NAPA (Network Analysis of Protein Adaptation)
is a computational framework for the reconstruction and analysis of networks of mutations
involved in the protein's evolution of new or improved functions, a.k.a. protein adaptation.
Mutation networks can be reconstructed from a protein's evolutionary history,
either in the from a multiple sequence alignment or a Bayesian phylogenetic tree ensemble.
Nodes are linked in the network when the corresponding mutations are found to co-occur
in the alignment or along phylogenetic tree branches. For a phylogeny based network, the
links can also be directed, based on observed ordering of pairs of mutations along the tree branches.
After network reconstruction graph-theoretical analysis can be performed to model properties
of a protein's adaptive evolution:
- Mutations likely driving adaptation
- by ranking the corresponding nodes in the network by network centrality.
- Important combinations of mutations from a protein's adaptive trajectories
- by centrality rankings of connected paths in the network.
- Sets of mutations acting synergisticly to improve protein function
- by detecting densely connected communities in the network.
NAPA is free for non-commercial use. For more details please refer to our
Commercial users should contact the
Johns Hopkins Technology Transfer office.
Current stable release is napa-1.1, last updated on 01/17/2017.
Source Code Releases
You can view the current source code on github.
01/17/2017 Initial release.
Please consult the
project wiki page
for installation and usage details.
Examples of running NAPA are described on the
quick start page.
- An alignment-based network of the CTX-M group 1 β-lactamases
evolving under selection for increased resistance to the extended spectrum antibiotic
- A directed phylogeny-based network of TEM β-lactamases evolving under selection
for resistance to cephalosporins:
Basic functionality of NAPA scripts developed in python should be available across all platforms.
- If you use our software for a publication, please cite the following:
Beleva Guthrie V, Allen J, Camps M, and Karchin R (2011)
Network models of TEM β-lactamase mutations coevolving under antibiotic
selection show modular structure and anticipate evolutionary trajectories.
PLoS Computational Biology. 7(9):e1002184
Software primary contact/developer
Violeta Beleva Guthrie: vbeleva at jhu dot edu