Application of machine learning, molecular modelling and structural data mining against antiretroviral drug resistance in HIV-1
- 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.
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Understanding the underlying resistance mechanism of Mycobacterium tuberculosis against Rifampicin by analyzing mutant DNA - directed RNA polymerase proteins via bioinformatics approaches
- Authors: Monama, Mokgerwa Zacharia
- Date: 2020
- Subjects: Mycobacterium tuberculosis , Rifampin , Drug resistance , Homology (Biology) , Tuberculosis -- Chemotherapy
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/167508 , vital:41487
- Description: Tuberculosis or TB is an airborne disease caused by the non-motile bacilli, Mycobacterium tuberculosis (MTB). There are two main forms of TB, namely, latent TB or LTB, asymptomatic and non-contagious version which according to the World Health Organization (WHO) is estimated to afflict over a third of the world’s population; and active TB or ATB, a symptomatic and contagious version which continues to spread, affecting millions worldwide. With the already high reported prevalence of TB, the emergence of drug-resistant strains has prompted the development of novel approaches to enhance the efficacy of known drugs and a desperate search for novel compounds to combat MTB infections. It was for this very purpose that this study was conducted. A look into the resistance mechanism of Rifampicin (Rifampin or RIF), one of the more potent first-line drugs, might prove beneficial in predicting the consequence of an introduced mutation (which usually occur as single nucleotide polymorphisms or SNPs) and perhaps even overcome it using appropriate therapeutic interventions that improve RIF’s efficacy. To accomplish this task, models of acceptable quality were generated for the WT and clinically relevant, RIF resistance conferring, SNPs occurring at codon positions D516, H526 and S531 (E .coli numbering system) using MODELLER. The models were accordingly ranked using GA341 and z-DOPE score, and subsequently validated with QMEAN, PROCHECK and VERIFY3D. MD simulations spanning 100 ns were run for RIF-bound (complex) and RIF-free (holo) DNA-directed RNA polymerase (DDRP) protein systems for the WT and SNP mutants using GROMACS. The MD frames were analyzed using RMSD, Rg and RMSF. For further analysis, MD-TASK was used to analyze the calculated dynamic residue networks (DRNs) from the generated MD frames, determining both change in average shortest path (ΔL) and betweenness centrality (ΔBC). The RMSD analysis revealed that all of the SNP complex models displayed a level instability higher than that of the WT complex. A majority of the SNP complex models were also observed to have similar compactness to the WT holo when looking at the calculated Rg. The RMSF results also hinted towards possible physiological consequences of the mutations (generally referred to as a fitness cost) highlighted by the increased fluctuations of the zinc-binding domain and the MTB SI α helical coiled coil. For the first time, to the knowledge of the authors, DRN analysis was employed for the DDRP protein for both holo and complex systems, revealing insightful information about the residues that play a key role in the change in distance between residue pairs along with residues that play an essential role in protein communication within the calculated RIN. Overall, the data supported the conclusions drawn by a recent study that only concentrated on RIF-resistance in rpoB models which suggested that the binding pocket for the SNP models may result in the changed coordination of RIF which may be the main contributor to its impaired efficacy.
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