Computational search for nature-derived dual-action inhibitors of HIV-1 reverse transcriptase and integrase: a potential strategy to mitigate drug resistance progression
- Authors: Mwiinga, Luyando
- Date: 2024-10-11
- Subjects: HIV (Viruses) , Reverse transcriptase , Antiretroviral agents , RDKit , Drug resistance , Docking
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/463930 , vital:76458
- Description: Human immunodeficiency virus Type 1 (HIV-1) is a devastating viral infection affecting millions worldwide and presents significant challenges in treatment and management. In 2022, approximately 39 million people were living with HIV with Sub-Saharan Africa having two thirds of these infections. Devastatingly, there were approximately 300 000 HIV/AIDS related deaths in Sub-Saharan Africa alone in 2022 alone. Antiretroviral therapy (ART) which is fundamental for HIV treatment, comprises of a combination of drugs such as nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTs), protease inhibitors (PIs) and integrase strand transfer inhibitors (INSTIs). However, although 28.7 million people out of the estimated 38.4 million people living with HIV in 2021 were receiving ART, the emergence of drug-resistant strains further complicates treatment efforts, highlighting the need for novel therapeutic approaches. This study aimed to address the challenges raised by drug resistance and significant side effects by identifying potential dual inhibitors against HIV-1 Reverse Transcriptase (RT) and Integrase (IN) using in silico techniques. RT RNase H and IN were chosen as targets for their shared dependency on Mg2+ ions within their active sites, which are crucial for catalytic activity. The selection of dual inhibitors was motivated by the fact that the virus would need to replicate at two points simultaneously to develop resistance, making it less likely. The objectives of this study included the creation of a natural derivative compound library using RDKit with the aid of SciFinder, utilizing (-)-epigallocatechin-3-O-gallate (EGCG), because of its dual inhibitory effects against RT and IN, as indicated by a study conducted by Sanna et al. 2019. The natural derivatives were chosen to take advantage of their chemical diversity and to explore potential novel therapeutic options for combating HIV drug resistance. The compound library created comprised of 125 203 compounds. Then docking studies were conducted to assess proteinligand binding. After the correlation of the RT and IN docking studies, 288 compounds were filtered to have potential dual inhibitory activity. Then quantitative estimation of druggability (QED) analysis identified three compounds with superior properties compared to EGCG and FDAapproved drug raltegravir (RAL). Molecular docking simulations revealed interactions between the inhibitors and the key active site residues of RT and IN, along with the chelation of at least one 3 Mg2+, suggesting the potential for enzymatic disruption. Furthermore, molecular dynamic (MD) simulations were then conducted to assess protein-ligand system behavior, through RMSD and RMSF analysis. The RMSD analysis uncovered instability in the IN-Sci30703 complex, leading to its exclusion as a potential dual action inhibitor. RMSF analysis for IN showed that all the inhibitors had the ability to limit the flexibility of the catalytic loop which is essential for catalytic activity. Therefore, further in vitro studies are required to evaluate the effectiveness of the remaining two EGCG derivatives (Sci33211 and Sci48919) in inhibiting RT and IN through the chelation of at least one Mg2+ ion to determine if they have superior dual inhibitory effects compared to EGCG. This study adds to the ongoing efforts to develop effective strategies against HIV-1 drug resistance and emphasizes the importance of continued research in this field. , Thesis (MSc) -- Faculty of Science, Biochemistry, Microbiology & Bioinformatics, 2024
- Full Text:
- Date Issued: 2024-10-11
- Authors: Mwiinga, Luyando
- Date: 2024-10-11
- Subjects: HIV (Viruses) , Reverse transcriptase , Antiretroviral agents , RDKit , Drug resistance , Docking
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/463930 , vital:76458
- Description: Human immunodeficiency virus Type 1 (HIV-1) is a devastating viral infection affecting millions worldwide and presents significant challenges in treatment and management. In 2022, approximately 39 million people were living with HIV with Sub-Saharan Africa having two thirds of these infections. Devastatingly, there were approximately 300 000 HIV/AIDS related deaths in Sub-Saharan Africa alone in 2022 alone. Antiretroviral therapy (ART) which is fundamental for HIV treatment, comprises of a combination of drugs such as nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTs), protease inhibitors (PIs) and integrase strand transfer inhibitors (INSTIs). However, although 28.7 million people out of the estimated 38.4 million people living with HIV in 2021 were receiving ART, the emergence of drug-resistant strains further complicates treatment efforts, highlighting the need for novel therapeutic approaches. This study aimed to address the challenges raised by drug resistance and significant side effects by identifying potential dual inhibitors against HIV-1 Reverse Transcriptase (RT) and Integrase (IN) using in silico techniques. RT RNase H and IN were chosen as targets for their shared dependency on Mg2+ ions within their active sites, which are crucial for catalytic activity. The selection of dual inhibitors was motivated by the fact that the virus would need to replicate at two points simultaneously to develop resistance, making it less likely. The objectives of this study included the creation of a natural derivative compound library using RDKit with the aid of SciFinder, utilizing (-)-epigallocatechin-3-O-gallate (EGCG), because of its dual inhibitory effects against RT and IN, as indicated by a study conducted by Sanna et al. 2019. The natural derivatives were chosen to take advantage of their chemical diversity and to explore potential novel therapeutic options for combating HIV drug resistance. The compound library created comprised of 125 203 compounds. Then docking studies were conducted to assess proteinligand binding. After the correlation of the RT and IN docking studies, 288 compounds were filtered to have potential dual inhibitory activity. Then quantitative estimation of druggability (QED) analysis identified three compounds with superior properties compared to EGCG and FDAapproved drug raltegravir (RAL). Molecular docking simulations revealed interactions between the inhibitors and the key active site residues of RT and IN, along with the chelation of at least one 3 Mg2+, suggesting the potential for enzymatic disruption. Furthermore, molecular dynamic (MD) simulations were then conducted to assess protein-ligand system behavior, through RMSD and RMSF analysis. The RMSD analysis uncovered instability in the IN-Sci30703 complex, leading to its exclusion as a potential dual action inhibitor. RMSF analysis for IN showed that all the inhibitors had the ability to limit the flexibility of the catalytic loop which is essential for catalytic activity. Therefore, further in vitro studies are required to evaluate the effectiveness of the remaining two EGCG derivatives (Sci33211 and Sci48919) in inhibiting RT and IN through the chelation of at least one Mg2+ ion to determine if they have superior dual inhibitory effects compared to EGCG. This study adds to the ongoing efforts to develop effective strategies against HIV-1 drug resistance and emphasizes the importance of continued research in this field. , Thesis (MSc) -- Faculty of Science, Biochemistry, Microbiology & Bioinformatics, 2024
- Full Text:
- Date Issued: 2024-10-11
Application of machine learning, molecular modelling and structural data mining against antiretroviral drug resistance in HIV-1
- Sheik Amamuddy, Olivier Serge André
- Authors: Sheik Amamuddy, Olivier Serge André
- Date: 2020
- Subjects: Machine learning , Molecules -- Models , Data mining , Neural networks (Computer science) , Antiretroviral agents , Protease inhibitors , Drug resistance , Multidrug resistance , Molecular dynamics , Renin-angiotensin system , HIV (Viruses) -- South Africa , HIV (Viruses) -- Social aspects -- South Africa , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/115964 , vital:34282
- Description: Millions are affected with the Human Immunodeficiency Virus (HIV) world wide, even though the death toll is on the decline. Antiretrovirals (ARVs), more specifically protease inhibitors have shown tremendous success since their introduction into therapy since the mid 1990’s by slowing down progression to the Acquired Immune Deficiency Syndrome (AIDS). However, Drug Resistance Mutations (DRMs) are constantly selected for due to viral adaptation, making drugs less effective over time. The current challenge is to manage the infection optimally with a limited set of drugs, with differing associated levels of toxicities in the face of a virus that (1) exists as a quasispecies, (2) may transmit acquired DRMs to drug-naive individuals and (3) that can manifest class-wide resistance due to similarities in design. The presence of latent reservoirs, unawareness of infection status, education and various socio-economic factors make the problem even more complex. Adequate timing and choice of drug prescription together with treatment adherence are very important as drug toxicities, drug failure and sub-optimal treatment regimens leave room for further development of drug resistance. While CD4 cell count and the determination of viral load from patients in resource-limited settings are very helpful to track how well a patient’s immune system is able to keep the virus in check, they can be lengthy in determining whether an ARV is effective. Phenosense assay kits answer this problem using viruses engineered to contain the patient sequences and evaluating their growth in the presence of different ARVs, but this can be expensive and too involved for routine checks. As a cheaper and faster alternative, genotypic assays provide similar information from HIV pol sequences obtained from blood samples, inferring ARV efficacy on the basis of drug resistance mutation patterns. However, these are inherently complex and the various methods of in silico prediction, such as Geno2pheno, REGA and Stanford HIVdb do not always agree in every case, even though this gap decreases as the list of resistance mutations is updated. A major gap in HIV treatment is that the information used for predicting drug resistance is mainly computed from data containing an overwhelming majority of B subtype HIV, when these only comprise about 12% of the worldwide HIV infections. In addition to growing evidence that drug resistance is subtype-related, it is intuitive to hypothesize that as subtyping is a phylogenetic classification, the more divergent a subtype is from the strains used in training prediction models, the less their resistance profiles would correlate. For the aforementioned reasons, we used a multi-faceted approach to attack the virus in multiple ways. This research aimed to (1) improve resistance prediction methods by focusing solely on the available subtype, (2) mine structural information pertaining to resistance in order to find any exploitable weak points and increase knowledge of the mechanistic processes of drug resistance in HIV protease. Finally, (3) we screen for protease inhibitors amongst a database of natural compounds [the South African natural compound database (SANCDB)] to find molecules or molecular properties usable to come up with improved inhibition against the drug target. In this work, structural information was mined using the Anisotropic Network Model, Dynamics Cross-Correlation, Perturbation Response Scanning, residue contact network analysis and the radius of gyration. These methods failed to give any resistance-associated patterns in terms of natural movement, internal correlated motions, residue perturbation response, relational behaviour and global compaction respectively. Applications of drug docking, homology-modelling and energy minimization for generating features suitable for machine-learning were not very promising, and rather suggest that the value of binding energies by themselves from Vina may not be very reliable quantitatively. All these failures lead to a refinement that resulted in a highly sensitive statistically-guided network construction and analysis, which leads to key findings in the early dynamics associated with resistance across all PI drugs. The latter experiment unravelled a conserved lateral expansion motion occurring at the flap elbows, and an associated contraction that drives the base of the dimerization domain towards the catalytic site’s floor in the case of drug resistance. Interestingly, we found that despite the conserved movement, bond angles were degenerate. Alongside, 16 Artificial Neural Network models were optimised for HIV proteases and reverse transcriptase inhibitors, with performances on par with Stanford HIVdb. Finally, we prioritised 9 compounds with potential protease inhibitory activity using virtual screening and molecular dynamics (MD) to additionally suggest a promising modification to one of the compounds. This yielded another molecule inhibiting equally well both opened and closed receptor target conformations, whereby each of the compounds had been selected against an array of multi-drug-resistant receptor variants. While a main hurdle was a lack of non-B subtype data, our findings, especially from the statistically-guided network analysis, may extrapolate to a certain extent to them as the level of conservation was very high within subtype B, despite all the present variations. This network construction method lays down a sensitive approach for analysing a pair of alternate phenotypes for which complex patterns prevail, given a sufficient number of experimental units. During the course of research a weighted contact mapping tool was developed to compare renin-angiotensinogen variants and packaged as part of the MD-TASK tool suite. Finally the functionality, compatibility and performance of the MODE-TASK tool were evaluated and confirmed for both Python2.7.x and Python3.x, for the analysis of normals modes from single protein structures and essential modes from MD trajectories. These techniques and tools collectively add onto the conventional means of MD analysis.
- Full Text:
- Date Issued: 2020
- Authors: Sheik Amamuddy, Olivier Serge André
- Date: 2020
- Subjects: Machine learning , Molecules -- Models , Data mining , Neural networks (Computer science) , Antiretroviral agents , Protease inhibitors , Drug resistance , Multidrug resistance , Molecular dynamics , Renin-angiotensin system , HIV (Viruses) -- South Africa , HIV (Viruses) -- Social aspects -- South Africa , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/115964 , vital:34282
- Description: Millions are affected with the Human Immunodeficiency Virus (HIV) world wide, even though the death toll is on the decline. Antiretrovirals (ARVs), more specifically protease inhibitors have shown tremendous success since their introduction into therapy since the mid 1990’s by slowing down progression to the Acquired Immune Deficiency Syndrome (AIDS). However, Drug Resistance Mutations (DRMs) are constantly selected for due to viral adaptation, making drugs less effective over time. The current challenge is to manage the infection optimally with a limited set of drugs, with differing associated levels of toxicities in the face of a virus that (1) exists as a quasispecies, (2) may transmit acquired DRMs to drug-naive individuals and (3) that can manifest class-wide resistance due to similarities in design. The presence of latent reservoirs, unawareness of infection status, education and various socio-economic factors make the problem even more complex. Adequate timing and choice of drug prescription together with treatment adherence are very important as drug toxicities, drug failure and sub-optimal treatment regimens leave room for further development of drug resistance. While CD4 cell count and the determination of viral load from patients in resource-limited settings are very helpful to track how well a patient’s immune system is able to keep the virus in check, they can be lengthy in determining whether an ARV is effective. Phenosense assay kits answer this problem using viruses engineered to contain the patient sequences and evaluating their growth in the presence of different ARVs, but this can be expensive and too involved for routine checks. As a cheaper and faster alternative, genotypic assays provide similar information from HIV pol sequences obtained from blood samples, inferring ARV efficacy on the basis of drug resistance mutation patterns. However, these are inherently complex and the various methods of in silico prediction, such as Geno2pheno, REGA and Stanford HIVdb do not always agree in every case, even though this gap decreases as the list of resistance mutations is updated. A major gap in HIV treatment is that the information used for predicting drug resistance is mainly computed from data containing an overwhelming majority of B subtype HIV, when these only comprise about 12% of the worldwide HIV infections. In addition to growing evidence that drug resistance is subtype-related, it is intuitive to hypothesize that as subtyping is a phylogenetic classification, the more divergent a subtype is from the strains used in training prediction models, the less their resistance profiles would correlate. For the aforementioned reasons, we used a multi-faceted approach to attack the virus in multiple ways. This research aimed to (1) improve resistance prediction methods by focusing solely on the available subtype, (2) mine structural information pertaining to resistance in order to find any exploitable weak points and increase knowledge of the mechanistic processes of drug resistance in HIV protease. Finally, (3) we screen for protease inhibitors amongst a database of natural compounds [the South African natural compound database (SANCDB)] to find molecules or molecular properties usable to come up with improved inhibition against the drug target. In this work, structural information was mined using the Anisotropic Network Model, Dynamics Cross-Correlation, Perturbation Response Scanning, residue contact network analysis and the radius of gyration. These methods failed to give any resistance-associated patterns in terms of natural movement, internal correlated motions, residue perturbation response, relational behaviour and global compaction respectively. Applications of drug docking, homology-modelling and energy minimization for generating features suitable for machine-learning were not very promising, and rather suggest that the value of binding energies by themselves from Vina may not be very reliable quantitatively. All these failures lead to a refinement that resulted in a highly sensitive statistically-guided network construction and analysis, which leads to key findings in the early dynamics associated with resistance across all PI drugs. The latter experiment unravelled a conserved lateral expansion motion occurring at the flap elbows, and an associated contraction that drives the base of the dimerization domain towards the catalytic site’s floor in the case of drug resistance. Interestingly, we found that despite the conserved movement, bond angles were degenerate. Alongside, 16 Artificial Neural Network models were optimised for HIV proteases and reverse transcriptase inhibitors, with performances on par with Stanford HIVdb. Finally, we prioritised 9 compounds with potential protease inhibitory activity using virtual screening and molecular dynamics (MD) to additionally suggest a promising modification to one of the compounds. This yielded another molecule inhibiting equally well both opened and closed receptor target conformations, whereby each of the compounds had been selected against an array of multi-drug-resistant receptor variants. While a main hurdle was a lack of non-B subtype data, our findings, especially from the statistically-guided network analysis, may extrapolate to a certain extent to them as the level of conservation was very high within subtype B, despite all the present variations. This network construction method lays down a sensitive approach for analysing a pair of alternate phenotypes for which complex patterns prevail, given a sufficient number of experimental units. During the course of research a weighted contact mapping tool was developed to compare renin-angiotensinogen variants and packaged as part of the MD-TASK tool suite. Finally the functionality, compatibility and performance of the MODE-TASK tool were evaluated and confirmed for both Python2.7.x and Python3.x, for the analysis of normals modes from single protein structures and essential modes from MD trajectories. These techniques and tools collectively add onto the conventional means of MD analysis.
- Full Text:
- Date Issued: 2020
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