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. 

Interactions between proteins are directly involved in most biological processes and are essential for the correct functioning of every form of life. The nature of protein-protein interactions allows functional assemblies of hundreds of protein chains. Given the enormous complexity and the pivotal role of protein interactions in life’s mechanics, the necessity to obtain a complete comprehension of such mechanisms is just as big as the challenge to achieve such knowledge. In the last few decades, experimental procedures constantly improved, dramatically increasing the available structural data for protein interactions. Unfortunately, experimental methods require a lot of time and resources and cannot always be applied with the same degree of success. Several computational methods have been developed in parallel with experimental procedures to overcome such limitations. Therefore, this thesis focused on screening existing computational methods and adopting them to improve the overall accuracy in solving structures of protein-complexes. In the first paper, I propose a simple rigid-body docking framework to test several interface predictors and their ability to drive a protein-protein docking procedure. Next, in the second paper, I display a method to adapt the trRosetta deep neural network to predict inter-residues distances and dihedral angle constraints for full protein complexes. The same concept is then improved in the third paper with FoldDock, an adaptation of Alphafold2 to work on multiple protein sequences and produce the corresponding complex. Finally, in the fourth paper, the FoldDock pipeline is applied to a large dataset of protein pairwise interactions derived from the hu.MAP and HuRI datasets, resulting in the characterization of more than 3000 high-confidence structural models.