Assigning Peptide Structure from Ion-Mobility Mass Spectrometry Collision Cross Section Data.

Area of Science:

• Computational chemistry
• Analytical chemistry
• Biophysics

Background:

• Ion mobility coupled with mass spectrometry (IM-MS) is a powerful tool for characterizing peptide ions.
• Collision cross section (CCS) values provide molecular-level insights into peptide conformations.
• Accurate prediction of peptide structures is challenging due to dynamic gas-phase intramolecular interactions.

Purpose of the Study:

• To systematically assess a computational workflow for peptide structure elucidation using IM-MS data.
• To investigate the impact of conformational sampling and quantum mechanical level of theory on CCS prediction accuracy.
• To establish a reliable method for assigning peptide structures based on experimental CCS values.

Main Methods:

• Utilized a computational workflow combining classical and density functional theory (DFT) methods.
• Performed extensive enumeration and optimization of peptide conformations.
• Employed D3-B3LYP/6-31G(d) and subsequently 6-31G(d,p) basis sets for quantum mechanical calculations.

Main Results:

• Good agreement between experimental and computed CCS values was achieved with adequate conformational sampling.
• Increasing the basis set to 6-31G(d,p) improved accuracy in cases with suboptimal agreement.
• The study demonstrated the feasibility of accurate peptide structure assignment.

Conclusions:

• Accurate peptide structure assignment is achievable through comprehensive conformational space sampling.
• The appropriate quantum mechanical level of theory is crucial for modeling gas-phase intramolecular interactions.
• The developed computational workflow provides a reliable approach for peptide structure elucidation using IM-MS.