Predictive Structure-Based Models of Malaria Resistance

基于预测结构的疟疾抗药性模型

• 批准号: 8265462
• 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/8265462
• 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; Screening procedure; 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; 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. PUBLIC HEALTH RELEVANCE: Approximately forty-one percent of the world's population lives in areas where malaria is transmitted and each year and it is estimated that 350-500 million cases of malaria occur worldwide. Two of the most prevalent malaria strains, Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) have developed clinical resistance to antifolate compounds that target the enzyme dihydrofolate reductase (DHFR) such as pyrimethamine and cycloguanil. Consequently, there remains a serious and immediate need for the development of novel antimalarial therapeutics that target drug-resistant strains. Using the proposed approach, we will develop predictive models of future Pf-DHFR and Pv-DHFR protein evolution that will facilitate hypothesis generation for likely future mutations in the wild. Afterthe completion of this SC3 research, we plan to integrate these predictive models into a comprehensive computational intelligence- based drug discovery platform thus providing the unique opportunity of getting a "head start" on drug discovery resulting in timely development of novel anti-malarial therapeutics to meet future needs. This approach will be tested on DHFR for novel antimalarial drug discovery; however, the methods developed can be applied broadly in early stage drug discovery and development.

描述（由申请人提供）：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）蛋白进化的预测模型，这将有助于对野生环境中可能的未来突变产生假设，从而在这些突变之前发现针对耐药菌株的新型抗疟疾治疗剂。通过这项SC 3研究，我们将对DHFR蛋白进化进行全面的基于结构的分析，以生成可能的氨基酸替换的位点特异性预测模型，并确定可能发生补偿性氨基酸替换的位置以应对选择压力。 研究将从使用从公共领域数据库获得的DHFR蛋白质序列生成全面的系统发育树开始。祖先序列将预测DHFR进化的关键分支。然后将为这些祖先序列中的每一个生成3D同源性模型，这些祖先序列将被顺序地添加到已经存在的基于结构的序列比对中，该序列比对由来自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。位点特异性氨基酸置换的预测模型将使用从计算智能和机器学习领域获得的工具和技术生成，包括HALTH和ANN。这些模型将通过使用前70%的概率进行测试和验证，以预测剩余的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的突变DHFR序列进行分析，以研究引起耐药性的种内分化。将产生代表疟原虫DHFR进化的下一步的假设DHFR序列和同源性模型。将使用现有的抗疟疾药物以及已知的DHFR抑制剂进行计算机对接实验。从这些研究中预测的蛋白质-配体相互作用的检查将为获得耐药性提供额外的见解。 通过这项工作，实现了研究和建模DHFR蛋白质进化的创新技术。将生成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，作为该方法的概念证明。该技术具有深远的益处，包括产生恶性疟原虫和间日疟原虫的种内分化和耐药性起源的假设，以及产生未来DHFR蛋白进化的预测模型的能力，提供了在野生耐药性发展之前获得药物发现的“领先优势”的独特机会。 公共卫生关系：大约41%的世界人口生活在疟疾传播的地区，据估计，每年全世界发生3.5 - 5亿例疟疾病例。两种最流行的疟疾菌株，恶性疟原虫（Pf）和间日疟原虫（Pv）已经对靶向酶二氢叶酸还原酶（DHFR）的抗叶酸化合物如乙胺嘧啶和环胍产生了临床抗性。因此，仍然迫切需要开发靶向耐药菌株的新型抗疟疾治疗剂。使用所提出的方法，我们将开发未来Pf-DHFR和Pv-DHFR蛋白进化的预测模型，这将有助于在野生环境中产生可能的未来突变的假设。在完成这项SC 3研究之后，我们计划将这些预测模型整合到一个全面的基于计算智能的药物发现平台中，从而提供一个独特的机会，在药物发现方面获得“领先优势”，从而及时开发新的抗疟疾疗法，以满足未来的需求。这种方法将在DHFR上进行测试，以发现新的抗疟药物;然而，所开发的方法可以广泛应用于早期药物发现和开发。