Overview

Residue Networks: Understanding the Time Evolution and Mutations of Proteins from a Graph Theoretical Perspective

Skill Level: Advanced

Language: English

Workload: 2 hours total

Topic: Computational structural biology

Overview: 

Lesson 1: Introduction to graphs and measures

Lesson 2: Proteins as graphs and residue networks

Lesson 3: Time evolution of residue networks and communities

Course Description: This online course introduces fundamental graph theory concepts and key network measures such as nodes, edges, degree and centrality before covering the construction of dynamic residue networks from molecular dynamics simulations. Participants learn to compute node and edge betweenness centrality for identifying critical amino acids and interactions, apply the Girvan–Newman algorithm to detect residue communities and track how their composition evolves as proteins undergo conformational changes. Insights from centrality and community dynamics illuminate ligand binding and mutation effects in protein complexes. Hands-on exercises leverage Python with ProDy, NetworkX, NumPy and VMD for visualization and analysis.

Course Contents:

Lesson 1: Investigation of a Toy Graph: Measures and Practical Analysis in Python

Lesson 2: Visual Exploration of a Protein Structure and Conversion to a Residue Network

Lesson 3: Analysis of a Protein Molecular Dynamics Simulation Using Graph Theory, Emphasizing Community Analysis

Who Should Enroll: Individuals interested in learning graph theory and protein dynamics.

Prerequisite(s):

Completion of the “Molecular Dynamics Simulations of Small Molecules” course

Basic knowledge of protein structure and molecular dynamics simulations

Intermediate proficiency in Python programming

Tools, libraries, frameworks used: Python, VAMD, NAMD

Learning Objectives
By the end of this course, participants can:

Explain fundamental graph theory concepts, including nodes, edges, degree and centrality

Construct residue networks from protein structures and MD trajectories

Calculate node and edge betweenness centrality to identify key residues and interactions

Apply the Girvan–Newman algorithm to detect and interpret residue communities

Track changes in network measures and community composition over the course of an MD simulation

Relate centrality and community dynamics to ligand binding and mutational effects in protein complexes

About the instructor(s): https://tfguclu.github.io/