ZymePackNet: rotamer-sampling free graph neural network method for protein sidechain prediction
Bibliographic record
Abstract
Abstract Protein sidechain conformation prediction, or packing, is a key step in many in silico protein modeling and design tasks. Popular protein packing methods typically rely on approximated energy functions and complex algorithms to search dense rotamer libraries. Inspired by the recent success of deep learning in protein modeling tasks, we present ZymePackNet, a graph neural network based protein packing tool that does not require a rotamer library, scoring functions or a search algorithm. We train regression models using protein crystal structures represented as graphs, which are employed sequentially to “germinate” the sidechain starting from atoms anchoring the protein backbone to the sidechains’ termini, followed by an iterative refinement stage. ZymePackNet is fast and accurate compared to state-of-the-art protein packing methods. We validate our model on three native backbone datasets achieving a mean average error of 16.6°, 24.1°, 42.1°, and 53.0° for sidechain dihedral angles ( χ 1 to χ 4 ). ZymePackNet captures complex physical interactions such as π stacking without explicitly accounting for it in the model; such effects are currently lacking in the energy terms used in traditional packing tools. Contact abmukho@vt.edu Supplementary information Supplementary data are available at Bioinformatics online.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".