Computational Structure-Based Protein Design

基于计算结构的蛋白质设计

• 批准号: 9915930
• 负责人: Bruce R. Donald
• 金额: $ 35.4万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9915930
• 关键词: Acceleration; Address; Affinity; Algorithm Design; Algorithmic Software; Algorithms; Amino Acid Sequence; Antibodies; Area; Basic Science; Binding; Biochemical; Biological; Candida glabrata; Capsid Proteins; Cell Proliferation; Cells; Clinical; Clinical Trials; Clinical Trials Design; Combinatorial Optimization; Community-Acquired Infections; Computer software; Crystallization; Cystic Fibrosis; Cystic Fibrosis Transmembrane Conductance Regulator; Designer Drugs; Development; Disease Resistance; Drug resistance; Escherichia coli; Foundations; Future; Genes; Glycoproteins; Grant; Guanosine Triphosphate Phosphohydrolases; HIV; HIV Antibodies; HIV envelope protein; HIV resistance; HIV therapy; HIV-1; Human; Influenza; Investigation; KRAS2 gene; Laboratories; Lead; Ligands; Malaria; Malignant Neoplasms; Mathematics; Measures; Medical Research; Membrane; Membrane Proteins; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Mutate; Mutation; Pancreatic Ductal Adenocarcinoma; Passive Immunization; Peptides; Pharmaceutical Preparations; Pharmacology; Process; Protein Engineering; Proteins; Proto-Oncogenes; Resistance; Side; Signal Transduction; Site; Specificity; Speed; Structure; Symptoms; System; Testing; Therapeutic; Thermodynamics; Time; Tuberculosis; Vancomycin resistant enterococcus; Vertebral column; antibiotic design; base; cystic fibrosis patients; design; env Gene Products; enzyme activity; experimental study; flexibility; immunogenic; improved; in vivo; inhibitor/antagonist; methicillin resistant Staphylococcus aureus; model design; molecular mechanics; molecular modeling; mutant; neutralizing antibody; new therapeutic target; novel; novel therapeutics; open source; pathogen; prospective; protein protein interaction; protein structure; protein transport; resistance mutation; response; software development; structured data; success; unnatural amino acids

Project Summary. Computational structure-based protein design is a transformative field with exciting prospects for advancing both basic science and translational medical research. My laboratory has developed new protein design algorithms and used them to predict MRSA resistance to new antibiotics; design a broadly neutralizing antibody VRC07-523LS against HIV with unprecedented breadth and potency that is now in clinical trials; design protein-peptide interactions to treat cystic fibrosis; perform antigenicity-guided structural design of HIV gp140 envelope protein (Env) trimer constructs to delineate mechanism and fix conformation; and design a new antigenic membrane-bound membrane proximal external region (MPER) trimer for examining immunogenic responses to the HIV viral coat protein gp41. Central to protein design methodology is the need to optimize the amino acid sequence, placement of side chains, and backbone conformations in protein structures. By developing advanced search and scoring algorithms for combinatorial optimization of protein and ligand structure and sequence, we showed that desired structure, affinity, and activity can be designed by (a) modeling improved molecular flexibility and (b) exploiting ensembles of structures for accurate predictions. Our suite of algorithms has mathematical guarantees on the solution quality (up to the accuracy of the input model, which includes the initial structures, molecular flexibility to be modeled, and an empirical molecular mechanics energy function). Specifically, our algorithms guarantee to compute the global minimum energy conformation (GMEC), a gap-free list of sequences and structures in order of predicted energy, and a provably-good approximation to the binding affinity by bounding partition functions over molecular ensembles. We propose to build on our foundation of protein design algorithms, called OSPREY, and apply them in areas of biochemical and pharmacological importance. We will (1) predict future resistance mutations in protein targets of novel drugs; (2) design inhibitors of protein:protein interactions to target today’s “undruggable” proteins; and (3) use OSPREY to redesign and improve broadly neutralizing HIV antibodies. Improvements to our protein design algorithms will be implemented to improve accuracy and scope, and we will advance the state of the art in protein design by making algorithmic and modeling improvements to accomplish the Aims (1- 3) above, including: the modeling of more protein/ligand flexibility and improved energy functions during large- scale design; new combinatorial optimization and energy-fitting methods to accelerate the design search; and design of affinity and specificity using novel multi-state design algorithms that model thermodynamic molecular ensembles. We will test our design predictions prospectively, by making novel predicted mutant proteins and performing biochemical, biological, and structural studies. We will also validate our algorithms retrospectively, using existing structures and data. All software will be released open-source.

项目摘要。基于计算结构的蛋白质设计是一个令人兴奋的变革领域 促进基础科学和转化医学研究的前景。我的实验室已经研发出 新的蛋白质设计算法，并用它们来预测MRSA对新抗生素的耐药性；设计一种广泛的 针对HIV的中和抗体VRC07-523LS具有前所未有的广度和效力，目前正在 临床试验；设计蛋白质-多肽相互作用治疗囊性纤维化；执行抗原性引导的结构 设计HIV gp140包膜蛋白(Env)三聚体结构以描述机制和确定构象； 并设计了一种新的抗原膜结合膜近端外区(MPER)三聚体 检测对HIV病毒外壳蛋白gp41的免疫原性反应。蛋白质设计方法学的核心 是否需要优化氨基酸序列、侧链的位置和主干构象 蛋白质结构。通过开发用于组合优化的高级搜索和评分算法 蛋白质和配体的结构和序列，我们证明了所需的结构、亲和力和活性都可以 通过(A)建模提高了分子的灵活性和(B)利用结构系综来精确地 预测。我们的算法套件在数学上保证了解的质量(最高可达 输入模型，包括初始结构、要模拟的分子柔性和一个经验模型 分子力学能量函数)。具体地说，我们的算法保证了计算全局最小值 能量构象(GMEC)，按预测能量的顺序排列的序列和结构的无间隙列表，以及 通过在分子系综上绑定配分函数来证明对结合亲和力的良好逼近。 我们建议建立在我们的蛋白质设计算法的基础上，称为鱼鹰，并将它们应用于领域 具有重要的生物化学和药理作用。我们将(1)预测未来蛋白质的抗药性突变 新药的靶点；(2)设计蛋白质抑制剂：以蛋白质相互作用为靶点的今天的“不能用药” 蛋白质；以及(3)使用鱼鹰重新设计和改进广泛中和艾滋病毒抗体。改进到 我们的蛋白质设计算法将被实施，以提高准确性和范围，我们将推进 通过改进算法和模型来实现以下目标的蛋白质设计的最新进展(1- 3)以上，包括：建模更多的蛋白质/配体灵活性和改进的能量功能在大- 规模设计；新的组合优化和能量拟合方法，以加快设计搜索；以及 利用模拟热力学分子的新型多态设计算法设计亲和力和专一性 合唱团。我们将前瞻性地测试我们的设计预测，通过制造新的预测突变蛋白质和 进行生化、生物学和结构研究。我们还将回溯验证我们的算法， 使用现有的结构和数据。所有软件都将以开源方式发布。