Assessment of protein-protein interfaces using graph neural networks
| dc.contributor.author | Virupaksha, Yashwanth Reddy, author | |
| dc.contributor.author | Ben-Hur, Asa, advisor | |
| dc.contributor.author | Anderson, Charles W., committee member | |
| dc.contributor.author | Adams, Henry Hugh, committee member | |
| dc.date.accessioned | 2021-09-06T10:25:09Z | |
| dc.date.available | 2021-09-06T10:25:09Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Proteins 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. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Virupaksha_colostate_0053N_16758.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/233751 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright 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. | |
| dc.subject | drug discovery | |
| dc.subject | graph convolution | |
| dc.subject | ranking protein-protein docking solutions | |
| dc.subject | graph attention | |
| dc.subject | deep learning | |
| dc.subject | graph neural networks | |
| dc.title | Assessment of protein-protein interfaces using graph neural networks | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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