A large multiscale detailed modelling of aptamers as anticancer therapeutics
- Authors: Mokgopa, Kabelo Phuti
- Date: 2025-04-02
- Subjects: Aptamer , MicroRNA , Drug discovery , Python (Computer program language) , Molecular dynamics , Cheminformatics , Bioinformatics
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/479174 , vital:78267
- Description: Cancer remains a leading cause of death worldwide, characterized by uncontrolled cell growth and spread. The challenge of effectively treating cancer has spurred interest in novel therapeutic strategies that target specific biological or biochemical mechanisms involved in cancer progression. Although many enzymes have been labelled as inducers of cancer development, microRNAs (miRNAs) are also emerging as significant contributors to cancer progression. This is because miRNAs play a crucial role in regulating gene expression, while cancer develops and grows due to genetic mutations, variations, and alterations. Among these miRNAs, miRNA-10b is notable for its involvement in promoting cancer cell proliferation, migration, and metastasis across various cancers, including breast cancer, glioblastoma, and esophageal squamous cell carcinoma. For this reason, we propose inhibiting miRNA-10b using RNA aptamers as a novel and promising approach for developing new anti-cancer therapeutics. RNA aptamers are short, non-coded, synthetic, and single-stranded nucleic acid molecules capable of binding to a wide range of targets, including metal ions, chemical compounds, proteins, cells, and microorganisms. They are used for a range of applications due to their well-known specificity and selectivity, starting from drug delivery to diagnostics. In this project we aimed to design and discover novel RNA aptamers that can effectively inhibit miRNA-10b using advanced computational methods. However, major challenges were encountered due to the lack of databases or tools available to design and predict secondary and tertiary structures of RNA aptamers at a large scale. Furthermore, no tools were available to perform high throughput virtual screening of these aptamers against macromolecular targets at a large scale. Prompted by that, we developed the T_SELEX program, which encompasses the various algorithms and tools dedicated to designing RNA aptamer sequences, predicting their secondary and tertiary structures, and, lastly, virtually screening aptamers. These algorithms and advanced tools are designed to handle the complexities of aptamer selection and virtual screening. By employing virtual screening methods, the aptamer discovery process was streamlined, offering a cost-effective and efficient alternative to traditional SELEX techniques. Prior to the main purpose application, the T_SELEX program was tested by designing aptamers for targeting HIV-1 protease, and a few applications were also done to assess its aptamer design approach. The study explored RNA aptamer sequences, revealing important insights into nucleotide composition, sequence patterns, and their role in aptamer efficacy and design. Analysis of secondary and tertiary structure predictions showed that Minimum Free Energy (MFE) values do not always correlate with structural compactness or complexity, with aptamers of similar MFE values exhibiting variations in attributes like loop size and guanine content. A novel Sequence Similarity Check (SSC) algorithm is introduced focused on internal sequence comparisons and secondary structures, revealing that aptamers with similar base compositions could have distinct folding states and stability. The Base Randomization Algorithm (BRA) generated RNA aptamer libraries was further benchmarked, highlighting a critical threshold for aptamer length and demonstrating Gaussian distribution in base compositions. Virtual screening of aptamers using the T_SELEX program against pre-miRNA-10b and their mature 5p and 3p arm, identified aptamers557 and 899 as effective binders for the 3p and 5p arms, respectively. Extensive quantum mechanical and molecular dynamics simulations confirmed the stability of the aptamer-RNA complexes. Due to the understanding of the flexibility of these RNA-RNA complexes, we further proposed the stability matrices method as a calculus-based method to evaluate the relative stability of the complexes without being biased during MD analysis. MM-GBSA calculations supported docking results, identifying aptamers like aptamers557, aptamer274 and aptamer734 as strong inhibitors of the 3p arm. Overall, this project has proposed novel approaches for aptamer in silico design and validation, particularly in targeting miRNA-10b for cancer therapy. , Thesis (MSc) -- Faculty of Science, Chemistry, 2025
- Full Text:
- Date Issued: 2025-04-02
- Authors: Mokgopa, Kabelo Phuti
- Date: 2025-04-02
- Subjects: Aptamer , MicroRNA , Drug discovery , Python (Computer program language) , Molecular dynamics , Cheminformatics , Bioinformatics
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/479174 , vital:78267
- Description: Cancer remains a leading cause of death worldwide, characterized by uncontrolled cell growth and spread. The challenge of effectively treating cancer has spurred interest in novel therapeutic strategies that target specific biological or biochemical mechanisms involved in cancer progression. Although many enzymes have been labelled as inducers of cancer development, microRNAs (miRNAs) are also emerging as significant contributors to cancer progression. This is because miRNAs play a crucial role in regulating gene expression, while cancer develops and grows due to genetic mutations, variations, and alterations. Among these miRNAs, miRNA-10b is notable for its involvement in promoting cancer cell proliferation, migration, and metastasis across various cancers, including breast cancer, glioblastoma, and esophageal squamous cell carcinoma. For this reason, we propose inhibiting miRNA-10b using RNA aptamers as a novel and promising approach for developing new anti-cancer therapeutics. RNA aptamers are short, non-coded, synthetic, and single-stranded nucleic acid molecules capable of binding to a wide range of targets, including metal ions, chemical compounds, proteins, cells, and microorganisms. They are used for a range of applications due to their well-known specificity and selectivity, starting from drug delivery to diagnostics. In this project we aimed to design and discover novel RNA aptamers that can effectively inhibit miRNA-10b using advanced computational methods. However, major challenges were encountered due to the lack of databases or tools available to design and predict secondary and tertiary structures of RNA aptamers at a large scale. Furthermore, no tools were available to perform high throughput virtual screening of these aptamers against macromolecular targets at a large scale. Prompted by that, we developed the T_SELEX program, which encompasses the various algorithms and tools dedicated to designing RNA aptamer sequences, predicting their secondary and tertiary structures, and, lastly, virtually screening aptamers. These algorithms and advanced tools are designed to handle the complexities of aptamer selection and virtual screening. By employing virtual screening methods, the aptamer discovery process was streamlined, offering a cost-effective and efficient alternative to traditional SELEX techniques. Prior to the main purpose application, the T_SELEX program was tested by designing aptamers for targeting HIV-1 protease, and a few applications were also done to assess its aptamer design approach. The study explored RNA aptamer sequences, revealing important insights into nucleotide composition, sequence patterns, and their role in aptamer efficacy and design. Analysis of secondary and tertiary structure predictions showed that Minimum Free Energy (MFE) values do not always correlate with structural compactness or complexity, with aptamers of similar MFE values exhibiting variations in attributes like loop size and guanine content. A novel Sequence Similarity Check (SSC) algorithm is introduced focused on internal sequence comparisons and secondary structures, revealing that aptamers with similar base compositions could have distinct folding states and stability. The Base Randomization Algorithm (BRA) generated RNA aptamer libraries was further benchmarked, highlighting a critical threshold for aptamer length and demonstrating Gaussian distribution in base compositions. Virtual screening of aptamers using the T_SELEX program against pre-miRNA-10b and their mature 5p and 3p arm, identified aptamers557 and 899 as effective binders for the 3p and 5p arms, respectively. Extensive quantum mechanical and molecular dynamics simulations confirmed the stability of the aptamer-RNA complexes. Due to the understanding of the flexibility of these RNA-RNA complexes, we further proposed the stability matrices method as a calculus-based method to evaluate the relative stability of the complexes without being biased during MD analysis. MM-GBSA calculations supported docking results, identifying aptamers like aptamers557, aptamer274 and aptamer734 as strong inhibitors of the 3p arm. Overall, this project has proposed novel approaches for aptamer in silico design and validation, particularly in targeting miRNA-10b for cancer therapy. , Thesis (MSc) -- Faculty of Science, Chemistry, 2025
- Full Text:
- Date Issued: 2025-04-02
Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents
- Authors: Sigauke, Lester Takunda
- Date: 2019
- Subjects: Peptides -- Synthesis , Macrocyclic compounds , Drug development , Drug discovery , Cardiovascular system -- Diseases -- Prevention , Proteins -- Synthesis
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/116056 , vital:34293
- Description: Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used.
- Full Text:
- Date Issued: 2019
- Authors: Sigauke, Lester Takunda
- Date: 2019
- Subjects: Peptides -- Synthesis , Macrocyclic compounds , Drug development , Drug discovery , Cardiovascular system -- Diseases -- Prevention , Proteins -- Synthesis
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/116056 , vital:34293
- Description: Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used.
- Full Text:
- Date Issued: 2019
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