Supplement: New computer for computationally designing peptides to interfere with p53-MDM2 and p53-sirtuin interaction

项目总结 具有可调节的结合亲和力、溶解度和特异性的多肽抑制剂可以中断 从细胞信号到病毒感染载体的生物学过程。不幸的是，这是一个尚未解决的挑战 设计一种具有特定寻找的相互作用特征的多肽。利用现有的功能 结构生物信息学，我们的目标是开发一个仅能明显结合的多肽的通用设计平台。 结合到一个目标蛋白上的特定结合部位。为此，我们通过使用一个 数据挖掘、分子对接和分子动力学模拟在串联式管道中的结合。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sirtuin结合伙伴服务于 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 大量已发表的数据可用于关于这种肿瘤抑制因子的对照研究，以及其 生物医学上的重要合作伙伴MDM2和SIRTUIN。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。

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 sirtuin 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 can be used for controls regarding this tumor suppressor, as well as its biomedically important partners MDM2 and sirtuin. 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.