Generating Mutant Protein Structures Using Advanced Techniques
Konu özeti
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Generating Mutant Protein Structures Using Advanced Techniques
Skill Level: Advanced - Research level
Language: English
Workload: 2 hours total
Topic: Computational structural biology and protein biophysics
Overview
This course provides a conceptual and practical overview of how structural biology tools can be applied to understand the effects of mutations on protein conformation, stability, and function. Participants are introduced to computational strategies that connect molecular dynamics, free energy calculations, and deep learning-based methods to predict and evaluate mutant protein structures. The course bridges physical models and data-driven approaches, enabling learners to interpret mutation-induced structural changes in a biophysical context.
The aim is to build a clear understanding of how molecular simulations and advanced modeling frameworks can be used to explore the thermodynamic and structural landscape of proteins upon mutation. By the end of this lecture, learners will be familiar with fundamental methodologies for analyzing mutation effects and generating mutant models using state-of-the-art computational tools.
Course Description
Generating Mutant Protein Structures Using Advanced Techniques is designed for participants who already have experience with molecular dynamics simulations and basic programming skills in Python. The lecture combines theoretical background with applied examples drawn from recent computational biology research.
The course consists of a prologue and three main lectures that progressively introduce methods for designing, modeling, and evaluating mutant protein structures:
Prologue – Why We Need to Understand Mutant Proteins
Lecture 1 – Mutation and Minimization (MuMi) for Mutant Protein Design
Lecture 2 – Free Energy Perturbation (FEP) for Mutant Structures and Relative Binding Energies
Lecture 3 – Deep Learning Approaches for Mutant Structures
The lecture emphasizes the structural and energetic consequences of amino acid substitutions and introduces thermodynamic cycles used to estimate relative stabilities of mutants. Participants will also explore deep learning models for predicting mutant conformations and compare their outcomes with physics-based simulations.
Course Contents
Prologue: Understanding mutation-induced structural perturbations
Lecture 1: Energy minimization and the MuMi workflow for mutant design
Lecture 2: Estimating relative free energies using FEP simulations
Lecture 3: Deep learning–based tools for generating and evaluating mutant structures
Who Should Enroll
This course is intended for graduate students, researchers, and professionals interested in computational modeling of protein mutations. It is suitable for learners who wish to extend their understanding of how structural biology and simulation-based approaches can be applied to mutation analysis.
Prerequisites
- Experience with terminal/command-line operations
- Basic to intermediate programming skills in Python
- Knowledge of protein structures and molecular dynamics simulations
- Completion of the ‘Molecular Dynamics Simulations of Small Molecules’ course is recommended
Tools, Libraries, and Frameworks Used
Participants will use VMD and NAMD for molecular modeling and simulations, as well as Python-based scripts for data analysis. Deep learning–based methods will be introduced for structural predictions of mutant proteins. The course also includes examples of integrating these tools to create a reproducible workflow for mutant structure generation.
Learning Objectives
By the end of this course, participants will be able to:
- Explain how mutations influence protein structure and stability.
- Apply minimization techniques for generating stable mutant models.
- Use Free Energy Perturbation (FEP) methods to calculate relative binding energies of mutants.
- Interpret thermodynamic cycles and evaluate the energetic costs of amino acid substitutions.
About the Instructor: https://tfguclu.github.io/
In Part 1 of Lecture 1, we skimmed through the Mutation and Minimization (MuMi) paper (DOI: 10.1021/acs.jpcb.4c04916) and discussed the biophysical context for performing single mutational scanning.
In Part 2, we explored the practical application of the MuMi approach, including the generation of mutant structures using Python and TCL scripts. The corresponding codes can be found at https://github.com/midstlab/MuMi_scheme.
Part 3 covered the analysis of the generated mutant structures, with related materials available at https://zenodo.org/records/11399775.
In Lecture 2, we examined free energy perturbation (FEP) simulations.
In Part 1, we discussed the theoretical basis of FEP simulations and thermodynamic cycles.
Using the codes from https://github.com/midstlab/FEP_mutational_scanning, we learned how to prepare FEP simulations.
The analysis of the resulting mutation energies was performed using a modified version of the script from https://github.com/midstlab/cyclescript. The files used during the lecture are provided in the FEP.zip file.
The research covered in this lecture has been published:
https://onlinelibrary.wiley.com/doi/10.1002/cpe.8371For additional reading on FEP simulations, see:
https://pubs.acs.org/doi/10.1021/acs.jctc.5c00193
https://pubs.acs.org/doi/full/10.1021/acs.jcim.0c01079
In Lecture 3, we briefly discussed deep learning–based methods for protein structure prediction. These approaches were introduced in the lecture “Prediction of Protein Structures Using Deep Learning Tools”, available at https://acikders.ulakbim.gov.tr/course/view.php?id=43
- Experience with terminal/command-line operations
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