Computational Structure-Based Protein Design

描述(由申请人提供)：基于计算结构的蛋白质设计是一个具有促进基础科学和转化医学研究的令人兴奋的前景的变革性领域。我的实验室开发了新的蛋白质设计算法，并将其用于设计治疗白血病的新药、重新设计一种酶以使当前的抗生素多样化、设计蛋白质与多肽的相互作用以治疗囊性纤维化、设计探针以分离广泛中和的艾滋病毒抗体，以及预测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、侧链的位置和主干构象。通过开发先进的搜索和评分算法来组合优化蛋白质和配体的结构和序列，我们证明了所需的结构、亲和力和活性可以通过(A)建模改善的分子灵活性和(B)利用结构集合进行准确预测来设计。我们的一套算法对解的质量有数学保证(达到输入模型的精度，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)。具体地说，我们的算法保证计算全局最小能量构象(GMEC)，一个按预测能量顺序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良好的结合亲和力近似。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、酶的动力学和稳定性、晶体结构、核磁共振结构、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，称为鱼鹰，并将它们应用于生化和药理学的重要领域。我们将(1)预测新药的蛋白质靶标的未来抗药性突变；(2)设计蛋白质的抑制剂：针对今天“无法下药的”蛋白质的蛋白质相互作用；(3)使用我们的设计方法来发现和改进广泛中和HIV-1抗体。我们将改进我们的蛋白质设计算法，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先进水平，以实现上述目标(1-3)，包括：在设计过程中对更多的蛋白质和配体灵活性进行建模；新的组合优化和能量约束方法，以加快设计搜索；以及使用模拟热力学分子集合的新型正负设计算法来设计亲和力和特异性。我们将通过制造新的预测突变蛋白质，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，对我们的算法进行回溯性验证。我们开发的所有软件都将以开源方式发布。

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.