Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing
Authors: Ye Du
Chen Yang
Nanxi Yu
Wanyu Lin
Qian Zhao
Shujun Wang
Venue: ICML 2025 poster (2025)
Keywords: De Novo Peptide Sequencing; Mass Spectrum; Latent Space Imputation;
Primary area: applications->chemistry_physics_and_earth_sciences
TL;DR: A new computational paradigm for addressing the missing fragmentation issue in mass spectrum for de novo peptide sequencing.

Abstract: *De novo* peptide sequencing is a fundamental computational technique for ascertaining amino acid sequences of peptides directly from tandem mass spectrometry data, eliminating the need for reference databases. Cutting-edge models encode the observed mass spectra into latent representations from which peptides are predicted auto-regressively. However, the issue of missing fragmentation, attributable to factors such as suboptimal fragmentation efficiency and instrumental constraints, presents a formidable challenge in practical applications. To tackle this obstacle, we propose a novel computational paradigm called $\underline{\textbf{L}}$atent $\underline{\textbf{I}}$mputation before $\underline{\textbf{P}}$rediction (LIPNovo). LIPNovo is devised to compensate for missing fragmentation information within observed spectra before executing the final peptide prediction. Rather than generating raw missing data, LIPNovo performs imputation in the latent space, guided by the theoretical peak profile of the target peptide sequence. The imputation process is conceptualized as a set-prediction problem, utilizing a set of learnable peak queries to reason about the relationships among observed peaks and directly generate the latent representations of theoretical peaks through optimal bipartite matching. In this way, LIPNovo manages to supplement missing information during inference and thus boosts performance. Despite its simplicity, experiments on three benchmark datasets demonstrate that LIPNovo outperforms state-of-the-art methods by large margins. Code is available at https://github.com/usr922/LIPNovo.

Latent Imputation before Prediction:
A New Computational Paradigm for De Novo Peptide Sequencing
Ye Du Chen Yang' Nanxi Yu?
Wanyu Lin
Qian Zhao
Shujun Wang' 145
Abstract
14000
LiPNovo (Ours)
De novo peptide sequencing is a fundamental com- putational technique for ascertaining amino acid sequences of peptides directly from tandem mass spectrometry data, eliminating the need for ref- erence databases. Cutting-edge models usually encode the observed mass spectra into latent rep- resentations from which peptides autoregressively. However, the issue of missing fragmentation, attributable to factors such sub- optimal fragmentation efficiency and instrumen- tal constraints, presents a formidable challenge in practical applications. To tackle this obstacle, we propose a novel computational paradigm called Latent Imputation before Prediction (I.PNovo). LIPNovo is devised to compensate for missing fragmentation information within observed spec- tra before executing the final peptide prediction. Rather than generating raw missing data, LIP- Novo performs imputation guided by the theoretical peak profile of the tar- get peptide sequence. The imputation process is conceptualized as see-prediction problem, uti- lizing a set of learnable peak queries to reason about the relationships among observed peaks and directly generate the latent representations of theoretical peaks through optimal bipartite match- ing. In this way, L.PNovo manages to supple- ment missing information during inference and thus boosts performance. Despite its simplicity, experiments on three benchmark datasets demon- strate that LIPNovo outperforms state-of-the-ar
Frequency of Instances
12000
CasaNovo (Baseline)
AdaNovo
0.8
Amino Acid-Leve Precision
10000
HelixNovo
8000
0.6
6000
0.4
4000
predicted
2000
0.2
0
0.0
0.6
0.8
Missing Fragmentation Ratio
Figure I. Comparison of amino acid-level precision between LIP- Novo (ours) and existing methods under varying missing frag- mentation ratios. As the missing ratio increases, performance deteriorates dramatically, highlighting the detrimental impact of the missing fragmentation issue. The proposed LIPNovo con- sistently outperforms existing methods across all missing ratios. Results are based the test set (i.e. the yeast species) from the Nine-species dataset (Tran et al., 2017).
methods by large margins. Code is available at httpss/github.commsss//LLLPPPP
the latent space,
1. Introduction
Peptide sequencing, i.e., determining the amino acid se- quences of peptides from observed mass spectra, is the cor- nerstone of the typical tandem mass spectrometry (MS) based proteomics workflow (Aebersold & Mann, 2003; Nesvizhskii al., 2007; Strauss et al., 2024). This tech- nique plays pivotal role in understanding protein structure and function (Lee al., 2007; Nowinski et al., 2012), hold- ing significant importance in various applications such as drug discovery (Lin et al., 2020), biomarker discovery (Mc- Donald Yates lii, 2002; Wenk et al., 2024), and medical research (Uzozie Aebersold, 2018; Macklin et al., 2020).
'Department of Biomedical Engineering, The Hong Kong Poly- technic University. Department Computing, The Hong Kong Polytechnic University. 'Department of Applied Biology and Chemical Technology. The Hong Kong Polytechnic University. "Research Institute for Smart Ageing. The Hong Kong Polytech- nie University. 'Research Institute for Artificial Intelligence of Things, The Hong Kong Polytechnic University. Correspondence to: Shujun Wang <shu-jun, wang polyu.edu.hk>.
Early approaches (Noor et al., 2021) solving this task rely on database search paradigm, where each spectrum is scored against a set of candidate peptides, and the highest- scoring peptide-spectrum match (PSM) is retrieved as the prediction. However, this approach is susceptible to certain limitations, such as limited coverage of the peptide space in the database and difficulties identifying new or rare
Proceedings of the 4dnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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LIPNovo: A New Computational Paradigm for De Novo Peptide Sequencing
peptides. In response to these limitations, the field has wit- nessed the emergence of the de novo peptide sequencing paradigm, propelled by the rapid advancements of deep learning (Tran et al., 2017). Unlike traditional database- based approaches, de novo sequencing does not depend on prior knowledge from a pre-constructed protein database. Instead, focuses on reconstructing peptide sequences di- reetly from the observed spectra. This capability facilitates the identification of previously unseen peptides such mAbs (Bandeira et al., 2008).
We build LIPNovo on top of the competitive CasaNovo baseline (Yilmaz et al., 2022; 2024). CasaNovo has recently undergone iterations with substantial performance improve- ments (Yilmaz et al., 2024). Experiments are conducted on three benchmark datasets (Zhou et al., 2024). The exper- imental results show that LIPNovo achieves significantly better results under different levels of missing fragmenta- tion ratios, as demonstrated in Figure 1. Overall, LIPNovo surpasses CasaNovo by +5.6%, +20.0%, and +11.2% in amino acid precision on the Nine-species, Seven-species, and HC-PT datasets, respectively. Moreover, LIPNovo out- performs tfte-of-thhearr methods by clear margins in amino acid-level. peptide- level, and post-translational modification (PTM))level metrics. Additionally, we also test the model performance w.r. the imputation quality, and the results sug- gest that the imputation quality correlates positively to the peptide sequencing performance. This further confirms the feasibility of our idea. To summarize, the core contributions of this work are as follows.
Current de novo sequencing methods (Tran et al., 2017:
Qiao et al., 2021; Yilmaz et al., 2022: Eloff et al., 2023; Xia et al., 2024; Yang et al., 2024; Yilmaz et al., 2024) typi- cally employ an encoder-decoder architecture. Within this framework, spectrum encoder transforms the observed spectra into latent representations, which are subsequently utilized by peptide decoder to generate amino acid se- quences in an auto-regressive manner. Nonetheless, the mass spectra frequently lack informative peaks (McDonnell et al., 2022; Mao et al., 2023; Zhou et al.. 2024; Yang et al., 2024), stemming from incomplete fragmentation of pre- cursor peptides or inherent limitations within tandem mass spectrometer. This deficiency results in insufficient infor- mation for reconstructing peptide sequences. Consequently, existing methods struggle to effectively model the intricate spectral patterns, leading to potential gaps hindering the performance of de novo peptide sequencing, as illustrated in Figure 1.
We introduce LIPNovo, a new computational paradigm that incorporates a latent space imputation step into de novo peptide sequencing, effectively addressing the challenge of missing fragmentation information.
LIPNovo formulates imputation as novel set pre- diction problem, utilizing bipartite matching to align predicted latent representations with ground truths and a carefully designed imputation loss function to guide the imputation process.
To bridge this gap, we propose Latent Imputation before Prediction (LIPNovo)) new computational paradigm that revolutionizes the de novo sequencing pipeline by incorpo- rating an imputation step. The imputation is formulated as a novel set prediction problem. Specifically, after modeling all pairwise interactions between observed peaks in a spectrum, LIPNovo integrates an imputation module, instantiated with standard transformer decoder (Vaswani, 2017) and sim- ple feed-forward networks, to predict latent representations of theoretical peaks corresponding to ideal fragmentation, specifically b- and y-ions of the target peptide (Zhou et al., 2024). By employing bipartite matching (Carion et al., 2020) to aligm predictions with ground truths and designing an imputation loss function to guide the imputation process, LIPNovo explicitly reconstructs missing fragmentation in- formation beyond the observed incomplete spectrum. This capability stems from leveraging certain informative noise peaks (Tabb et al., 2003; Yang et al., 2024) and capturing the missing pattern from a large-scale training dataset. Through imputation, LIPNovo functions as a signal enhancement mechanism that directly reduces ambiguitics, thus elucidat- ing patterns and dependencies between the spectrum and the peptide, in stark contrast previous methods that rely solely incomplete spectra.
LIPNovo achieves substantial performance gains over existing state-of- of-the-art methods across multiple bench- mark datasets, demonstrating its superiority.
2. Related Work
2.1. De Novo Peptide Sequencing
Deep learning techniques (LeCun et al., 2015) have signif- icantly transformed the field of de novo peptide sequenc- ing. Among them, DeepNovo (Tran et al., 2017) pioneered the use of deep neural network that integrates CNN and LSTM networks. Then, PepNet (Liu et al., 2023) employed fully CNN network, while GraphNovo (Mao et al., 2023) introduced a two-stage graph-based network that finds the optimal path among observed peaks to guide the sequence prediction. Recently, CasaNovo (Yilmaz et al.. 2022) intro- duced powerful transformers for peptide sequencing, which greatly improves performance. Specifically, CasaNovo adopts an encode-then-decode pipeline that establishes di- rect mappings from observed spectra to peptides, now a mainstream framework for the task. Under this framework, Yilmaz et al. (Yilmaz et al., 2024) incorporated a beam search strategy (Freitag & Al-Onaizan, 2017) to improve
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LIPNNoo: A New Computational Paradigm for De Novo Peptide Sequencing
Training only
Bipartite Matching
($)
Peak Encoder
Spectrum Encoder
Prediction Head
Imputtttio
Theoretical Spectrum
No peaks
weight sharing
Filtration
Spectrum Decoder
Peak Encoder
Spectrum Encoder
Imputation Module
C
Precursor Enoodar
Embedding Layer
Observed
Incomplete
Spectrum
Learnable Peak Queries
Precursor
Spectrum Representation
Latent Imputation
Peptide Prediction
Figure 2. Illustration of the computational paradigm of LIPNovo. During training. LIPNovo generates a thcoretical spectrum based on the target peptide (Figure 3), which is then embedded using the spectrum encoder, along with the observed spectrum. Then, LIPNovo learns impute the latent representation of the theoretical peaks. Bipartite matching is utilized enable unique matching between imputed results and ground truths, followed by a tailed imputation training objective Limquntion. Finally. the highly confident imputation results concatenated with the original spectrum representations and imput into the peptide decoder predict the peptide sequence. During inference, the upper part discarded, climinating the need for the theoretical spectrum during testing. "[S]" is the stop token.
performance. Moreover, ContraNovo (Jin et al., 2024) incor- porated contrastive learning and π r-HelixNovo (Yang et al., 2024) generates complementary spectra as supplementary inputs, operating under the prior that b- and y-ions should appear symmetrically in the observed mass spectrum. In addition, AdaNovo (Xia et al., 2024) introduced an adaptive training algorithm that uses conditional mutual information to improve the identification of PTM. A comprehensive com- parison of these methods is provided by NovoBench (Zhou et al., 2024) under fair experimental settings. Different from these methods, our approach directly imputes missing frag- mentation information before prediction, forming new end-to-end computational paradigm.
encoders (Kingma, 2013), and diffusion models (Zheng & Charoenphakdee, 2022). Moreover, advanced methods that ensemble multiple imputation methods (Li al., 2024b) or incorporate domain-specifi knowledge (Yang al., 2024; Hayat & Hasan, 2024) have been explored to enhance im- putation accuracy and address various missing data mech- anisms. In this work, we tackle the missing fragmentation issue in the de novo peptide sequencing task from an im- putation perspective, based on the prior that certain noise ions can provide useful information for peptide recognition (Tabb al., 2003; Yang et al., 2024) and that an explicit imputation objective can be obtained during training.
3. Methodology
2.2. Missing Data Imputation
3.1. Preliminary
Missing data is common challenge across various real- world applications, impacting the reliability of analyses. Imputation techniques (Sun et al., 2023; Li et al., 2024a) have proven essential in mitigating the issues associated with incomplete datasets. Early methods (Lin & Tsai, 2020) utilized traditional machine learning techniques for imputa- tion, including K-Nearest Neighbors (KNN) (Zhang, 2012), MissForest imputation (Stekhoven & Bühlmann, 2012), and Multiple Imputation by Chained Equations (MICE) (White al., 20. Due to the ineffectiveness of these approaches in capturing inherent data patterns, recent studies (Sun et al., 2023) designed deep learning-based strategies to overcome these limitations. This includes utilizing generative adver- sarial networks (Goodfellow al., 2020), variational auto-
De novo peptide sequencing is the process of translating the mass spectra obtained from the tandem mass spectrometer into amino acid sequences. As shown in Figure 2, the mass spectra are histograms showing the intensity plotted against the mass-to-charge (m/z) values of the ions, which result from the fragmentation of the intact peptides, usually known as the precursors.
Formally, a mass spectrum can be represented as x {m, 4.) N' where (m4, 1,) denotes the pair m/z and intensity, and N signifies the number of peaks. The intensity is unitless but correlates monotonically with the quantity of ions contributing to the observed peaks (Yilmaz al., 2022).
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LIPNovo: A New Computational Paradigm for De Novo Peptide Sequencing
The n/z values correspond to the prefixes (i.e., b-ions) and suffixes (ie., y-ions) of the peptide sequence. Additionally, the precursor, denoted t (Mprecx Cpree) comprising the
16
45
13
P E
PEPOIDE
P
E
mass Mprre E R and charge state Oprec E {1.2, 10), is also crucial for peptide identification, as the peptide mass should align within a specified tolerance of the to- tal precursor mass. The peptide sequence can be denoted as y 1_ where each yt belongs to the amino acid vocabulary including 20 canonical amino acids and their post-translational modifications. L is the peptide length that can vary among different peptides. Thus, de novo peptide se- quencing requires a model that leverages and to predict the probability of y. This can be formulated as the product of the conditional probabilities of each amino acid:
Theoretical Spectrum
by o0 by
Figure 3. Illustration of theoretical spectrum calculation. For ex- ample, by splitting the position at 'E' and 'P", we can derive the b2 ion (PE) and the y5 ion (PTIDE). The masses of these two ions can be calculated using the mass table of amino acid residues. Here, we assume a charge of +1 and set the intensity to 100%.
As such, denoted as z ( | where each 2i E R4, Notably, x is permutation invariant, meaning that the order of the peaks in the mass spectrum does not influence the peptide identification results.
get set of mass spectrum representation,
Plylx,t) II P(u 3,,,
where リー! = (リj ) denotes all amino acids that precede yt in the peptide sequence.
3.2.2. LATENT SPACE IMPUTATION
After encoding the spectral features, traditional meth- ods (Yilmaz al., 2022; Xia al., 2024; Yang et al., 2024; Yilmaz et al., 2024) directly predict the amino acid sequence from them. However, the observed spectrum usually ex- hibits varying degrees of missing informative peaks, making it challenging to rely solely on z for peptide prediction.
3.2. Latent Imputation before Prediction
In typical scenarios, the spectrum æ may exhibit missing peaks that are crucial for peptide identification, resulting in performance degradation. To address this, the central idea of our work is to directly impute the missing information before peptide prediction. The overall pipeline of our L.PP Novo is shown in Figure 2, which comprises three steps: spectrum representation, latent space imputation, and the final peptide prediction.
To address this issue, we introduce a latent space imputation step prior peptide prediction. Specifically, we begin by calculating the mass value m', of each ideal fragmentation (i.e., one of all b- and y-ions) derived from splitting the precursor peptide, as shown in Figure 3. Then, the inten- sity value is estimated as I = m Ji,,,,,, IN), based on the prior that fragmentations should occur in consis- tently high quantities in an ideal scenario (Yang et al., 2024). Therefore, the theoretical spectrum can be represented as x' {m/. In where the charge state is assumed to be +1, and N" = 2(T. 1) denotes the number of ideal fragmentation.
3.2.1. SPECTRUM REPRESENTATION
Following CasaNovo (Yilmaz et al., 2022), we first trans- form æ = {(m;, 1 into a d-dimensional embedding using a peak encoder. Specifically, the peak encoder applies fixed sinusoidal embedding (Vaswani, 2017) to each m,, where each feature in the embedding f Rd is defined as
sin mm//
Amax Amain
( (Amia 2//d)
for j S d/2
Afterwards, we encode z' using the same spectrum encod- ing method discussed above, generating the theoretical spec- trum representation z' = { z' contains sufficient information facilitate peptide prediction, and can there- fore serve as the imputation objective for complementing z. Thus, we construct an imputation module & (-) parame- terized by 8, which takes z as input and directly produces a set of predictions, yielding the following optimization objective:
cos
mm//
Amaa
) for j > d/2 27
Amtn
(2)
where, Amax and Amin are set to 10, 000 and 0.001, respec- tively. Concurrently, I; is projected into the d-dimensional space via a linear layer. And the m/z and intensity embed- dings are integrated by summing them to generate the peak embedding.
Subsequently, a spectrum encoder, implemented using standard transformer encoder, is employed to convert the peak embeddings into the latent representation space. The spectrum encoder utilizes the self-attention mechanism to capture and model the relationships between observed peaks.
i arg min Limputation (D (0),)))²')
0
where Limputation represents a suitable distance metric.
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LIPNovo: A New Computational Paradigm for De Novo Peptide Sequencing
3.2.3. IMPUTATION VIA BIPARTITE MATCHING
3.2.4, ARCHITECTURE OF IMPUTATION MODULE
In Eq. (3), the target z' typically varies in length across different peptide instances, presenting variable-length set prediction challenge. To tackle this, our LIPNovo gener- ates a fixed-size set of M predictions in a single forward pass through the imputation module, where M is hyper- parameter chosen to exceed the typical number of theoretical peaks in a spectrum. A key challenge during training is eval- uating &o (z) with respect to z' to ensure unique matching between the two sets. To overcome this, we employ the optimal bipartite matching between predictions and ground truths, followed by optimizing peak-specific losses.
We then illustrate the architecture of Qg. As mentioned previously, two types of outputs (i.e. the imputed results and their probabilities) are required by the imputation pro- cess. To this end, we implement 4g using a two-branch architecture that shares a common imputation decoder. The imputation decoder is built using a standard transformer decoder. To accommodate set prediction, we introduce set of learnable query vectors, denoted as q = {q; 1/- where each qj E TRd.. These query vectors are randomly initialized and input to the imputation decoder, with the observed spec- trum representations x as encoded memories to produce total of M embeddings, as illustrated in Figure 2. On top of them, two separate feed-forward networks (FFN) are applied: one generates the imputed representations, while the other (appended with sigmoid activation) produces the probabilities required in Eq. (6). This design is simple and efficient, enabling the parallelized prediction of latent representations for all theoretical peaks, thus providing an effective instantiation for De.
Specifically, given (=)=) E MRd and z' E TR-N xd with M > N², we first pad x' by appending Ø to the end of it to align its length with (z)... To find a bipartite matching between (z(z) and z', inspired by (Carion et al., 2020), we search for a permutation of M elements
M with the
lowest cost:
ô- = arg min o d
Maath (Do(z)o()): 2/)²
(4)
3.2.5. PEPTIDE PREDICTION
After the imputation step, we combine with do(2) to predict the amino acid sequence. Note that the set D (z) may contain predictions associated with Ø3. To address this, we apply a probability threshold T to filter 2z(2), resulting in a refined set of predictions:
where (j) indexes prediction from the imputation mod- ule, and LMatc measures the matching cost between the ground truth and the indexed prediction. In calculating LMarch. we consider the pairwise mean squared error (MSE) between predictions and targets, as well as the probability of whether each prediction is responsible for true target or not (i.e., Ø). Thus, denoting Po(j) E [0, 1] the probability associated with prediction indexed by (j)), we define the matching cost as
2 = {zz 2 E (b(())A)>>7)
where py denotes the predicted probability for 2j. & is then combined with z and fed into a peptide decoder to predict the amino acid sequence in an auto-regressive manner. The peptide decoder is also a transformer decoder, followed by prediction head composed of linear layer and softmax activation function. As in previous works (Yilmaz et al., 2022). the serial prediction process begins with the precursor as input, which is embedded using the same method as the spectrum. The prediction is supervised by the amino acid cross-entropy (CE) loss LCE((E; z)), where Ler is defined as:
CMachh (Do (Po(2))()) [,)()))() 22112+ - (1z + (1
(1- - Po(i)),. (5)
where 1 represents the indicator function.
In practice, the optimal assignment can be efficiently com- puted by the Hungarian algorithm (Kuhn, 1955). After solving 8, we calculate the imputation loss in Eq. (3) as
L Cce(c) = Σ log P (v) c,). j=1
(8)
N'
1
Imputattion (0)()),²')=
(o(())00)
2]12+
Furthermore, to generate meaningful theoretical spectrum representations 2' as imputation targets, we utilize the same peptide decoder to decode z' and employ the CE loss (i.e., CCE(x)) for supervision. Finally, the total training objective of LIPNovo is the combination of three losses:
N'
N
M
log Pa(i)
Σ N1
log (1 - -Pao))
M
1=1
(6)
Liotal = Cce((E; x)) + Lce(2') + Limputation
(9)
where the first term computes the MSE between matched pairs, while the second term aims to maximize the proba- bility of predictions corresponding to the ground truth and minimize the probability of those corresponding to Øs.
Remark 3.1. After training. LIPNovo is capable of recon- structing missing information from incomplete spectra dur- ing testing. This capability stems from two perspectives.
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LIPNovo: A New Computational Paradigm for De Novo Peptide Sequencing
Table 1. Empiriaal comparison with state-of-the-ar art methods Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. i denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.
Amino Leie-Level Performance
Peptide-Level Performance
Method
Nine-species Recall
Sevenespecies
HC-PT
Nine-species
Seven-species
HC-PT
Prec.
Prec. Recall
Prec.
Recall
Prec.
AUC
Prec. AUC
Prec.
AUC
PEAKS (Ma et al.. 2003)
0.748
0.428
DeepNovo (Tran et al., 2017)
0.696
0.638
0.492
0.433
0.531
0.534
0.428
0.376
0.204
0.136
0.313
0.255
PointNovo (Qiao et al., 2021)
0.740
0.671
0.196
0.169
0.623
0.622
0,480
0.436
0.022
0.007
0.419
0.373
InstaNovo (Floff al., 2023)
0.420
0.395
0.192
0.176
0.289
0.285
0.164
0.123
0.031
0.009
0.057
0.034
CasaNovo (Yilmaz al., 2024)
0.697
0.696
0.322
0.327
0.442
0.453
0.481
0,439
0.119
0.084
0.211
0.177
AdaNovo (Xia et al., 2024)
0.698
0.709
0.379
0.385
0.442
0.451
0.505
0,469
0.174
0.135
0.212
0.178
AdaNovo' (Xia et al., 2024) IT n-HelixNovo (Yang et al., 2024)
0.681
0.681
0.403
0.405
0.492
0.496
0,473
0.439
0.189
0.149
0.289
0.254
0.765
0.758
0.481
0.472
0.588
0.582
0.517
0.453
0.234
0.173
0.356
0.318
x-
"-HelixNovoo' (Yang et al., 2024)
0.765
0.752
0.465
0.462
0.532
0.537
0.509
0.431
0.218
0.156
0.301
0.261
Baseline" (Yilmaz et al., 2024)
0.741
0.740
0.357
0.366
0.525
0.530
0.529
0.493
0.159
0.119
0.324
0.290
LIPNovo (Ours)
0.797
0.797
0.557
0.560
0.637
0.643
0.582
0.547
0.327
0.281
0.458
0.427
widely used in previous studies, contains mass spectra from nine different species. The yeast species is utilized as the test set, while the other eight species are used for training and evaluation. The Seven-species dataset consists of mass spectra from seven species, where the yeast species is set as the test set and the other six species are used for training and evaluation. The HC-PT dataset contains mass spectra of human-orige origin peptides, including synthetic tryptic peptides that represent all canonical human proteins and isoforms,
Table 2. Empirical comparison with state-of the- methods on Nine-species, Seven- sevensspecies, and HC-PT datasets in PTM level performance. i denotes our retrained results, and other results are given by NovoBench. The best results are marked in bold.
Sinc-sp
Speenes
HC-PT
Method
Prec.
Recall
Frec.
Recall
Prec.
Recall
DeepNovo
0.576
0.529
0.391
0.373
0.626
0.615
PointNovo
0.629
0.546
0.117
0.094
0.676
0.740
InstaNovo
0.443
0.294
0.126
0.115
0.350
0.261
CasaNovo
0.706
0,566
0,360
0.251
0.501
0,460
AdaNovo
0.652
0.570
0.448
0.321
0.552
0.482
as well as peptides generated by altemnativ proteases and human leukocyte antigen peptides.
AdaNovo
0.678
0.552
0.430
0.356
0.562
0,532
R-HelixNovo
0.680
0.598
0.473
0.366
0.568
0.667
x- HellixNovo'
0.723
0.593
0.362
0.370
0.632
0.566
Evaluation Metries. We evaluate the peptide sequencing
Baseline!
0.755
0.601
0.368
0.292
0.550
0.582
performance using amino acid-level precision and recall, peptide-level precision and area under the precision-recall curve (AUC), as well as precision and recall of PTM identi- fication.
LIPNovo (Ours)
0.765
0.656
0.604
0.498
0.732
0.745
From a local perspective, some noise peaks in the spectrum correspond to informative ions (Yang et al., 2024; Tabb et al., 2003), which arise due to incorrect cleavage patterns of theoretical ions. Therefore, these peaks can still provide valuable information for imputation. From a global perspec- tive, although a single spectrum instance may lack sufficient information to reconstruct missing peaks, the imputation model is trained across the entire dataset. This allows the model to learn patterns of missing information by leveraging cross-instance correlations, enabling it to infer the latent rep- resentations of missing fragments. The imputation module explicitly reconstructs the missing fragments in the latent space, which reduces ambiguity in the incomplete spectra, and enables the model to better capture the dependencies between the spectrum and the peptide sequence.
4.2. Implementation Details
Both the spectrum encoder and peptide decoder consist of 9 layers, with a hidden dimension of 512, an FFN layer dimension of 1024, and attention heads. The maximum number of peaks is set to 150, while the maximum peptide length is 100. Following (Yang et al., 2024), we employ the complementary spectrum additional input. The amino acid vocabulary comprises 20 canonical amino acids and 3 PTMs (oxidation of methionine and deamidation of as- paragine or glutamine), in addition to stop token [$] used to indicate the end of the sequence. For the imputation mod- ule, the imputation decoder has 3 layers, and the number of peak queries is set to 100. The filter threshold 7 is set to 0.8. Models are trained with batch size of 32 for 30 epochs. The learning rate is 5e-4, with a weight decay of le-5. The learning rate is linearly increased from zero to the peak value in 100k warm-up steps, followed by a cosine-shaped decay. For LCE, we use label smoothing of 0.01. During inference, beam search strategy with a beam size of is utilized for all models. The parameters are kept consistent
4. Experiment
4.1. Experimental Setup
Datasets. Following the benchmark (Zhou et al., 2024), we conduct experiments on three datasets: Nine-species (Tran et al., 2017). Seven-species (Tran et al., 2017), and HC- PT (Eloff et al., 2023). The Nine-species dataset, the most
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LIPNovo: A New Computational Paradigm for De Novo Peptide Sequencing
Table Leave-one-out cross validation compared to hascline on the Nine-species dataset. i means our re- re-trnined results.
Table 5. Component ablation. "Impu." denotes the imputation mod- ule, and Lee(=') means the CE loss supervised on the theoretical spectrum. "Comp." means the complementary spectrum.
Species
Method
Amino Acid
Peptide
PTM
Prec.
Recall
Prec.
AUC
Prec.
Recall
Amino Acid Level
Peptide Level
Baseline'
0.743
0.745
0.559
0.523
0.797
0.689
Baseline
Impa.
(z')
Comp.
Prec.
Rocal
Pree.
AUC
Bacillus
I.I
0.806
0.807
0.607
0.581
0.855
0.739
x
x
0,741
0.740
0.529
0.493
x
0.755
0.755
0.537
0.500
Clam Ba,
Baseline' !
0.754
0.752
0.544
0.509
0.742
0.591
x
0.766
0.764
0.546
0,513
I.1
0.805
0.805
0.591
0.563
0.764
0.647
0.782
Honeyh.
Baseline'
0.761
0.757
0.554
0.519
0.762
0.624
0.797
0.797
0.582
0.547
L.INovo
0.807
0.806
0.606
0.577
0.769
0.675
Baseline'
0.769
0.770
0.575
0,540
0.784
0.612
in precision compared to AdaNovo and +1 +11.3% compared to x ---eeixNovo. Additionally, LIPNovo demonstrates the highest AUC among the compared methods. It also exceeds PointNovo, the second-best method on the HC-PT dataset, by +5.4%. These results highlight the superiority of LIP- Novo as powerful de novo sequencing method, reinforcing its potential as a new computational paradigm in training peptide sequencing models.
Human
I.PNovo
0.805
0.596
0.567
0.772
0.672
M.mazei
Baseline
0.754
0.754
0.558
0.532
0.779
0.582
LIPNovo
0.807
0.807
0.596
0.565
0.812
0.630
Baseline
0.753
0.752
0.558
0.52]
0.799
0
Mouse
LIPNovo
0.808
0.807
0.607
0.579
0.803
0.667
Ricebean
Baseline
0.753
0.755
0.549
0,510
0.760
0,593
LIPNovo
0.794
0.796
0.577
0,545
0.764
0.634
Baseline
0.717
0.716
0.506
0.464
0.710
0.540
Tomato
LIPNovo
0.800
0.798
0.577
0.544
0.777
0.627
PTM-Level Comparison. PTMs represent a critical aspect of protein function and regulation, making PTM-level com- parison valuable for evaluating the efficacy of sequencing models (Xia et al., 2024). In this regard, LIPNovo also exhibits remarkable performance. Specifically, as shown in Table 2. LIPNovo outperforms AdaNovo by +8.7%, +17.4% and +17.0% in PTM precision across three datasets. Addi- tionally, benefiting from the imputation mechanism, LIP- Novo achieves significant improvements in PTM recall, with increases of +5.5% and +12.5% compared to the second-best method on Nine- and Seven-species datasets, respectively.
Baseline
0.751
0.750
0.550
0.515
0.767
0.605
Mean
1.1o
0.804
0.804
0.595
0.565
0.790
0.661
Table 4. Comparison with Goao (Mao et al., 2023).
Peptide Level
Amino Acid Lovel
Method
Params
Recall
AUC
Recall
Prec.
GraphNovo
88. 2M
0.712
0.876
0.874
LIPNovo
68.4M
0.729
0.707
0.876
0.882
for all datasets. All experiments were performed using an NVIDIA GeForce RTX 4090 GPU.
4.3. Comparison with State-of-the-arts
Leave-One-Our Cross Validation. We also perform leave- one-out cross-validation that takes turns selecting a species as the test set and trains on the other eight species. The re- sults are shown in Table 3, where "mean" is computed by av- eraging across the eight test species. LIPNovo consistently demonstrates improvements in most scenarios. On average, LIPNovo outperforms the baseline by +5.3%, +4.5%, and +2.3% in precision across the three performance levels.
We compare LIPNovo with several established de novo sequencing competitors, including DeepNovo (Tran al., 2017), PointNovo (Qiao al., 2021), InstaNovo (Eloff et al., 2023), CasaNovo (Yilmaz et al., 2024), AdaNovo (Xia et al., 2024), and n-HelixNovo (Yang et al., 2024). Notably, we retrain a CasaNovo as the direct baseline of our LIPNovo with the same configure configurations. The results are summarized in Tables 1 and 2.
Comparison with GraphNovo. We also compare I.PNovo with GraphNovo (Mao et al., 2023). Training GraphNovo is resource-intensive, making it impractical to retrain on bench- mark datasets. To ensure a fair comparison, we trained LIP- Novo on the dataset collected in GraphNovo. As shown in Table LIPNovo outperforms GraphNovo by +1.7% in pep- tide recall. Notably, GraphNovo is two-stage model that is not only computationally demanding but also has larger parameter count. In contrast, LIPNovo offers a simpler and more efficient end-to-end solution while maintaining superior performance, demonstrating its effectiveness and practicality.
Amino Acid-Level Comparison. LIPNovo achieves the highest amino acid-level precision and recall across all datasets. Specifically, on the Nine-species dataset, LIPNovo reaches a precision of 0.797 and recall of 0.797, outper- forming the closest competitor T-HelixNovo by +3.2% and +4.5%. It also surpasses the powerful software PEAKS by +5.1% % in precision. In the Seven-species and HC-PT, LIPNovo also sets new record with a high performance.
Peetide-Level Comparison. Peptide-level performance crucial for assessing the practical utility of the models, as the primary objective of the peptide sequencing task is to accurately assign a complete peptide sequence to each observed spectrum. As shown in Table 1, LIPNovo substan- tially outperforms previous methods across all datasets in the peptide level, achieving mean improvements of +13.9%
4.4. Ablation Study
Component Ablation. We evaluate the contribution of each component in LIPNovo on the Nine-species dataset. The
7

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LIPNovo: A New Computational Paradigm for De Novo Peptide Sequencing
Table 7. Parameters vs. model performance.
is the extension.
Table 6. Sensitivity to hyper-parameters. "Prec.A" and "Prea.P"
means amino acid and peptide precision, respectively.
Method
Params
Amino Acid Level
Peptide Level
Prec.
Recall
Prec.
AUC
M
Prec.A
Prec.P
Layer
Prec.A
Prec.P
Prec.A
Prec.P
Baseline
47. 4M
0.741
0.740
0.529
0.493
0.741
0.529
0.741
0.529
0.6
0.790
0.571
Baseline
69.4M
0.750
0.751
0.539
0.494
50
0.787
0.564
0.796
0.574
0.7
0.794
0.569
LIPNovo
68.4M
0.797
0.797
0.582
0.547
100
0.797
0.582
0.797
0.582
0.8
0.797
0.582
150
0.792
0.574
0.793
0.579
0.9
0.785
0.569
6cco
1.
UPhoas Upper
8100
Seven spectes
Pfovoo (uri Casollove fBase hn)
12002-
HCPP
LPNoao (bun)) Casaaovo (Bssetine)
seco
48co
F7te Frecson
Tapiade AJC
12
0.
Adlicoo HoliNovo
10000-
Adahooo
Gets tieettoon
2200
3350
2030
Indlatiove
2500
4200
Arrtto Fol--oooLoo
E002
1600-
0.5
0.1
0.2
74 PMC
Agp Pm
No. 060
VIN Pre
2000
Imputation
2002
Figure 5. Imputation quality vs. model performance. (Left) A smaller imputation loss corresponds to higher performance. (Right) The upper bound of LIPNovo. obtained by directly using ground truth representations instead of predicted representations for the theoretical spectrum the test set.
0.0
0.4
0.5
0.5
Miolig Frammetattion
Mivdnng
Ratio
Figure 4. Missing fragmentation ratio vs. model performance. LIP-
Novo outperforms existing methods under various missing frag- mentation ratios on Seven-species and HC-PT datasets.
high value, the degree of improvement becomes limited. For instance, on the HC-PT dataset, at missing ratios ranging from 0.7 to 0.8, the improvement is merely +0.8%. We attribute this to the significant loss of signal peaks, which hinders effective imputation for the representations of the- oretical peaks, consequently leading to reduced accuracy. Addressing this challenge represents potential future work, focusing on compensating for missing information under conditions of exceptionally high fragmentation proportions. This includes leveraging additional information, such as cross-sample correlations, and designing more effective im- putation mechanisms.
results are presented in Table 5. Comparing rows 4 and 1, we observe that using the imputation mechanism alone leads to significant improvements. The (="(=') which supervises the learning of theoretical spectrum representations, is also crucial; its removal results in noticeable performance drop (row 4 vs. row 3). We then test the impact of complementary spectrum (Yang et al., 2024). Comparing row 5 and row 4, we find that it contributes an additional +1.5% increase in amino acid precision and + +1.3% in peptide AUC. Overall, these results demonstrate the performance of each module, with the primary performance improvement stemming from our proposed imputation mechanism.
Enhancing imputation quality can improve peptide se- quencing performance. We visualize the imputation loss values vs. sequencing performance in Figure 5. The results are based on the test set of the Nine-species dataset. Note that the model predictions are not directly influenced by the imputation loss, as the imputation loss exclusively guides the generation of theoretical spectrum representations rather than peptide prediction. The figure shows that when the im- putation loss minimal (indicating high imputation quality), the model achieves exceptional performance. Conversely, as the imputation quality deteriorates, the model performance also declines. This suggests that enhancing the accuracy of imputation can aid in improving sequencing performance. In Figure 5, we also show the upper bound performance of LIPNovo, which is obtained by directly using ground truth representations of theoretical spectra instead of imputed re- sults for peptide prediction during testing. The upper bound achieves an amino acid precision of 0.939 and peptide pre- cision of 0.842. This further validates the feasibility of our core idea that improving imputation quality is an effective way to boost peptide sequencing.
Hyper-Parameter Sensitivity Analysis. We then test the sensitivity of LIPNovo to three key byper-parameters: the number of peak queries M, the layer count of the imputation module, and the threshold 7 in Eq. (7). The experiments, conducted on the Nine-species dataset, are detailed in Table 6. For M, if the number of theoretical peaks exceeds M, we only choose the fist M peaks as imputation targets. The results indicate that M=100 yields the optimal performance, with no further performance gains observed with higher values. Additionally, we find that setting the layer count to 3 and to 0.8 results in the best performance. These parameters are maintained consistently on other datasets.
4.5. Analysis
LIPNovo effectively mitigates the impact of missing frag- ments. We further investigate the performance in handling various missing fragmentation ratios, which is calculated by dividing the number of missing peaks by the number of ideal peaks in spectrum, as in (Zhou et al., 2024). As shown in Figure 1 and 4, LIPNovo consistently achieves performance improvements under almost all missing ratios. An intrigu- ing observation is that as the missing ratio reaches a very
The performance enhancement originates from the im- putation mechanism rather than the additional parame-
8

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LIPNovo: A New Computational Paradigm for De Novo Peptide Sequencing
ters. While LIPNovo integrates imputation module that introduces training parameters, this raises the question of whether the performance boost is merely due to the in- creased parameter number. To address this, we extend the baseline model by increasing the number of layers to 12 and adding an FFN to roughly match LIPNovo's parameter count. As shown in Table 7, this extension results in only a marginal improvement, whereas LIPNovo still outperforms it by significant margin.
Eloff, K., Kalogeropoulos, K., Morell, O., Mabona, A., Jes- persen, J. B., Williams, W., van Beljouw, S. P., Skwark, M., Laustsen, A. H., Brouns, S. J., al. De novo peptide sequencing with instanovo: Accurate, database- free pep- tide identification for large scale proteomics experiments. bioRxiv, pp. 2023-08, 2023.
Freitag, M. and Al-Onaizan, Y. Beam search strate- gies for neural machine translation. arXiv preprint 0rXiv:1702.01866 2017.
5. Conclusion
Goodfellow, I., Pouget-Abadie J., Mirza, M. Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative adversarial networks. Communications of the ACM, 63(1 (3(11):133 139--14444 2020.
In this work, we present LIPNovo, a new computational framework that enhances de now peptide sequencing through imputation. We frame imputation as novel set prediction problem, utilizing optimal bipartite matching and three
Hayat, A. and Hasan, M. R. Claim your data: Enhancing im- putation accuracy with contextual large language models. arXiv preprini arXiv:2405. 17712, 2024.
tailed imputation loss function. Experiments benchmark datasets demonstrate that LIPNovo achieves state-of-the-ar performance across wide range of metrics, highlighting its potential to advance proteomics research.
Jin, Z., Xu, S., Zhang, X., Ling, T., Dong, N., Ouyang, W., Gao, Z., Chang, C., and Sun, S. Contranovo: A contrastive learning approach to enhance de novo peptide sequencing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 144-152, 2024.
Acknowledgements
This work partially supported by RGC Collaborative Research Fund (No. C5055-24G), the Start-up Fund of The Hong Kong Polytechnic University (No. P0045999), the Seed Fund of the Research Institute for Smart Ageing (No.
Kingma, D. P. Auto-encoding variational bayes. arXiv preprint arXiv:1312.660 2013.
P0050946), and SollguuaPPolyU Joint Research Initiative Fund (No. PO056509).
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Impact Statement
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LIPNovo: A New Computational Paradigm for De Novo Peptide Sequencing
Appendix
A. Background
To assist readers who may not familiar with proteomics, particularly tandem mass spectrometry analysis and the task of de novo peptide sequencing, we provide a brief background overview. Proteomics is the large-scale study of the structure, function, and interactions of proteins within biological system, aimed understanding cellular processes and biological mechanisms (Blackstock & Weir, 1999). An essential component of proteomics is the identification of proteins in biological samples (e.g., within living organism). Tandem mass spectrometry has become the primary high-throg throughput technique for protein identification. As illustrated in Figure 6, the standard shotgun proteomics workflow (Wolters et al., 2001) starts with enzymatic digestion of proteins, generating a mixture of peptides. These peptides, known as precursors, are then analyzed by a mass spectrometer, which measures their mass-to-charge (m/z) ratios during the first sean (MS1). The peptides are subsequently fragmented using techniques such collision-inducee dissociation (CID) et al., 1995) and higher-energy collisional dissociation (Ose) & Mann, 2004), producing fragments that analyzed in second scan (MS2). During this fragmentation, peptides break randomly along their backbone, creating ions that correspond the peptide's prefixes (i.e., b-ions) and suffixes (i.e., y-ions). The resulting MS2 spectrum consists of peaks characterized by m/z values and intensities. While m/z values are measured with high precision, intensity values, although less precise, are proportional to the number of ions contributing to each peak. The task of de novo peptide sequencing involves developing machine learning models that take MS2 data, along with corresponding precursor information, as input to predict the peptide sequence responsible for generating the observed mass spectrum. This prediction followed by the assembly of various peptide segments determine the complete protein sequence. The whole process is critical for decoding the complexities of the proteome, offering deep insights into the molecular underpinnings of biological systems and advancing our understanding of cellular functions and disease mechanisms.
Biological samples
Mixture of peptides
Mass spectrometer
The first scan M51
De novo Sequencing
Assembly
E
Protein sequence
Peptide sequence
The second scan MS2
Figure Illustration of the identification workflow shotgun proteomics (Wolters al., 2001).
B. More Details
Table 8 presents comprehensive information the dataset partitioning to assist replicating our work. The average peptide length is obtained from NovoBench (Zhou et al., 2024).
C. More Results
Figure provides a detailed comparison of LIPNovo with other methods across three datasets, analyzing amino acid-level recall and peptide-level recall under varying missing fragmentation ratios. The results demonstrate that LIPNovo consistently outperforms existing approaches.
12

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LIPNovo: A New Computational Paradigm for De Novo Peptide Sequencing
Table 8. Statistics of three benchmark datasets (Zhou et al.. 2024).
Dataset
Training
Validation
# Testing
Average Peptide Length
PTM class
Nine-species (Tran et al., 2017) Seven-species (Tran et al., 2017)
499, 402
28, 572
27, 142
15.01
3
009
17, 740
17, 094
15.79
3
HC-PT (Eloff et al., 2023)
213, 284
25, 718
536
12.53
Nine species
Nine species
14000
14000
0.8
LIPNovo (Ours)
LIPNovo (Ours)
12000
CasaNovo (Baseline)
12000
CasaNovo (Baseline)
Frequency Instances
0.8
Amino Acid-Leve Recall
Frequency Instances
AdaNovo
AdaNovo
0.6
Peptide Level Recail
10000
He ixNovo
10000
HelixNovo
8000
0.6
8000
0.4
6000
6000
0.4
4000
4000
0.2
2000
2000
0.2
0.0
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.4
0.6
0.8
Missing fragmentation Ratio
Missing Fragmentation Ratio
Seven-species
Seven-species
8000
8000
LIPNovo (Ours)
LIPNovo (Ours)
Frequency Instances
CasaNovo (Baseline)
Arnino Acid-Lee Level Recall
Frequency of Instances
CasaNovo (Baseline)
0.4
6000
AdaNovo
0.6
6000
AdaNovo
Peptide Level Reca
HelixNovo
He ixNovo
4000
4000
0.2
2000
0.2
2000
0.0
0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Missing Fragmentation Ratio
Missing Fragmentation Ratio
HC-PT
HC-PT
12000
LIPNovo (Ours)
12000
LIPNovo (Ours)
CasaNovo (Baseline)
0.8
CasaNovo (Baseline)
Frequency Instances
10000
AdaNovo
Amino Acid-Level Recall
Frequency Instances
10000
AdaNovo
0.6
Peptide Level Recall
He ixNovo
HelixNovo
8000
8000
0.4
6000
6000
0.4
4000
4000
0.2
0.2
2000
2000
0.0
0.0
0.2
0.4
0.6
1.0
Missing Fragmentation Ratio
Missing Fragmentation Ratio
Figure 7. Comparison amino acid recall (left column) and peptide recall (right column) under different missing fragmentation ratios between LIPNovo and state-of of-the-tttttt art methods three datasets.
13


Reviews:
[{"review_id": "7BnfKwluJm", "reviewer": "Reviewer_7ddk", "summary": "This paper presents a novel computational paradigm called **LIPNovo** for **de novo peptide sequencing**, addressing the problem of missing fragmentation information commonly encountered in mass spectrometry data. Unlike existing methods that rely on incomplete spectra, LIPNovo performs latent space imputation before prediction. The method formulates the imputation process as a set prediction problem, introducing a set of learnable peak queries to generate latent representations of theoretical peaks. Optimal bipartite matching is employed to align these predictions with ground truths, and an imputation loss function is designed to guide the process. Experiments conducted on the **Nine-species, Seven-species, and HC-PT datasets** demonstrate that LIPNovo significantly outperforms state-of-the-art methods across various metrics, including amino acid-level, peptide-level, and PTM-level performance. The proposed model achieves substantial improvements over the competitive baseline CasaNovo, confirming the effectiveness of the imputation mechanism. Ablation studies and sensitivity analyses further validate the contributions of each module. LIPNovo offers a powerful new approach to enhance peptide sequencing accuracy and robustness.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the positive feedback on our work's novelty and the recognition that our method significantly improves the robustness of the model, achieves SOTA performance, and demonstrates generalization and efficiency. Our responses to the concerns are as follows.\n\n**Q1: Limitations of theoretical spectrum generation.**\n\nWe make some clarifications about the theoretical spectrum generation. \n\nIn our work, the theoretical spectra are computed from ground truth peptide sequences in the training set, where PTMs are explicitly considered. This ensures that peptides with different PTMs yield distinct theoretical spectra, and the mass deviations introduced by modifications are properly accounted for during training.\n\nImportantly, theoretical spectra are only used during training. Our method does **not require theoretical spectrum computation during inference**.\n\nLIPNovo is trained to map incomplete observed spectra to their corresponding theoretical spectrum representations. This acts as a **signal enhancement mechanism**, helping to mitigate deviations between observed and theoretical spectra and clarify spectral patterns, ultimately improving peptide sequence prediction.\n\nIn more complex fragmentation scenarios (e.g. Electron Transfer Dissociation producing $c/z$ ions), the theoretical spectra can be extended to include these additional ion types. LIPNovo remains compatible with such scenarios **without requiring changes to its computational paradigm**, further demonstrating its flexibility.\n\n**Q2: Computational efficiency analysis.**\n\nThanks for the valuable suggestion. We evaluate the computational overhead compared to the baseline in **Table 1**, which reports the inference time and GPU memory usage. The values are the mean across examples in the Nine-Species test set. The results show that LIPNovo increases the inference time by 14.82 ms (**+1.94%**) per example, and consumes an additional 98MB of GPU memory. All experiments were conducted on a server equipped with:\n\n- CPU:  Intel(R)Xeon(R)Gold 5418Y @ 2.00GHz (96 cores,AVX2)\n\n- GPU:  NVIDIA GeForce RTX 4090 (24GB)\n\n- OS: Ubuntu 20.04.6 LTS\n\n**Table 1:**  Comparison of computational overhead compared to baseline.\n\n|                     | Inference Time (ms) | **GPU Memory** (MB) |\n| ------------------- | ------------------- | ------------------- |\n| Baseline (CasaNovo) | 764.91              | 678                 |\n| **LIPNovo (Ours)**  | 779.73              | 776                 |\n\n**Q3: Improvement direction for extreme missing scenarios.**\n\nThanks for the thoughtful suggestion. In **Lines 425-436**, we have discussed such limitations and introduced possible improvement directions. As indicated, one potential way is utilizing cross-sample information for missing peak imputation. To investigate this, we conduct a preliminary exploration to incorporate **cross-sample learning** into LIPNovo. Specifically, for each spectrum we search for the top one most similar spectrum (i.e., reference spectrum) from the training set with mass constraints, following [1]. Spectral similarity implies possible peptide similarity, where shared fragments can offer additional information for imputation. The reference spectrum is encoded by the spectrum encoder, and the resulting representation is fed into the imputation module to assist imputation. \n\nThe results are presented in **Table 2**. As shown, under the high missing ratio range [0.7, 0.8), while the LIPNovo improves the baseline by only 0.86%, the simple extension **LIPNovo+Cross-Sample** achieves a **2.22%** improvement. On average, this extension enhances baseline performance by **8.38%** (*v.s.* 6.58% by LIPNovo).\n\n**Table 2:** Experiments on LIPNovo's improvement direction for extreme missing scenarios. Amnio-acid precision (%) is reported.\n\n| Missing Ratio Range       | [0,0.1) | [0.1,0.2) | [0.2, 0.3) | [0.3, 0.4) | [0.4, 0.5) | [0.5, 0.6) | [0.6, 0.7) | [0.7, 0.8) | [0.8, 0.9) | [0.9,1.0] | Mean   |\n| ------------------------- | ------- | --------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | --------- | ------ |\n| Baseline (CasaNovo)       | 75.399  | 57.784    | 40.009     | 28.595     | 24.021     | 19.532     | 13.706     | 10.628     | 10.517     | 4.998     | 28.519 |\n| LIPNovo                   | 88.265  | 73.296    | 53.276     | 39.715     | 30.740     | 24.026     | 16.029     | 11.490     | 9.333      | 4.854     | 35.102 |\n| **LIPNovo+Cross-Sample**  | 88.946  | 74.296    | 55.983     | 41.622     | 33.831     | 26.047     | 17.712     | 12.849     | 11.069     | 6.624     | 36.898 |\n| $\\Delta$ *w.r.t* Baseline | 13.547  | 16.512    | 15.974     | 13.027     | 9.810      | 6.515      | 4.006      | 2.221      | 0.552      | 1.626     | 8.379  |\n| $\\Delta$ *w.r.t* LIPNovo  | 0.681   | 1.000     | 2.707      | 1.907      | 3.091      | 2.021      | 1.683      | 1.359      | 1.736      | 1.770     | 1.796  |\n\n[1]. Xia et al.  SearchNovo, ICLR 2025.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "The method of this paper has been verified through experiments. No theory is proposed that requires additional proof.", "experimental_designs_or_analyses": "Yes. The method of this paper has been verified through experiments.", "relation_to_broader_scientific_literature": "This paper presents a novel computational paradigm called **LIPNovo** for **de novo peptide sequencing**, addressing the problem of missing fragmentation information commonly encountered in mass spectrometry data", "essential_references_not_discussed": "N/A.", "other_strengths_and_weaknesses": "### **Advantages**\n1. **Innovation**:\n- It is novel to propose to fill in missing fragment information in the latent space instead of directly operating on the original mass spectrometry data.\n- Bipartite matching is introduced into mass spectrometry data analysis, and the matching strategy in the field of target detection is borrowed to effectively solve the problem of predictive alignment of variable-length theoretical peaks.\n\n2. **Technical Contribution**:\n- An end-to-end filling-prediction framework is designed, and the filling process is guided by the latent representation of the theoretical spectrum, which significantly improves the robustness of the model to incomplete mass spectrometry data.\n- The positive correlation between filling quality and sequencing performance is verified experimentally, and the necessity of each module is proved by ablation experiments and parameter analysis.\n\n3. **Experimental Results**:\n- On three mainstream data sets (Nine-species, Seven-species, HC-PT), LIPNovo has achieved SOTA in amino acid, peptide and PTM recognition, especially in the case of high fragment missing rate.\n- The generalization and efficiency of the method were further verified by cross-validation and comparison with GraphNovo.\n\n---\n\n### **Improvement suggestions**\n1. **Limitations of theoretical spectrum generation**:\n- The theoretical spectrum relies on the ideal fragments of the target peptide (such as b/y ions), but in real scenarios, unknown peptides or complex PTMs may cause deviations in the theoretical spectrum. It is recommended to supplement the discussion on how to alleviate such problems.\n\n2. **Computational efficiency analysis**:\n- Filling modules may increase inference time, but the article does not mention the comparison of computational overhead (such as inference speed/memory usage with the baseline model). It is recommended to supplement relevant analysis to evaluate the practicality of the method.\n\n3. **Improvement direction for extreme missing scenarios**:\n- Experiments show that the performance improvement is limited when the fragment missing rate is >70%. It is recommended to further explore possible solutions (such as cross-sample learning or introducing auxiliary information) in the discussion section and design targeted experiments.", "other_comments_or_suggestions": "Please see above.", "questions_for_authors": "N/A.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FUOXPu0OX4", "reviewer": "Reviewer_Dr28", "summary": "This paper proposes LIPNovo, which is devised to compensate for missing fragmentation information within observed spectra before executing the final peptide prediction.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer for the recognition that the proposed method is novel, and the evaluation is fair, which addresses the key issue of missing fragments in de novo peptide sequencing and can advance progress in this field. We address the reviewer's question as follows.\n\n**Q: It is better to demonstrate the experimental results to see whether this method is orthogonal to HelixNovo. which is also a method for solving the missing fragment problem. Using the complementary spectra as the LIPNovo's input.** \n\nThanks for the thoughtful suggestion. $\\pi$-HelixNovo and LIPNovo actually **address different aspects of the missing fragmentation issue**. \n\n$\\pi$-HelixNovo handles the case where at least one ion ($b$ or $y$) in a pair is present, using the **complementary spectrum**. Specifically, if the $b$ ion is observed but the paired $y$ ion is missing, $\\pi$-HelixNovo can recover the missing $y$ ion by subtracting the $b$ ion's mass from the precursor mass, which generates a complementary spectrum. However, $\\pi$-HelixNovo cannot handle cases where a pair of $b$ and $y$ ions are all missing, which is how the missing ratio is calculated according to [2]. \n\n**In contrast, LIPNovo is designed to impute all types of missing scenarios.** This is achieved by leveraging an imputation module that predicts the theoretical spectrum in a latent space, thus it effectively addresses problems that $\\pi$-HelixNovo cannot resolve. This has been further evaluated through ablation experiments (as shown in **Table 3**). Specifically, \"Baseline+ Complementary Spectrum\" improves peptide precision by 0.8%. In contrast, \"Baseline + our Imputation mechanism\" achieves a **+4.0%** increase. Moreover, \"Baseline + Imputation + Complementary Spectrum\" further leads to 1.3% increase, because the complementary spectrum can provide additional information for the imputation process.\n\n**Table 3:** Experiments regarding the complementary spectrum used in $\\pi$-HelixNovo.\n\n| Method                                                       | Peptide Precision | Peptide AUC  | Amnio-acid Precision | Amnio-acid Recall |\n| ------------------------------------------------------------ | ----------------- | ------------ | -------------------- | ----------------- |\n| Baseline (CasaNovo)                                          | 0.529             | 0.493        | 0.741                | 0.740             |\n| Baseline + **Complementary Spectrum**                        | 0.537(+0.9%)      | 0.500(+0.7%) | 0.755(+1.4%)         | 0.755(+1.5%)      |\n| Baseline + **Imputation Module**                             | 0.569(+4.0%)      | 0.536(+4.3%) | 0.782(+4.1%)         | 0.782(+4.2%)      |\n| Baseline + **Imputation Module** + **Complementary Spectrum** | 0.582(+5.3%)      | 0.547(+5.4%) | 0.797(+5.6%)         | 0.797(+5.7%)      |\n\n[2]. Zhou et al. NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics, NeurIPS 2024 D&B track.", "claims_and_evidence": "The claims made in the submission are supported by clear and convincing evidence", "methods_and_evaluation_criteria": "The proposed method is novel, and the evaluation is fair.", "theoretical_claims": "This paper has no theoretical proofs.", "experimental_designs_or_analyses": "I check the validity of the experiments.", "relation_to_broader_scientific_literature": "Introduce missing data imputation into the de novo peptide sequencing.", "essential_references_not_discussed": "No essential references not discussed", "other_strengths_and_weaknesses": "This paper addresses the key issue of missing fragments in de novo peptide sequencing, which can advance progress in this field.", "other_comments_or_suggestions": "No other comments or suggestions.", "questions_for_authors": "It is better to demonstrate the experimental results to see whether this method is orthogonal to HelixNovo, which is also a method for solving the missing fragment problem. Using the complementary spectra as the LIPNovo's input.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UBQy0Rj0Lo", "reviewer": "Reviewer_2qTi", "summary": "This work aims to design peptide sequence based on observed mass spectra, addressing the issue of missing fragmentation. This problem is due to the incomplete fragmentation of precursor peptides or inherent limitations within tandem mass spectrometer. The author design a bipartite matching algorithm to impute the missing information.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We acknowledge with gratitude for the reviewer's appreciation that our work's motivation is quite clear and supported by the experimental results, as well as the recognition that the design of proposed algorithm is generally reasonable. We address the reviewer's concerns in detail below.\n\n**Q1: The weakness of this work may can be the overall algorithm design is not that impressive and cannot be adapted\nto other fields. However, personally I like such application paper with simple yet effective approach.**\n\nThanks for the constructive feedback and appreciation of our simple yet effective approach. While our algorithm is specifically  designed for peptide sequencing, it may offer insights into other computational biology areas facing similar challenges.  For instance, in single-cell transcriptomics [3], expression values appear undetected due to technical limitations rather than true biological absence, and the number of missing expression values varies across cells. Our method could serve as inspiration for developing analogous solutions in such contexts.\n\n**Q2: The imputation may also introduce additional time consumption. Could the authors also provide the time portion of imputation in the entire pipeline.**\n\nThanks for the valuable suggestion. We evaluate the computational overhead of the imputation module in **Table 4**, which reports the **inference time and proportion** (averaged over the Nine-Species test set) of each module in LIPNovo. The results show that the imputation module accounts for only **0.46%** of the total pipeline runtime. \n\nIn **Table 5**, we also provide the training time comparison, which shows that our LIPNovo adds 8 minutes per training epoch compared to the baseline model. All experiments were conducted on a server equipped with:\n\n- CPU: Intel(R)Xeon(R)Gold 5418Y @ 2.00GHz (96 cores,AVX2)\n\n- GPU: NVIDIA GeForce RTX 4090 (24GB)\n\n- OS: Ubuntu 20.04.6 LTS\n\n**Table 4:** Inference time (ms) and proportion of each module in LIPNovo.\n\n| Module         | Spectrum Representation | Imputation | Peptide Prediction | Total  |\n| -------------- | ----------------------- | ---------- | ------------------ | ------ |\n| **Time (ms)**  | 6.03                    | **3.60**   | 770.10             | 779.73 |\n| **Proportion** | 0.77%                   | **0.46%**  | 98.77%             | 100%   |\n\n**Table 5:** Training time (minutes) for an epoch ($499,402$ samples with batch size=$32$) compared to the baseline model.\n\n| Method              | Time (minutes) |\n| ------------------- | -------------- |\n| Baseline (CasaNovo) | 55             |\n| **LIPNovo (Ours)**  | 63             |\n\n\n\n[3]. Deng et al. Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning, Nature Methods, 2019.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "The proposed algorithm, Latent Imputation before Prediction, generally makes sense and is well-motivated.", "theoretical_claims": "This work does not have theoretical claim.", "experimental_designs_or_analyses": "The experimental results show great performance improvement. The authors design extensive experiments and ablation studies to show the validness.", "relation_to_broader_scientific_literature": "None.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "1. The motivation is quite clear and supported by the experimental results.\n\n2. The design of proposed algorithms is generally reasonable.\n\n3. The weakness of this work may can be the overall algorithm design is not that impressive and cannot be adapted to other fields. However, personally I like such application paper with simple yet effective approach.\n\n4. The imputation may also introduce additional time consumption. Could the authors also provide the time portion of imputation in the entire pipeline.", "other_comments_or_suggestions": "None.", "questions_for_authors": "None.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes LIPNovo, a framework aimed at addressing the (common) issue of missing fragmentation data in de novo peptide sequencing. The method joins set-prediction withg optimal bipartite matching to fill in missing spectral data within latent spaces, thus improving peptide prediction accuracy. The paper was met positively by the reviewers, who acknowledged its novelty and robustness of experimental validation. Strengths cited include the model's significant improvements over the CasaNovo baseline and its demonstration of generalisation and efficiency.

Review scores were 3, 4, 3. While one reviewer initially expressed concerns about the theoretical spectrum generation and computational efficiency, the authors effectively addressed these in the rebuttal. A detailed examination of cross-sample learning for handling extreme missing scenarios further validated the approach. Overall, reviewers highlighted the innovative nature of the imputation mechanism within latent spaces, marking it as a notable advancement in peptide sequencing methodologies.

Given the well-substantiated empirical results and the positive reassessment by the reviewers after the rebuttal, I recommend accepting this paper.
Decision: Accept (poster)