Over the last two decades, mass spectrometry (MS) has emerged as a key approach for addressing challenging problems in structural biochemistry. Sophisticated MS techniques in conjunction with covalently-labeled protein residues yield exposure information useful in protein structure prediction. Surface induced dissociation (SID) allows for estimation of the dissociation energy of mostly non-covalent interactions at protein-protein interfaces, as well as complex stoichiometry. However, easy and reliable translation of this information into accurate structural protein and protein complex models remains particularly challenging, due to a lack of standardized and automated methods. We developed and validated software tools that use covalent labeling MS data to guide protein structure prediction and that allow SID data to improve protein-protein docking in Rosetta. 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. 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. To evaluate the effectiveness of our covalent-labeling guided structure prediction algorithm we benchmarked its performance using four proteins of known structure for which we have obtained experimental covalent labeling data. For all benchmark proteins, models with lower average RMSDs were built when MS scores were included. Our covalent-labeling guided structure prediction algorithm proved to be a powerful hybrid method allowing for covalent labeling MS data to be used in protein structure prediction. For the SID-guided protein complex structure prediction, we have shown that there is a strong correlation between structural features (such as interface area, buried hydrophobic surface area) and experimental SID dissociation energy. This allowed for discrimination of different quaternary structural models based on SID measurements in the form of a novel SID based scoring function, which is used in combination with Rosetta’s scoring function. This scoring function has been successful in rescoring experiments, showing an improved ability over Rosetta alone to select poses with low RMSD to the native.

Computational protein structure prediction guided by covalent labeling and SID mass spectrometry data

2019 Spring National Meeting

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