aesop 1.1.0

Module for analyzing electrostatics with protein structures

AESOP

(A)nalysis of (E)lectrostatic (S)tructures (o)f (P)roteins

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 potentials of 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