Structure-informed machine learning-based steric zipper prediction.

<p><b>a.)</b> Comparison of structure-based 3D profiling method to structure-informed machine learning-based approach. Top panel shows structure-based zipperDB methodology. Sequence is divided into hexapeptide segments, individually modeled onto NNQQNY backbone where the energetics...

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Bibliographic Details
Main Author:	Samantha Zink (22120627) (author)
Other Authors:	Songrong Qu (22120630) (author), Thomas Holton (491084) (author), Eesha Shankar (22120633) (author), Paulina Stanley (22120636) (author), David S. Eisenberg (1325538) (author), Michael R. Sawaya (217747) (author), Jose A. Rodriguez (1982692) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Molecular Biology Pharmacology Developmental Biology Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> predicting predictive model allows predictions show enrichment like structures remain cell wall reorganization amyloid formation across zipper forming segments steric zipper propensity informed machine learning machine learning zipper profiles peptide segments amyloid fold adopt amyloid ten years recent progress rapid proteome proteome level proteins involved potential category new approach facilitated structure experimental characterization designed sequences based prediction
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Structure-informed machine learning-based steric zipper prediction.

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