Computational prediction of intra- and intermolecular interactions, specifically intra- protein residue-residue interactions and the interaction sites in between proteins and other macromolecules, are critical for understanding numerous biological processes. The existing methods fall short in estimating the quality of intra-protein interactions. Moreover, the methods for predicting intermolecular interactions fail to harness some of the latest technological advancements such as advances in pretrained protein and RNA language models and struggle to effectively integrate predicted structural information, thus limiting their predictive modeling accuracy. Hence, my objectives include (1) the development of computational methods for protein structure modeling through the estimation of intra-protein interactions, (2) the development of computational methods for predicting protein- protein interaction sites leveraging the latest deep learning architectures and predicted structural information, and (3) extending the scope beyond protein-protein interactions to develop novel computational methods to predict protein-nucleic acid interactions informed by protein and RNA language models. The major benefits of achieving these objectives for the broader scientific community are the following: (1) intra-protein interaction estimation methods have the potential to enhance the accuracy of protein structure modeling, and (2) the methods for predicting protein-protein and protein-nucleic acid interaction will deepen our understanding of biomolecular interactions in cell, even when experimentally determined molecular structures are not available. / Doctor of Philosophy / My research focuses on developing accurate computational predictive modeling methods centering to biomolecular interactions. Proteins, one of the most important biomolecules, fold into stable three-dimensional forms to perform specific tasks in the cell. Recognizing the importance of this information in the absence of ground truth three-dimensional structures, I developed computational methods to predict the folded three-dimensional structures of proteins, utilizing intra-protein atomic interactions and for the quality estimation of those interactions. Since proteins not only fold themselves but also interact with other proteins and biomolecules such as nucleic acids, which is crucial for many biological processes, I expanded my research from intra-protein interactions to predicting interactions between proteins and other molecules. In particular, using advanced computational techniques, I developed methods for predicting protein-protein and protein-nucleic acid interactions. The research outcomes not only outperform existing state-of-the-art computational methods by overcoming their limitations but also have potential applications in designing effective therapies and combating diseases, ultimately improving the health sector through their large-scale predictability. All the scientific tools resulting from the research are publicly available, fascinating knowledge sharing and collaboration within and beyond the scientific community.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/120785 |
| Date | 30 July 2024 |
| Creators | Roche, Rahmatullah |
| Contributors | Computer Science and#38; Applications, Bhattacharya, Debswapna, Murali, T. M., Haspel, Nurit, Zhang, Liqing, Heath, Lenwood S. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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