Computational Proteomics and Protein Modeling

Graduate / Professional

This course explores advanced computational proteomics and protein modeling approaches. Students gain hands-on experience analyzing bulk, single-cell, and imaging-based proteomics data. The course also covers cutting-edge machine learning and deep learning techniques, including transformer models, graph neural networks, and generative models, for protein-related tasks such as structure and function prediction and protein optimization. Special attention is given to applications in immunology. This course is ideal for students interested in computational biology, AI-driven structural biology, and computational immunology. Prerequisites: mathematics and statistics (linear algebra; calculus [for optimization]; probability/statistics), Python for data analysis (NumPy, Pandas, plotting, Jupyter; basic Git/conda), machine learning basics (regression/classification, model evaluation, intro to deep learning [PyTorch]). Recommended: basic biology (proteins [sequence/structure], high-level proteomics), advanced machine learning (transformers, GNNs, generative models, command-line tools). HTBA