Utilization of Hydrophobic Microenvironment Sensitivity in Diethylpyrocarbonate Labeling for Protein Structure Prediction

Abstract

Diethylpyrocarbonate (DEPC) labeling analyzed with mass spectrometry can provide important insights into higher order protein structures. It has been previously shown that neighboring hydrophobic residues promote a local increase in DEPC concentration such that serine, threonine, and tyrosine residues are more likely to be labeled despite low solvent exposure. In this work, we developed a Rosetta algorithm that used the knowledge of labeled and unlabeled serine, threonine, and tyrosine residues and assessed their local hydrophobic environment to improve protein structure prediction. Additionally, DEPC-labeled histidine and lysine residues with higher relative solvent accessible surface area values (i.e., more exposed) were scored favorably. Application of our score term led to reductions of the root-mean-square deviations (RMSDs) of the lowest scoring models. Additionally, models that scored well tended to have lower RMSDs. A detailed tutorial describing our protocol and required command lines is included. Our work demonstrated the considerable potential of DEPC covalent labeling data to be used for accurate higher order structure determination.

Accounting for Neighboring Residue Hydrophobicity in Diethylpyrocarbonate Labeling Mass Spectrometry Improves Rosetta Protein Structure Prediction

“…1,9,10,12,13,24−26,30−35 Previously, we incorporated the first DEPC-guided Rosetta score term based on the labeling sensitivity of Ser, Thr, and Tyr (STY) residues to a hydrophobic microenvironment and based on the solvent exposure of His and Lys residues. 9 Labeled and unlabeled STY residues with low relative solvent accessible surface area (SASA) were rewarded based on the number of hydrophobic neighbors. Labeled residues with more hydrophobic neighbors and unlabeled residues with fewer hydrophobic residues were rewarded based on our previous work that indicated neighboring hydrophobic residues promoted an increased local concentration of DEPC and thus facilitated labeling.…”

“…Experimental data collection for myoglobin, 8 β2m, 8 ubiquitin, 8 lysozyme, 36 HGH, 8 and carbonic anhydrase 37 has been described and published previously, with DEPC labeling status data for His, Lys, Ser, Thr, and Tyr residues for each protein. 9 Experimental data for sfGFP has been included in Table S1.…”

“…4,6−8 Knowledge of DEPClabeled and unlabeled residues coupled with computational methods can be used for protein structure elucidation. 9 Computational methods in combination with MS and other experimental data have been previously shown to support protein structure prediction efforts across different modeling platforms. 9−20 Hydrogen−deuterium exchange (HDX) data from MS and nuclear magnetic resonance and chemical cross-linking (XL) data have been used for successful protein modeling.…”

Advances in mass spectrometry‐based footprinting of membrane proteins

“…The integration of MS data with those from other structural methods, including cryo-EM and NMR, presents an opportunity to study increasingly complex systems by using an integrative structural biology approach. In addition, integrating the MS data into a structural model can be complex when there is no highresolution structure, especially for dynamic systems that rely on the computer simulation (e.g., Alphafold), but some progress is being made on this front by the efforts of Lindert and coworkers [100,101]. The interaction of biomolecules (e.g., RNA) with MPs will also complement our understanding of MPs [102].…”

Simulation of energy-resolved mass spectrometry distributions from surface-induced dissociation

“…The resulting structural information can then be used to better understand the roles of the specific proteins in biological processes. These sparse data have also been combined with computational modeling methods (17,18) to improve the accuracy of structural predictions (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29).…”