Multiscale Computational Studies of PEG Chain Length Effects on HER2 mAb Fc Structure and Binding Energetics
Bibliographic record
Abstract
HER2 overexpression in breast cancer drives aggressive disease, treated clinically with monoclonal antibodies such as trastuzumab and pertuzumab. While effective, these therapies are limited by suboptimal pharmacokinetics and tumor penetration. Polyethylene glycol (PEG) conjugation can extend circulation half-life but may alter Fc-mediated interactions and receptor binding. Here, we used a multiscale computational framework to quantify PEGylation effects on the pertuzumab Fc domain. Structural models were generated with AlphaFold2, refined with RoseTTAFold2 to incorporate G0F-type glycans at Asn297, and site-specifically PEGylated via hydrazone linkages in UCSF ChimeraX. Aglycosylated control and PEGylated variants (1, 2, and 4 kDa) underwent 100 ns all-atom molecular dynamics simulations in GROMACS. Increasing PEG size produced stepwise RMSD elevations (1.1-13 nm) and hinge expansion (21.37-63.53 Å). RMSF analysis revealed domain-specific mobility shifts within the Fc region: CH2 flexibility in the control, CH3 mobility in the 1 and 2 kDa variants, and CH2/CH3 destabilization (>2.0 nm) in the 4 kDa system. Principal component analysis showed PC1 (45-70% variance) capturing hinge-closing and CH2 inward motion in controls, versus CH3 separation and CH2-CH3 displacement in PEGylated forms; the 4 kDa variant exhibited pronounced flexibility (PC2 ∼20 to 25%). Complementary backbone dihedral and hydrogen bond analyses showed localized torsional relaxation and a reduction in CH2-CH3 hydrogen bond occupancy in PEGylated systems, confirming the structural basis of hinge expansion. Vector projections indicated steric and entropic disruption of interdomain hydrogen bonds, suggesting reduced Fcγ receptor engagement. These results reveal PEG size-dependent structural perturbations, providing a molecular basis for diminished HER2 affinity and guiding rational design of PEGylated mAbs with optimized pharmacokinetics and preserved effector function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".