Developing Computational Methods to Aid Infectious Disease Therapeutics Through Analysis of Protein Function Evolution

通过分析蛋白质功能进化开发计算方法来辅助传染病治疗

• 批准号: MR/K020420/1
• 负责人: Nicholas Furnham
• 金额: $ 43.28万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FK020420%2F1
• 关键词: Developing; Computational; Methods; Aid; Infectious

The recent revolution in high throughput DNA sequencing, started by the Human Genome Project, has led to large collections of data on a diverse set of organisms. This notably includes the parasitic, bacterial and viral agents that cause infectious diseases, as well as the organisms that are responsible for disease transmission. The emergence of this data offers new and exciting opportunities to understand these disease-causing agents and to develop novel therapeutics.An outstanding and challenging problem is to understand the functions of the proteins encoded by these genomes. Time and resources limit the number whose function can be experimentally determined; therefore methods for predicting function are of paramount importance. Moreover, new methods are required when applied to infectious diseases due to the complex relationships between the host organism and the disease causing agent. These associations also have implications for assessing which drugs are suitable for use against infectious diseases and for the development of new therapeutics.An understanding of the complex biochemical relationships that will facilitate the identifying of new drug targets for infectious diseases requires bringing together a range of diverse biological information. The best method for achieving this is using a multidisciplinary approach interfacing biology, chemistry and computer science techniques. In collaboration with colleagues at the London School of Hygiene and Tropical Medicine, the European Bioinformatics Institute and University College London, I will develop a unique computational resource specifically to handle genomes associated with infectious diseases that:- brings together relationships between protein sequences and their molecular structures, putting them into an evolutionary context as well as establishing measures of similarity between the functions of these proteins.- uses the data captured to develop a new method to predict the function of proteins by defining rules bases on the systematic analysis of cases where changes in function occur between related proteins and determining the features of that change.From the outset of the project, the methods developed will be applied to specific problems in infectious disease research, combined with validating predictions in collaboration with experimental groups. I will start by addressing the key enzymes involved in new drug treatments for Chagas disease, the most important parasitic infection in the Americas, with the aim of providing a better understanding of drug-resistance mechanisms. Predictions and functional annotations of the Trypanosoma and Leishmania genomes, the causative agents of sleeping sickness/Chagas disease and Leishmaniasis respectively, will be made to test the methods and to gain insight into how well they can contribute to enhancing the annotations of these genomes. Insights gained from the application and validation process will be used to further enhance the methods developed, ultimately enabling them to be used on any infectious disease agent. The resource and methods developed will also be used to identify new drug targets and possible unintended interactions between the drug and other proteins that may result in side effects in patients. An immediate application will seek to add value to the results of high-throughput drug screens against schistosomes. This trematode worm causes the world's second most socio-economically devastating parasitic disease (highlighted by he World Health Organization). The aim will be to identify which protein(s) the drug(s) might be targeting, and to determine if there is potential for adverse interaction in the human host. The research will eventually be of use in a clinical setting, with the real possibility of helping fight the huge variety of infectious diseases suffered by millions.

最近由人类基因组计划发起的高通量 DNA 测序革命导致了关于不同生物体的大量数据收集。这尤其包括引起传染病的寄生虫、细菌和病毒，以及导致疾病传播的生物体。这些数据的出现为了解这些致病因子和开发新的治疗方法提供了令人兴奋的新机会。一个突出且具有挑战性的问题是了解这些基因组编码的蛋白质的功能。时间和资源限制了可以通过实验确定功能的数量；因此，预测功能的方法至关重要。此外，由于宿主生物体和致病因子之间的复杂关系，在应用于传染病时需要新的方法。这些关联对于评估哪些药物适合用于对抗传染病以及开发新疗法也具有重要意义。了解复杂的生化关系将有助于确定传染病的新药物靶点，需要汇集一系列不同的药物生物信息。实现这一目标的最佳方法是使用生物学、化学和计算机科学技术相结合的多学科方法。我将与伦敦卫生和热带医学学院、欧洲生物信息学研究所和伦敦大学学院的同事合作，开发一种独特的计算资源，专门用于处理与传染病相关的基因组，该资源： - 汇集蛋白质序列与其分子之间的关系结构，将它们置于进化背景中，并建立这些蛋白质功能之间相似性的衡量标准。-使用捕获的数据开发一种新方法，通过基于对发生变化的情况的系统分析来定义规则来预测蛋白质的功能在功能上发生在相关蛋白质之间确定这种变化的特征。从项目一开始，开发的方法将应用于传染病研究中的具体问题，并与实验组合作验证预测。我将首先讨论美洲最重要的寄生虫感染恰加斯病新药治疗中涉及的关键酶，目的是更好地了解耐药机制。将对锥虫和利什曼原虫基因组（分别是昏睡病/查加斯病和利什曼病的病原体）进行预测和功能注释，以测试这些方法并深入了解它们如何有助于增强这些基因组的注释。从应用和验证过程中获得的见解将用于进一步增强所开发的方法，最终使其能够用于任何传染病病原体。开发的资源和方法还将用于识别新的药物靶点以及药物与其他蛋白质之间可能导致患者副作用的意外相互作用。立即应用将寻求增加针对血吸虫的高通量药物筛选结果的价值。这种吸虫引起世界上第二大对社会经济破坏性最大的寄生虫病（世界卫生组织强调）。目的是确定药物可能针对哪些蛋白质，并确定在人类宿主中是否存在潜在的不良相互作用。这项研究最终将应用于临床，真正有可能帮助数百万人对抗各种各样的传染病。

项目成果

Complementary Sources of Protein Functional Information: The Far Side of GO.

蛋白质功能信息的补充来源：GO 的另一面。

• DOI: 10.1007/978-1-4939-3743-1_19
• 发表时间: 2017
• 期刊: Methods in molecular biology (Clifton, N.J.)
• 作者: Furnham N

Chopping and Changing: the Evolution of the Flavin-dependent Monooxygenases.

• DOI: 10.1016/j.jmb.2016.07.003
• 发表时间: 2016-07-31
• 期刊: Journal of molecular biology
• 影响因子: 5.6
• 作者: Mascotti ML;Juri Ayub M;Furnham N;Thornton JM;Laskowski RA
• 通讯作者: Laskowski RA

Known Allergen Structures Predict Schistosoma mansoni IgE-Binding Antigens in Human Infection.

• DOI: 10.3389/fimmu.2015.00026
• 发表时间: 2015
• 期刊: Frontiers in immunology
• 影响因子: 7.3
• 作者: Farnell EJ;Tyagi N;Ryan S;Chalmers IW;Pinot de Moira A;Jones FM;Wawrzyniak J;Fitzsimmons CM;Tukahebwa EM;Furnham N;Maizels RM;Dunne DW
• 通讯作者: Dunne DW

The evolution of enzyme function in the isomerases.

• DOI: 10.1016/j.sbi.2014.06.002
• 发表时间: 2014-06
• 期刊: CURRENT OPINION IN STRUCTURAL BIOLOGY
• 影响因子: 6.8
• 作者: Cuesta, Sergio Martinez;Furnham, Nicholas;Rahman, Syed Asad;Sillitoe, Ian;Thornton, Janet M.
• 通讯作者: Thornton, Janet M.

Large-Scale Analysis Exploring Evolution of Catalytic Machineries and Mechanisms in Enzyme Superfamilies.

• DOI: 10.1016/j.jmb.2015.11.010
• 发表时间: 2016-01-29
• 期刊: Journal of molecular biology
• 影响因子: 5.6
• 作者: Furnham N;Dawson NL;Rahman SA;Thornton JM;Orengo CA
• 通讯作者: Orengo CA