Proteins are the driving force behind most cellular processes. Traditional methods for determining protein structure are limited to probing only a few static structures of a given protein. However, molecular dynamics simulation presently allows for the determination of the dynamics of a protein. In this thesis, I introduce AWSEM-Suite, a coarse-grained force field that has recently shown good performance in protein structure prediction experiments. This force field is based on physical principles and neural network-based machine learning. I describe the composition of the force field, as well as two new energy terms: the template-based and coevolutionary-guided terms.

After discussing the problem of protein structure prediction, I showcase how we built an online server for AWSEM-Suite, describing the path between the input and output. Additionally, I discuss other techniques for structural analysis, including frustration protein structure refinement, a physics-based method that complements current methods.

In the last part of this thesis, I introduce two applications: solving phase problems in X-ray crystallography and exploring the translocation mechanism of bacteriophage T7 helicase gp4 using AWSEM-Suite. Our results show that AWSEM-Suite provides better results than the state-of-the-art program I-TASSER-MR in finding phase information for a given protein. Furthermore, AWSEM-Suite can successfully predict the key loops that interact with ssDNA during gp4 translocation. We explore the intermediate structures of action, highlighting the possible mechanism of the role of electrostatic effects exerted by the binding and release of ATP molecules.

In these chapters, we explore several related questions concerning protein structure prediction, protein structure refinement, and specific biological questions using AWSEM-Suite. In summary, our studies demonstrate that AWSEM-Suite is a powerful technology for exploring protein dynamics and predicting protein structure.