Machine Learning for Protein Science and Engineering

Christian Dallago, Peter Koo, Kevin Yang , Ananthan Nambiar

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English

Cold Spring Harbor Laboratory Press
04 June 2025

Physical chemistry; Biochemistry; Molecular biology; Biotechnology

Summary
Details

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.

By: Christian Dallago, Peter Koo, Kevin Yang, Ananthan Nambiar
Imprint: Cold Spring Harbor Laboratory Press
Dimensions: Height: 254mm, Width: 178mm, Spine: 16mm
Weight: 635g
ISBN: 9781621824800
ISBN 10: 1621824802
Pages: 250
Publication Date: 04 June 2025
Audience: General/trade , ELT Advanced
Format: Hardback
Publisher's Status: Active