Leveraging Deep Learning Model Embeddings for Protein Property Prediction and Design
Polizzi Lab at Harvard University
Deep learning has transformed our ability to extract meaningful representations from protein sequences and structures. These embeddings capture rich biochemical, evolutionary, and biophysical information that can be leveraged for a wide range of downstream tasks. In this talk, I will discuss recent advances in learning and applying protein embeddings for property prediction and design. We will compare how embeddings are generated in graph neural networks like AlphaFold2 and ProteinMPNN as well as in protein language models. I will show how these representations can be used to build accurate predictors of protein properties with the goal of introducing concepts necessary to understand the AF2Bind model.