Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds

Fei Cai; Yuehua Wei; Daniel Kirchhofer; Andrew Chang; Yingnan Zhang

doi:10.1371/journal.pcbi.1012609

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Open Access

Peer-reviewed

Research Article

Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds

Fei Cai,

Roles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

Affiliation Departments of Biological Chemistry, Genentech, Inc., South San Francisco, California, United States of America
⨯
Yuehua Wei,

Roles Data curation, Formal analysis, Methodology

Affiliation Departments of Biological Chemistry, Genentech, Inc., South San Francisco, California, United States of America
⨯
Daniel Kirchhofer,

Roles Data curation, Formal analysis, Investigation, Project administration, Supervision, Writing – review & editing

Affiliation Departments of Biological Chemistry, Genentech, Inc., South San Francisco, California, United States of America
⨯
Andrew Chang ,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: andrew.chang@deepseq.ai (AC); zhang.yingnan@gene.com (YZ)

Affiliation DeepSeq.AI, Inc., San Francisco, California United States of America
⨯
Yingnan Zhang

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: andrew.chang@deepseq.ai (AC); zhang.yingnan@gene.com (YZ)

Affiliation Departments of Biological Chemistry, Genentech, Inc., South San Francisco, California, United States of America

https://orcid.org/0009-0004-1268-0477

⨯

Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds

Fei Cai,
Yuehua Wei,
Daniel Kirchhofer,
Andrew Chang,
Yingnan Zhang

Published: November 18, 2024
https://doi.org/10.1371/journal.pcbi.1012609

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Protein folding
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Peptide libraries
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Protein structure prediction
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Next-generation sequencing
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Machine learning
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Alanine
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Alanine scanning
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