Machine Learning for Protein Structure Prediction - Algorithm Space - Part 1

Explore protein structure prediction and machine learning fundamentals in this comprehensive lecture from Harvard Medical School researcher Nazim Bouatta, who leads the OpenFold project. Gain insights into the revolutionary AlphaFold2 neural network model and its impact on structural biology, starting with basic protein biology concepts. Learn about co-evolutionary approaches, various algorithmic methods for protein structure prediction, and how proteins can be analyzed using Convolutional Neural Networks (CNNs). Discover end-to-end differentiable approaches, the role of attention mechanisms in capturing long-range dependencies, and get a concise overview of AlphaFold2's architecture. As part of a special lecture series on Machine Learning and Protein Folding, this 90-minute session provides essential knowledge for understanding how cutting-edge AI technologies are transforming our ability to predict protein structures from amino acid sequences.