Identification of novel compounds against Plasmodium falciparum Cytochrome bc1 Complex inhibiting the trans-membrane electron transfer pathway: an In Silico study
- Authors: Chebon, Lorna Jemosop
- Date: 2022-10-14
- Subjects: Malaria , Plasmodium falciparum , Molecular dynamics , Antimalarials , Molecules Models , Docking , Cytochromes , Drug resistance , Computer simulation , Drugs Computer-aided design , System analysis
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/365666 , vital:65774 , DOI https://doi.org/10.21504/10962/365666
- Description: Malaria continues to be a burden globally with a myriad of challenges deterring eradication efforts. With most antimalarials facing drug resistance, such as atovaquone (ATQ), alternative compounds that can withstand resistance are warranted. The Plasmodium falciparum cytochrome b (PfCytb), a subunit of P. falciparum cytochrome bc1 complex, is a validated drug target. Structurally, cytochrome b, cytochrome c1, and iron sulphur protein (ISP) subunits form the catalytic domain of the protein complex having heme bL, heme bH and iron-sulphur [2FE-2S] cluster cofactors. These cofactos have redox centres to aid in the electron transfer (ET) process. These subunits promote ET mainly through the enzyme’s ubiquinol oxidation (Qo) and ubiquinone reduction (Qi) processes in the catalytic domain. ATQ drug has been used in the prevention and treatment of uncomplicated malaria by targeting PfCytb protein. Once the mitochondrial transmembrane ET pathway is inhibited, it causes a collapse in its membrane potential. Previously reported ATQ drug resistance has been associated with the point mutations Y268C, Y268N and Y268S. Thus, in finding alternatives to the ATQ drug, this research aimed to: i) employ in silico approaches incorporating protein into phospholipid bilayer for the first time to understand the parasites’ resistance mechanism; ii) determine any sequence and structural differences that could be explored in drug design studies; and iii) screen for PfCytb-iron sulphur protein (Cytb-ISP) hit compounds from South African natural compound database (SANCDB) and Medicines for Malaria Venture (MMV) that can withstand the identified mutations. Using computational tools, comparative sequence and structural analyses were performed on the cytochrome b protein, where the ultimate focus was on P. falciparum cytochrome b and its human homolog. Through multiple sequence alignment, motif discovery and phylogeny, differences between P. falciparum and H. sapiens cytochrome b were identified. Protein modelling of both P. falciparum and H. sapiens cytochrome b - iron sulphur protein (PfCytb-ISP and HsCytb-ISP) was performed. Results showed that at the sequence level, there were few amino acid residue differences because the protein is highly conserved. Important to note is the four-residue deletion in Plasmodium spp. absent in the human homolog. Motif analysis discovered five unique motifs in P. falciparum cytochrome b protein which were mapped onto the predicted protein model. These motifs were not in regions of functional importance; hence their function is still unknown. At a structural level, the four-residue deletion was observed to alter the Qo substrate binding pocket as reported in previous studies and confirmed in this study. This deletion resulted in a 0.83 Å structural displacement. Also, there are currently no in silico studies that have performed experiments with P. falciparum cytochrome b protein incorporated into a phospholipid bilayer. Using 350 ns molecular dynamics (MD) simulations of the holo and ATQ-bound systems, the study highlighted the resistance mechanism of the parasite protein where the loss of active site residue-residue interactions was identified, all linked to the three mutations. The identified compromised interactions are likely to destabilise the protein’s function, specifically in the Qo substrate binding site. This showed the possible effect of mutations on ATQ drug activity, where all three mutations were reported to share a similar resistance mechanism. Thereafter, this research work utilised in silico approaches where both Qo active site and interface pocket were targeted by screening the South African natural compounds database (SANCDB) and Medicines for Malaria Venture (MMV) compounds to identify novel selective hits. SANCDB compounds are known for their structural complexity that preserves the potency of the drug molecule. Both SANCDB and MMV compounds have not been explored as inhibitors against the PfCytb drug target. Molecular docking, molecular dynamics (MD) simulations, principal component, and dynamic residue network (DRN; global and local) analyses were utilised to identify and confirm the potential selective inhibitors. Docking results identified compounds that bound selectively onto PfCytb-ISP with a binding energy ≤ -8.7 kcal/mol-1. Further, this work validated a total of eight potential selective compounds to inhibit PfCytb-ISP protein (Qo active site) not only in the wild-type but also in the presence of the point mutations Y268C, Y268N and Y268S. The selective binding of these hit compounds could be linked to the differences reported at sequence/residue level in chapter 3. DRN and residue contact map analyses of the eight compounds in holo and ligand-bound systems revealed reduced residue interactions and decreased protein communication. This suggests that the eight compounds show the possibility of inhibiting the parasite and disrupting important residue-residue interactions. Additionally, 13 selective compounds were identified to bind at the protein’s heterodimer interface, where global and local analysis confirmed their effect on active site residues (distal location) as well as on the communication network. Based on the sequence differences between PfCytb and the human homolog, these findings suggest these selective compounds as potential allosteric modulators of the parasite enzyme, which may serve as possible replacements of the already resistant ATQ drug. Therefore, these findings pave the way for further in vitro studies to establish their anti-plasmodial inhibition levels. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
- Date Issued: 2022-10-14
- Authors: Chebon, Lorna Jemosop
- Date: 2022-10-14
- Subjects: Malaria , Plasmodium falciparum , Molecular dynamics , Antimalarials , Molecules Models , Docking , Cytochromes , Drug resistance , Computer simulation , Drugs Computer-aided design , System analysis
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/365666 , vital:65774 , DOI https://doi.org/10.21504/10962/365666
- Description: Malaria continues to be a burden globally with a myriad of challenges deterring eradication efforts. With most antimalarials facing drug resistance, such as atovaquone (ATQ), alternative compounds that can withstand resistance are warranted. The Plasmodium falciparum cytochrome b (PfCytb), a subunit of P. falciparum cytochrome bc1 complex, is a validated drug target. Structurally, cytochrome b, cytochrome c1, and iron sulphur protein (ISP) subunits form the catalytic domain of the protein complex having heme bL, heme bH and iron-sulphur [2FE-2S] cluster cofactors. These cofactos have redox centres to aid in the electron transfer (ET) process. These subunits promote ET mainly through the enzyme’s ubiquinol oxidation (Qo) and ubiquinone reduction (Qi) processes in the catalytic domain. ATQ drug has been used in the prevention and treatment of uncomplicated malaria by targeting PfCytb protein. Once the mitochondrial transmembrane ET pathway is inhibited, it causes a collapse in its membrane potential. Previously reported ATQ drug resistance has been associated with the point mutations Y268C, Y268N and Y268S. Thus, in finding alternatives to the ATQ drug, this research aimed to: i) employ in silico approaches incorporating protein into phospholipid bilayer for the first time to understand the parasites’ resistance mechanism; ii) determine any sequence and structural differences that could be explored in drug design studies; and iii) screen for PfCytb-iron sulphur protein (Cytb-ISP) hit compounds from South African natural compound database (SANCDB) and Medicines for Malaria Venture (MMV) that can withstand the identified mutations. Using computational tools, comparative sequence and structural analyses were performed on the cytochrome b protein, where the ultimate focus was on P. falciparum cytochrome b and its human homolog. Through multiple sequence alignment, motif discovery and phylogeny, differences between P. falciparum and H. sapiens cytochrome b were identified. Protein modelling of both P. falciparum and H. sapiens cytochrome b - iron sulphur protein (PfCytb-ISP and HsCytb-ISP) was performed. Results showed that at the sequence level, there were few amino acid residue differences because the protein is highly conserved. Important to note is the four-residue deletion in Plasmodium spp. absent in the human homolog. Motif analysis discovered five unique motifs in P. falciparum cytochrome b protein which were mapped onto the predicted protein model. These motifs were not in regions of functional importance; hence their function is still unknown. At a structural level, the four-residue deletion was observed to alter the Qo substrate binding pocket as reported in previous studies and confirmed in this study. This deletion resulted in a 0.83 Å structural displacement. Also, there are currently no in silico studies that have performed experiments with P. falciparum cytochrome b protein incorporated into a phospholipid bilayer. Using 350 ns molecular dynamics (MD) simulations of the holo and ATQ-bound systems, the study highlighted the resistance mechanism of the parasite protein where the loss of active site residue-residue interactions was identified, all linked to the three mutations. The identified compromised interactions are likely to destabilise the protein’s function, specifically in the Qo substrate binding site. This showed the possible effect of mutations on ATQ drug activity, where all three mutations were reported to share a similar resistance mechanism. Thereafter, this research work utilised in silico approaches where both Qo active site and interface pocket were targeted by screening the South African natural compounds database (SANCDB) and Medicines for Malaria Venture (MMV) compounds to identify novel selective hits. SANCDB compounds are known for their structural complexity that preserves the potency of the drug molecule. Both SANCDB and MMV compounds have not been explored as inhibitors against the PfCytb drug target. Molecular docking, molecular dynamics (MD) simulations, principal component, and dynamic residue network (DRN; global and local) analyses were utilised to identify and confirm the potential selective inhibitors. Docking results identified compounds that bound selectively onto PfCytb-ISP with a binding energy ≤ -8.7 kcal/mol-1. Further, this work validated a total of eight potential selective compounds to inhibit PfCytb-ISP protein (Qo active site) not only in the wild-type but also in the presence of the point mutations Y268C, Y268N and Y268S. The selective binding of these hit compounds could be linked to the differences reported at sequence/residue level in chapter 3. DRN and residue contact map analyses of the eight compounds in holo and ligand-bound systems revealed reduced residue interactions and decreased protein communication. This suggests that the eight compounds show the possibility of inhibiting the parasite and disrupting important residue-residue interactions. Additionally, 13 selective compounds were identified to bind at the protein’s heterodimer interface, where global and local analysis confirmed their effect on active site residues (distal location) as well as on the communication network. Based on the sequence differences between PfCytb and the human homolog, these findings suggest these selective compounds as potential allosteric modulators of the parasite enzyme, which may serve as possible replacements of the already resistant ATQ drug. Therefore, these findings pave the way for further in vitro studies to establish their anti-plasmodial inhibition levels. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
- Date Issued: 2022-10-14
The characterization of GTP Cyclohydrolase I and 6-Pyruvoyl Tetrahydropterin Synthase enzymes as potential anti-malarial drug targets
- Khairallah, Afrah Yousif Huseein
- Authors: Khairallah, Afrah Yousif Huseein
- Date: 2022-04-08
- Subjects: Antimalarials , Plasmodium falciparum , Malaria Chemotherapy , Malaria Africa , Drug resistance , Drug development , Molecular dynamics
- Language: English
- Type: Doctoral thesis , text
- Identifier: http://hdl.handle.net/10962/233784 , vital:50127 , DOI 10.21504/10962/233784
- Description: Malaria remains a public health problem and a high burden of disease, especially in developing countries. The unicellular protozoan malaria parasite of the genus Plasmodium infects about a quarter of a billion people annually, with an estimated 409 000 death cases. The majority of malaria cases occurred in Africa; hence, the region is regarded as endemic for malaria. Global efforts to eradicate the disease led to a decrease in morbidity and mortality rates. However, an enormous burden of malaria infection remains, and it cannot go unnoticed. Countries with limited resources are more affected by the disease, mainly on its public health and socio-economic development, due to many factors besides malaria itself, such as lack of access to adequate, affordable treatments and preventative regimes. Furthermore, the current antimalarial drugs are losing their efficacy because of parasite drug resistance. The emerged drug resistance has reduced the drug efficacy in clearing the parasite from the host system, causing prolonged illness and a higher risk of death. Therefore, the emerged antimalarial drug resistance has hindered the global efforts for malaria control and elimination and established an urgent need for new treatment strategies. When the resistance against classical antimalarial drugs emerged, the class of antifolate antimalarial medicines became the most common alternative. The antifolate antimalarial drugs target the malaria parasite de novo folate biosynthesis pathway by limiting folate derivates, which are essential for the parasite cell growth and survival. Yet again, the malaria parasite developed resistance against the available antifolate drugs, rendering the drugs ineffective in many cases. Given the previous success in targeting the malaria parasite de novo folate biosynthesis pathway, alternative enzymes within this pathway stand as good targets and can be explored to develop new antifolate drugs with novel mechanisms of action. The primary focus of this thesis is to contribute to the existing and growing knowledge of antimalarial drug discovery. The study aims to characterise the malaria parasite de novo folate synthesis pathway enzymes guanosine-5'-triphosphate (GTP) cyclohydrolase I (GCH1) and 6-pyruvoyl tetrahydropterin synthase (PTPS) as alternative drug targets for malaria treatment by using computational approaches. Further, discover new allosteric drug targeting sites within the two enzymes' 3D structures for future drug design and discovery. Sequence and structural analysis were carried out to characterise and pinpoint the two enzymes' unique sequence and structure-based features. From the analyses, key sequence and structure differences were identified between the malaria parasite enzymes relative to their human homolog; the identified sites can aid significantly in designing and developing new antimalarial antifolate drugs with good selectivity toward the parasites’ enzymes. GCH1 and PTPS contain a catalytically essential metal ion in their active site; therefore, force field parameters were needed to study their active sites accurately during all-atom molecular dynamic simulations (MD). The force field parameters were derived through quantum mechanics potential energy surface scans of the metals bonded terms and evaluated via all-atom MD simulations. Proteins structural dynamics is imperative for many biological processes; thus, it is essential to consider the structural dynamics of proteins whilst understanding their function. In this regard, the normal mode analysis (NMA) approach based on the elastic network model (ENM) was employed to study the intrinsic dynamics and conformations changes of GCH1 and PTPS enzymes. The NMA disclosed essential structural information about the protein’s intrinsic dynamics and mechanism of allosteric modulation of their binding properties, further highlighting regions that govern their conformational changes. The analysis also disclosed hotspot residues that are crucial for the proteins' fold stability and function. The NMA was further combined with sequence motif results and showed that conserved residues of GCH1 and PTPS were located within the identified key structural sites modulating the proteins' conformational rearrangement. The characterized structural features and hotspot residues were regarded as potential allosteric sites of important value for the design and development of allosteric drugs. Both GCH1 and PTPS enzymes have never been targeted before and can provide an excellent opportunity to overcome the antimalarial antifolate drug resistance problem. The data presented in this thesis contribute to the understanding of the sequence, structure, and global dynamics of both GCH1 and PTPS, further disclose potential allosteric drug targeting sites and unique structural features of both enzymes that can establish a solid starting point for drug design and development of new antimalarial drugs of a novel mechanism of actions. Lastly, the reported force field parameters will be of value for MD simulations for future in-silico drug discovery studies involving the two enzymes and other enzymes with the same Zn2+ binding motifs and coordination environments. The impact of this research can facilitate the discovery of new effective antimalarial medicines with novel mechanisms of action. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
- Date Issued: 2022-04-08
- Authors: Khairallah, Afrah Yousif Huseein
- Date: 2022-04-08
- Subjects: Antimalarials , Plasmodium falciparum , Malaria Chemotherapy , Malaria Africa , Drug resistance , Drug development , Molecular dynamics
- Language: English
- Type: Doctoral thesis , text
- Identifier: http://hdl.handle.net/10962/233784 , vital:50127 , DOI 10.21504/10962/233784
- Description: Malaria remains a public health problem and a high burden of disease, especially in developing countries. The unicellular protozoan malaria parasite of the genus Plasmodium infects about a quarter of a billion people annually, with an estimated 409 000 death cases. The majority of malaria cases occurred in Africa; hence, the region is regarded as endemic for malaria. Global efforts to eradicate the disease led to a decrease in morbidity and mortality rates. However, an enormous burden of malaria infection remains, and it cannot go unnoticed. Countries with limited resources are more affected by the disease, mainly on its public health and socio-economic development, due to many factors besides malaria itself, such as lack of access to adequate, affordable treatments and preventative regimes. Furthermore, the current antimalarial drugs are losing their efficacy because of parasite drug resistance. The emerged drug resistance has reduced the drug efficacy in clearing the parasite from the host system, causing prolonged illness and a higher risk of death. Therefore, the emerged antimalarial drug resistance has hindered the global efforts for malaria control and elimination and established an urgent need for new treatment strategies. When the resistance against classical antimalarial drugs emerged, the class of antifolate antimalarial medicines became the most common alternative. The antifolate antimalarial drugs target the malaria parasite de novo folate biosynthesis pathway by limiting folate derivates, which are essential for the parasite cell growth and survival. Yet again, the malaria parasite developed resistance against the available antifolate drugs, rendering the drugs ineffective in many cases. Given the previous success in targeting the malaria parasite de novo folate biosynthesis pathway, alternative enzymes within this pathway stand as good targets and can be explored to develop new antifolate drugs with novel mechanisms of action. The primary focus of this thesis is to contribute to the existing and growing knowledge of antimalarial drug discovery. The study aims to characterise the malaria parasite de novo folate synthesis pathway enzymes guanosine-5'-triphosphate (GTP) cyclohydrolase I (GCH1) and 6-pyruvoyl tetrahydropterin synthase (PTPS) as alternative drug targets for malaria treatment by using computational approaches. Further, discover new allosteric drug targeting sites within the two enzymes' 3D structures for future drug design and discovery. Sequence and structural analysis were carried out to characterise and pinpoint the two enzymes' unique sequence and structure-based features. From the analyses, key sequence and structure differences were identified between the malaria parasite enzymes relative to their human homolog; the identified sites can aid significantly in designing and developing new antimalarial antifolate drugs with good selectivity toward the parasites’ enzymes. GCH1 and PTPS contain a catalytically essential metal ion in their active site; therefore, force field parameters were needed to study their active sites accurately during all-atom molecular dynamic simulations (MD). The force field parameters were derived through quantum mechanics potential energy surface scans of the metals bonded terms and evaluated via all-atom MD simulations. Proteins structural dynamics is imperative for many biological processes; thus, it is essential to consider the structural dynamics of proteins whilst understanding their function. In this regard, the normal mode analysis (NMA) approach based on the elastic network model (ENM) was employed to study the intrinsic dynamics and conformations changes of GCH1 and PTPS enzymes. The NMA disclosed essential structural information about the protein’s intrinsic dynamics and mechanism of allosteric modulation of their binding properties, further highlighting regions that govern their conformational changes. The analysis also disclosed hotspot residues that are crucial for the proteins' fold stability and function. The NMA was further combined with sequence motif results and showed that conserved residues of GCH1 and PTPS were located within the identified key structural sites modulating the proteins' conformational rearrangement. The characterized structural features and hotspot residues were regarded as potential allosteric sites of important value for the design and development of allosteric drugs. Both GCH1 and PTPS enzymes have never been targeted before and can provide an excellent opportunity to overcome the antimalarial antifolate drug resistance problem. The data presented in this thesis contribute to the understanding of the sequence, structure, and global dynamics of both GCH1 and PTPS, further disclose potential allosteric drug targeting sites and unique structural features of both enzymes that can establish a solid starting point for drug design and development of new antimalarial drugs of a novel mechanism of actions. Lastly, the reported force field parameters will be of value for MD simulations for future in-silico drug discovery studies involving the two enzymes and other enzymes with the same Zn2+ binding motifs and coordination environments. The impact of this research can facilitate the discovery of new effective antimalarial medicines with novel mechanisms of action. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
- Date Issued: 2022-04-08
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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