-
Loading metrics
Open Access
Peer-reviewed
Research Article
Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes
-
Samantha Zink ,
Contributed equally to this work with: Samantha Zink, Songrong Qu
Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Department of Chemistry and Biochemistry; UCLA-DOE Institute for Genomics and Proteomics, STROBE, NSF Science and Technology Center, University of California, Los Angeles (UCLA), Los Angeles, California, United States of America
⨯ -
Songrong Qu ,
Contributed equally to this work with: Samantha Zink, Songrong Qu
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – review & editing
Affiliation Department of Chemistry and Biochemistry; UCLA-DOE Institute for Genomics and Proteomics, STROBE, NSF Science and Technology Center, University of California, Los Angeles (UCLA), Los Angeles, California, United States of America
⨯ -
Thomas Holton,
Roles Project administration, Resources, Software, Validation, Visualization, Writing – review & editing
Affiliation Departments of Chemistry and Biochemistry and Biological Chemistry, Howard Hughes Medical Institute, UCLA-DOE Institute, Molecular Biology Institute, UCLA, Los Angeles, California, United States of America
⨯ -
Eesha Shankar,
Roles Data curation, Investigation, Validation, Writing – review & editing
Affiliation Department of Chemistry and Biochemistry; UCLA-DOE Institute for Genomics and Proteomics, STROBE, NSF Science and Technology Center, University of California, Los Angeles (UCLA), Los Angeles, California, United States of America
⨯ -
Paulina Stanley,
Roles Data curation, Investigation, Validation, Writing – review & editing
Affiliation Department of Chemistry and Biochemistry; UCLA-DOE Institute for Genomics and Proteomics, STROBE, NSF Science and Technology Center, University of California, Los Angeles (UCLA), Los Angeles, California, United States of America
⨯ -
David S. Eisenberg,
Roles Conceptualization, Supervision, Writing – original draft, Writing – review & editing
Affiliation Departments of Chemistry and Biochemistry and Biological Chemistry, Howard Hughes Medical Institute, UCLA-DOE Institute, Molecular Biology Institute, UCLA, Los Angeles, California, United States of America
⨯ -
Michael R. Sawaya ,
Roles Conceptualization, Formal analysis, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing
* E-mail: jrodriguez@mbi.ucla.edu (JAR); sawaya@mbi.ucla.edu (MRS)
Affiliation Departments of Chemistry and Biochemistry and Biological Chemistry, Howard Hughes Medical Institute, UCLA-DOE Institute, Molecular Biology Institute, UCLA, Los Angeles, California, United States of America
⨯ -
Jose A. Rodriguez
Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing
* E-mail: jrodriguez@mbi.ucla.edu (JAR); sawaya@mbi.ucla.edu (MRS)
Affiliation Department of Chemistry and Biochemistry; UCLA-DOE Institute for Genomics and Proteomics, STROBE, NSF Science and Technology Center, University of California, Los Angeles (UCLA), Los Angeles, California, United States of America
⨯
Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes
- Samantha Zink,
- Songrong Qu,
- Thomas Holton,
- Eesha Shankar,
- Paulina Stanley,
- David S. Eisenberg,
- Michael R. Sawaya,
- Jose A. Rodriguez
-
- Published: August 25, 2025
- https://doi.org/10.1371/journal.pcbi.1013395