Machine Learning for Protein Science and Engineering

Click to Enlarge				Machine Learning for Protein Science and Engineering *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 • ~~$61.00~~ 54.90 ISBN 978-1-621824-80-0 You save: 10% You will receive free shipping on this item at checkout. Free shipping offer applies to direct website purchases by individual U.S. customers only. This title also available in: Hardcover

Description Contents 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

Machine Learning for Protein Science and Engineering

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