2022
DOI: 10.1021/jasms.1c00373
|View full text |Cite
|
Sign up to set email alerts
|

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

Abstract: Covalent labeling mass spectrometry allows for protein structure elucidation via covalent modification and identification of exposed residues. Diethylpyrocarbonate (DEPC) is a commonly used covalent labeling reagent that provides insight into structure through the labeling of lysine, histidine, serine, threonine, and tyrosine residues. We recently implemented a Rosetta algorithm that used binary DEPC labeling data to improve protein structure prediction efforts. In this work, we improved on our modeling effort… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
8
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
5
1

Relationship

4
2

Authors

Journals

citations
Cited by 9 publications
(8 citation statements)
references
References 42 publications
0
8
0
Order By: Relevance
“…Interestingly, some Ser and Thr residues (i.e., Thr77, Ser81, Thr89, and Ser147), which are near the epitope and are only partially buried upon adalimumab binding, actually increase in DEPC labeling when adalimumab is present (Figure D). These residues undergo increased labeling because adalimumab binding creates nearby hydrophobic pockets that increase the local DEPC concentration, as has been previously shown, ,,, allowing these weakly nucleophilic residues to react more extensively than they do when exposed in unbound TNFα.…”
Section: Resultsmentioning
confidence: 90%
See 1 more Smart Citation
“…Interestingly, some Ser and Thr residues (i.e., Thr77, Ser81, Thr89, and Ser147), which are near the epitope and are only partially buried upon adalimumab binding, actually increase in DEPC labeling when adalimumab is present (Figure D). These residues undergo increased labeling because adalimumab binding creates nearby hydrophobic pockets that increase the local DEPC concentration, as has been previously shown, ,,, allowing these weakly nucleophilic residues to react more extensively than they do when exposed in unbound TNFα.…”
Section: Resultsmentioning
confidence: 90%
“…For all three mAbs studied, most (76%) of the peptides that decrease significantly in deuterium uptake include epitope residues. Similarly, most (70%) of labelable epitope residues decrease in DEPC-CL, and the remaining 30% are Ser, Thr, or Tyr residues that become newly labeled upon mAb binding because of the creation of a hydrophobic pocket that enables DEPC reactivity. ,,, The complementarity between the two methods can be best observed with the TNFα complex with adalimumab. TNFα experiences decreases in HDX that span the majority of the protein sequence.…”
Section: Discussionmentioning
confidence: 99%
“…This linker region is not expected to be buried upon complex formation, but evidently, these residues experience a change in microenvironment upon complex formation. In previous work, it was shown that the reactivity of weakly nucleophilic residues such as Ser, Thr, and Tyr is increased when flanked by nearby hydrophobic groups, so a decrease in labeling might indicate that these residues become even more exposed in the complex. Ser258, which is located in the flexible linker that connects the P2 and P3 domains, also may undergo a decrease in labeling for similar reasons.…”
Section: Resultsmentioning
confidence: 99%
“…An assortment of different techniques can be used to measure various types of structural information. For example, ion mobility (IM) can provide information on size and shape, chemical cross-linking (XL) can provide information on residue distances and contacts, and covalent labeling can provide information on solvent accessibility and flexibility of residues. 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 , to improve the accuracy of structural predictions. …”
Section: Introductionmentioning
confidence: 99%