Computational Active-Site Redesign and Binding Prediction via Molecular Ensembles

描述(由申请人提供)：实现新的分子功能需要改变分子络合物形成的能力。通过改变酶的活性部位来改变酶与底物的相互作用，从而改变酶的功能。我们提出了一种新的蛋白质重新设计算法，它结合了基于统计力学的集成计算结合常数的方法和分支定界剪枝算法的速度和完备性。此外，我们还提出了一种有效的确定性近似算法，能够将我们的评分函数逼近到任意精度。我们的基于集成的算法使用基于旋转异构体的配分函数灵活地对蛋白质和配体进行建模，在酶的重新设计、蛋白质与配体结合的预测以及计算机辅助药物设计中都有应用。在初步研究中，我们重新设计了非核糖体多肽合成酶Gramiidin合成酶A(NRPS GRSA-PHEA)的苯丙氨酸特异性腺化结构域。使用对GRSA-PHEA结合和非结合状态的配分函数使用旋转式近似的系综评分，使用通过搜索可能的活性位点突变空间来计算预测的新的活性位点序列，将酶的专一性切换到亮氨酸(Leu)和酪氨酸(Tyr)。在体外创造了硅胶突变体中得分最高的突变体，并测定了结合活性和催化活性。几个排名靠前的突变在特异性上表现出预期的变化，从Phe到Leu或Tyr。当考虑蛋白质灵活性和分子集成进行蛋白质设计时，一个主要的挑战是基于集成的重新设计算法的发展，该算法可以有效地修剪突变和构象。提出的K*(“K-STAR”)方法将基于Boltzmann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，并使突变搜索同时考虑到配体和蛋白质的灵活性在计算上是可行的。除了重新设计PHEA，K算法还将用于重新编程其他NRPS结构域的特异性，其产品包括天然抗生素、抗真菌药物、抗病毒药物、免疫抑制剂和抗肿瘤药物。我们还将使用我们的算法重新设计两个限制性内切酶(RES)，并将应用K来设计CAL(囊性纤维化跨膜电导调节因子相关配体)PDZ结构域的多肽抑制剂。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质，并通过生化活性分析和确定新的晶体结构来验证我们的预测。我们将在体外和体内测试预测的CAL结合多肽。 项目简介：酶的重新设计为我们对蛋白质的理解提供了一个很好的测试。我们研究的长期目标是开发新的算法来计划基于结构的定点突变到蛋白质的活性部位，以改变其功能。我们将开发通用规划软件，可以对许多蛋白质的特异性进行重新编程，包括NRPS结构域，其产品包括天然抗生素、抗真菌药物、抗病毒药物、免疫抑制剂和抗肿瘤药物。这些工程化的酶应该能够组合生物合成新的药理活性化合物，为药物设计带来新的线索。我们的算法在限制性内切酶和CAL PDZ结构域上的拟议应用可能(分别)导致生物技术的进步和囊性纤维化的新的治疗干预措施。

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.