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How artificial intelligence is reengineering protein engineering.

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Title: How artificial intelligence is reengineering protein engineering.
Authors: Listgarten, Jennifer (AUTHOR), Jiang, Hanlun (AUTHOR)
Source: Science. 4/9/2026, Vol. 392 Issue 6794, p159-166. 8p.
Subjects: Artificial intelligence, Protein engineering, Machine learning, Bioinformatics, Computational biology, Biocatalysis, Protein structure prediction, Probabilistic generative models
Abstract: Over the past decades, protein engineering has matured into a field of its own, driven by computational modeling and high-throughput wet lab experiments, with broad application in therapeutics, diagnostics, agriculture, and manufacturing. In recent years, artificial intelligence (AI) has further propelled protein engineering by enabling more efficient search through high-dimensional sequence space for proteins with desired properties. Notable AI-based advances encompass generative modeling of sequences, backbone structure, and atoms; tailoring general versions of such models to design proteins with specific properties; modeling for extraction of protein representations and scoring candidate protein sequences; and developing techniques for library design, including synthesis-aware approaches. Herein we discuss these advances, emphasizing a unifying view through a statistical interpretation of modern AI approaches. Editor's summary: Proteins, with their varied structure and chemistry, are the prime actors of biology and have long been targets for in vitro and in silico engineering. Generative protein models and other artificial intelligence (AI) tools are now being integrated into experimental workflows. Listgarten and Jiang reviewed advances in AI methods and discuss how statistical principles are being used to transform protein engineering through conditional generative modeling. Beyond the sizable advances so far, current challenges include designing functional enzymes, disordered proteins, and binders of all kinds, problems for which we currently lack sufficient training data. —Michael A. Funk [ABSTRACT FROM AUTHOR]
Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: How artificial intelligence is reengineering protein engineering.
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  Data: <searchLink fieldCode="AR" term="%22Listgarten%2C+Jennifer%22">Listgarten, Jennifer</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Hanlun%22">Jiang, Hanlun</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Science%22">Science</searchLink>. 4/9/2026, Vol. 392 Issue 6794, p159-166. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+engineering%22">Protein engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+biology%22">Computational biology</searchLink><br /><searchLink fieldCode="DE" term="%22Biocatalysis%22">Biocatalysis</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+structure+prediction%22">Protein structure prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+generative+models%22">Probabilistic generative models</searchLink>
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  Data: Over the past decades, protein engineering has matured into a field of its own, driven by computational modeling and high-throughput wet lab experiments, with broad application in therapeutics, diagnostics, agriculture, and manufacturing. In recent years, artificial intelligence (AI) has further propelled protein engineering by enabling more efficient search through high-dimensional sequence space for proteins with desired properties. Notable AI-based advances encompass generative modeling of sequences, backbone structure, and atoms; tailoring general versions of such models to design proteins with specific properties; modeling for extraction of protein representations and scoring candidate protein sequences; and developing techniques for library design, including synthesis-aware approaches. Herein we discuss these advances, emphasizing a unifying view through a statistical interpretation of modern AI approaches. Editor's summary: Proteins, with their varied structure and chemistry, are the prime actors of biology and have long been targets for in vitro and in silico engineering. Generative protein models and other artificial intelligence (AI) tools are now being integrated into experimental workflows. Listgarten and Jiang reviewed advances in AI methods and discuss how statistical principles are being used to transform protein engineering through conditional generative modeling. Beyond the sizable advances so far, current challenges include designing functional enzymes, disordered proteins, and binders of all kinds, problems for which we currently lack sufficient training data. —Michael A. Funk [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1126/science.aec8444
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        Text: English
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        PageCount: 8
        StartPage: 159
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Protein engineering
        Type: general
      – SubjectFull: Machine learning
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      – SubjectFull: Bioinformatics
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      – SubjectFull: Computational biology
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      – SubjectFull: Biocatalysis
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      – SubjectFull: Protein structure prediction
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      – SubjectFull: Probabilistic generative models
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            – D: 09
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              Text: 4/9/2026
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              Y: 2026
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