Crii: OAC: development of a modular framework for the modeling of peptide and protein binding to membranes

This Project Grant award of $175,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a modular framework for modeling the binding of peptides and proteins to cell membranes. The key products to be delivered include:

A software "mover" implemented in the Rosetta C++ codebase to predict the placement of proteins and peptides in cell membranes, an application of this mover compatible with the RosettaScripts framework, standalone Python scripts that can be integrated with external modeling tools, and a web server for predicting protein-membrane binding. This work aims to create flexible, robust, and accurate computational tools to model membrane protein structure and function, which are critical for understanding disease mechanisms and developing potential therapies. The award is held by Butler University in Indianapolis, Indiana and is scheduled for completion by July 2026.

Abstract

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.- subawards are not planned for this award.