This tutorial describes how to calculate signature dynamics for a family of proteins with similar structures using Elastic Network Models. This method creates an ensemble of aligned structures and calculates statistics such as means and standard deviations on various dynamic properties including mode profiles, mean square fluctuations and cross-correlation matrices. It also includes tools for classifying family members based on their sequence, structure and dynamics.

The theory and usage of this toolkit will be described in [SZ18].

Required Programs

The latest version of ProDy is recommended along with NumPy and Matplotlib. IPython is highly recommended for interactive usage.

Getting Started

This tutorial contains three parts. In the first part, we will have a quick walk-through on the SignDy calculations and functions using the example of type-I periplasmic binding protein (PBP-I) domains, in which case the data is convienient collected from the Dali server [LH10]. The second part will be a more detailed periplasmic binding protein (PBP-I) domains, in which case the data is convieniently collected from the Dali server [LH10]. The second part will be review how to use the CATH database [IS21] to build the ensemble. The third part will be a more detailed tutorial on building an ensemble ‘manually’ from scratch, and try to reproduce the figures presented in [SZ18].

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 the ProDy, NumPy and Matplotlib packages.

In [1]: from prody import *

In [2]: from numpy import *

In [3]: from matplotlib.pyplot import *

In [4]: 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 SignDy Calculations in your research, please cite the following paper:

[SZ18](1, 2) Zhang S, Li H, Krieger J, Bahar I. Shared signature dynamics tempered by local fluctuations enables fold adaptability and specificity. Mol. Biol. Evol. 2019 36(9):2053–2068
[LH10](1, 2) Holm L, Rosenström P. Dali server: conservation mapping in 3D. Nucleic Acids Res. 2010 10(38):W545-9
[IS21]Sillitoe I, Bordin N, Dawson N, Waman VP, Ashford P, Scholes HM, Pang CSM, Woodridge L, Rauer C, Sen N, Abbasian M, Le Cornu S, Lam SD, Berka K, Varekova IH, Svobodova R, Lees J, Orengo CA. CATH: increased structural coverage of functional space. Nucleic Acids Res. 2021 49(D1):D266-D273