Computational Structure-Based Protein Design

基于计算结构的蛋白质设计

• 批准号: 8628215
• 负责人: Bruce R. Donald
• 金额: $ 31.93万
• 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 2018-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8628215
• 关键词: Address; Affinity; Algorithm Design; Algorithmic Software; Algorithms; Amino Acid Sequence; Antibiotics; Antibodies; Area; Avidity; Basic Science; Binding; Biochemical; Biological; Candida glabrata; Cells; Chemicals; Chloride Ion; Chlorides; Clinical; Combinatorial Optimization; Community-Acquired Infections; Cystic Fibrosis; Cystic Fibrosis Transmembrane Conductance Regulator; DHFR gene; Data; Defect; Designer Drugs; Development; Disease Resistance; Drug resistance; Enzyme Kinetics; Enzyme Stability; Enzymes; Epithelial; Folic Acid Antagonists; Foundations; Future; Genetic; Glycoproteins; Goals; Grant; HIV; HIV Envelope Protein gp120; HIV-1; Human; Influenza; Investigation; Laboratories; Lead; Ligands; Malaria; Malignant Neoplasms; Measurement; Measures; Medical Research; Membrane Proteins; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Molecular Models; Molecular Structure; Mutate; Mutation; Passive Immunization; Peptides; Pharmaceutical Preparations; Process; Proteins; Reaction; Resistance; Side; Site; Solutions; Specificity; Speed; Structure; Symptoms; System; Testing; Therapeutic; Therapeutic antibodies; Thermodynamics; Tuberculosis; Validation; Vancomycin resistant enterococcus; Vertebral column; Viral; antibiotic design; base; computer studies; cystic fibrosis patients; design; env Gene Products; enzyme activity; flexibility; improved; inhibitor/antagonist; leukemia; methicillin resistant Staphylococcus aureus; model design; molecular mechanics; molecular modeling; mutant; nanobodies; neutralizing antibody; novel; open source; pathogen; protein protein interaction; protein structure; protein transport; public health relevance; research study; resistance mutation; response; software development; virus envelope

DESCRIPTION (provided by applicant): 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 design new drugs for leukemia, redesign an enzyme to diversify current antibiotics, design protein-peptide interactions to treat cystic fibrosis, design probes to isolate broadly neutralizin HIV antibodies, and predict MRSA resistance to new antibiotics. 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 suit 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 tested our algorithms prospectively, and experimental validation included construction of mutant proteins, measurement of binding affinity, enzyme kinetics and stability, crystal structures, NMR structures, viral neutralization, and in-cell activity. 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 our design methodology to discover and improve broadly neutralizing HIV-1 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 and ligand flexibility during design; new combinatorial optimization and energy-bounding methods to accelerate the design search; and design of affinity and specificity using novel positive and negative 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 we develop will be released open-source.

描述（由申请人提供）：基于计算结构的蛋白质设计是一个变革性的领域，具有推进基础科学和转化医学研究的令人兴奋的前景。我的实验室开发了新的蛋白质设计算法，并利用它们来设计治疗白血病的新药，重新设计酶以使当前的抗生素多样化，设计蛋白质-肽相互作用以治疗囊性纤维化，设计探针以分离广泛中和的HIV抗体，并预测MRSA对新抗生素的耐药性。蛋白质设计方法的核心是需要优化蛋白质结构中的氨基酸序列、侧链的位置和骨架构象。通过开发用于蛋白质和配体结构和序列的组合优化的高级搜索和评分算法，我们表明可以通过（a）建模改进的分子柔性和（B）利用结构的集合进行精确预测来设计期望的结构、亲和力和活性。我们的一套算法对解决方案的质量有数学保证（高达输入模型的精度，包括初始结构，要建模的分子柔性和经验分子力学能量函数）。具体来说，我们的算法保证计算全局最小能量构象（GMEC），一个无间隙的序列和结构的列表，以预测的能量，和一个证明良好的近似的结合亲和力，通过绑定配分函数在分子系综。我们前瞻性地测试了我们的算法，实验验证包括构建突变蛋白，测量结合亲和力，酶动力学和稳定性，晶体结构，NMR结构，病毒中和和细胞内活性。我们建议建立在我们的蛋白质设计算法的基础上，称为OSPREY，并将其应用于生物化学和药理学重要性的领域。我们将（1）预测新药物蛋白质靶点的未来耐药突变;（2）设计蛋白质：蛋白质相互作用的抑制剂，以靶向今天的“不可药物化”蛋白质;（3）使用我们的设计方法来发现和改进广泛中和的HIV-1抗体。我们将对蛋白质设计算法进行改进，以提高准确性和范围，我们将通过改进算法和建模来推进蛋白质设计的最新技术，以实现上述目标（1-3），包括：在设计过程中建模更多的蛋白质和配体灵活性;新的组合优化和能量约束方法，以加速设计搜索;以及使用模拟热力学分子系综的新颖的阳性和阴性设计算法设计亲和力和特异性。我们将通过制造新型预测突变蛋白质并进行生化、生物和结构研究来前瞻性地测试我们的设计预测。我们还将使用现有的结构和数据回顾性地验证我们的算法。我们开发的所有软件都将开源发布。