Introduction

This tutorial demonstrates how to use ESSA for determining the essential sites that would significantly alter the global/functional dynamics of a protein upon ligand binding [KB20]. ESSA emulates ligand binding by increasing the node density around each scanned residue. This is achieved by adding extra nodes at the positions of the specific residue’s heavy atoms (other than the C-alpha atoms that define the reference network in GNM or ANM). Changes in the global mode spectrum in response to crowding near each scanned residue is measured by the mean shift in the frequency of selected softest modes after pairwise matching of the modes between the reference and perturbed models. For quantifying essentiality, we convert the mean shifts to z-scores. Essential residues correspond to hot regions in terms of ligand binding and/or allostery.

Integration of ESSA with pocket information has been successful in predicting the allosteric pockets for apo and holo structures of proteins [KB20]. The pockets obtained from the Fpocket algorithm [LGV09] are rank-ordered using the ESSA score calculated based on the residues lining each pocket. Further screening using local hydrophobic density (LHD, a pocket feature of Fpocket) improves the predictions. In the second part of this tutorial, we will demonstrate how to use our automated protocol [KB20] that combines ESSA scores, pocket geometry and LHD for detecting allosteric pockets.

The example used in this tutorial for ESSA profile generation and the prediction of allosteric pockets is TEM-1 beta-lactamase (PDB id: 1pzo), which we have previously studied (see Figure S2 and Table S2 of [KB20] and the third image on the ESSA webpage).

Required Programs

The latest version of ProDy is required for ESSA. Additionally, Fpocket 3.0 and Pandas are required for the ESSA-based allosteric pocket prediction.

Getting Started

We recommend that you will follow this tutorial by typing commands in an IPython session, e.g.:

$ ipython

or with pylab environment:

$ ipython --pylab

First, we will make necessary imports from ProDy, NumPy, Matplotlib, and Pandas packages.

In [1]: from prody import *

In [2]: from numpy import *

In [3]: from matplotlib.pyplot import *

In [4]: from pandas import *

In [5]: ion()

We have included these imports in every part of the tutorial, so that code copied from the online pages is complete. You do not need to repeat imports in the same Python session.

How to Cite

If you benefited from ESSA in your research, please cite the following paper:

[KB20]

(1, 2, 3, 4) Kaynak B.T., Bahar I., Doruker P., Essential site scanning analysis: A new approach for detecting sites that modulate the dispersion of protein global motions, Comput. Struct. Biotechnol. J. 2020 18:1577-1586.

Additionally, if you performed ESSA-based allosteric site prediction in your research, please also cite the following paper for Fpocket:

[LGV09]

Le Guilloux, V., Schmidtke P., Tuffery P., Fpocket: An open source platform for ligand pocket detection, BMC Bioinformatics 2009 10:168.