Automated Protein Affinity Optimization using a 1D-CNN Deep Learning Model
Bibliographic record
Abstract
Abstract Functional biologics design is a multi-objective optimization problem often with competing design objectives. We report on a novel deep learning based protein sequence prediction framework, ZymeSwapNet, that can be customized to handle a wide range of quantifiable design objectives, a current limitation of traditional protein design methods. We train a simple convolutional neural network (1D-CNN) on nonredundant curated protein crystal structures, using a set of geometric and topological features that describes a local protein environment, to predict the likelihood of each amino acid type for residue sites in the design region. While the model can be directly used to rank templates derived from mutagenesis campaigns, we extend the scope by developing a sequence/mutation generator that optimizes the desired multivariate distribution using a Monte-Carlo sampling. Using a case study – the design of a stable heterodimeric Fc (HetFc) antibody domain – we show that we can further include a Metropolis criterion to bias the sampling to enhance features such as the heterodimeric binding specificity, in addition to original sampling objective of enhancing stability. We demonstrate that ZymeSwapNet can generate stable HetFc designs, within minutes that had taken several rounds of rational structure and physical force-field based modeling attempts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".