2. Mapping biological interfaces by CLMS
A crosslinking reagent makes a short covalent bridge between two
biomolecules in proximity, i.e. freezing them, that occurs when they are
bound through specific molecular contacts, thereby usefully aiding the
identification of an unknown binding partner interacting with a host
protein, especially in proteome-wide studies (Figure 1) (Götze et al.,
2019; Holding 2015; Piersimoni and Sinz 2020). Due to poor site-specific
reactivity and/or lack of highly sensitive analytical tools,
residue-level elucidation of a binding interface via chemical
crosslinking had not been possible until controlled crosslinking
chemistry and high-sensitivity mass spectrometry emerged into practice
(Mishra et al., 2020; Yang et al., 2016).
Among a variety of chemical reactivity available, to date, a
photo-activated functionality such as diazirine provides the most
effective resolution of crosslinks due to in situ initiation and
the fast kinetics of chemical steps. Additionally, a carbene formed from
diazirine upon UV irradiation shows an equivalent crosslinking
reactivity to all proteinogenic amino acids, avoiding biased estimation
in the following mass spetrometric analyses. An unnatural amino acid
containing a photo-activated residue (UAA) can be genetically and
site-specifically inserted into a protein or a photo-activated moiety
can be synthetically incorporated into a peptide or a small molecule
(Chen et al., 2020; Pham et al., 2013; Yang et al., 2016).
Alternatively, a natural amino acid residue such as amine or thiol in a
protein is derivatized with N-hydroxysuccinimide (NHS) ester in a
heterobifunctional linker that also contains a photo-activated group for
a subsequent crosslinking reaction to a binding partner.
A conventional homobifunctional linker bearing NHS esters is still
widely applicable not only for its ease of manipulation but also for
newly introduced functionalities like MS-cleavable moiety, isotope
labeling, or well-defined spatial constraints which, to a greater
extent, facilitate automated mass data analyses and computational
modeling with high confidence (Iacobucci et al., 2018; Ihling et al.,
2020; Rappsilber 2011). For more details about the workflow of CLMS,
crosslinking chemistries, and data processing which are beyond the scope
of this review, interested readers are referred to several recent
reviews (Chavez et al., 2019; Holding 2015; Piersimoni and Sinz 2020).