SBIR Phase I: Structure-based drug discovery for intrinsically disordered proteins

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业化潜力是能够发现危及生命的疾病的药物。一组与癌症和神经退行性疾病有关的蛋白质，以及细菌和病毒内部的许多蛋白质，由于它们在疾病中的作用而成为理想的治疗靶点。由于这些蛋白质具有挑战性的结构特征，设计治疗方法来调节它们是困难的。具体来说，这些蛋白质不断改变形状，而不是像现有的治疗目标那样保持刚性。该项目旨在开发一个计算实验平台，以促进发现变形蛋白质的治疗方法。这个小企业创新研究（SBIR）第一阶段项目促进了基于结构的药物发现内在无序蛋白（IDPs），一类与危及生命的疾病有关的柔性蛋白。IDPs的极端灵活性使其具有多种细胞功能，但却模糊了其构象的实验细节。正因为如此，传统的药物发现方法缺乏设计和优化调节IDPs的配体所需的结构细节，也没有成功开发出针对IDPs的药物。该项目的第一个目标是开发一种基于人工智能化学的分子动力学算法，用于精确建模给定IDP采用的构象。在第二个目标中，这些结构模型将与核磁共振片段筛选相结合，以获得概念验证IDP与观察到的结合IDP的任何小分子片段之间相互作用的详细模型。在第三个目标中，结构模型将指导有希望的片段的融合，扩展和优化，以设计用于调节概念验证IDP的主要候选物，生物物理实验将验证和指导计算假设。总之，这些目标为IDPs的基于结构的药物发现提供了一个系统的平台，以及一组靶向概念验证蛋白的主要候选物。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercialization potential of this Small Business Innovation Research (SBIR) Phase I project is to enable drug discovery for life-threatening diseases. A group of proteins involved in cancers and neurodegenerative disorders, as well as many proteins inside bacteria and viruses, are desirable therapeutic targets due to their role in disease. Designing therapeutics to regulate these proteins is difficult because of their challenging structural features. Specifically, these proteins constantly shift shapes instead of remaining rigid like existing therapeutic targets. This project aims to develop a computational-experimental platform to facilitate the discovery of therapeutics for shape-shifting proteins. This Small Business Innovation Research (SBIR) Phase I project facilitates structure-based drug discovery for intrinsically disordered proteins (IDPs), a class of flexible proteins implicated in life-threatening diseases. The extreme flexibility of IDPs enables their diverse cellular functions, but obscures experimental details about their conformations. Because of this, traditional drug discovery methods have lacked the structural details needed to design and optimize ligands that regulate IDPs, and no drugs have been successfully developed that target IDPs. The first aim of this project is to develop an AI-chemistry-based molecular dynamics algorithm for accurately modeling conformations adopted by a given IDP. In the second aim, these structural models will be coupled with NMR fragment screens in order to obtain detailed models of the interactions between the proof-of-concept IDP and any small molecule fragments observed to bind the IDP. In the third aim, the structural models will direct the fusion, extension, and optimization of promising fragments to design lead candidates for regulating the proof-of-concept IDP, and biophysical experiments will validate and guide computational hypotheses. Together, these aims provide a systematic platform for structure-based drug discovery for IDPs, as well as a set of lead candidates for targeting the proof-of-concept protein.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.