Comprehensive Tutorial for Computational Methods of Protein Structure Prediction Incorporating Mass Spectrometry Data

Here we present a series of tutorials demonstrating the use of various methods which integrate structural mass spectrometry (MS) data with computational protein structure prediction methods. We give usage examples of widely used modeling frameworks, including Rosetta-based approaches (ab initio modeling, comparative modeling, and protein-protein docking) and deep learning methods such as AlphaFold2. We then describe strategies for incorporating covalent labeling, ion mobility, and surface-induced dissociation MS data into these workflows through Rosetta scoring terms and specialized applications. Finally, we provide instructions on calculating structural metrics, such as solvent accessibility, collision cross sections, and energy-resolved MS data and comparing them to actual MS data. We also introduce new PyRosetta implementations of the PARCS algorithm and the SID_ERMS_Rescore application. Together, these tutorials provide a comprehensive framework for integrating computational modeling with structural MS to enhance protein structure prediction.