Virupaksha, Yashwanth Reddy, authorBen-Hur, Asa, advisorAnderson, Charles W., committee memberAdams, Henry Hugh, committee member2021-09-062021-09-062021https://hdl.handle.net/10217/233751Proteins are fundamental building blocks of cellular function. They systematically interact with other proteins to make life happen. Understanding these protein-protein interactions is important for obtaining a detailed understanding of protein function and to enable the process of drug and vaccine design. Experimental methods for studying protein interfaces including X-ray crystallography, NMR, and Cryo-electron microscopy, are expensive, time consuming, and sometimes unsuccessful due to the unstable nature of many protein-protein interactions. Computational docking experiments are a cheap and fast alternative. Docking algorithms produce a large number of potential solutions that are then ranked by quality. However, current scoring methods are not good enough for finding a docking solution that is close to the native structure. That has led to the development of machine learning methods for this task. These methods typically involve extensive engineering of features to describe the protein complex, and are not very successful at identifying good quality solutions among the top ranks. In this thesis, we propose a scoring technique that uses graph neural networks that function at the atomic level to learn the interfaces of docked proteins without the need for feature engineering. We evaluate our model and show that it performs better than commonly used docking methods and deep learning methods that use 3D CNNs.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.drug discoverygraph convolutionranking protein-protein docking solutionsgraph attentiondeep learninggraph neural networksText