Module for analyzing electrostatics with protein structures
Project description
# aesop-python
**AESOP**: Analysis of Electrostatic Structures of Proteins
Authors: Reed Harrison, Rohith Mohan, and Dimitrios Morikis
## Framework
- AESOP is a computational framework to explore electrostatic structures within proteins. The library depends on external tools including: APBS, PDB2PQR, Modeller, and ProDy
- **Atomic Selections**
- All selection strings must be made according to the style of ProDy (http://prody.csb.pitt.edu/manual/reference/atomic/select.html)
- **Examples**
- All materials for example cases are provided in the tests folder
- **Documentation**
- HTML documentation provided within the docs folder
- **Dependencies**
- APBS and PDB2PQR
- Required Python libraries: numpy, scipy, prody, matplotlib, modeller, griddataformats
- Optional Python libraries: multiprocessing
## Methods
- **Alascan**
- Perform a computational alanine scan on a provided protein structure using a side-chain truncation scheme
- Association free energies for mutatants (relative to the parent) may be predicted if 2 or more selection strings are provided
- Users may restrict mutations to some region of the protein structure
- **DirectedMutagenesis**
- Perform a directed mutagenesis scan on a provided protein structure using Modeller to swap amino acids
- Association free energies for mutatants (relative to the parent) may be predicted if 2 or more selection strings are provided
- Mutations must be specified
- **ElecSimilarity**
- Compare electrostatic potential vector fields between multiple protein structures
- If structures are very dissimilar, the user should superpose coordinates for each protein structure according to their desired method
## General Utilities
- aesop.plotScan()
- Show bargraph summary of results from computational mutagenesis methods (Alascan, DirectedMutagenesis)
- aesop.plotESD()
- Show heatmap summary of results from methods exploring electrostatic similarity (ElecSimilarity)
- aesop.plotDend()
- Show dendrogram summary of results from methods exploring electrostatic similarity (ElecSimilarity)
## Notes
- We recommend using Anaconda to aid in installation of Python scientific libraries
- Depending on your platform, ProDy may need to be installed with an executable
**AESOP**: Analysis of Electrostatic Structures of Proteins
Authors: Reed Harrison, Rohith Mohan, and Dimitrios Morikis
## Framework
- AESOP is a computational framework to explore electrostatic structures within proteins. The library depends on external tools including: APBS, PDB2PQR, Modeller, and ProDy
- **Atomic Selections**
- All selection strings must be made according to the style of ProDy (http://prody.csb.pitt.edu/manual/reference/atomic/select.html)
- **Examples**
- All materials for example cases are provided in the tests folder
- **Documentation**
- HTML documentation provided within the docs folder
- **Dependencies**
- APBS and PDB2PQR
- Required Python libraries: numpy, scipy, prody, matplotlib, modeller, griddataformats
- Optional Python libraries: multiprocessing
## Methods
- **Alascan**
- Perform a computational alanine scan on a provided protein structure using a side-chain truncation scheme
- Association free energies for mutatants (relative to the parent) may be predicted if 2 or more selection strings are provided
- Users may restrict mutations to some region of the protein structure
- **DirectedMutagenesis**
- Perform a directed mutagenesis scan on a provided protein structure using Modeller to swap amino acids
- Association free energies for mutatants (relative to the parent) may be predicted if 2 or more selection strings are provided
- Mutations must be specified
- **ElecSimilarity**
- Compare electrostatic potential vector fields between multiple protein structures
- If structures are very dissimilar, the user should superpose coordinates for each protein structure according to their desired method
## General Utilities
- aesop.plotScan()
- Show bargraph summary of results from computational mutagenesis methods (Alascan, DirectedMutagenesis)
- aesop.plotESD()
- Show heatmap summary of results from methods exploring electrostatic similarity (ElecSimilarity)
- aesop.plotDend()
- Show dendrogram summary of results from methods exploring electrostatic similarity (ElecSimilarity)
## Notes
- We recommend using Anaconda to aid in installation of Python scientific libraries
- Depending on your platform, ProDy may need to be installed with an executable
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