Computational Structure-Based Protein Design

基于计算结构的蛋白质设计

基本信息

  • 批准号:
    8628215
  • 负责人:
  • 金额:
    $ 31.93万
  • 依托单位:
  • 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

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。

项目成果

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Bruce R. Donald其他文献

Discovery, characterization, and redesign of potent antimicrobial thanatin orthologs from emChinavia ubica/em and emMurgantia histrionica/em targeting emE. coli/em LptA
从 emChinavia ubica/em 和 emMurgantia histrionica/em 中发现、表征和重新设计针对 emE. coli/em LptA 的强效抗菌 thanatin 直系同源物
  • DOI:
    10.1016/j.yjsbx.2023.100091
  • 发表时间:
    2023-12-01
  • 期刊:
  • 影响因子:
    5.100
  • 作者:
    Kelly Huynh;Amanuel Kibrom;Bruce R. Donald;Pei Zhou
  • 通讯作者:
    Pei Zhou
Resistor: an algorithm for predicting resistance mutations using Pareto optimization over multistate protein design and mutational signatures
Resistor:一种使用多态蛋白质设计和突变特征的帕累托优化来预测抗性突变的算法
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    N. Guerin;A. Feichtner;Eduard Stefan;T. Kaserer;Bruce R. Donald
  • 通讯作者:
    Bruce R. Donald
span style=color:#0070C0;font-family:quot;Calibriquot;,quot;sans-serifquot;;font-size:12pt;An Efficient Parallel Algorithm for Accelerating Computational Protein Design/span
一种加速计算蛋白质设计的高效并行算法
  • DOI:
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    5.8
  • 作者:
    Yichao Zhou;Wei Xu;Bruce R. Donald;Jianyang Zen
  • 通讯作者:
    Jianyang Zen
A theory of manipulation and control for microfabricated actuator arrays
微加工执行器阵列的操纵和控制理论

Bruce R. Donald的其他文献

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{{ truncateString('Bruce R. Donald', 18)}}的其他基金

Diversity Supplement: Computational and Experimental Studies of Protein Structure and Design
多样性补充:蛋白质结构和设计的计算和实验研究
  • 批准号:
    10579649
  • 财政年份:
    2022
  • 资助金额:
    $ 31.93万
  • 项目类别:
Computational and Experimental Studies of Protein Structure and Design
蛋白质结构和设计的计算和实验研究
  • 批准号:
    10554322
  • 财政年份:
    2022
  • 资助金额:
    $ 31.93万
  • 项目类别:
Computational and Experimental Studies of Protein Structure and Design
蛋白质结构和设计的计算和实验研究
  • 批准号:
    10727023
  • 财政年份:
    2022
  • 资助金额:
    $ 31.93万
  • 项目类别:
Computational and Experimental Studies of Protein Structure and Design
蛋白质结构和设计的计算和实验研究
  • 批准号:
    10793426
  • 财政年份:
    2022
  • 资助金额:
    $ 31.93万
  • 项目类别:
Computational and Experimental Studies of Protein Structure and Design
蛋白质结构和设计的计算和实验研究
  • 批准号:
    10330495
  • 财政年份:
    2022
  • 资助金额:
    $ 31.93万
  • 项目类别:
Deep Topological Sampling of Protein Structures
蛋白质结构的深度拓扑采样
  • 批准号:
    9304913
  • 财政年份:
    2017
  • 资助金额:
    $ 31.93万
  • 项目类别:
Automated NMR Assignment and Protein Structure Determination
自动 NMR 分配和蛋白质结构测定
  • 批准号:
    7940504
  • 财政年份:
    2009
  • 资助金额:
    $ 31.93万
  • 项目类别:
Computational Structure-Based Protein Design
基于计算结构的蛋白质设计
  • 批准号:
    9915930
  • 财政年份:
    2008
  • 资助金额:
    $ 31.93万
  • 项目类别:
Computational Active-Site Redesign and Binding Prediction via Molecular Ensembles
通过分子整体的计算活性位点重新设计和结合预测
  • 批准号:
    8025987
  • 财政年份:
    2008
  • 资助金额:
    $ 31.93万
  • 项目类别:
Computational Active-Site Redesign and Binding Prediction via Molecular Ensembles
通过分子整体的计算活性位点重新设计和结合预测
  • 批准号:
    7462701
  • 财政年份:
    2008
  • 资助金额:
    $ 31.93万
  • 项目类别:

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