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

Infectious Diseases: Targeting Meningitis

SBKB [doi:10.1038/sbkb.2012.140]
Technical Highlight - May 2013
Short description: Screens with substrates and inhibitors, followed by molecular modeling, reveal details of an enzyme's ligand binding specificity.

Model of NmAPN with its most potent inhibitor, with interactions indicated in green. Figure courtesy of Artur Mucha.

Bacterial pathogens that plague the human population are becoming increasingly resistant to broad-spectrum antibiotics. In order to develop new antibiotics, a clear understanding of the mode of action of potential bacterial targets is necessary, including knowledge of the contacts between enzymes and their substrates, to design drugs that thwart those interactions and stop the pathogen in its tracks. Mucha and colleagues (PSI MCSG) have now performed such an analysis for the alanine aminopeptidase of Neisseria meningitides (NmAPN) —a pathogen that can cause meningitis, a disease still prevalent in the developing world.

First, the authors determined the substrate specificity of NmAPN. As this enzyme catalyzes the removal of N-terminal amino acids from substrate proteins, a library of fluorogenic substrates was used to measure hydrolysis rates. The analysis revealed that the enzyme prefers substrates with bulky, hydrophobic side chains: L-homoarginine, L-arginine and L-alanine were the top three substrates. Substrate preferences were notably different than those of human APN, a dissimilarity that could be important in the design of drug inhibitors specific to the bacterial enzyme.

Next, a set of six inhibitors was tested; these were organophosphorus compounds derived from arginine and homophenylalanine, which yielded inhibition constants ranging from 0.05μM to 2.5μM. The researchers then modeled the NmAPN–inhibitor complexes, based on known structures of native NmAPN and Plasmodium falciparum APN in complex with an inhibitor. This analysis pinpointed the specific interactions responsible for tighter binding and thus greater inhibitory activity of the best inhibitor for NmAPN. This integrated work should aid in the treatment of meningitis through the design of inhibitors of NmAPN with high affinity and specificity.

Irene Kaganman


  1. E. Węglarz-Tomczak et al. An integrated approach to the ligand binding specificity of Neisseria meningitidis M1 alanine aminopeptidase by fluorogenic substrate profiling, inhibitory studies and molecular modeling.
    Biochimie. 95, 419-428 (2013). doi:10.1016/j.biochi.2012.10.018

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