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Computationally optimized anti-staphylococcal biotherapeutics

计算优化的抗葡萄球菌生物疗法

基本信息

批准号：
8415825
负责人：
Chris Bailey-Kellogg
金额：
$ 23.86万
依托单位：
DARTMOUTH COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-02-01 至 2015-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8415825
关键词：
Algorithms Anti-Bacterial Agents Antibiotic Resistance Antibiotics Antibodies Bacteria Bacterial Proteins Benchmarking Biological Assay Biological Response Modifier Therapy Cardiovascular system Cell Wall Cellular Immunity Clinical Communities Community Hospitals Computational algorithm Cytolysis Dependency Development Drug Industry Drug resistance Engineering Enzymes Evaluation Exhibits Eye Infections Fluorescence Gene Library Genes Genetic Engineering Genus staphylococcus Goals Gram-Positive Bacteria Hospitals Human Immune response Immunocompetence Immunophenotyping Incidence Individual Infection Inflammatory Kinetics Left Libraries Life Lung Lysostaphin LytA enzyme MHC Class II Genes Mediating Medical Methicillin Resistance Modeling Molecular Multi-Drug Resistance Mus Mutagenesis Mutation Outcome Predisposition Proteins Recombinants Resistance Saccharomyces cerevisiae Serum Side Skin Staphylococcus aureus Structure T cell response T-Cell Activation T-Lymphocyte T-Lymphocyte Epitopes Technology Therapeutic Therapeutic Agents Time Transgenic Mice Transgenic Organisms Translations Variant Virulence bactericide base clinical application combat combinatorial cytokine design high throughput screening immunogenicity in vivo killings member methicillin resistant Staphylococcus aureus mouse model mutant next generation novel novel therapeutics pathogen screening small molecule therapeutic protein

项目摘要

DESCRIPTION (provided by applicant): Antibiotic resistance complicates the majority of Staphylococcus aureus (S. aureus) infections, as a full two thirds of hospital-associated S. aureus infections and ~50% of those acquired in the community are now methicillin-resistant (MRSA). The increasing incidence of multi-drug resistance in S. aureus and other bacteria underscores the need for next-generation antibiotics capable of combating these dangerous pathogens. While traditional small molecule antibiotics inhibit genetically-encoded intracellular enzymes, an alternative strategy is to employ recombinant versions of natural lytic enzymes such as Staphylococcus simulans lysostaphin (ssLST), which acts by catalytic degradation of the cell wall and may therefore have lower susceptibility to evolved resistance. Unfortunately, as a bacterial protein itself, ssLST is known to drive a potent immune response, providing a major barrier to clinical development of ssLST therapies. This proposal hypothesizes that by integrating novel computational deimmunization algorithms with cutting- edge biomolecular engineering and immunogenicity screening technologies, we can redesign ssLST at the molecular level so as to maintain wild-type stability and catalytic function while simultaneously reducing immunogenicity. Two complementary approaches to developing deimmunized ssLST variants will be pursued in parallel. Aim 1 seeks to computationally design and experimentally evaluate a small number of variants predicted to have simultaneously good activity and reduced immunogenicity. The design algorithms will employ detailed modeling of sequence and structure in order to select optimal sets of deimmunizing mutations. The bactericidal activity of the engineered variants will be quantified by determination of Minimal Inhibitory Concentration (MIC), Minimal Bactericidal Concentration (MBC), and S. aureus lysis kinetics. The immunogenicity of the engineered variants will be assessed in a transgenic mouse model using antibody titers, inflammatory cytokine secretion, and T cell activation as readouts. Aim 2 seeks to computationally design combinatorial libraries predicted to be enriched in variants with reduced immunogenicity, and then employ high-throughput activity screening to identify active variants for further evaluation. The design algorithms will optimize primarily for immunogenicity in selecting mutations for library construction, leaving the screens to identify highly active library members for detailed characterization as in Aim 1. Successfully achieving these aims will result in powerful algorithms for optimizing individual variants and libraries of therapeutic proteins, a broadly applicable fluorescence-based assay enabling ultra-high-throughput screening of genetically-engineered antibacterial proteins, and fully functional, non-immunogenic, anti- staphylococcal biocatalysts potentially useful in treating drug-resistant S. aureus infections.

描述（由申请人提供）：抗生素耐药性使大多数金黄色葡萄球菌（S.金黄色葡萄球菌）感染，作为整整三分之二的医院相关的S.金黄色葡萄球菌感染和~50%的那些在社区获得的现在是耐甲氧西林（MRSA）。耐多药发生率的增加是导致耐药的主要原因。金黄色葡萄球菌和其他细菌的感染强调了对能够对抗这些危险病原体的下一代抗生素的需求。虽然传统的小分子抗生素会抑制遗传编码的细胞内酶，但另一种策略是采用天然溶解酶的重组版本，例如模拟葡萄球菌溶葡萄球菌酶（ssLST），其通过催化降解细胞壁发挥作用，因此可能对进化的耐药性具有较低的敏感性。不幸的是，作为一种细菌蛋白质本身，ssLST已知会驱动一种有效的免疫应答，这为ssLST疗法的临床开发提供了一个主要障碍。该提案假设通过将新的计算去免疫算法与尖端生物分子工程和免疫原性筛选技术相结合，我们可以在分子水平上重新设计ssLST，以保持野生型稳定性和催化功能，同时降低免疫原性。两种互补的方法来开发去免疫ssLST变体将并行进行。目的1寻求通过计算设计和实验评估少量预测同时具有良好活性和降低的免疫原性的变体。设计算法将采用序列和结构的详细建模，以选择去免疫突变的最佳集合。通过测定最小抑制浓度（MIC）、最小杀菌浓度（MBC）和S.金黄色葡萄球菌裂解动力学将在转基因小鼠模型中使用抗体滴度、炎性细胞因子分泌和T细胞活化作为读数来评估工程化变体的免疫原性。目的2寻求通过计算设计预测富含具有降低的免疫原性的变体的组合文库，然后采用高通量活性筛选来鉴定活性变体以用于进一步评估。设计算法将主要针对免疫原性进行优化，以选择突变用于文库构建，留下筛选以鉴定高活性文库成员用于如目的1中的详细表征。成功实现这些目标将产生用于优化治疗性蛋白质的个体变体和文库的强大算法、能够实现遗传工程化抗菌蛋白的超高通量筛选的广泛适用的基于荧光的测定、以及可能用于治疗耐药链球菌的全功能、非免疫原性、抗葡萄球菌生物催化剂。金黄色葡萄球菌感染