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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:

  • 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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  • Date Issued: 2019

Bioinformatics tool development with a focus on structural bioinformatics and the analysis of genetic variation in humans

  • Authors: Brown, David K
  • Date: 2018
  • Subjects: Bioinformatics , Human genetics -- Variation , High performance computing , Workflow management systems , Molecular dynamics , Next generation sequencing , Human Mutation Analysis (HUMA)
  • Language: English
  • Type: text , Thesis , Doctoral , PhD
  • Identifier: http://hdl.handle.net/10962/60708 , vital:27820
  • Description: This thesis is divided into three parts, united under the general theme of bioinformatics tool development and variation analysis. Part 1 describes the design and development of the Job Management System (JMS), a workflow management system for high performance computing (HPC). HPC has become an integral part of bioinformatics. Computational methods for molecular dynamics and next generation sequencing (NGS) analysis, which require complex calculations on large datasets, are not yet feasible on desktop computers. As such, powerful computer clusters have been employed to perform these calculations. However, making use of these HPC clusters requires familiarity with command line interfaces. This excludes a large number of researchers from taking advantage of these resources. JMS was developed as a tool to make it easier for researchers without a computer science background to make use of HPC. Additionally, JMS can be used to host computational tools and pipelines and generates both web-based interfaces and RESTful APIs for those tools. The web-based interfaces can be used to quickly and easily submit jobs to the underlying cluster. The RESTful web API, on the other hand, allows JMS to provided backend functionality for external tools and web servers that want to run jobs on the cluster. Numerous tools and workflows have already been added to JMS, several of which have been incorporated into external web servers. One such web server is the Human Mutation Analysis (HUMA) web server and database. HUMA, the topic of part 2 of this thesis, is a platform for the analysis of genetic variation in humans. HUMA aggregates data from various existing databases into a single, connected and related database. The advantages of this are realized in the powerful querying abilities that it provides. HUMA includes protein, gene, disease, and variation data and can be searched from the angle of any one of these categories. For example, searching for a protein will return the protein data (e.g. protein sequences, structures, domains and families, and other meta-data). However, the related nature of the database means that genes, diseases, variation, and literature related to the protein will also be returned, giving users a powerful and holistic view of all data associated with the protein. HUMA also provides links to the original sources of the data, allowing users to follow the links to find additional details. HUMA aims to be a platform for the analysis of genetic variation. As such, it also provides tools to visualize and analyse the data (several of which run on the underlying cluster, via JMS). These tools include alignment and 3D structure visualization, homology modeling, variant analysis, and the ability to upload custom variation datasets and map them to proteins, genes and diseases. HUMA also provides collaboration features, allowing users to share and discuss datasets and job results. Finally, part 3 of this thesis focused on the development of a suite of tools, MD-TASK, to analyse genetic variation at the protein structure level via network analysis of molecular dynamics simulations. The use of MD-TASK in combination with the tools developed in the previous parts of this thesis is showcased via the analysis of variation in the renin-angiotensinogen complex, a vital part of the renin-angiotensin system.
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  • Date Issued: 2018

HUMA: A platform for the analysis of genetic variation in humans

  • Authors: Brown, David K , Tastan Bishop, Özlem
  • Date: 2018
  • Language: English
  • Type: text , article
  • Identifier: http://hdl.handle.net/10962/124653 , vital:35642 , https://doi.10.1002/humu.23334
  • Description: The completion of the human genome project at the beginning of the 21st century, along with the rapid advancement of sequencing technologies thereafter, has resulted in exponential growth of biological data. In genetics, this has given rise to numerous variation databases, created to store and annotate the ever-expanding dataset of known mutations. Usually, these databases focus on variation at the sequence level. Few databases focus on the analysis of variation at the 3D level, that is, mapping, visualizing, and determining the effects of variation in protein structures. Additionally, these Web servers seldom incorporate tools to help analyze these data. Here, we present the Human Mutation Analysis (HUMA) Web server and database. HUMA integrates sequence, structure, variation, and disease data into a single, connected database. A user-friendly interface provides click-based data access and visualization, whereas a RESTfulWebAPI provides programmatic access to the data. Tools have been integrated into HUMA to allow initial analyses to be carried out on the server. Furthermore, users can upload their private variation datasets, which are automatically mapped to public data and can be analyzed using the integrated tools. HUMA is freely accessible at https://huma.rubi.ru.ac.za.
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  • Date Issued: 2018

Development of Bioinformatics Infrastructure for Genomics Research:

MD-TASK: a software suite for analyzing molecular dynamics trajectories

  • 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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  • Date Issued: 2017

PRIMO: an interactive homology modeling pipeline

  • 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.
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  • Date Issued: 2017

Role of structural bioinformatics in drug discovery by computational SNP analysis: analyzing variation at the protein level

  • Authors: Brown, David K , Tastan Bishop, Özlem
  • Date: 2017
  • Language: English
  • Type: text , article
  • Identifier: http://hdl.handle.net/10962/125921 , vital:35832 , https://doi.10.1016/j.gheart.2017.01.009
  • Description: With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps toward an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as genome-wide association studies and candidate gene association studies. However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as high throughput sequencing. In this paper, we discuss the contributions of structural bioinformatics to drug discovery, focusing particularly on the analysis of nonsynonymous single nucleotide polymorphisms. We conclude by suggesting a protocol for future analyses of the structural effects of nonsynonymous single nucleotide polymorphisms on proteins and protein complexes.
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  • Date Issued: 2017

Structure-based analysis of single nucleotide variants in the renin-angiotensinogen complex:

  • Authors: Brown, David K , Olivier, Sheik Amamuddy , Tastan Bishop, Özlem
  • Date: 2017
  • Language: English
  • Type: text , article
  • Identifier: http://hdl.handle.net/10962/147994 , vital:38700 , https://doi.org/10.1016/j.gheart.2017.01.006
  • Description: The renin-angiotensin system (RAS) plays an important role in regulating blood pressure and controlling sodium levels in the blood. Hyperactivity of this system has been linked to numerous conditions including hypertension, kidney disease, and congestive heart failure. Three classes of drugs have been developed to inhibit RAS. In this study, we provide a structure-based analysis of the effect of single nucleotide variants (SNVs) on the interaction between renin and angiotensinogen with the aim of revealing important residues and potentially damaging variants for further inhibitor design purposes.
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  • Date Issued: 2017

The role of structural bioinformatics in drug discovery via computational SNP analysis–a proposed protocol for analyzing variation at the protein level:

  • Authors: Brown, David K , Tastan Bishop, Özlem
  • Date: 2017
  • Language: English
  • Type: text , article
  • Identifier: http://hdl.handle.net/10962/162914 , vital:40996 , doi: 10.1016/j.gheart.2017.01.009
  • Description: With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps towards an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as Genome-Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS). However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as High Throughput Sequencing (HTS).
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  • Date Issued: 2017

JMS: a workflow management system and web-based cluster front-end for the Torque resource manager

  • Authors: Brown, David K , Musyoka, Thommas M , Penkler, David L , Tastan Bishop, Özlem
  • Date: 2015
  • Language: English
  • Type: text , article
  • Identifier: http://hdl.handle.net/10962/148049 , vital:38705 , https://arxiv.org/abs/1501.06907
  • Description: Complex computational pipelines are becoming a staple of modern scientific research. Often these pipelines are resource intensive and require days of computing time. In such cases, it makes sense to run them over distributed computer clusters where they can take advantage of the aggregated resources of many powerful computers. In addition to this, researchers often want to integrate their workflows into their own web servers. In these cases, software is needed to manage the submission of jobs from the web interface to the cluster and then return the results once the job has finished executing.
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  • Date Issued: 2015

JMS: an open source workflow management system and web-based cluster front-end for high performance computing

  • Authors: Brown, David K , Penkler, David L , Musyoka, Thommas M , Tastan Bishop, Özlem
  • Date: 2015
  • Language: English
  • Type: text , article
  • Identifier: http://hdl.handle.net/10962/162880 , vital:40993 , doi:10.1371/journal.pone.0134273
  • Description: Complex computational pipelines are becoming a staple of modern scientific research. Often these pipelines are resource intensive and require days of computing time. In such cases, it makes sense to run them over high performance computing (HPC) clusters where they can take advantage of the aggregated resources of many powerful computers. In addition to this, researchers often want to integrate their workflows into their own web servers. In these cases, software is needed to manage the submission of jobs from the web interface to the cluster and then return the results once the job has finished executing. We have developed the Job Management System (JMS), a workflow management system and web interface for high performance computing (HPC). JMS provides users with a user-friendly web interface for creating complex workflows with multiple stages. It integrates this workflow functionality with the resource manager, a tool that is used to control and manage batch jobs on HPC clusters. As such, JMS combines workflow management functionality with cluster administration functionality.
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  • Date Issued: 2015

SANCDB: a South African natural compound database

  • Authors: Hatherley, Rowan , Brown, David K , Musyoka, Thommas M , Penkler, David L , Faya, Ngonidzashe , Lobb, Kevin A , Tastan Bishop, Özlem
  • Date: 2015
  • Language: English
  • Type: text , article
  • Identifier: http://hdl.handle.net/10962/148337 , vital:38730 , DOI: 10.1186/s13321-015-0080-8
  • Description: Natural products (NPs) are important to the drug discovery process. NP research efforts are expanding world-wide and South Africa is no exception to this. While freely-accessible small molecule databases, containing compounds isolated from indigenous sources, have been established in a number of other countries, there is currently no such online database in South Africa.
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  • Date Issued: 2015
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