Characterizing protein dynamics can uncover the mechanism for protein function. Chemical crosslinking coupled with highly sensitive mass spectrometry is a structural biology technology developed in recent ten years. The distance information between specific amino acids in protein can be obtained by crosslinking. CXMS is becoming more popular owing to its high sensitivity and no limitation on the molecular weight of the protein system. CXMS distance restraints are usually applied to Cα or Cβ atoms of the crosslinked residues, with upper bounds over 20Å. The incorporation of loose CXMS restraints only marginally improves the resolution of the calculated structures. 

In a study pubilished in Structure on Aug 6^th , a research team led by Dr. TANG Chun from Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, developed a new method to study protein structure and dynamics based on chemical crosslinking coupled with mass spectrometry (CXMS).

The research group compared and analyzed published CXMS experimental data. With the inspiration from nuclear magnetic resonance, they developed a new method of using the Cω atom as the distance restraint point. With the flexible side-chain explicitly represented, the reformatted restraint can be applied to the modification group instead, with an upper bound of 6Å or less. The use of the new type of restraints not only affords better-resolved structures but also uncovers protein dynamics. 

The research group has a keen interest in developing new methods for characterizing biomolecular dynamics. They have published several papers on the computational analysis of CXMS data, which include the development of CXMS calculation methods to study the dynamic structures of protein complexes, modeling of the protein excited-state structures from "over-length" chemical crosslinks, developing the new method integrates CXMS, Small-angle X-ray scattering, and single-molecule FRET data.

Recently, they also systematically characterized the distribution characteristics of crosslinking distance, and proposed a more accurate crosslinking distance distribution model.