Computationally optimized anti-staphylococcal biotherapeutics

计算优化的抗葡萄球菌生物疗法

• 批准号: 8226022
• 负责人: Chris Bailey-Kellogg
• 金额: $ 21.26万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-01 至 2014-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8226022
• 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; 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; Screening procedure; 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; small molecule; therapeutic protein

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. PUBLIC HEALTH RELEVANCE: Staphylococcus simulans lysostaphin (ssLST) is a highly effective anti-staphylococcal biocatalyst that efficiently kills Staphylococcus aureus pathogens, including methicillin-resistant S. aureus (MRSA). Unfortunately, as a bacterial protein itself, ssLST is known to drive a potent immune response that can result in loss of efficacy. This proposal hypothesizes that the integration of novel computational deimmunization algorithms with cutting-edge biomolecular engineering and immunogenicity screening technologies can produce ssLST variants that have wild-type stability and catalytic function but reduced immunogenicity. These designer enzymes could be powerful therapeutics for MRSA infections.

描述(申请人提供)：抗生素耐药性使大多数金黄色葡萄球菌(金黄色葡萄球菌)感染复杂化，因为整整三分之二的医院相关金黄色葡萄球菌感染和约50%的社区获得者现在对甲氧西林耐药(MRSA)。金黄色葡萄球菌和其他细菌中多重耐药的发生率不断增加，这突显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抑制遗传编码的细胞内酶，但另一种策略是使用天然裂解酶的重组版本，如模拟葡萄球菌溶葡萄球菌酶(SsLST)，它通过催化降解细胞壁发挥作用，因此可能对进化的耐药性敏感性较低。不幸的是，作为一种细菌蛋白本身，ssLST被认为可以驱动强大的免疫反应，这为ssLST疗法的临床开发提供了主要障碍。这一建议假设，通过将新的计算去免疫算法与前沿的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，以便在降低免疫原性的同时保持野生型的稳定性和催化功能。将同时采取两种互补的方法来开发去免疫的ssLST变异体。目的1试图通过计算机设计和实验评估少数预测同时具有良好活性和降低免疫原性的变异体。设计算法将使用序列和结构的详细建模，以选择最佳的去免疫突变集。通过测定最小抑菌浓度(MIC)、最小杀菌浓度(MBC)和金黄色葡萄球菌裂解动力学来量化工程变异体的杀菌活性。在转基因小鼠模型中，将使用抗体滴度、炎性细胞因子分泌和T细胞激活作为读数来评估工程变体的免疫原性。目的2试图通过计算设计预测富含免疫原性降低的变异体的组合文库，然后使用高通量活性筛选来识别活性变异体以进行进一步的评价。设计算法将主要针对选择构建文库的突变的免疫原性进行优化，使筛选能够识别高活性的文库成员以进行详细的表征，如目标1所示。成功实现这些目标将导致用于优化治疗蛋白质的单个变体和文库的强大算法，一种广泛适用的基于荧光的分析方法，能够超高通量筛选基因工程抗菌蛋白，以及可能用于治疗耐药金黄色葡萄球菌感染的全功能、非免疫原性、抗葡萄球菌生物催化剂。 公共卫生相关性：模拟葡萄球菌溶葡萄球菌酶(SsLST)是一种高效的抗葡萄球菌生物催化剂，可有效杀死金黄色葡萄球菌病原体，包括耐甲氧西林金黄色葡萄球菌(MRSA)。不幸的是，作为一种细菌蛋白本身，ssLST已知会驱动一种强大的免疫反应，从而导致疗效丧失。这一建议假设，将新型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，可以产生具有野生型稳定性和催化功能但免疫原性降低的ssLST变体。这些设计的酶可能是治疗MRSA感染的强大疗法。