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.

人类基因组项目开始的高吞吐量DNA测序的近期革命导致大量有关各种生物体的数据收集。这特别包括引起传染病的寄生虫，细菌和病毒剂，以及导致疾病传播的生物。这些数据的出现为了解这些引起疾病的药物并开发新的治疗学提供了新的令人兴奋的机会。一个杰出而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其功能的数字；因此，预测功能的方法至关重要。此外，由于寄主生物体与引起疾病的疾病之间的复杂关系，将新方法应用于传染病。这些关联还具有评估哪些药物适合用于感染疾病的药物和新疗法的发展。对复杂的生物化学关系的理解将有助于识别新药物的感染性疾病目标，需要将一系列多种生物学信息融合在一起。实现此目的的最佳方法是使用多学科方法接口生物学，化学和计算机科学技术。欧洲生物信息学研究所和伦敦大学学院与伦敦卫生和热带医学学院的同事合作，我将开发一种独特的计算资源，专门用于处理与感染性疾病相关的基因组，这些基因组将蛋白质序列及其分子结构之间的关系汇集在一起​​，并将其构成蛋白质的捕获，从而使这些蛋白质的捕获量相似 - 这些蛋白质与这些概念相似 - 这些概念的功能 - 这些概念的功能 - 这些功能的这些功能 - 一种新方法来预测蛋白质的功能，通过在相关蛋白质之间发生功能发生变化和确定该变化的功能变化的情况下定义规则碱基的功能。从项目的发表开始，开发的方法将应用于传染病研究中的特定问题，结合了与实验组的验证预测。我将首先解决与美洲最重要的寄生虫感染新药物治疗有关的关键酶，以便更好地了解药物耐药机制。锥虫瘤和利什曼原虫基因组的预测和功能注释，分别是熟睡的疾病/chagas疾病和利什曼病的病因，以测试这些方法并深入了解它们如何为增强这些基因组的注释做出贡献。从应用和验证过程中获得的见解将用于进一步增强开发的方法，最终使它们能够用于任何传染病剂。开发的资源和方法还将用于确定药物与其他蛋白质之间可能导致患者副作用的药物和其他蛋白质之间可能的意外相互作用。立即应用将寻求为针对血吸虫的高通量药物筛查的结果增加价值。这种Trematode蠕虫会导致世界上第二最社会经济上毁灭性的寄生疾病（由HE世界卫生组织强调）。目的是确定药物可能靶向的蛋白质，并确定人宿主是否存在不良相互作用的可能性。这项研究最终将在临床环境中使用，真正的可能性有助于与数百万遭受的各种传染病作斗争。

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