Predictive Structure-Based Models of Malaria Resistance

基于预测结构的疟疾抗药性模型

• 批准号: 8669015
• 负责人: David Hecht
• 金额: $ 8.1万
• 依托单位: SOUTHWESTERN COLLEGE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8669015
• 关键词: Amino Acid Sequence; Amino Acid Substitution; Amino Acids; Antibiotics; Antimalarials; Area; Bacteria; Base Sequence; Biological Neural Networks; Clinical; Computer Simulation; Coupled; Data; Databases; Development; Dihydrofolate Reductase; Dihydrofolate Reductase Inhibitor; Docking; Drug resistance; Enzymes; Evolution; Folic Acid Antagonists; Frequencies; Future; Genbank; Generations; Goals; Head Start Program; Homology Modeling; Human; Infectious Diseases Research; Intelligence; Ligands; Light; Location; Machine Learning; Malaria; Methodology; Methods; Modeling; Mutation; Peptide Sequence Determination; Pharmaceutical Preparations; Phylogenetic Analysis; Phylogeny; Plasmodium; Plasmodium falciparum; Plasmodium vivax; Play; Population; Population Sizes; Positioning Attribute; Proteins; Public Domains; Pyrimethamine; Research; Resistance; Sequence Alignment; Sequence Analysis; Sequence Homology; Series; Site; Staging; Statistical Models; Structure; Study models; Techniques; Technology; Testing; Therapeutic; Time; Training; Trees; base; cycloguanil; drug candidate; drug development; drug discovery; innovative technologies; insight; markov model; meetings; mutant; novel; pathogen; predictive modeling; pressure; research study; resistant strain; response; screening; therapeutic target; tool

DESCRIPTION (provided by applicant): The goal of this proposed effort is to develop predictive models of future Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) dihydrofolate reductase (DHFR) protein evolution that will facilitate hypothesis generation for likely future mutations in the wild, leading to discovery of novel anti-malarial therapeutics againt drug resistant strains in advance of these mutations. Through this SC3 research, we will perform a comprehensive structure-based analysis of DHFR protein evolution in order to generate site-specific predictive models of likely amino acid replacements and identify locations where compensating amino acid replacements may be occurring in response to selection pressures. Research will commence with generation of a comprehensive phylogenetic tree using DHFR protein sequences obtained from public domain databases. Ancestral sequences will be predicted for key clades in DHFR evolution. 3D homology models will then be generated for each of these ancestral sequences that will be added sequentially to an already existing structure-based sequence alignment generated from a superposition of experimentally determined x-ray crystal structures of wild-type (wt) DHFR from 22 species. Predictive models of site-specific amino acid replacements will be generated using tools and techniques taken from the field of computational intelligence and machine learning that include HMMs and ANNs. These models will be tested and validated by using the first 70% of the phylogeny in order to predict the remaining 30%. Using the insights gained from these predictive models, an analysis will be performed with mutant DHFR sequences of P. falciparum and P. vivax to study the intraspecies differentiation that gave rise to drug resistance. Hypothetical DHFR sequences and homology models representing next steps in Plasmodium DHFR evolution will be generated. In silico docking experiments will be performed with existing anti-malarial drugs as well as known inhibitors of DHFR. Examination of the predicted protein-ligand interactions from these studies will provide additional insights into the acquisition of drug resistance. Through this proposed effort, an innovative technology for studying and modeling DHFR protein evolution is realized. Predictive models of potential next steps in P. falciparum and P. vivax DHFR evolution will be generated as a proof of concept of this approach. This technology has far-reaching benefits including the generation of hypotheses for intraspecies differentiation and origins of drug resistance in P. falciparum and P. vivax as well as the ability to generate predictive models of future DHFR protein evolution providing the unique opportunity of getting a "head start" on drug discovery before drug resistance develops in the wild.

描述(申请人提供)：这项拟议工作的目标是开发未来恶性疟原虫(PF)和间日疟原虫(PV)二氢叶酸还原酶(DHFR)蛋白质进化的预测模型，以促进对未来可能在野外发生的突变的假说产生，导致在这些突变之前发现针对耐药菌株的新型抗疟疾疗法。通过这项SC3研究，我们将对DHFR蛋白质的进化进行全面的基于结构的分析，以便生成可能的氨基酸替换的特定位置预测模型，并识别可能发生补偿性氨基酸替换的位置，以响应选择压力。研究将开始于使用从公共领域数据库获得的DHFR蛋白质序列来生成全面的系统发育树。DHFR进化中的关键分支的祖先序列将被预测。然后，将为这些祖先序列中的每一个生成3D同源模型，这些序列将被顺序地添加到已经存在的基于结构的序列比对，该序列比对是由来自22个物种的野生型(Wt)DHFR的实验确定的x射线晶体结构的叠加生成的。将使用来自计算智能和机器学习领域的工具和技术，包括隐马尔可夫模型和神经网络，生成特定部位氨基酸替换的预测模型。这些模型将通过使用系统发育的前70%进行测试和验证，以便预测剩余的30%。利用从这些预测模型中获得的见解，将对恶性疟原虫和间日疟原虫突变的dhfr序列进行分析，以研究导致耐药性的种内分化。假想的dhfr序列和代表疟原虫dhfr进化下一步的同源模型将被产生。在电子对接实验中，将使用现有的抗疟疾药物以及已知的DHFR抑制剂进行实验。对这些研究中预测的蛋白质-配体相互作用的检验将为获得耐药性提供更多的见解。通过这一提出的努力，实现了一种研究和模拟DHFR蛋白质进化的创新技术。恶性疟原虫和间日疟原虫DHFR进化的潜在下一步的预测模型将被生成作为该方法的概念的证明。这项技术具有深远的好处，包括为恶性疟原虫和间日疟原虫产生种内分化和耐药性起源的假说，以及生成未来DHFR蛋白进化的预测模型的能力，提供了在野生耐药之前在药物发现方面抢占先机的独特机会。

项目成果

Modeling the evolution of drug resistance in malaria.

• DOI: 10.1007/s10822-012-9618-2
• 发表时间: 2012-12
• 期刊: JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
• 影响因子: 3.5
• 作者: Hecht, David;Fogel, Gary B.
• 通讯作者: Fogel, Gary B.

Structure-based analysis of Bacilli and plasmid dihydrofolate reductase evolution.

基于结构的芽孢杆菌和质粒二氢叶酸还原酶进化分析。

• DOI: 10.1016/j.jmgm.2016.10.011
• 发表时间: 2017
• 期刊: Journal of molecular graphics & modelling
• 影响因子: 2.9
• 作者: Alotaibi,Mona;Reyes,BenDelos;Le,Tin;Luong,Phuong;Valafar,Faramarz;Metzger,RobertP;Fogel,GaryB;Hecht,David
• 通讯作者: Hecht,David