Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models
Bibliographic record
Abstract
Abstract In-silico prediction of protein mutant stability, measured by the difference in Gibbs free energy change (ΔΔ G ), is fundamental for protein engineering. Current sequence-to-label methods typically employ the two-stage pipeline: ( i ) encoding mutant sequences using neural networks ( e . g ., transformers), followed by ( ii ) the ΔΔ G regression from the latent representations. Although these methods have demonstrated promising performance, their dependence on specialized neural network encoders significantly increases the complexity. Additionally, the requirement to individually compute latent representations for each mutant site negatively impacts computational efficiency and poses the risk of overfitting . This work proposes the Venus-M axwell framework, which reformulates mutation ΔΔ G prediction as a sequence-to-landscape task. In Venus-M axwell , mutations of a protein and their corresponding ΔΔ G values are organized into a landscape matrix, allowing our framework to learn the ΔΔ G landscape of a protein with a single forward and backward pass during training. Besides, to facilitate future works, we also curated a large-scale ΔΔ G dataset with strict controls on data leakage and redundancy to ensure robust evaluation. Venus-M axwell is compatible with multiple protein language models and enables these models for accurate and efficient ΔΔ G prediction. For example, when integrated with the ESM-IF, Venus-M axwell achieves higher accuracy than ThermoMPNN with 10× faster in inference speed (despite having 50× more parameters than ThermoMPNN). The training codes, model weights, and datasets are publicly available at https://github.com/ai4protein/Venus-MAXWELL .
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".