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Machine Learning for Protein Science and Engineering


Book Series:  A Cold Spring Harbor Perspectives in Biology Collection
Subject Area(s):  Molecular Biology; Biochemistry

Edited by Peter K. Koo, Cold Spring Harbor Laboratory; Christian Dallago, Technical University of Munich; Ananthan Nambiar, University of Illinois at Urbana-Champaign; Kevin K. Yang, Microsoft Research Lab

Download a Free Excerpt from Machine Learning for Protein Science and Engineering:

Preface
Artificial Intelligence Learns Protein Prediction
Index

© 2025 • 166 pages, illustrated (30 color), index
Hardcover • $79 55.30
ISBN  978-1-621824-80-0
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Description

Machine learning techniques are having a huge impact on how biologists study and understand proteins. Protein structure prediction has been revolutionized, and new tools are improving functional annotation of proteins, as well as opening up new possibilities for protein design.

Written and edited by experts in the field, this collection from Cold Spring Harbor Perspectives in Biology explores the rapidly evolving intersection of machine learning and protein science. The contributors review various approaches for learning representations of proteins, as well as statistical models of co-evolution and large-scale homology searches, which have important implications for protein structure prediction. In addition, they examine applications of machine learning for functional annotation of proteins and variant effect prediction.

The collection also explores generative models for protein sequence and structure and looks at the environmental impact of applying these tools, acknowledging the need to balance technological advancement with sustainable computing. It is therefore an essential reference for all scientists interested in both learning more about these techniques and implementing them in research institutions.

Contents

Preface
Artificial Intelligence Learns Protein Prediction
Michael Heinzinger and Burkhard Rost
Building Representation Learning Models for Antibody Comprehension
Justin Barton, Aretas Gaspariunas, Jacob D. Galson, and Jinwoo Leem
Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information
Jerry C. Dinan, James W. McCormick, and Kimberly A. Reynolds
Petabase-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
Variant Effect Prediction in the Age of Machine Learning
Yana Bromberg, R. Prabakaran, Anowarul Kabir, and Amarda Shehu
Is Novelty Predictable?
Clara Fannjiang and Jennifer Listgarten
Exploring the Protein Sequence Space with Global Generative Models
Sergio Romero-Romero, Sebastian Lindner, and Noelia Ferruz
Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models
Jue Wang, Joseph L. Watson, and Sidney L. Lisanza
Backbone Conditional Protein Sequence Design
Justas Dauparas
Environmental Impacts of Machine Learning Applications in Protein Science
Loïc Lannelongue and Michael Inouye
Index