A reproducible molecular dynamics benchmark for ADC epitope triage in EGFR and HER2

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The paper presents a reproducible molecular-dynamics benchmarking framework to triage antibody-drug conjugate (ADC) epitopes before peptide-level experimental validation, using EGFR and HER2 as model targets. It runs paired simulations of antigen alone versus antibody-bound antigen for three therapeutic antibody–antigen pairs (HER2–Trastuzumab, EGFR–Cetuximab, and EGFR–Panitumumab), summarizes simulations into ranked antigen regions, and evaluates performance with rank-based enrichment metrics with uncertainty estimates when panel size is small. For HER2–Trastuzumab and EGFR–Cetuximab, the ranked regions are compared to published peptide-level HDX–MS protection patterns, while EGFR–Panitumumab is evaluated using proxy labels derived from structural contacts and mutational mapping of the EGFR domain III footprint due to the lack of peptide-level labels. The study explicitly positions itself as a benchmarking/triage layer rather than a predictor of absolute deuterium uptake or clinical efficacy. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract Antibody–drug conjugate programs often commit to an epitope before peptide–level experimental evidence is available. I present a reproducible molecular–dynamics benchmark for pre–HDX epitope triage in EGFR and HER2, evaluated on three therapeutic antibody–antigen pairs (HER2–Trastuzumab, EGFR–Cetuximab, and EGFR–Panitumumab). For each system, paired simulations of antigen alone and antibody–bound antigen are summarized into ranked antigen regions suitable for designing an initial HDX–MS peptide panel. Performance is quantified with rank–based enrichment metrics appropriate for imbalanced labels, together with uncertainty estimates where panel size is small. For systems with published peptide–level HDX–MS labels (HER2–Trastuzumab and EGFR–Cetuximab), ranked regions are compared to reported protection patterns. For EGFR–Panitumumab, where peptide–level HDX–MS labels are not used here, evaluation is performed against proxy labels derived from structural contacts and mutational mapping of the EGFR domain III footprint. This work is framed as a reproducible benchmarking and triage layer rather than a predictor of absolute deuterium uptake or clinical efficacy. Success is defined as enrichment of known or proxy–positive regions near the top of the ranked list under a locked evaluation protocol.
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I present a reproducible molecular–dynamics benchmark for pre–HDX epitope triage in EGFR and HER2, evaluated on three therapeutic antibody–antigen pairs (HER2–Trastuzumab, EGFR–Cetuximab, and EGFR–Panitumumab). For each system, paired simulations of antigen alone and antibody–bound antigen are summarized into ranked antigen regions suitable for designing an initial HDX–MS peptide panel. Performance is quantified with rank–based enrichment metrics appropriate for imbalanced labels, together with uncertainty estimates where panel size is small. For systems with published peptide–level HDX–MS labels (HER2–Trastuzumab and EGFR–Cetuximab), ranked regions are compared to reported protection patterns. For EGFR–Panitumumab, where peptide–level HDX–MS labels are not used here, evaluation is performed against proxy labels derived from structural contacts and mutational mapping of the EGFR domain III footprint. This work is framed as a reproducible benchmarking and triage layer rather than a predictor of absolute deuterium uptake or clinical efficacy. Success is defined as enrichment of known or proxy–positive regions near the top of the ranked list under a locked evaluation protocol. Structural Biology antibody–drug conjugates epitope triage molecular dynamics solvent accessible surface area HDX–MS pre–screening EGFR HER Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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