Title: DualMPNN: Harnessing Structural Alignments for High-Recovery Inverse Protein Folding
Venue: NeurIPS 2025
Accepted: yes
Keywords: Protein Inverse Folding

Structure Alignments

MPNN

High Recovery
Abstract: Inverse protein folding addresses the challenge of designing amino acid sequences that fold into a predetermined tertiary structure, bridging geometric and evolutionary constraints to advance protein engineering.  Inspired by the pivotal role of multiple sequence alignments (MSAs) in structure prediction models like AlphaFold, we hypothesize that structural alignments can provide an informative prior for inverse folding. In this study, we introduce DualMPNN, a dual-stream message passing neural network that leverages structurally homologous templates to guide amino acid sequence design of predefined query structures. DualMPNN processes the query and template proteins via two interactive branches, coupled through alignment-aware cross-stream attention mechanisms that enable exchange of geometric and co-evolutionary signals. Comprehensive evaluations across on CATH 4.2, TS50 and T500 benchmarks demonstrate DualMPNN achieves state-of-the-art recovery rates of 65.51\%, 70.99\%, and 70.37\%, significantly outperforming base model ProteinMPNN by 15.64\%, 16.56\%, 12.29\%, respectively. Further template quality analysis and structural foldability assessment underscore the value of structural alignment priors for protein design.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on the task of inverse protein folding, where a protein backbone structure is given and a plausible amino acid sequence needs to be generated that folds into the given structure. A key contribution of the paper is the use of templates selected from a database using structural alignments, inspired by the common use of multiple sequence alignments (MSA) in forward folding. Specifically, the paper leverages Foldseek to find similar structures to the query structure that needs to be inverse-folded, and then uses these similar structures and the associated sequences to inform the sequence prediction of the new, given structure. This is implemented with a model called DualMPNN, a dual-stream message passing network that in parallel processes the query and the template structures. The method is validated on sequence recovery, perplexity and foldability metrics, and favourable results compared to baselines are reported.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n- The use of structural alignments to find templates that can be leveraged in the inverse-folding task is an interesting and original idea and promising research direction.\n- When well-aligned templates are available (TM-Score > approx. 0.5), the proposed method outperforms previous methods in terms of sequence recovery rate and perplexity.\n- The proposed DualMPNN architecture is novel and appropriately designed for the task at hand.\n- Generally, inverse folding is a highly relevant task in protein design, an important scientific field with impactful applications. Hence, the paper addresses a problem of significance.\n\n**Weaknesses:**\n- The method is inspired by MSAs in forward folding. MSA databases used in folding models are usually huge, consisting of many millions of sequences. In contrast, the paper leverages only the protein databank (PDB) to perform the structural alignments. However, the PDB is orders of magnitude smaller. I wonder whether this is reason for concern; this aspect is not discussed. For instance, could this lead to overfitting to the small PDB? What is the effect of different sizes of the database to find structural alignments? Related, the authors could extend this to not only the PDB, but also the AlphaFold Database (AFDB), which would offer a much larger database to find structural alignments (this may actually boost performance). I think the size of the database from which the templates are identified deserves more discussion and quantitative analysis.\n- The paper uses perplexity and sequence recovery as its two main metrics. However, in inserve-folding there can be multiple sequences that fold into the same structure. Hence, if the model outputs a different sequence than the ground truth one, it may be perfectly fine so long as this sequence still folds into the query structure. Therefore, I believe that sequence recovery and perplexity with the ground truth sequence should be interpreted very carefully. Instead, I believe that foldability is a critical metric -- ultimately, we are fine to predict whatever sequence we want, as long as the correct structure is recovered. However, on foldability, the proposed method is actually worse than ProteinMPNN (see avg RMSD in Table 3), the most popular inverse folding model in the literature (which does not rely on structure templates) -- this is concerning and needs more discussion or should be addressed.\n- In general, one would expect the model to be as good as ProteinMPNN when no templates are used or the templates are not well aligned, and better when useful templates can be found (high TM scores). However, as seen in Figure 2, the method performs worse than ProteinMPNN when the templates are not well aligned (TM score < 0.5). This is concerning.\n- Related, the method outperforms ProteinMPNN on sequence recovery when TM score>0.5. In general, this further supports the concern that the model is essentially overfitting to the templates, i.e. it only achieves good performance when the template TM score is high, i.e. when structures in the template database can be found that are highly similar to the query structure. In those cases, possibly the model largely copies the template sequence? The relation and similarity of the template and predicted sequences is not appropriately discussed or analyzed, unfortunately.\n- It would be interesting to analyze the types of sequences that DualMPNN predicts with the types of sequences that ProteinMPNN predicts. Which of them are more diverse? Does some have certain dominant amino acid types? It is known that ProteinMPNN-predicted sequences follow a distribution different from native sequences. However, I would expect DualMPNN's sequences to follow a more native-like sequence distribution, considering that native sequences inform the sequence prediction through the templates. This deserves a more detailed analysis.\n- The clarity, writing and presentation of the paper could be improved: For instance, the paper discusses many very low-level implementation details in the main text, some of which are difficult to follow (some related questions below). But at the end, the method uses relatively standard message passing neural networks, just with two streams connected via attention (which is an appropriate design, as pointed out under strengths). It would be better to present the non-novel details in the appendix, and instead focus in the main paper on the key novelties and save space for more discussions, analyses and experiments.\n\n**Conclusion:** In summary, I think the direction of using structural alignments to inform sequence prediction in inverse folding is an interesting idea and promising research direction. However, there are many questions and concerns and I don't think the paper is ready for publication in its current form. Hence, I am leaning towards rejection.\n\nQUESTIONS:\nI have a few further questions and comments:\n- Please introduce $L_q$ in equation (1).\n- Discussion around equation (3): When exactly is an aligned i-j pair between query and template protein matched? The text does not describe the exact criteria for matching residue pairs -- and most likely those come with hyperparameters that could be worth exploring.\n- Line 132: I assume in $A_{template}=...$, it should be $j_t$ and not $i_t$?\n- Line 205 and following: Why is the number of neighbors for each aggregated node so different in the query MPNN and the template MPNN? It's 48 and 4, respectively.\n- Line 213: The authors say they use \"easy-search\" mode. What does this mean? The author should explain the search algorithm and not just list Foldseek commands.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a dual stream network architecture for the inverse folding problem which incorporates information from structural alignments by cross-attention between two instances of a base inverse folding model such as ProteinMPNN. The authors demonstrate improved performance compared to baselines and highlight the importance of good templates.\n\nSTRENGTHS AND WEAKNESSES:\nThe proposed idea is simple and clean, and the text is well written and easy to follow (apart from a few typos, see below).  The inverse folding problem is currently very relevant in the context of protein design, and the experiments are well conducted and focus on a few key aspects such as ablations and the dependence on template quality.\n\nI am only concerned about data leakage from the alignment step, especially when provided during evaluation. One might argue that DualMPNN gains unfair advantage through the additional information which was not available to ProteinMPNN. As the similarity threshold is quite high (99%), a near-identical template could potentially be selected for a test protein, in which case the low-hanging fruit for the model is simply to map the template sequence to the query sequence. It would be good if the authors take great care to discuss this point in the paper, as it is a little sensitive topic.\n\nI, in principle, would recommend to accept the paper as it is well executed, but I reserve to change my assessment based on the author discussion of this critical point.\n\nQUESTIONS:\n- Leakage, see above. Are templates at test time limited to training samples or is data leakage otherwise prevented?\n- Is the template strictly required at inference or can the model be run without? Is the performance then comparable to a bad template (with low TM) or worse?\n- How were the hyperparameters for the similarity threshold (99%) and the neighbors in query and template model (which also differ strongly) chosen?\n- How long did it take to run the alignment for all training data? This might also be relevant in the limitations. Is the dataset available?\n\nThe following questions are more for my own interest, I don’t require them to be answered:\n\n- Does the template branch focus more on the node label or the geometric edge label information? Do you have any indications how this is weighted, eg through an ablation of the geometric edge features?\n- It would be interesting to see how DualMPNN performs on structural holdouts (eg from FoldSeek clustering) compared to ProteinMPNN, which would point towards generalization to novel folds, although I understand that this is out of scope for this rebuttal.\n\nLIMITATIONS:\n- As mentioned above, if the runtime is significantly increased through the alignment, that should be mentioned.\n- Also, while the availability of templates is discussed, the authors discard this with the argument that in a real-world scenario there will likely be some template available. I would argue here that one of the primary applications of inverse folding models is the sequence design for novel protein folds, where such templates might not be available. (I would just recommend here to let the limitation stand as it is without trying to remedy it.)", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents DualMPNN, a dual-stream message passing neural network that improves inverse protein folding by leveraging structurally aligned templates through TM-align and attention-based feature fusion. The model shows significant gains in sequence recovery and perplexity over existing methods (e.g. ProteinMPNN), particularly when high-quality templates are available. Extensive ablation and benchmark evaluations demonstrate the model’s robustness and architectural soundness.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- Comprehensive ablation studies demonstrate the contribution of each architectural component: template node initialization improves recovery from 49.9% to 61.3%, cross-stream attention further boosts it to 64.8%, and TM-score modulation leads to a final recovery rate of 65.4%, highlighting the additive benefit of each mechanism.\n\n- Meaningful improvement over baselines: DualMPNN outperforms ProteinMPNN and GraDe-IF across standard benchmarks, consistently achieving higher recovery rates and lower perplexity across CATH, TS50, and T500 datasets.\n\n - The model avoids overfitting in low-TM-score regions due to the masking of unaligned residues and the use of TM-score–weighted attention, which downregulates unreliable template signals. This is supported by empirical evidence: in Figure 2(a), proteins with TM-score < 0.3 show no significant drop in recovery rate compared to ProteinMPNN; and in Figure 2(b), low TM-score cases (blue and orange points) cluster near the diagonal, indicating that DualMPNN neither helps nor harms when template similarity is poor.\n\n- Attention score modulation by the global TM-score allows the model to selectively leverage high-quality templates, leading to recovery gains over 30% when TM-score > 0.9.\n\nLimitations\n\n- Triviality of the core insight: While the paper quantifies the relationship between TM-score and sequence recovery, the central finding, i.e. that higher structural similarity leads to better recovery, is intuitive. \n\n- Potential data leakage: The authors only exclude near-identical templates (TM-score > 0.99, same PDB ID), and hence, residual homology may still bias results. The absence of explicit sequence identity filtering raises concerns about the true independence of training and test pairs.\n\n- Lack of functional assessment: The study evaluates sequence recovery but does not test whether the generated sequences are functional, foldable, or biologically plausible beyond AlphaFold confidence metrics.\n\n - No direct test for hallucination: The paper claims robustness to low-quality templates, but does not evaluate whether it generates unrealistic or misleading sequences in such cases. There is no analysis of output diversity, entropy, or structural viability in weak-alignment settings.\n\nQUESTIONS:\nSuggestions for improvement\n\n- Stricter homology filtering: To ensure generalization and avoid template leakage, consider filtering training/test pairs using sequence identity thresholds (e.g., <30%), in addition to TM-score and PDB-based exclusions.\n\n- Include SCOP-based evaluations: Evaluating the model on SCOP-classified domains would help validate whether DualMPNN generalizes across different evolutionary and topological classifications, complementing the current CATH-based benchmarks.\n\n - Why not use AlphaFold3? Given the availability of AlphaFold3, the authors should either validate foldability using the updated model or strongly justify the continued use of AlphaFold2. Additionally, they could leverage structures excluded from AlphaFold2 training but included in AlphaFold3 as a valuable test set for assessing generalization.\n\n - Incorporate functional or structural plausibility metrics: Beyond recovery and perplexity, authors could evaluate conservation of functional motifs, residue-level confidence, or validate structure-function relationships via experimental assays or in silico function prediction.\n\n- Analyze diversity and entropy under poor templates: To support the claim of robustness under low-quality alignments, the model’s output diversity and per-residue entropy could be measured and compared to baselines, particularly in low TM-score regimes.\n\n- Several complementary metrics could provide deeper insight into the biological plausibility, structural consistency, and functional relevance of the inferred sequences. For instance, structural similarity metrics such as RMSD after in silico folding, confidence scores from AlphaFold (e.g., pLDDT), and physicochemical assessments such as packing quality or solvation energy can help evaluate whether the designed sequences are not only recoverable but also foldable and functionally viable.\n\nLIMITATIONS:\nLimitations: The authors could strengthen the discussion by acknowledging key limitations, such as:\n\n- There is a lack of functional validation for generated sequences.\n\n- Potential data leakage due to residual homology between training and test sets.\n\n- Reliance on AlphaFold2 for foldability without discussing more recent tools like AlphaFold3.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-06"}