Advanced Certificate in Computational Protein Engineering for Design

• Protein Structure Prediction: An in-depth study of methods and tools used to predict protein structure, including homology modeling, ab initio modeling, and threading.
• Molecular Dynamics Simulations: Understanding the principles and applications of molecular dynamics simulations in computational protein engineering.
• Sequence Analysis and Alignment: Techniques for analyzing and aligning protein sequences to identify conserved domains and predict function.
• Protein Design and Optimization: Strategies for designing and optimizing proteins using computational methods, including de novo design, directed evolution, and consensus design.
• Protein-Ligand Interactions: The study of protein-ligand interactions and their role in drug design, including molecular docking and scoring functions.
• Protein-Protein Interactions: Understanding the principles and methods for predicting and engineering protein-protein interactions, including docking and interface analysis.
• Computational Approaches to Protein Folding: An exploration of the latest computational methods for predicting protein folding, including coarse-grained and all-atom simulations.
• Massively Parallel Computing in Protein Engineering: The application of massively parallel computing techniques, such as GPU and cluster computing, to accelerate protein engineering simulations.
• Machine Learning in Computational Protein Engineering: The application of machine learning algorithms and techniques to predict protein structure, function, and interactions.

These units provide a comprehensive overview of the field of computational protein engineering for design, and equip learners with the skills and knowledge necessary to succeed in this exciting area of research.