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.

Figure 1.

Violin plot demonstrating the relative frequency of different hydrophobic neighbor count values for labeled STY (blue, includes 24 residues) and unlabeled STY (orange, includes 22 residues) residues with relative SASA values of 5–35% from benchmark protein crystal structures. Mean and extrema are shown on the plot.

Figure 2.

Overview of the DEPC score term (depc_ms) algorithm. The DEPC score term required CL-MS labeling data (residue numbers and label status) as input, along with input structures, which were either generated with homology or ab initio modeling (a). The relative SASA was calculated for all residues listed in the input file (b). If the residue was STY, additionally the hydrophobic neighbor count was calculated for residues with relative SASA between 5 and 35% (c). Labeled HK residues with higher relative SASA were rewarded (d). Labeled STY residues with more hydrophobic neighbors and unlabeled residues with less hydrophobic neighbors were rewarded as well (e).

Figure 3.

Score versus RMSD to the crystal structure for 10,000 ab initio models for a, Rosetta without DEPC labeling data and b, Rosetta with DEPC labeling data. Best scoring models are marked by a black star and shown in color aligned to the crystal structure (grey). P_near values are listed.

Figure 4.

Score versus RMSD to the crystal structure for 3,500 homology models for a, Rosetta without DEPC labeling data and b, Rosetta with DEPC labeling data. Best scoring models are marked by a black star and shown in color aligned to the crystal structure (grey). P_near values are listed.