PSI Structural Biology Knowledgebase

PSI | Structural Biology Knowledgebase
Header Icons

Related Articles
Signaling: A Platform for Opposing Functions
May 2015
Protein Folding and Misfolding: It's the Journey, Not the Destination
March 2015
Molecular Portraits of the Cell
February 2015
Nuclear Pore Complex: A Flexible Transporter
February 2015
Nuclear Pore Complex: Higher Resolution of Macromolecules
February 2015
Nuclear Pore Complex: Integrative Approach to Probe Nup133
February 2015
Piecing Together the Nuclear Pore Complex
February 2015
Updating ModBase
January 2015
Transmembrane Spans
December 2014
Mining Protein Dynamics
May 2014
Novel Proteins and Networks: Assigning Function
May 2014
Cancer Networks: Predicting Catalytic Residues from 3D Protein Structures
November 2013
The Immune System: A Brotherhood of Immunoglobulins
June 2013
The Immune System: Super Cytokines
June 2013
Infectious Diseases: Targeting Meningitis
May 2013
PDZ Domains
April 2013
Protein Interaction Networks: Adding Structure to Protein Networks
April 2013
Design and Discovery: Flexible Backbone Protein Redesign
February 2013
Pocket changes
July 2012
Predictive protein origami
July 2012
Refining protein structure prediction
March 2012
Metal mates
February 2012
Devil is in the details
January 2012
Playing while you work
November 2011
Docking and rolling
October 2011
Fit to serve
October 2011
Rosetta hone
July 2011
Structure from sequence
July 2011
An easier solution for symmetry
June 2011
Solutions in the solution
June 2011
Regulating nitrogen assimilation
January 2011
Guard cells pick up the SLAC
December 2010
Alpha/Beta Barrels
October 2010
Modeling RNA structures
May 2010
Deducing function from small structural clues
February 2010
Spot the pore
January 2010
Network coverage
November 2009
GPCR modeling: any good?
August 2009
Protein modeling made easy
July 2009
Model proteins in your lunch break
April 2009
Click for cancer-protein interactions
December 2008
Modeling with SAXS
October 2008
Designing activity
September 2008

Technology Topics Modeling

The Immune System: A Brotherhood of Immunoglobulins

SBKB [doi:10.1038/sbkb.2012.145]
Technical Highlight - June 2013
Short description: The Brotherhood algorithm groups proteins into functional families using indirect sequence similarity.

The Brotherhood method, an intermediate sequence-based clustering algorithm, is used to assign functional families within the human Ig super family. Reprinted with permission from Elsevier. 1

Until detailed structures can be readily generated for any protein, effective computational methods are necessary to predict protein function. What a protein does can be inferred by sequence similarity to other proteins with known functions. Yet this strategy omits functional relationships characterized by low sequence similarity, as is often the case across large protein superfamilies. Relaxing similarity thresholds can help, but leads to many false functional assignments.

Fiser, Almo and colleagues (PSI NYSGRC and IFN) introduce the Brotherhood algorithm, which uses intermediate sequence analysis to boost the accuracy of detecting related molecules. The Brotherhood intermediate sequence analysis measures the relatedness of two proteins by the degree of overlap in their BLAST-derived sets of similar, or 'intermediate,' proteins. Since the approach can cluster very weakly related proteins, the Brotherhood algorithm retains high specificity by normalizing the number of intermediate sequences by the total number of related sequences.

The authors used the Brotherhood method to classify 561 cell surface or secreted proteins within the immunoglobulin superfamily (IGSF). The IGSF is a diverse superfamily of regulatory proteins that mediate cell adhesion and immunity. At an empirically determined threshold of 45% intermediate protein overlap, Brotherhood largely recapitulates 14 well-curated families, generating only about half the number of singletons as the commonly used programs CD-HIT, which is based on pairwise BLAST similarities, and SCI-PHY, which uses multiple alignments and phylogenomic inferences.

The more inclusive clusters allow new functional predictions, which are highlighted in a family of five characterized nectin and four nectin-like proteins. Brotherhood analysis predicted five new members, including the class-I-restricted T-cell-associated molecule (CRTAM), which it classified as a nectin-like protein.

The researchers used molecular replacement to determine the crystal structure of the Ig-V portion of the CRTAM extracellular domain at 2.3-Å resolution (PDB 3RBG). CRTAM exhibits an antiparallel dimer structure, conserved binding interface residues and a gene structure similar to nectin-like proteins, thus validating its classification. The analysis predicts an unexpected function for CRTAM in mediating homodimer transinteractions.

The Brotherhood classification will help prioritize candidates for structural studies of the IGSF in order to shed light on these important therapeutic targets for cancer and infectious and autoimmune diseases.

Tal Nawy


  1. R. Rubinstein et al. Functional classification of immune regulatory proteins.
    Structure. (11 April 2013). doi:10.1016/j.str.2013.02.022

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