Quality assessment of protein structures using graph convolutional networks
| dc.contributor.author | Roy, Soumyadip, author | |
| dc.contributor.author | Ben-Hur, Asa, advisor | |
| dc.contributor.author | Blanchard, Nathaniel, committee member | |
| dc.contributor.author | Zhou, Wen, committee member | |
| dc.date.accessioned | 2024-05-27T10:31:49Z | |
| dc.date.available | 2024-05-27T10:31:49Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The prediction of protein 3D structure is essential for understanding protein function, drug discovery, and disease mechanisms; with the advent of methods like AlphaFold that are capable of producing very high quality decoys, ensuring the quality of those decoys can provide further confidence in the accuracy of their predictions. In this work we describe Qε, a graph convolutional network that utilizes a minimal set of atom and residue features as input to predict the global distance test total score (GDTTS) and local distance difference test score (lDDT) of a decoy. To improve the model's performance, we introduce a novel loss function based on the ε-insensitive loss function used for SVM-regression. This loss function is specifically designed for the characteristics of the quality assessment problem, and provides predictions with improved accuracy over standard loss functions used for this task. Despite using only a minimal set of features, it matches the performance of recent state-of-the-art methods like DeepUMQA. The code for Qε is available at https://github.com/soumyadip1997/qepsilon. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Roy_colostate_0053N_18196.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/238364 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights.license | This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 United States License. https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode | |
| dc.title | Quality assessment of protein structures using graph convolutional 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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