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Special Lectures on Machine Learning and Protein Folding

The CMSA will host a series of three 90-minute lectures on the subject of machine learning for protein folding.

Location: G10, CMSA, 20 Garden Street, Cambridge MA 02138

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.

Thursday, Feb 9, 2023
3:30–5:00 pm ET
Lecture 1: Machine learning for protein structure prediction, Part 1: Algorithm space

AlphaFold2 impacts on experimental structural biology. Co-evolutionary approaches. Space of ‘algorithms’ for protein structure prediction. Proteins as images (CNNs for protein structure prediction). End-to-end differentiable approaches. Attention and long-range dependencies. AlphaFold2 in a nutshell.

Thursday, Feb 16, 2023: 3:30–5:00 pm ET
3:30–5:00 pm ET

Lecture 2: AlphaFold2 architecture.

Turning the co-evolutionary principle into an algorithm: EvoFormer. Structure module and symmetry principles (equivariance and invariance). OpenFold: retraining AlphaFol2 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.

Thursday, March 9, 2023
3:30–5:00 pm ET
Lecture 3: Limitations of AlphaFold2 and evolutionary ML pipelines.

Current single sequence models. Protein language models (LM): single sequence + LM embeddings. Combining LM models with Frenet-Serret construction for protein structure prediction. Applying AlphaFold2 and OpenFold for language models.

https://cmsa.fas.harvard.edu/event/protein-folding/