Module #1 Introduction to Protein Folding Overview of protein folding, its importance, and the challenges of understanding the process
Module #2 Thermodynamics of Protein Folding Introduction to thermodynamic principles governing protein folding, including free energy landscapes
Module #3 Kinetics of Protein Folding Kinetic models of protein folding, including rate constants and folding pathways
Module #4 Computational Methods for Protein Structure Prediction Overview of computational approaches for predicting protein structure, including ab initio and template-based methods
Module #5 Molecular Dynamics Simulations Introduction to molecular dynamics simulations, including force fields, integration algorithms, and parallelization
Module #6 Monte Carlo Simulations Introduction to Monte Carlo simulations, including Markov chains and Metropolis algorithms
Module #7 Coarse-Grained Models Coarse-grained models for protein folding, including lattice models and Go-like models
Module #8 All-Atom Models All-atom models for protein folding, including CHARMM and AMBER force fields
Module #9 Free Energy Calculations Methods for calculating free energy differences, including thermodynamic integration and free energy perturbation
Module #10 Enhanced Sampling Techniques Enhanced sampling techniques for protein folding, including replica exchange and bias-exchange metadynamics
Module #11 Protein Folding Landscapes Analysis of protein folding landscapes, including funnel-shaped landscapes and kinetic transitioning
Module #12 Machine Learning in Protein Folding Introduction to machine learning methods for protein folding, including neural networks and support vector machines
Module #13 Deep Learning for Protein Structure Prediction Deep learning methods for protein structure prediction, including convolutional neural networks and recurrent neural networks
Module #14 Evolutionary Algorithms Evolutionary algorithms for protein folding, including genetic algorithms and evolution strategies
Module #15 Protein-Ligand Interactions Computational methods for predicting protein-ligand interactions, including docking and scoring functions
Module #16 Case Studies in Protein Folding In-depth analysis of specific protein folding problems, including amyloid formation and protein misfolding diseases
Module #17 High-Performance Computing in Protein Folding Parallelization and high-performance computing strategies for large-scale protein folding simulations
Module #18 Data Analysis and Visualization Methods for analyzing and visualizing protein folding data, including clustering, dimensionality reduction, and molecular visualization
Module #19 Force Field Development Development and validation of force fields for protein folding simulations
Module #20 Methodological Challenges Challenges and limitations of computational methods for protein folding, including sampling and force field inaccuracies
Module #21 Experimental Validation Experimental methods for validating computational predictions of protein folding, including NMR and X-ray crystallography
Module #22 Computational Tools and Resources Overview of computational tools and resources for protein folding, including Rosetta, GROMACS, and Amber
Module #23 Protein Folding and Evolution Evolutionary perspectives on protein folding, including the evolution of protein function and fold
Module #24 Disease-Related Protein Folding Protein folding in the context of human disease, including protein misfolding and aggregation
Module #25 Course Wrap-Up & Conclusion Planning next steps in Computational Methods in Protein Folding career
