Contents

Preface

1. Artificial Intelligence Learns Protein Prediction

Michael Heinzinger and Burkhard Rost

2. Building Representation Learning Models for Antibody Comprehension

Justin Barton, Aretas Gaspariunas, Jacob D. Galson, and Jinwoo Leem

3. Engineering Proteins Using Statistical Models of Co-Evolutionary Sequence Information

Jerry C. Dinan, James W. McCormick, and Kimberly A. Reynolds

4. Petasbase-scale Homology Search for Structure Prediction

Sewon Lee, Gyuri Kim, Eli Levy Karin, Milot Mirdita, Sukhwan Park, Rayan Chikhi, Artem Babaian, Andriy Kryshtafovych, and Martin Steinegger

5. Variant Effect Prediction in the Age of Machine Learning

Yana Bromberg, R. Prabakaran, Anowarul Kabir, and Amarda Shehu

6. Is Novelty Predictable?

Clara Fannjiang and Jennifer Listgarten

7. Exploring the Protein Sequence Space with Global Generative Models

Sergio Romero-Romero, Sebastian Lindner, and Noelia Ferruz

8. Protein Design Using Structure-Prediction Networks: Alphafold and RoseTTAFold as Protein Structure Foundation Models

Joseph L. Watson, Sidney L. Lisanza, Jue Wang

9. Backbone Conditional Protein Sequence Design

Justas Dauparas

10. Environmental Impacts of Machine Learning Applications in Protein Science

Loïc Lannelongue and Michael Inouye

Index