Protein Structure Prediction

This lecture covers the application of maximum entropy modeling in predicting protein structure from sequence data. It discusses the challenges in inferring protein structure, the importance of amino acid correlations, and the use of pairwise maximum entropy models. The lecture also explores the analysis of residue pairs for 3D contact prediction, the limitations of structure prediction methods, and recent developments in protein structure prediction, including deep learning approaches like AlphaFold2. Additionally, it highlights the various applications of maximum entropy models in protein sequences, such as mutation effect prediction, protein-protein interaction prediction, and protein design.