Title: Computational Protein Structure Prediction Guided by Cryo-EM and Mass Spectrometry Data
Predicting a protein’s tertiary structure from the primary amino acid sequence has been hailed the holy grail of computational structural biology. The Rosetta software package has been shown to be one of the most successful tools for computational protein structure prediction and other modeling tasks. Our group develops software that is building on Rosetta and allows for incorporation of cryo-EM and mass spectrometry (MS) data to be effectively used to guide protein structure prediction. We developed an iterative cryo-EM guided Rosetta-MD refinement protocol for membrane proteins. By applying density-guided Rosetta-MD iteratively, we were able to refine the predicted structures of five membrane proteins to atomic resolutions. Additionally, we are using covalent labeling and surface induced dissociation (SID) mass spectrometry data to improve protein structure prediction. We developed a custom MS-scoring function that favorably scores exposure of labeled residues in protein models and this functionality was implemented into the Rosetta protein structure prediction software. In combination with Rosetta’s powerful energy function, we can accurately predict protein structure from MS labeling data. Additionally, we used SID data to develop a structure-based method for calculating interface energy. This allows for discrimination of different quaternary structural models based on SID measurements using a novel SID based scoring function, which is used in combination with Rosetta’s scoring function.