CRII: OAC: Development of a modular framework for the modeling of peptide and protein binding to membranes

膜蛋白结合膜以发挥其生物学功能，如受体和转运蛋白，其功能对人类健康至关重要。它们构成了近一半已批准药物的靶点，因此是治疗多种疾病的重要靶点。了解这些疾病机制的研究工作受益于预测蛋白质在膜中的准确位置。然而，现有的计算预测方法通常作为独立的应用程序执行，并且它们与其他蛋白质建模工具结合使用不能自动化。为了解决这些差距，这项工作开发了一个有效的框架，用于在细胞膜中准确放置膜蛋白，同时能够与其他经常用于以模块化方式设计或评估膜蛋白结构的计算工具集成。这种方法有助于对具有潜在治疗用途的蛋白质和肽进行建模，包括开发专门设计用于承担功能的抗菌肽和蛋白质，以及研究导致各种疾病的蛋白质变体。这项工作的目的是开发计算工具，用于模拟蛋白质结合膜，可以以多种方式使用，使用Rosetta套件作为平台。设计的工具可以在Rosetta框架内部或外部使用。该项目的智力价值是其方法，以模块化和灵活的方式优化膜坐标预测方法的性能，鲁棒性和准确性。作为该项目的一部分设计的工具将包括开发a）Rosetta mover，其实现为Rosetta C++源代码，以预测任何给定蛋白质或肽的膜放置，B）该mover的应用程序与Rosetta框架兼容，c）独立和容器化的Python脚本，其可以与外部建模软件流水线连接，以及d）用于预测蛋白质与膜结合的网络服务器。该奖项反映了NSF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而被认为值得支持。

Membrane proteins bind membranes to serve their biological functions, such as receptors and transporters, whose functions are critical for human health. They constitute the targets of nearly half of all approved drugs and thus are important targets for treating myriad diseases. Research efforts to understand these disease mechanisms benefit from predicting the accurate placement of proteins in membranes. However, the existing computational prediction methods typically perform as standalone applications, and their use in conjunction with other protein modeling tools cannot be automated. To address these gaps, this work develops an efficient framework for the accurate placement of membrane proteins in cell membranes while enabling integration with other computational tools frequently used to design or assess membrane protein structure in a modular manner. This approach facilitates the modeling of proteins and peptides with potential therapeutic use, including developing antimicrobial peptides and proteins specifically designed to assume functions and the investigation of protein variants that cause various diseases. This work aims to develop computational tools for modeling protein binding to membranes that can be used in multiple ways, using the Rosetta suite as a platform. The designed tools can be used within or outside the Rosetta framework. The intellectual merit of this project is its approach that optimizes the performance, robustness, and accuracy of membrane coordinate prediction methods in a modular and flexible manner. The tools designed as part of this project will encompass the development of a) a Rosetta mover implemented to the Rosetta C++ source code to predict membrane placement of any given protein or peptide, b) an application of this mover compatible with the RosettaScripts framework, c) standalone and containerized Python scripts that can be pipelined with external modeling software, and d) a web server for the prediction of protein binding to membranes.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.