Supplement: Student support for computationally designing peptides to interfere with p53-MDM2 and p53-sirtuin interaction

联系PD/PI：雅各布斯，唐纳德 项目摘要 多肽抑制剂具有可调节的结合亲和力、溶解性和特异性特征，可以干扰生物 从细胞信号到病毒感染载体的过程。不幸的是，设计一种 具有特定寻找的相互作用特性的多肽。利用现有的结构性功能 生物信息学，我们的目标是开发一个多肽的通用设计平台，该平台将仅与 在一个靶蛋白上的特定结合部位。为此，我们通过使用一个 将数据挖掘、分子对接和分子动力学模拟结合在一个串行式管道中。这个 验证阶段是对排名靠前的候选对象的绑定特性进行实验测量。连续的 将在遍历排序的列表时执行实验。当结果被汇编、监督时 机器学习将迭代地应用于重新排序候选列表以识别具有结合的多肽 所寻求的特征。在这个项目中，P53蛋白及其MDM2和SIRT1结合伙伴提供 作为一个模型系统，将进行一系列战略性的系统实验。重要的是，因为p53 是人体内调节细胞功能、转录和增殖的关键中枢蛋白，有 大量已发表的数据将用于建立关于这种肿瘤抑制因子的对照，以及 它在生物医学上的重要合作伙伴MDM2和SIRT1。实验设计的格式提供了一种测试 涉及多个结合配对的多变量结合目标。与理性设计策略相比 对于小分子，开发基于多肽的先导化合物的潜力要大得多。 该项目的一个成果将是两个独立的公共领域软件工具。第一个，称为PepStream，将 使用开放的存储库生成按与目标位置结合的倾向排序的候选多肽列表 序列和结构数据。第二种是有监督的机器学习工具，它与 对候选名单进行连续重新排序以提高成功率的实验测量结果。 项目摘要/摘要第6页

Contact PD/PI: JACOBS, Donald Project Summary With tunable binding affinity, solubility, and specificity characteristics, peptide inhibitors can interrupt biological processes from cell signaling to viral infection vectors. Unfortunately, it is an unsolved challenge to design a peptide to possess specifically sought interaction characteristics. Leveraging current capabilities in structural bioinformatics, we aim to develop a general design platform for peptides that will bind appreciably only to a specific binding site on one target protein. To this end, we rank order candidate peptides by employing a combination of data-mining, molecular docking, and molecular dynamics simulation in a serial-style pipeline. The verification stage is to experimentally measure the binding characteristics of the top candidates. Successive experiments will be performed as the ranked ordered list is traversed. As the results are compiled, supervised machine learning will be iteratively applied to re-rank the candidate list to identify peptides with binding characteristics that are sought. In this project, the p53 protein and its MDM2 and SIRT1 binding partners serve as a model system where a strategic set of systematic experiments will be performed. Importantly, because p53 is a critical hub protein in humans that modulates cellular function, transcription, and proliferation, there is considerable published data that will be used to establish controls regarding this tumor suppressor, as well as its biomedically important partners MDM2 and SIRT1. The format of the experimental design affords testing of a multivariate binding objective involving more than one binding partner. Compared to rational design strategies for small molecules, the potential for the development of a peptide-based lead compound is considerably higher. An outcome of this project will be two separate public-domain software tools. The first, called pepStream, will generate a candidate list of peptides ordered by propensity to bind to a target site using open repositories of sequence and structural data. The second, is a supervised machine learning tool that is integrated with the results from experimental measurements for successively re-ranking the candidate list to enhance success rates. Project Summary/Abstract Page 6