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

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.