Nazim Bouatta | Machine learning for protein structure prediction, Part 2: AlphaFold2 architecture

Special Lectures on Machine Learning and Protein Folding 2/16/23 Lecture 2

Speaker: Nazim Bouatta, Harvard Medical School

Abstract: AlphaFold2, a neural network-based model which predicts protein structures from amino acid sequences, is revolutionizing the field of structural biology. This lecture series, given by a leader of the OpenFold project which created an open-source version of AlphaFold2, will explain the protein structure problem and the detailed workings of these models, along with many new results and directions for future research.

Lecture 2: Machine learning for protein structure prediction, Part 2: AlphaFold2 architecture

Abstract: Turning the co-evolutionary principle into an algorithm: EvoFormer. Structure module and symmetry principles (equivariance and invariance). OpenFold: retraining AlphaFold2 and insights into its learning mechanisms and capacity for generalization. Applications of variants of AlphaFold2 beyond protein structure prediction: AlphaFold Multimer for protein complexes, RNA structure prediction.