AlphaFold2, we use the pipeline FoldDock, which is an adaption of AlphaFold2 on protein complexes, for protein-protein interactions (PPIs) of two human interactome datasets and construct a PPI network. Next, we conduct a benchmark study of AlphaFold-Multimer in multi-chain protein complexes with 2 to 6 chains and examine how different evaluation scores affect the prediction assessment. In the last paper, we predict the large protein complexes starting from subcomponents using AlphaFold2 and a Monte Carlo Tree Search algorithm. The studies in this thesis show that deep learning approaches can yield reliable results in predicting protein complex structures, and there is ample potential for further improvement.