Determining the unbinding events and conserved motions associated with the pyrazinamide release due to resistance mutations of Mycobacterium tuberculosis pyrazinamidase:
- Amamuddy, Olivier S, Musyoka, Thommas M, Boateng, Rita A, Zabo, Sophakama, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Musyoka, Thommas M , Boateng, Rita A , Zabo, Sophakama , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148869 , vital:38781 , https://doi.org/10.1016/j.csbj.2020.05.0099
- Description: Pyrazinamide (PZA) is the only first-line antitubercular drug active against latent Mycobacterium tuberculosis (Mtb). It is activated to pyrazinoic acid by the pncA-encoded pyrazinamidase enzyme (PZase). Despite the emergence of PZA drug resistance, the underlying mechanisms of resistance remain unclear. This study investigated part of these mechanisms by modelling a PZA-bound wild type and 82 mutant PZase structures before applying molecular dynamics (MD) with an accurate Fe2+ cofactor coordination geometry.
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- Authors: Amamuddy, Olivier S , Musyoka, Thommas M , Boateng, Rita A , Zabo, Sophakama , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148869 , vital:38781 , https://doi.org/10.1016/j.csbj.2020.05.0099
- Description: Pyrazinamide (PZA) is the only first-line antitubercular drug active against latent Mycobacterium tuberculosis (Mtb). It is activated to pyrazinoic acid by the pncA-encoded pyrazinamidase enzyme (PZase). Despite the emergence of PZA drug resistance, the underlying mechanisms of resistance remain unclear. This study investigated part of these mechanisms by modelling a PZA-bound wild type and 82 mutant PZase structures before applying molecular dynamics (MD) with an accurate Fe2+ cofactor coordination geometry.
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Impact of early pandemic stage mutations on molecular dynamics of SARS-CoV-2 Mpro:
- Amamuddy, Olivier S, Verkhivker, Gennady M, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Verkhivker, Gennady M , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/162330 , vital:40835 , https://0-doi.org.wam.seals.ac.za/10.1021/acs.jcim.0c00634
- Description: A new coronavirus (SARS-CoV-2) is a global threat to world health and economy. Its dimeric main protease (Mpro), which is required for the proteolytic cleavage of viral precursor proteins, is a good candidate for drug development owing to its conservation and the absence of a human homolog. Improving our understanding of Mpro behavior can accelerate the discovery of effective therapies to reduce mortality. All-atom molecular dynamics (MD) simulations (100 ns) of 50 mutant Mpro dimers obtained from filtered sequences from the GISAID database were analyzed using root-mean-square deviation, root-mean-square fluctuation, Rg, averaged betweenness centrality, and geometry calculations.
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- Authors: Amamuddy, Olivier S , Verkhivker, Gennady M , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/162330 , vital:40835 , https://0-doi.org.wam.seals.ac.za/10.1021/acs.jcim.0c00634
- Description: A new coronavirus (SARS-CoV-2) is a global threat to world health and economy. Its dimeric main protease (Mpro), which is required for the proteolytic cleavage of viral precursor proteins, is a good candidate for drug development owing to its conservation and the absence of a human homolog. Improving our understanding of Mpro behavior can accelerate the discovery of effective therapies to reduce mortality. All-atom molecular dynamics (MD) simulations (100 ns) of 50 mutant Mpro dimers obtained from filtered sequences from the GISAID database were analyzed using root-mean-square deviation, root-mean-square fluctuation, Rg, averaged betweenness centrality, and geometry calculations.
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Impact of emerging mutations on the dynamic properties the SARS-CoV-2 main protease: an in silico investigation
- Amamuddy, Olivier S, Verkhivker, Gennady M, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Verkhivker, Gennady M , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163035 , vital:41006 , doi: 10.1021/acs.jcim.0c00634
- Description: The new coronavirus (SARS-CoV-2) is a global threat to world health and its economy. Its main protease (Mpro), which functions as a dimer, cleaves viral precursor proteins in the process of viral maturation. It is a good candidate for drug development owing to its conservation and the absence of a human homolog. An improved understanding of the protein behaviour can accelerate the discovery of effective therapies in order to reduce mortality. 100 ns all-atom molecular dynamics simulations of 50 homology modelled mutant Mpro dimers were performed at pH 7 from filtered sequences obtained from the GISAID database. Protease dynamics were analysed using RMSD, RMSF, Rg, the averaged betweenness centrality and geometry calculations. Domains from each Mpro protomer were found to generally have independent motions, while the dimer-stabilising N-finger region was found to be flexible in most mutants.
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- Authors: Amamuddy, Olivier S , Verkhivker, Gennady M , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163035 , vital:41006 , doi: 10.1021/acs.jcim.0c00634
- Description: The new coronavirus (SARS-CoV-2) is a global threat to world health and its economy. Its main protease (Mpro), which functions as a dimer, cleaves viral precursor proteins in the process of viral maturation. It is a good candidate for drug development owing to its conservation and the absence of a human homolog. An improved understanding of the protein behaviour can accelerate the discovery of effective therapies in order to reduce mortality. 100 ns all-atom molecular dynamics simulations of 50 homology modelled mutant Mpro dimers were performed at pH 7 from filtered sequences obtained from the GISAID database. Protease dynamics were analysed using RMSD, RMSF, Rg, the averaged betweenness centrality and geometry calculations. Domains from each Mpro protomer were found to generally have independent motions, while the dimer-stabilising N-finger region was found to be flexible in most mutants.
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Integrated computational approaches and tools for allosteric drug discovery:
- Amamuddy, Olivier S, Veldman, Wade, Manyumwa, Colleen, Khairallah, Afrah, Agajanian, Steve, Oluyemi, Odeyemi, Verkhivker, Gennady M, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Veldman, Wade , Manyumwa, Colleen , Khairallah, Afrah , Agajanian, Steve , Oluyemi, Odeyemi , Verkhivker, Gennady M , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163012 , vital:41004 , https://doi.org/10.3390/ijms21030847
- Description: Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.
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- Authors: Amamuddy, Olivier S , Veldman, Wade , Manyumwa, Colleen , Khairallah, Afrah , Agajanian, Steve , Oluyemi, Odeyemi , Verkhivker, Gennady M , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163012 , vital:41004 , https://doi.org/10.3390/ijms21030847
- Description: Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.
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Computational analysis of missense mutations from the human Macrophage Migration Inhibitory Factor (MIF) protein by Molecular Dynamics Simulations and Dynamic Residue Network Analysis:
- Kimuda, Phillip M, Brown, David K, Amamuddy, Olivier S, Ross, Caroline J, Matovu, Enock, Tastan Bishop, Özlem
- Authors: Kimuda, Phillip M , Brown, David K , Amamuddy, Olivier S , Ross, Caroline J , Matovu, Enock , Tastan Bishop, Özlem
- Date: 2019
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163238 , vital:41021 , https://doi.org/10.21955/aasopenres.1115054.1
- Description: Missense mutations are changes in the DNA that result in a change in the amino acid sequence. Depending on their location within the protein they can have a negative impact on how the protein functions. This is especially important for proteins involved in the body’s response to infection and diseases. Macrophage migration inhibitory factor (MIF) is one such protein that functions to recruit white blood cells to sites of inflammation.
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- Authors: Kimuda, Phillip M , Brown, David K , Amamuddy, Olivier S , Ross, Caroline J , Matovu, Enock , Tastan Bishop, Özlem
- Date: 2019
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163238 , vital:41021 , https://doi.org/10.21955/aasopenres.1115054.1
- Description: Missense mutations are changes in the DNA that result in a change in the amino acid sequence. Depending on their location within the protein they can have a negative impact on how the protein functions. This is especially important for proteins involved in the body’s response to infection and diseases. Macrophage migration inhibitory factor (MIF) is one such protein that functions to recruit white blood cells to sites of inflammation.
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Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics:
- Amamuddy, Olivier S, Bishop, Nigel T, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148126 , vital:38712 , DOI: 10.1038/s41598-018-36041-8
- Description: The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class.
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- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148126 , vital:38712 , DOI: 10.1038/s41598-018-36041-8
- Description: The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class.
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MODE-TASK: Large-scale protein motion tools
- Ross, Caroline J, Nizami, B, Glenister, Michael, Amamuddy, Olivier S, Atilgan, Ali R, Atilgan, Canan, Tastan Bishop, Özlem
- Authors: Ross, Caroline J , Nizami, B , Glenister, Michael , Amamuddy, Olivier S , Atilgan, Ali R , Atilgan, Canan , Tastan Bishop, Özlem
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125206 , vital:35746 , http://dx.doi.org/10.1101/217505
- Description: Conventional analysis of molecular dynamics (MD) trajectories may not identify global motions of macromolecules. Normal Mode Analysis (NMA) and Principle Component Analysis (PCA) are two popular methods to quantify large-scale motions, and find the “essential motions”; and have been applied to problems such as drug resistant mutations (Nizami et al., 2016) and viral capsid expansion (Hsieh et al., 2016). MODE-TASK is an array of tools to analyse and compare protein dynamics obtained from MD simulations and/or coarse grained elastic network models. Users may perform standard PCA, kernel and incremental PCA (IPCA). Data reduction techniques (Multidimensional Scaling (MDS) and t-Distributed Stochastics Neighbor Embedding (t-SNE)) are implemented. Users may analyse normal modes by constructing elastic network models (ENMs) of a protein complex. A novel coarse graining approach extends its application to large biological assemblies.
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- Authors: Ross, Caroline J , Nizami, B , Glenister, Michael , Amamuddy, Olivier S , Atilgan, Ali R , Atilgan, Canan , Tastan Bishop, Özlem
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125206 , vital:35746 , http://dx.doi.org/10.1101/217505
- Description: Conventional analysis of molecular dynamics (MD) trajectories may not identify global motions of macromolecules. Normal Mode Analysis (NMA) and Principle Component Analysis (PCA) are two popular methods to quantify large-scale motions, and find the “essential motions”; and have been applied to problems such as drug resistant mutations (Nizami et al., 2016) and viral capsid expansion (Hsieh et al., 2016). MODE-TASK is an array of tools to analyse and compare protein dynamics obtained from MD simulations and/or coarse grained elastic network models. Users may perform standard PCA, kernel and incremental PCA (IPCA). Data reduction techniques (Multidimensional Scaling (MDS) and t-Distributed Stochastics Neighbor Embedding (t-SNE)) are implemented. Users may analyse normal modes by constructing elastic network models (ENMs) of a protein complex. A novel coarse graining approach extends its application to large biological assemblies.
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Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks:
- Amamuddy, Olivier S, Bishop, Nigel T, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148261 , vital:38724 , https://0-doi.org.wam.seals.ac.za/10.1186/s12859-017-1782-x
- Description: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs.
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- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148261 , vital:38724 , https://0-doi.org.wam.seals.ac.za/10.1186/s12859-017-1782-x
- Description: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs.
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MD-TASK: a software suite for analyzing molecular dynamics trajectories
- Brown, David K, Penkler, David L, Amamuddy, Olivier S, Ross, Caroline J, Atilgan, Ali R, Atilgan, Canan, Tastan Bishop, Özlem
- Authors: Brown, David K , Penkler, David L , Amamuddy, Olivier S , Ross, Caroline J , Atilgan, Ali R , Atilgan, Canan , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125138 , vital:35735 , https://doi.10.1093/bioinformatics/btx349
- Description: Molecular dynamics (MD) determines the physical motions of atoms of a biological macromolecule in a cell-like environment and is an important method in structural bioinformatics. Traditionally, measurements such as root mean square deviation, root mean square fluctuation, radius of gyration, and various energy measures have been used to analyze MD simulations. Here, we present MD-TASK, a novel software suite that employs graph theory techniques, perturbation response scanning, and dynamic cross-correlation to provide unique ways for analyzing MD trajectories.
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- Authors: Brown, David K , Penkler, David L , Amamuddy, Olivier S , Ross, Caroline J , Atilgan, Ali R , Atilgan, Canan , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125138 , vital:35735 , https://doi.10.1093/bioinformatics/btx349
- Description: Molecular dynamics (MD) determines the physical motions of atoms of a biological macromolecule in a cell-like environment and is an important method in structural bioinformatics. Traditionally, measurements such as root mean square deviation, root mean square fluctuation, radius of gyration, and various energy measures have been used to analyze MD simulations. Here, we present MD-TASK, a novel software suite that employs graph theory techniques, perturbation response scanning, and dynamic cross-correlation to provide unique ways for analyzing MD trajectories.
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