Computational Structure-Based Protein Design

基于计算结构的蛋白质设计

基本信息

批准号：
8826756
负责人：
Bruce R. Donald
金额：
$ 31.48万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
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/8826756
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 Health 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 research study resistance mutation response software development virus envelope

项目摘要

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领域，在推进基础科学和转化医学研究方面具有令人兴奋的前景。我的实验室开发了新的蛋白质设计算法，并用它们设计治疗白血病的新药，重新设计酶以使现有抗生素多样化，设计蛋白质-肽相互作用来治疗囊性纤维化，设计探针来分离广泛中和 HIV 抗体，并预测 MRSA 对新抗生素。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、侧链的位置和主链构象。通过开发用于蛋白质和配体结构和序列组合优化的先进搜索和评分算法，我们表明可以通过以下方式设计所需的结构、亲和力和活性：(a) 建模改进的分子灵活性；(b) 利用结构集合进行准确预测。我们的算法套件对解决方案质量具有数学保证（直到输入模型的准确性，其中包括初始结构、要建模的分子灵活性以及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）、按预测能量顺序排列的序列和结构的无间隙列表，以及通过分子集合上的边界配分函数来证明对结合亲和力的良好近似。我们前瞻性地测试了我们的算法，实验验证包括突变蛋白的构建、结合亲和力的测量、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。我们建议建立在称为 OSPREY 的蛋白质设计算法基础上，并将其应用于具有重要生化和药理学意义的领域。我们将（1）预测新药蛋白质靶点未来的耐药突变； (2) 设计蛋白质抑制剂：蛋白质相互作用以针对当今的“不可成药”蛋白质； (3) 使用我们的设计方法来发现和改进广泛中和 HIV-1 抗体。我们将改进蛋白质设计算法，以提高准确性和范围，并且我们将通过算法和建模改进来推进蛋白质设计的最新技术，以实现上述目标 (1-3)，包括：在设计过程中对更多蛋白质和配体灵活性进行建模；新的组合优化和能量限制方法可加速设计搜索；使用模拟热力学分子整体的新型正负设计算法进行亲和力和特异性设计。我们将通过制造新的预测突变蛋白并进行生化、生物学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。