Bontha, Mridula, authorBen-Hur, Asa, advisorBeveridge, J. Ross, committee memberKing, Emily J., committee member2021-06-072021-06-072021https://hdl.handle.net/10217/232476Proteins play a vital role in most biological processes, most of which occur through interactions between proteins. When proteins interact they form a complex, whose functionality is different from the individual proteins in the complex. Therefore understanding protein interactions and their interfaces is an important problem. Experimental methods for this task are expensive and time consuming, which has led to the development of docking methods for predicting the structures of protein complexes. These methods produce a large number of potential solutions, and the energy functions used in these methods are not good enough to find solutions that are close to the native state of the complex. Deep learning and its ability to model complex problems has opened up the opportunity to model protein complexes and learn from scratch how to rank docking solutions. As a part of this work, we have developed a 3D convolutional network approach that uses raw atomic densities to address this problem. Our method achieves performance which is on par with state-of-art methods. We have evaluated our model on docked protein structures simulated from four docking tools namely ZDOCK, HADDOCK, FRODOCK and ClusPro on targets from Docking Benchmark Data version 5 (DBD5).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.Text