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

基本信息

批准号：
7462701
负责人：
Bruce R. Donald
金额：
$ 31.12万
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 2012-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7462701
关键词：
Active Sites Algorithms Amino Acids Anabolism Antifungal Antibiotics Antineoplastic Agents Antiviral Agents Binding Binding Proteins Biochemical Biological Assay Biological Models Biotechnology Catalysis Cations Chemicals Cities Complex Computer Assisted Computer Simulation Computer software Cystic Fibrosis Cystic Fibrosis Transmembrane Conductance Regulator DNA Restriction Enzymes Development Drug Design Effectiveness Engineering Entropy Enzymes Evaluation Exhibits Future Generations Goals Gramicidin Hybrids Immunosuppressive Agents In Vitro Lead Leucine Libraries Ligand Binding Ligands Ligase Measures Methods Modeling Modification Molecular Molecular Conformation Mutation Peptides Phenylalanine Pliability Proteins Reaction Recovery Research Score Side Site Specificity Speed Statistical Mechanics Structure Substrate Interaction System Techniques Testing Therapeutic Intervention Time Tyrosine Vertebral column Work adenylate base combinatorial design desire enzyme substrate improved in vivo inhibitor/antagonist mutant novel novel therapeutics open source peptide synthase predictive modeling research study

项目摘要

DESCRIPTION (provided by applicant): Realization of novel molecular function requires the ability to alter molecular complex formation. Enzymatic function can be altered by changing enzyme-substrate interactions via modification of an enzyme's active site. We propose a new algorithm for protein redesign, which combines a statistical mechanics-derived ensemble-based approach to computing the binding constant with the speed and completeness of a branch-and-bound pruning algorithm. In addition, we propose an efficient, deterministic approximation algorithm, capable of approximating our scoring function to arbitrary precision. Our ensemble-based algorithm, which flexibly models both protein and ligand using rotamer-based partition functions, has application in enzyme redesign, the prediction of protein-ligand binding, and computer-aided drug design. In preliminary studies, we redesigned the phenylalanine-specific adenylation domain of the non-ribosomal peptide synthetase Gramicidin Synthetase A (NRPS GrsA-PheA). Ensemble scoring, using a rotameric approximation to the partition functions of the bound and unbound states for GrsA-PheA, was used to switch the enzyme specificity toward leucine (Leu) and tyrosine (Tyr), using novel active site sequences computationally predicted by searching through the space of possible active site mutations. The top-scoring in silico mutants were created in vitro, and binding and catalytic activity were measured. Several of the top-ranked mutations exhibit the desired change in specificity from Phe to Leu or Tyr. When considering protein flexibility and molecular ensembles for protein design, a major challenge has been the development of ensemble-based redesign algorithms that efficiently prune mutations and conformations. The proposed K* ("K-star") method generalizes Boltzmann-based scoring to ensembles and applies the result to protein design. K prunes the vast majority of conformations, thereby reducing execution time and making a mutation search that considers both ligand and protein flexibility computationally feasible. In addition to redesigning PheA, the K algorithm will be used to reprogram the specificity of other NRPS domains, whose products include natural antibiotics, antifungals, antivirals, immuno- suppressants, and antineoplastics. We will also use our algorithms to redesign two restriction endonucleases (REs), and will apply K to design peptide inhibitors for the CAL (Cystic fibrosis transmembrane conductance regulator Associated Ligand) PDZ domain. Our algorithms will predict NRPS and RE mutants with putative novel function, and we will create the mutant proteins, and test our predictions by using biochemical activity assays and determining new crystal structures. We will test the predicted CAL-binding peptides both in vitro and in vivo. Project Narrative: Enzyme redesign provides a good test of our understanding of proteins. The long-term goal of our research is to develop novel algorithms to plan structure-based site-directed mutations to a protein's active site in order to modify its function. We will develop general planning software that can reprogram the specificity of many proteins, including NRPS domains, whose products include natural antibiotics, antifungals, antivirals, immuno- suppressants, and antineoplastics. These engineered enzymes should enable combinatorial biosynthesis of novel pharmacologically-active compounds, yielding new leads for drug design. The proposed application of our algorithms to restriction endonucleases and the CAL PDZ domain could lead to (respectively) biotechnology advances and novel therapeutic interventions for cystic fibrosis.

描述（由申请人提供）：新分子功能的实现需要改变分子复合物形成的能力。通过修饰酶的活性位点来改变酶与底物的相互作用，可以改变酶的功能。我们提出了一种用于蛋白质重新设计的新算法，它将统计力学衍生的基于集成的方法与分支定界修剪算法的速度和完整性相结合来计算结合常数。此外，我们提出了一种有效的、确定性的近似算法，能够将我们的评分函数近似到任意精度。我们基于集成的算法使用基于旋转异构体的分配函数灵活地对蛋白质和配体进行建模，可应用于酶重新设计、蛋白质-配体结合的预测和计算机辅助药物设计。在初步研究中，我们重新设计了非核糖体肽合成酶短杆菌肽合成酶 A (NRPS GrsA-PheA) 的苯丙氨酸特异性腺苷酸化结构域。整体评分，使用 GrsA-PheA 的结合和未结合状态的分配函数的旋转异构体近似，用于将酶特异性切换到亮氨酸 (Leu) 和酪氨酸 (Tyr)，使用通过搜索计算预测的新活性位点序列可能的活性位点突变的空间。计算机模拟中得分最高的突变体是在体外创建的，并测量了结合和催化活性。几个排名靠前的突变表现出从 Phe 到 Leu 或 Tyr 所需的特异性变化。在考虑用于蛋白质设计的蛋白质灵活性和分子整体时，一个主要挑战是开发基于整体的重新设计算法，该算法可以有效地修剪突变和构象。所提出的 K*（“K-star”）方法将基于玻尔兹曼的评分推广到集成，并将结果应用于蛋白质设计。 K 修剪了绝大多数构象，从而减少了执行时间，并使考虑配体和蛋白质灵活性的突变搜索在计算上可行。除了重新设计 PheA 之外，K 算法还将用于重新编程其他 NRPS 域的特异性，其产品包括天然抗生素、抗真菌药、抗病毒药、免疫抑制剂和抗肿瘤药。我们还将使用我们的算法重新设计两种限制性内切酶 (RE)，并将应用 K 设计 CAL（囊性纤维化跨膜电导调节相关配体）PDZ 结构域的肽抑制剂。我们的算法将预测具有假定新功能的 NRPS 和 RE 突变体，我们将创建突变蛋白，并通过使用生化活性测定和确定新的晶体结构来测试我们的预测。我们将在体外和体内测试预测的 CAL 结合肽。项目叙述：酶的重新设计很好地检验了我们对蛋白质的理解。我们研究的长期目标是开发新的算法来规划蛋白质活性位点基于结构的定点突变，以修改其功能。我们将开发总体规划软件，可以重新编程许多蛋白质的特异性，包括 NRPS 结构域，其产品包括天然抗生素、抗真菌药、抗病毒药、免疫抑制剂和抗肿瘤药。这些工程酶应该能够实现新型药理活性化合物的组合生物合成，为药物设计提供新的线索。我们的算法在限制性核酸内切酶和 CAL PDZ 结构域中的拟议应用可能会（分别）带来生物技术的进步和囊性纤维化的新治疗干预措施。