SANCDB: an update on South African natural compounds and their readily available analogs
- Diallo, Bakary N, Glenister, Michael, Musyoka, Thommas M, Lobb, Kevin A, Taştan Bishop, Özlem
- Authors: Diallo, Bakary N , Glenister, Michael , Musyoka, Thommas M , Lobb, Kevin A , Taştan Bishop, Özlem
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/451154 , vital:75023 , xlink:href="https://doi.org/10.1186/s13321-021-00514-2"
- Description: The dimeric dihydropyrimidine dehydrogenase (DPD), metalloenzyme, an adjunct anti-cancer drug target, contains highly specialized 4 × Fe2+4S2−4 clusters per chain. These clusters facilitate the catalysis of the rate-limiting step in the pyrimidine degradation pathway through a harmonized electron transfer cascade that triggers a redox catabolic reaction. In the process, the bulk of the administered 5-fluorouracil (5-FU) cancer drug is inactivated, while a small proportion is activated to nucleic acid antimetabolites. The occurrence of missense mutations in DPD protein within the general population, including those of African descent, has adverse toxicity effects due to altered 5-FU metabolism. Thus, deciphering mutation effects on protein structure and function is vital, especially for precision medicine purposes. We previously proposed combining molecular dynamics (MD) and dynamic residue network (DRN) analysis to decipher the molecular mechanisms of missense mutations in other proteins. However, the presence of Fe2+4S2−4 clusters in DPD poses a challenge for such in silico studies. The existing AMBER force field parameters cannot accurately describe the Fe2+ center coordination exhibited by this enzyme. Therefore, this study aimed to derive AMBER force field parameters for DPD enzyme Fe2+ centers, using the original Seminario method and the collation features Visual Force Field Derivation Toolkit as a supportive approach. All-atom MD simulations were performed to validate the results. Both approaches generated similar force field parameters, which accurately described the human DPD protein Fe2+4S2−4 cluster architecture. This information is crucial and opens new avenues for in silico cancer pharmacogenomics and drug discovery related research on 5-FU drug efficacy and toxicity issues.
- Full Text:
- Date Issued: 2021
- Authors: Diallo, Bakary N , Glenister, Michael , Musyoka, Thommas M , Lobb, Kevin A , Taştan Bishop, Özlem
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/451154 , vital:75023 , xlink:href="https://doi.org/10.1186/s13321-021-00514-2"
- Description: The dimeric dihydropyrimidine dehydrogenase (DPD), metalloenzyme, an adjunct anti-cancer drug target, contains highly specialized 4 × Fe2+4S2−4 clusters per chain. These clusters facilitate the catalysis of the rate-limiting step in the pyrimidine degradation pathway through a harmonized electron transfer cascade that triggers a redox catabolic reaction. In the process, the bulk of the administered 5-fluorouracil (5-FU) cancer drug is inactivated, while a small proportion is activated to nucleic acid antimetabolites. The occurrence of missense mutations in DPD protein within the general population, including those of African descent, has adverse toxicity effects due to altered 5-FU metabolism. Thus, deciphering mutation effects on protein structure and function is vital, especially for precision medicine purposes. We previously proposed combining molecular dynamics (MD) and dynamic residue network (DRN) analysis to decipher the molecular mechanisms of missense mutations in other proteins. However, the presence of Fe2+4S2−4 clusters in DPD poses a challenge for such in silico studies. The existing AMBER force field parameters cannot accurately describe the Fe2+ center coordination exhibited by this enzyme. Therefore, this study aimed to derive AMBER force field parameters for DPD enzyme Fe2+ centers, using the original Seminario method and the collation features Visual Force Field Derivation Toolkit as a supportive approach. All-atom MD simulations were performed to validate the results. Both approaches generated similar force field parameters, which accurately described the human DPD protein Fe2+4S2−4 cluster architecture. This information is crucial and opens new avenues for in silico cancer pharmacogenomics and drug discovery related research on 5-FU drug efficacy and toxicity issues.
- Full Text:
- Date Issued: 2021
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.
- Full Text:
- Date Issued: 2018
- 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.
- Full Text:
- Date Issued: 2018
PRIMO: an interactive homology modeling pipeline
- Hatherley, Rowan, Brown, David K, Glenister, Michael, Tastan Bishop, Özlem
- Authors: Hatherley, Rowan , Brown, David K , Glenister, Michael , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148282 , vital:38726 , doi: 10.1371/journal.pone.0166698
- Description: The development of automated servers to predict the three-dimensional structure of proteins has seen much progress over the years. These servers make calculations simpler, but largely exclude users from the process. In this study, we present the PRotein Interactive MOdeling (PRIMO) pipeline for homology modeling of protein monomers. The pipeline eases the multi-step modeling process, and reduces the workload required by the user, while still allowing engagement from the user during every step. Default parameters are given for each step, which can either be modified or supplemented with additional external input. PRIMO has been designed for users of varying levels of experience with homology modeling. The pipeline incorporates a user-friendly interface that makes it easy to alter parameters used during modeling.
- Full Text:
- Date Issued: 2017
- Authors: Hatherley, Rowan , Brown, David K , Glenister, Michael , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148282 , vital:38726 , doi: 10.1371/journal.pone.0166698
- Description: The development of automated servers to predict the three-dimensional structure of proteins has seen much progress over the years. These servers make calculations simpler, but largely exclude users from the process. In this study, we present the PRotein Interactive MOdeling (PRIMO) pipeline for homology modeling of protein monomers. The pipeline eases the multi-step modeling process, and reduces the workload required by the user, while still allowing engagement from the user during every step. Default parameters are given for each step, which can either be modified or supplemented with additional external input. PRIMO has been designed for users of varying levels of experience with homology modeling. The pipeline incorporates a user-friendly interface that makes it easy to alter parameters used during modeling.
- Full Text:
- Date Issued: 2017
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