Title: Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Deep learning

Protein language model

Protein stability

Protein engineering
Abstract: In-silico prediction of protein mutant stability, measured by the difference in Gibbs free energy change ($\Delta \Delta G$), is fundamental for protein engineering.
Current sequence-to-label methods typically employ two-stage pipelines: (i) encoding mutant sequences using neural networks (e.g., transformers), followed by (ii) the  $\Delta \Delta G$ 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 compute latent representations individually for each mutant sequence negatively impacts computational efficiency and poses the risk of overfitting.
This work proposes the Venus-MAXWELL framework, which reformulates mutation $\Delta \Delta G$ prediction as a sequence-to-landscape task.
In Venus-MAXWELL, mutations of a protein and their corresponding $\Delta \Delta G$ values are organized into a landscape matrix, allowing our framework to learn the $\Delta \Delta G$ landscape of a protein with a single forward and backward pass during training. To this end, we curated a new  $\Delta \Delta G$ benchmark dataset with strict controls on data leakage and redundancy to ensure robust evaluation.
Leveraging the zero-shot scoring capability of protein language models (PLMs), Venus-MAXWELL effectively utilizes the evolutionary patterns learned by PLMs during pre-training.
More importantly, Venus-MAXWELL is compatible with multiple protein language models.
For example, when integrated with the ESM-IF, Venus-MAXWELL achieves higher accuracy than ThermoMPNN with 10$\times$ faster in inference speed (despite having 50$\times$ more parameters than ThermoMPNN).
The training codes, model weights, and datasets are publicly available at https://github.com/ai4protein/Venus-MAXWELL.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces Maxwell: a framework to fine-tune a pre-trained model to predict the $\\Delta\\Delta G$ values for all single substitution mutations for a reference protein, moving from sequence-label to sequence-landscape modeling. An unmasked reference protein is fed through a pre-trained protein language model, which returns per position logits. The reference sequence logits are subtracted, and the resulting matrix is interpreted as a mutation landscape. The logits are via a composite loss function trained to approximate experimental $\\Delta\\Delta G$ values. The paper curates a representative stability dataset which is used for benchmarking, where the proposed model outperforms other models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. Well-motivated problem to move from sequence-label to sequence-landscape mappings. I appreciate the efficiency comparisons.\n2. Simple idea of learning multiple $\\Delta\\Delta G$ values via regression on a full backbone.\n3. Beats ThermoMPNN across metrics.\n4. Well-written paper with clear notation.\n5. Rigorous handling of datasets and curation of novel stability dataset.\n\nWeaknesses\n\n1. The novelty of the approach and the methodological contributions are limited. Perhaps this is outweighed by the increased utility of the model given its rapid inference. I am however missing clear cases where Maxwell would have a substantial impact. Directed evolution is used as an example, but it is unclear to me exactly how this would work given the lack of multi-mutant predictions.\n2. Confusing ad-hoc loss function where the Pearson correlation between experimental labels and model “logits” is maximized while the MSE between the labels and MLP-transformed logits is minimized. I do not understand the use of the MLP. If the role of the MLP is to map from logits to $\\Delta\\Delta G$s, why is this not a core component of the model? See related questions. Perhaps I’m misunderstanding something.\n3. I am similarly missing a qualitative analysis of when the model performs well or poorly. Perhaps the model is just consistently better across proteins and dataset sizes, but it would nonetheless be illustrative to see.\n4. No code in the supplementary materials for reproducibility.\n\nI will consider raising my score if the weaknesses - particularly 1 and 2 - are adequately and convincingly addressed.\n\nQUESTIONS:\n1. L44-46: As you point out later, most PLMs are either encoder-only or decoder-only. To state that most methods fail to harness the learned evolutionary patterns due to them only using the encoder while ignoring the decoder is therefore not entirely accurate in my opinion. For encoder-only models, the language model heads are typically deliberately simple MLPs which map from the latent representation to logits.\n2. L65: I think that the claim that existing stability datasets exhibit significant redundancy, data leakage, and misalignment should be quantitatively supported. Presumably, this should be easy to show. I think a figure - perhaps just in the appendix - showing these existing issues would show the benefit of the introduced dataset.\n3. L129: Phrasing: “primarily composed of mainly consisting of”\n4. Figure 2: I think the figure is slightly misleading w.r.t. the one-hot encodings as it shows these being fed directly into the PLM which would be odd. From the appendix, I can see that this is not actually the case as the input sequence is tokenized in two ways: regular tokenization for the PLM and one hot for the PLM output. I think this should be clarified in L148-150.\n5. Loss function. I am generally confused by the loss function, particularly the inconsistent use of the MLP and the motivation to optimize the Pearson correlation.\n    1. Regarding equation 10, the Pearson correlation coefficient captures the linear correlation between the predicted and ground truth values. In that case, it is not actually a ranking, right? I think labeling the loss as a ranking loss is incorrect and misleading. Have you explored using ranking losses? \n    2. L186: I am confused about the role of the MLP. The aim of the main architecture is to predict $\\Delta\\Delta G$ values, yet an MLP is used in the *loss* function to map from a logit-like space to $\\Delta\\Delta G$ space to compute the MSE loss. This necessarily means that the model outputs are not actually $\\Delta\\Delta G$s. Why is that? Wouldn’t it make more sense to have the MLP as a component after the element-wise subtraction? \n    3. In a similar vein, why is the MLP only used for the MSE loss and not the Pearson loss? \n    4. Is the MLP in the MSE loss trained jointly with the rest of the architecture or is it trained separately? \n6. Section 5.2: How are the zero-shot scores computed? For zero-shot scoring using sequence-only PLMs, a position would typically be masked and the logits of the masked position conditioned on non-masked residues would typically serves as the fitness proxy. Is this how the values are computed? Or are full sequence (pseudo-) likelihood computed? If so, how? If the sequences are non-masked, does this not result in very peaked distributions, and wouldn’t it benefit encoder-only models to be scored using masking?\n7. L190: The weighing factor of the losses is 0.1. Why this values? Did you experiment with others?\n8. Figure 4: In the related works, many $\\Delta\\Delta G$ predictors are referenced. I think it could be beneficial and fairly straightforward to include more of these in the results to add more granularity.\n9. Is Maxwell consistently better than the other baselines? Or are there instances where it is outperformed? I think an analysis or discussion of some qualitative results would be interesting to see, such as potential failures modes or instances where Maxwell is superior.\n10. I have some questions regarding the role of Maxwell for directed evolution, which is proposed as a use case for the model. Since Maxwell cannot handle multiple mutations, what does DE with Maxwell actually look like? At each step, would a single mutation be suggested, the resulting variant would then be used as input for the next round? ESM-IF1 which is used as PLM is structure conditioned. This presumably means that the structure would change over time, particularly if stability is optimized. Wouldn’t structure prediction be a potential bottleneck which would overrule the inference-speed gains of the proposed architecture? I think some more motivation for how Maxwell is used in a DE pipeline would help show the utility of the model.\n11. You mention on lines 330-331 that Maxwell can potentially be used for diverse protein function prediction. How would this work? Would it be using the same framework but just substituting the stability data with other datasets?\n\nLIMITATIONS:\nI think that the limitations of the framework should be discussed slightly more. This relates to the mentioned weaknesses and raised questions. E.g., when does the model fail? How sensitive is it to the structure quality? How can be used for DE if it cannot handle multiple mutations?", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-08"}

comments: SUMMARY:
This manuscript addresses the problem of predicting protein mutant stability across a sequence of interest. Existing frameworks model the relationship between each mutant sequence and its corresponding stability score for every mutant/score pair, resulting in prohibitively high training and inference costs.

The authors propose using a mutation landscape matrix constructed by taking the difference between the matrix derived from the wild-type sequence and the matrix obtained from a protein language model. In computational experiments, this matrix is shown to perform better than existing frameworks such as Rosetta, ThermoMPNN, and ESM-IF+MLP. Moreover, the effectiveness of the proposed approach is demonstrated by its fast prediction capability.

STRENGTHS AND WEAKNESSES:
Strength
- Demonstrated efficiency and performance

QUESTIONS:
This reviewer can understand that the proposed method is more efficient than existing methods, but cannot understand why it outperforms the existing methods in terms of prediction accuracy, since the existing methods explicitly learn from mutated sequences, whereas the proposed method learns them only implicitly through language models.

LIMITATIONS:
The authors addressed the limitations of their work.
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-27

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MAXWELL, a framework for predicting protein mutation stability. Instead of predicting individual mutation effects separately, MAXWELL predicts an entire mutation landscape matrix in a single forward pass. The authors demonstrate improved computational efficiency while maintaining competitive predictive performance compared to existing sequence-to-label methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n* The proposed method is simple yet novel and sound. This matrix-based approach allows the model to learn relationships between different mutations more effectively, as mutations at different positions can influence each other through the shared representation. Moreover, the reported test-time speedups (10× faster than ThermoMPNN, orders of magnitude faster than MLP baselines) make this approach valuable for some protein engineering applications.\n* The dataset construction is rigorous. The dataset curation process is well-documented and addresses a common issue in many papers of the field: test data leakage. The authors use mmseq to filter the sequences with >30% identity between train/test sets and then conduct further verifications using pairwise global sequence alignment.\n\nWeakness:\n* Several aspects of the experimental setup lack proper justification or transparency. For example, the weighting factor λ = 0.1 in the joint loss appears arbitrary and the MLP architecture details (dimensions of W₁, W₂) are not specific. Plus, It's unclear whether \"optimal performance\" in tuning learning rates refers to validation loss (and how is the validation set constructed?), or inadvertently test set performance, which would be problematic.\n* The introduction is not well-written and weakens the motivation for the project. Overall, the introduction fails to adequately motivate why learning mutation landscapes jointly is superior to independent predictions beyond computational efficiency. I am confused by the  \"initial performance of ΔΔG prediction is equivalent to the zero-shot prediction performance of the PLM\" (lines 62-64) since the model is also fine-tuned in the proposed method.\n\nQUESTIONS:\n1. Experiment details: Can you provide more details on the hyperparameter tuning process? Specifically:\n* What was the search space for the learning rate grid search?\n* How was \"optimal performance\" defined during hyperparameter selection?\n* Can you provide ablation studies for the loss weighting factor λ?\n* What are the specific dimensions used in the MLP projection layer? How sensitive is performance to these choices?\n\n2. Can you detail the advantage of your method compared to the conventional approach beyond computational efficiency? Can you clarify the statement about \"equivalent to zero-shot prediction performance\"?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Matrix-wise landscape learning(MAXWELL), a novel framework for efficiently learning protein mutation stability landscapes. MAXWELL transforms the mutation prediction task from the traditional sequence-to-label approach to a sequence-to-landscape paradigm, leveraging protein language models to predict the ∆∆G of protein mutations in a matrix-driven manner. The framework enables the learning of an entire mutation landscape with just a single forward and backward propagation, significantly improving computational efficiency. Additionally, the authors constructed a large-scale protein mutation ∆∆G dataset with strict controls on data leakage and redundancy to ensure robust model evaluation. Experimental results demonstrate that MAXWELL outperforms existing state-of-the-art methods in both prediction accuracy and computational efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1.MAXWELL employs a matrix-driven scoring approach, transforming mutation prediction to comprehensive landscape prediction. By integrating both ranking loss and MSE loss during training, it enables more holistic optimization of model performance.  \n\n2. Compared to traditional methods, MAXWELL demonstrates significant speed improvements in both training and inference, allowing efficient processing of large-scale mutation data. \n\n3. The framework is compatible with various PLMs, effectively leveraging evolutionary information from pre-trained models to enhance prediction accuracy.  \n\nWeaknesses\n\n1.MAXWELL primarily focuses on modeling single-point saturation mutations, and does not yet address the more complex multi-point mutation combinatorial effects, which limits its applicability in complex mutational scenarios.  \n\n2.The paper lacks a detailed theoretical explanation for why the probabilities of mutant and wild-type proteins can be used to predict mutation scores and ∆∆G values. Although a probability-based scoring formula is proposed, the underlying theoretical basis and assumptions are not sufficiently elaborated.  \n\n3.The experimental validation in this study was conducted only on the author-constructed Test12K dataset, without additional testing on publicly available benchmarks.\n\n4.The paper only compares the proposed framework with zero-shot and MLP baselines , omitting comparisons with other fine-tuning frameworks.\n\nQUESTIONS:\n1.In Equation (2), the order of probability calculations for the mutant and wild-type proteins appears inconsistent with the sequence described in the \"Method\" section. Could this be a typographical error?  \n\n2.Does the training set contain a sufficient number of mutant variants to ensure scalability across all mutation sites and amino acid types? If the MASK matrix is overly sparse, could this adversely affect the model’s generalization capability?  \n\n3.Did the authors attempt to combine MSE and ranking loss during MLP training to validate the effectiveness of introducing matrix transformations and mutation scoring?  \n\n4.MAXWELL fine-tunes all parameters of the PLM, whereas the MLP baseline freezes the PLM’s parameters. Could this discrepancy lead to performance differences, and if so, has this been quantified to isolate the contribution of the proposed architecture?\n\nLIMITATIONS:\nYes, the authors have discussed limitations in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}