Refinement of AlphaFold-Predicted Models Using Cryo-EM Density Maps and Enhancement of Protein Secondary Structure Topologies With Residue Contacts

Proteins play an important role in almost every biological process. Understanding the mechanism of protein function requires knowledge of three-dimensional (3D) structures. Traditionally, the determination of 3D structures has presented significant challenges. However, Cryo-Electron Microscopy (Cryo-EM) has revolutionized the field of structural biology, providing a powerful technique for atomic structure determination. This dissertation delves into the potential of cryoEM in two ways. First, this dissertation presents a new flexible fitting approach, utilizing Normal Mode Analysis (NMA) and Elastic Network Models (ENMs) to refine AlphaFold-predicted models by optimizing the structures to match cryo-EM density maps. This approach identifies the optimal mode elongation vector that minimizes the negative cross-correlation between the simulated map generated from the deformed structures and the target map. The methodological affirmation in this dissertation includes local and global optimization methods, three different sets of basis functions, masked and box-cropped density maps, and two cross-correlation similarity measures. Four AlphaFold models with notable discrepancies from the true structure were tested, showing significant improvements in structure prediction accuracy. Second, an exploratory approach combined cryoEM density maps with residue contact information to enhance the secondary structure topology. This approach integrates three sources of information: sequence segments, amino acid contact pairs, and traces at the secondary structure level. A test involving fourteen cases demonstrates that the accuracy of predicted secondary structures is crucial for determining protein topologies. If the secondary structure prediction is reasonably accurate, utilizing significant long-range contact pairs is particularly effective in improving the rank of the topology for proteins with a large number of secondary structures. Through these focused efforts, this dissertation aims to contribute to a deeper understanding of protein function at the molecular level. This understanding has the potential to open new doors in fields, including drug discovery and novel treatments.