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

联系PD/PI：JACOBS，Donald 项目摘要 肽抑制剂具有可调的结合亲和力、溶解度和特异性特征，可以干扰生物活性， 从细胞信号传导到病毒感染载体。不幸的是，这是一个悬而未决的挑战，设计一个 肽具有特别寻求的相互作用特性。利用现有的结构能力 生物信息学，我们的目标是开发一个通用的设计平台，肽，将结合明显只有一个 一个靶蛋白上的特异性结合位点。为此，我们通过采用 数据挖掘、分子对接和分子动力学模拟的组合。的 验证阶段是通过实验测量最佳候选物的结合特性。连续 将在遍历经排序的有序列表时执行实验。随着结果的汇编，监督 机器学习将迭代地应用于对候选列表进行重新排序以识别具有结合的肽 所追求的特征。在这个项目中，p53蛋白及其MDM 2和SIRT 1结合伴侣 作为一个模型系统，其中将进行一系列战略性的系统实验。重要的是，因为p53 是人类中调节细胞功能、转录和增殖的关键枢纽蛋白， 大量已发表的数据将用于建立关于这种肿瘤抑制因子的控制，以及 它的重要生物医学伙伴MDM 2和SIRT 1。实验设计的形式提供了一个测试， 多变量绑定目标涉及一个以上的绑定伴侣。与理性设计策略相比 对于小分子，开发基于肽的先导化合物的潜力相当高。 该项目的成果将是两个独立的公共领域软件工具。第一个叫做pepStream， 使用开放的肽库生成肽的候选列表，所述肽的候选列表通过结合至靶位点的倾向来排序， 序列和结构数据。第二，是一个有监督的机器学习工具，与 从实验测量中获得的结果，用于连续地重新排列候选列表以提高成功率。 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