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| Crosslinkomics---A New Era of Mapping Protein-Protein Interactions |
| Jie Luo* and Jeff Ranish |
| Institute for Systems Biology, 401 Terry Ave N, Seattle, WA 98109, USA |
| *Corresponding author: |
Jie Luo
Institute for Systems Biology, 401 Terry Ave N
Seattle
WA 98109, USA
E-mail: jluo@systemsbiology.org |
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| Received December 12, 2011; Accepted December 15, 2011; Published December 17, 2011 |
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| Citation: Luo J, Ranish J (2011) Crosslinkomics---A New Era of Mapping Protein-
Protein Interactions. J Proteomics Bioinform 4: viii-viii. doi:10.4172/jpb.100000e8 |
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| Copyright: © 2011 Luo J, et al. This is an open-access article distributed under
the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and
source are credited. |
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| A major goal of proteomics is to figure out how proteins interact
with one another through networks and within complexes. The
development of integrated chemical crosslinking, Mass spectrometric
and computation approaches (CXMS) have recently emerged as
powerful technologies to study protein interactions and have the
potential to significantly advance the field of protein-interaction
mapping. The identified crosslinked peptides (two peptides linked
by specific crosslinkers), can be used to infer sites of protein-protein
interactions and put distance constraints on interacting sites based on
the properties of the crosslinkers. Recent reports on the on mapping
of interactions within large assemblages such the 15 subunit RNA
polymerase II-TFIIF complex [EMBO J 29, 717-726] , the 53 subunit
Ribosome[JPR, 10(8):3604-3616] and a partially purified prepartion
of the 12 subunit RNA polymerase II [MCP, mcp.M111.008318]
suggest that the field of CXMS has matured to the point where its use
for mapping protein interactions in large compelxes will soon become
more routine. CROSSLINKOMICS, which uses mass spectrometry and
computational approaches to identify chemically crosslinked peptides
in a high throughput and unbiased manner, is emerging as a new and
promising OMICS which has the potential to revolutionize the study of
protein-protein interactions. |
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| For the past decade, however, the CXMS approach has been limited
primarily to the analysis of single proteins or small complexes. Three
major hurdles have to be conquered for crosslinkomics to be useful
for the large scale and routine study of protein-protein interactions,
especially for the global mapping of protein-protein interactions.
The first hurdle involves the detection of the typically low abundance
crosslinked peptides in samples of high complexity. Several approaches
have been devised to facilitate detection of peptide crosslinks
during MS analysis. They include the use of crosslinking reagents
that produce diagnostic fragmentation patterns during collisioninduced
dissociation (CID), isotope-coded crosslinkers or proteins,
isotopic labeling of peptides derived from crosslinking reaction, SCX
fractionation and enrichment of crosslinked products via affinity
handles. Most of these approaches, however, require relatively large
amounts of starting material which is due in part to generally poor
crosslinking and enrichment efficiency. The requirement for relatively
large amounts of starting material is the major hurdle at present that
prevents the widespread usage of this technique for mapping proteinprotein
interactions. |
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| The second hurdle is how to identify the crosslinked spectra and the
corresponding crosslinked peptides. Identification of the crosslinked
peptides is a formidable challenge because crosslinked peptides typically
generate fragmentation spectra that are very complex and difficult to
interpret. The explosion of database search space that occurs when
combinations of all possible crosslinked peptide pairs is considered,
severely limits the ability of search algorithms to distinguish true
positives from false positives. Thus, most search algorithms require
construction of sample-specific databases to limit the number of
potential peptide pairs to consider during database searching. Current
algorithms can handle on the order of 30-50 proteins. One strategy that
facilitates confident identification of crosslinked peptides in complex
samples is the use of “MS labile” crosslinkers that fragment either by in
source decay or by CID (MS2) to transform crosslinked peptides into
two modified peptides which can in turn be selected for CID (MS3) by
data dependent routines, and identified by search algorithms such as
Sequest or Mascot that are commonly used to identify linear peptides.
The reduced complexity of the MS3 spectra facilitates confident
peptide identification and alleviates the limitation of searching samplespecific
databases. Various MS labile crosslinkers have been developed,
including linkers that contain an Aspartyl-Prolyl-bond (D-P), various
forms of a carbon-sulfur (C-S) bond, a urea moiety, and a Rink moiety.
These methods have also encounters issues such as hydrophobicity,
crosslinking and/or enrichment efficiency, multiple MS2 fragments
and/or MS3 sensitivity. This issue is the main issue most labs work
on at present and major progress has been made in identifying the
crosslinking peptides. |
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| The third hurdle for crosslinkomics is how to interpret the
crosslinking data, which provides information about the vicinity of the
crosslinking sites, not necessary the sites of interactions. Computational
tools need to be built to integrate various sources of crosslinking data,
with other structural approaches to permit generation of refined models
of prtoein complexes structure. This hurdle has been least addressed so
far, but it will become more and more important as CXMS becomes
more routine. Until then, crosslinkomics can become an efficient
and powerful approach to study protein-protein interactions. Thus,
the furture direction to the realization of mapping protein-protein
interactions in test tubes will rely on the progresses from all three areas:
crosslinking efficiency and MS sensitivity increasement, the confidence
and the efficiency of spectra identification and the structure modeling. |
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