PSI Structural Biology Knowledgebase

PSI | Structural Biology Knowledgebase
Header Icons
E-Collection

Related Articles
Microbiome: Expanding the Gut Gene Catalog
November 2014
Complex Search
September 2014
Repairing a Rift
September 2014
iTRAQing the Ubiquitinome
July 2014
Immunity: Clustering Immunoglobulins
June 2014
Mining Protein Dynamics
May 2014
Design and Discovery: Identifying New Enzymes and Metabolic Pathways
January 2014
Epigenetics: Tracing Histone Demethylase Inhibitors
December 2013
Cancer Networks: Predicting Catalytic Residues from 3D Protein Structures
November 2013
Protein-Nucleic Acid Interaction: Inhibition Through Allostery
July 2013
Infectious Diseases: Targeting Meningitis
May 2013
Protein Interaction Networks: Reading Between the Lines
April 2013
Design and Discovery: A Cocktail for Proteins Without ID
February 2013
Targeting Enzyme Function with Structural Genomics
July 2012
More in one
June 2012
Disordered Proteins
February 2012
RNA Chaperone NMB1681
July 2011
Capsid assembly in motion
April 2011
One at a time
April 2011
A growing family
February 2011
Predicting functions within a superfamily
January 2011
Isoxanthopterin Deaminase
November 2010
Scaling up mutational scanning
November 2010
Alpha/Beta Barrels
October 2010
Mre11 Nuclease
May 2010
Assigning protein function: GeMMA
April 2010
Face off
October 2009

Technology Topics Annotation/Function

Immunity: Clustering Immunoglobulins

SBKB [doi:10.1038/sbkb.2014.205]
Technical Highlight - June 2014
Short description: An algorithm identifies functional clusters of cell surface-anchored and secreted immunoglobulin superfamily proteins through the comparison of conserved regions and taking into account known protein–protein interaction data.

PICTree classification of the 477 human IgSF proteins into functional families (green circles indicate known binding partners). Figure courtesy of Andras Fiser.

As more sequencing data become available, new tools to generate insights about protein function are sorely needed. Clustering algorithms that rely solely on pair-wise sequence similarity can miss functionally related proteins that share little sequence identity.

To generate predictions of functionally related families among the pharmacologically important immunoglobulin superfamily (IgSF) proteins, Fiser and colleagues (PSI NYSGRC) trained their algorithm to identify proteins that bind the same ligand in a similar manner. This algorithm, named PICTree, first compares sequence profile-based hidden Markov models, which amplifies signals from conserved regions that generally correlate with functional importance. In addition to improving clustering, the algorithm was calibrated on a dataset that included ligand-interaction data from the STRING protein interaction database. Further, the algorithm places more emphasis on sequence similarity within the N-terminal domain, which is frequently involved in ligand binding within cell-surface IgSF proteins.

Analysis of the 477 human cell-surface or secreted IgSF proteins resulted in the identification of 83 clusters with 2–34 members in each, and 87 singletons. Toward the researchers' goal of defining ligand interactions for all IgSF proteins, they predicted the function of a previously uncharacterized protein, VSIG8, in this initial analysis.

Of the five IgSF functional pairs in the training dataset that PICTree failed to identify, four required additional experimental information to ascertain if the pairs indeed share common binding modes. The authors also propose the incorporation of protein-specific binding site information in future versions of the algorithm: for example, in secreted IgSF proteins, unlike cell-surface ones, the binding site could lie outside the N-terminus.

For now, large-scale structural genomics efforts could benefit from information about functional families as well as single interactors that currently lack such information, in order to prioritize targets for experimental analysis.

Irene Kaganman

References

  1. E.H. Yap et al. Functional clustering of immunoglobulin superfamily proteins with protein-protein interaction information calibrated hidden Markov model sequence profiles.
    J. Mol. Biol. 426, 945-961 (2014). doi:10.1016/j.jmb.2013.11.009

Structural Biology Knowledgebase ISSN: 1758-1338
Funded by a grant from the National Institute of General Medical Sciences of the National Institutes of Health