Title: Towards Open-Search De Novo Peptide Sequencing via Mass-Based Zero-Shot Learning
Venue: NeurIPS 2025
Accepted: no
Keywords: Adversarial Networks

Attention Models

Computational Biology and Bioinformatics

Multitask and Transfer Learning

One-Shot/Low-Shot Learning Approaches

Regression
Abstract: Proteins are the main drivers of biochemical processes and play a pivotal role in almost all cellular functions. Through post-translational modifications (PTMs), residues within a protein can be chemically modified to fine-tune the protein's function in the cellular context. Despite the importance of PTMs, the plethora of deep learning-based de novo peptide sequencing (DNPS) models, which, in contrast to database searching approaches, predict peptide sequence solely from tandem mass spectra without any reference organism database, can only predict peptide sequences with a limited set of PTMs. This is because they rely on fixed vocabularies that map residue tokens to non-generalizable learned embeddings. To overcome this limitation, we propose a novel approach that leverages the fact that amino acids and their derivatives are characterized by their mass, a generalizable feature that enables zero-shot learning. Specifically, we reformulate DNPS as a mass prediction problem instead of a multiclass classification problem, where the model predicts the mass of the next residue instead of its token representation. To facilitate generalization to unseen PTMs, we leverage an adversarial multi-task learning scheme by supplementing the training data of experimental spectra with simulated spectra that mimic spectra containing unseen residues. We show that our approach allows the prediction of previously unseen PTMs, providing a promising proof of concept for mass-based representations as a path towards true open-search DNPS.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe prediction of post-translational modifications (PTMs) on peptides is important due to their vital biological functions. Existing DNPS models can only predict a limited set of PTMs that are present in the training data. This paper proposes a novel approach that reformulates PTM prediction as a mass prediction problem, which allows for the identification of previously unseen PTMs.\n\nSTRENGTHS AND WEAKNESSES:\n**strengths：**\n\n1.Nearly all previous DNPS models are incapable of predicting PTMs unseen during training. This is an inherent limitation of their architecture, as they can only identify PTMs included in a predefined 'vocabulary'. In contrast, the model proposed in this study is a rare example for performing open-search DNPS.\n\n\n2.The approach presented in this paper—reformulating DNPS as a mass prediction problem instead of a multiclass classification problem—is both novel and promising.\n\n**weaknesses：**\n\n1.My primary concern is the model's use of teacher forcing during the testing phase, a fact the authors clearly acknowledge. In this setup, the model predicts the remainder of a peptide based on a partial ground truth sequence. This is a severe form of label leakage, rendering the model inapplicable to any practical DNPS scenario, where ground truth information is entirely unavailable. \n\nIt is a fundamental consensus in the deep learning community, i think, that the testing process must be completely free from such direct label leakage.\n\n\n2.Another major concern is the complete absence of performance comparisons against any baseline DNPS models; the paper only presents its own model's results in isolation. While existing baselines may not support the task of open-search DNPS, they could and should have been included in several experiments for a fair evaluation.\n\nFor example, the task of \"Mass regression evaluation on seen amino acid combinations\" (Section 4.2.1, Figure 2A) is unrelated to the open-search. A direct comparison could have been performed in one of two ways: by converting the discrete amino acid outputs of baseline DNPS models into scalar mass values, or by converting this proposed model's scalar mass output back into discrete labels (which the authors explicitly state is possible). Similarly, baseline models could have readily participated in the experiments presented in Section 4.2.3 (Figure 4B) and those involving simulated data in Section 4.2.2, either by training or testing them on the same data.\n\nWithout any performance comparison against existing DNPS models, it is difficult to ascertain the true contribution and advancement presented in this paper.\n\n3.Given that the main innovation is the ability to predict unknown PTMs, the experiments should have logically focused on rare, low-data modifications not covered by previous DNPS models. Instead, the authors chose methionine oxidation and cysteine carbamidomethylation for their experiments—two of the most common and data-rich PTMs that nearly all existing DNPS models already predict.\n\nAs a result, the experiments is not solid to demonstrate the model's most crucial selling point: its claimed ability to perform open-search DNPS for unknown modifications.\n\n\n4.Lack of Comprehensive Metrics. The evaluation metrics include 'mass accuracy' and 'residue identification' but neglect the most critical metric for DNPS tasks: peptide-level precision. This metric serves as the principal indicator of a DNPS model's practical utility, as the ultimate goal of DNPS is to assign complete and accurate peptide sequences to each mass spectrum. After all, a correctly identified unknown PTM holds little value if the overall peptide sequence is incorrect.\n\n5.The model's reported experimental performance is underwhelming. For instance, the authors acknowledge that the \"absolute prediction quality for unseen residues remained limited,\" with the recall for cysteine carbamidomethylation being as low as 3.18% and for methionine oxidation only 14.41%. This underwhelming performance is also visually evident. Figures 2A, 3C, and 4D clearly show that when the model predicts unknown PTMs, the distribution of its predicted masses deviates significantly from the actual ground truth mass of the PTM.\n\nQUESTIONS:\nAs noted in the weaknesses section, my primary questions for the authors are:\n1. Why were no performance comparisons made against any existing DNPS models, even on tasks where such comparisons would be directly applicable?\n2. Why does the evaluation lack peptide-level metrics, such as peptide precision, given that these are the most critical indicators of a model's practical utility for the DNPS task?\n\n3.The presentation of Section 4.2 seems to follow a progressive, step-by-step structure that introduces the model's components sequentially. Why was this framed as a series of 'main results' rather than as a formal ablation study, which is typically used to demonstrate the individual contribution of each component?\n\nLIMITATIONS:\nI want to state clearly that the problem this paper focus—open-search de novo peptide sequencing (DNPS) for unknown PTMs—is a critically important and exceptionally difficult task in peptide sequencing and proteomics. However, this manuscript reads more like a cursory experimental report than a substantive, innovative contribution with practical significance.\n\nThis paper undertakes a preliminary exploration into the important field of open-search DNPS and presents a series of experimental results. Although the proposed model is novel, its performance is poor, and its methodology is critically flawed by the use of teacher forcing during the inference stage.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 1, "ORIGINALITY": 3, "DATE": "2025-06-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel de novo peptide sequencing approach based on mass regression and multi-task learning, aiming to achieve \"zero-shot generalization\" to unseen post-translational modifications (PTMs). The model is jointly trained on experimental spectra and large-scale simulated spectra, with the introduction of an adversarial discriminator attempting to align the distributions of experimental and simulated domains in the latent space.\nWhile the work introduces promising methodological innovations, more rigorous comparisons against state-of-the-art methods, deeper analysis of adversarial mechanisms, and significantly expanded validation of zero-shot PTM generalization are essential.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The methodology demonstrates innovation by reformulating peptide sequence prediction as a continuous mass regression task, overcoming limitations of fixed token-based approaches. This theoretically enables zero-shot generalization potential.\n2. The multi-task learning strategy, integrating both experimental and simulated spectra, enhances the model's overall robustness and partial PTM generalization capability.\n3. The authors transparently disclose the methodological limitations of their work, demonstrating commendable scientific rigor.\n\nWeaknesses:\n1. Lack of systematic quantitative comparison against established baselines (e.g., Casanovo, AdaNovo, π-PrimeNovo, ContraNovo). Only indirect or partial qualitative mentions of one or two baseline models are provided; no comprehensive comparative tables are presented.\n2. Limited generalization to novel PTMs (e.g., C[Carbamidomethyl]). Evaluation is restricted to only two common modification types (M[Oxidation] and C[Carbamidomethyl]), lacking broader coverage. Crucially, no direct performance comparison to other baseline methods on unseen PTMs.\n3. Insufficient explanation of the adversarial discriminator mechanism. Key design choices—such as the rationale for the adversarial loss weight selection (e.g., justification for -50), and the impact of discriminator capacity on domain alignment and generalization—lack detailed experimental validation.\n\nQUESTIONS:\nRequired Revisions:\n1. Systematic baseline comparison: Include quantitative performance tables comparing against Casanovo, AdaNovo, π-PrimeNovo, ContraNovo and so on.\n2. Comprehensive generalization assessment: There are “over 400 known PTM types”, please extend PTM generalization testing beyond M[Oxidation] and C[Carbamidomethyl]. And conduct direct comparisons with leading de novo peptide sequencing methods on multiple unseen PTM types.\n3. Clarify data partitioning: Explicitly state the criteria for preventing data leakage (e.g., sequence identity thresholds, spectral similarity-based splits) to ensure rigorous evaluation.\n4. Detail Multi-task learning mechanisms: \n(1) Specify the training relationship between the discriminator and main model (e.g., alternating or joint updates?).\n(2) Provide mathematical formula for the composite adversarial loss term (LAdv).\n(3) Justify the selection of the adversarial weight (weight -50) with empirical or theoretical rationale.\n(4) Ablation studies on adversarial design: \na. Different discriminator architectures.\nb. Varying adversarial loss weights.\n\nLIMITATIONS:\n1.\tThe generalization scope is severely constrained: PTM evaluations are limited to only M[Oxidation] and C[Carbamidomethyl], failing to reflect model capability across hundreds of biologically relevant PTMs.\n2.\tNo comprehensive quantitative benchmarking against state-of-the-art models is provided.\n3.\tThe adversarial design choices lack rigorous ablation studies and mechanistic analysis.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors present a novel method for de novo peptide sequencing which specifically allows for the prediction of arbitrary post translational modifications by predicting the *mass* of the residue instead of the identity of the residue. Test show that with multi task training, the model is able to predict the mass of residues that were unseen during training.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\nThis is a really interesting and genuinely novel idea, as far as I know. The choice to predict mass should, in theory, allow for predicting arbitrary modifications and therefore allow interesting generalization. The use of multi-task training with real and experimental data is also interesting and shows good results.\n\nIn general, the paper is also fairly clear, and the methodology and results are well explained. There are a few details (enumerated below) which could further improve clarity.\n\nOverall, a good paper.\n\n## Weaknesses\nThere are a few issues of clarity and missing information that would be good to clear up\n\n### Inline results vs. Table\nResults are reported generally inline inside the text. This is common for papers submitting to more bio-focused venues, but I think adopting the table result reporting convention could improve clarity given that it is what readers of NeurIPS papers often expect. Having a single table that reports the various different models trained along with some baselines with a few standard metrics (maybe the average mass error, accuracy of residue calling, and performance on unseen modifications) would help the reader clearly understand what was done and what the effects were.\n\n### Comparison to other models\nResults against other models are not reported. This is somewhat ok, given that the authors are testing something new (ability to generalize to unseen PTMs) but there are still comparisons that can be made. The authors even report in the text, \"While these recall values were somewhat lower than those reported by classification-based transformer models such as AdaNovo,\" but do not provide the actual numbers. These should be provided both inline after this statement and in the table suggested above.\n\n### Further discussion of mass prediction vs. residue classification\nIt is hard for me to believe that mass prediction would perform adequately in the real world due to the similarity in mass between many residues. The impact of this on recall should be stated clearly.\n\n### Missing training and architecture details\nThe authors should provide (perhaps in the supplement) the architecture and training details for each model. Some of this is provided for the adversarial model, but not the others.\n\n## Extensions\nI believe the authors could train a model with multiple objectives for both mass prediction *and* residue classification, with a binary \"has PTM\" classifier as well. This could allow the best of both worlds, where the model defaults to the residue classifier if there is no PTM (allowing it to distinguish between similar-mass residues) and to mass prediction otherwise.\n\nQUESTIONS:\nSee the weaknesses and extensions section above.\n\nI am giving this paper an accept - I still believe the weaknesses I discussed should be addressed, but they are fairly minor so I am confident the authors can address them in the rebuttal.\n\nIf the authors perform the extension I suggested and it substantially improves results, I would consider raising to a strong accept.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

comments: SUMMARY:
This paper presents a novel approach to de novo peptide sequencing by reframing it as a mass regression problem, which enables the model to perform zero-shot identification of PTMs. The transformer-based model is trained using a multi-task learning framework that combines experimental spectra with diverse simulated spectra, and it employs an adversarial scheme to align the representations of these different data types.

STRENGTHS AND WEAKNESSES:
Strengths
- The author leverages the physical mass of residues as a generalizable feature, which is different from fixed-vocabulary classification and helps in handling novel PTMs.

Weaknesses

Major
- All performance metrics are obtained under teacher forcing. This is a significant limitation that prevents an assessment of the model's real-world de novo sequencing accuracy.
- The end-to-end peptide identification performance (e.g., peptide recall) is not reported.
- The model appears to predict the mass of an unseen methionine oxidation (M+O, ~147.04 Da), but since this is very close to the mass of phenylalanine (F, ~147.07 Da) in the training set, it almost certainly reflects memorization rather than true generalization. This weak claim distracts from the model’s actual capabilities.

Minor
- Figure 2 B: cystein -> cysteine
- Line 108: zero-short -> zero-shot
- Line 157: Mulit -> Multi

QUESTIONS:
How does the model perform under autoregressive decoding without teacher forcing?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 1
ORIGINALITY: 2
DATE: 2025-07-03