- Title
- Bioinformatics tool and web server development focusing on structural bioinformatics applications
- Creator
- Nabatanzi, Margaret
- ThesisAdvisor
- Bishop, Özlem Taştan
- Subject
- Structural bioinformatics
- Subject
- Proteins Structure
- Subject
- Protein structure prediction
- Subject
- Proteins Conformation
- Subject
- Protein complex
- Date
- 2022-10-14
- Type
- Academic theses
- Type
- Doctoral theses
- Type
- text
- Identifier
- http://hdl.handle.net/10962/365700
- Identifier
- vital:65777
- Identifier
- DOI https://doi.org/10.21504/10962/365700
- Description
- This thesis is divided into two main sections: Part 1 describes the design, and evaluation of the accuracy of a new web server – PRotein Interactive MOdeling (PRIMO-Complexes) for modeling protein complexes and biological assemblies. The second part describes the development of bioinformatics tools to predict HIV-1 drug resistance and support bioinformatics research and education. Recent technological advances have resulted in a tremendous increase in the number of sequences and protein structures deposited in the Universal Protein Resource Knowledgebase (UniProtKB) and the Protein Data Bank (PDB). However, the number of sequences has increased at a higher rate compared with the experimentally solved multimeric protein structures. This is partly due to advances in high-throughput sequencing technology. To fill this protein sequence-structure gap, computational approaches have been developed to predict protein structures from available sequences. Computational approaches include template-based and ab initio modeling with the former being the most reliable. Template-based modeling process can be achieved using either standalone software or automated modeling web servers. However, using standalone software requires familiarity with command-line interfaces as well as utilising other intermediate programs which could be daunting to novice users. To alleviate some of these problems, the modeling process has been automated, however, it still has numerous challenges. To date, only a few web servers that support multimeric protein modeling have been developed and even these provide little, if any user involvement in the process. To address some of these issues, a new web server – PRIMO-Complexes – was developed to model protein complexes and biological assemblies. The existing PRIMO web server could only model monomeric proteins. Part 1 of this thesis provides a detailed account of the development and evaluation of PRIMO-Complexes. The rationale for developing this new web server was based on the understanding that most proteins function as protein multimers and often the ligand-binding sites, and enzyme active sites are located at the protein-protein interfaces. It, therefore, necessitated developing capabilities for modeling multimeric proteins. PRIMO-Complexes web server was developed using the Waterfall system development life cycle model, is based on the Django web framework and makes use of high-performance computing resources to execute jobs. The accuracy of the algorithms embedded in PRIMO- Complexes was evaluated and the results were promising. Additionally, PRIMO-Complexes performs comparatively well in relation to other web servers that offer multimeric protein modeling. Another unique feature of PRIMO-Complexes is its interactivity. The webserver was developed with capabilities for allowing users to model multimeric proteins with an appreciable degree of control over the process. In the second part of the thesis several other bioinformatics tools are described, for example, a webserver for predicting HIV-1 drug resistance, the RUBi protein model repository, and a bioinformatics web portal for education and research resources. RUBi protein model repository stores verified theoretical models built using various modeling approaches. This enables users to easily access models to reproduce and/or further the research. This is described in chapter 5. Chapter 6 describes the design and development of the Human Immunodeficiency type 1 Resistance Predictor (HIV-1 ResPredictor), a web application that employs artificial neural networks (ANN) to predict drug resistance in patients infected with HIV-1 subtype B. The ANNs and subtype classifiers performed well making this web application potentially useful to both clinicians and researchers in this era of personalised medicine. Finally, chapter 7 describes a bioinformatics education web portal that equips students with information on how to use bioinformatics online resources. Being aware of these resources is not enough without a deeper understanding and guidance on how to apply bioinformatics methods to solve practical problems. This web portal was aimed at familiarising students with the basic terminology and approaches in structural bioinformatics. Students will potentially gain skills to conduct real-life bioinformatics research to obtain biological insights.
- Description
- Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Format
- computer, online resource, application/pdf, 1 online resource (209 pages), pdf
- Publisher
- Rhodes University, Faculty of Science, Biochemistry and Microbiology
- Language
- English
- Rights
- Nabatanzi, Margaret
- Rights
- Use of this resource is governed by the terms and conditions of the Creative Commons "Attribution-NonCommercial-ShareAlike" License (http://creativecommons.org/licenses/by-nc-sa/2.0/)
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RU Department of Biochemistry and Microbiology (2015-)
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | NABATANZI-PHD-TR22-201.pdf | 4 MB | Adobe Acrobat PDF | View Details Download |