Bridging the Gap between Database Search and \emph{De Novo} Peptide Sequencing with SearchNovo
Authors: Jun Xia
Sizhe Liu
Jingbo Zhou
Shaorong Chen
hongxin xiang
Zicheng Liu
Yue Liu
Stan Z. Li
Venue: ICLR 2025 Poster (2025)
Keywords: Protein Identification
Mass Spectrum
Proteomics
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We introduce SearchNovo, a novel framework that synergistically integrates the strengths of database search and \emph{de novo} sequencing to enhance peptide sequencing.

Abstract: Accurate protein identification from mass spectrometry (MS) data is fundamental to unraveling the complex roles of proteins in biological systems, with peptide sequencing being a pivotal step in this process. The two main paradigms for peptide sequencing are database search, which matches experimental spectra with peptide sequences from databases, and \emph{de novo} sequencing, which infers peptide sequences directly from MS without relying on pre-constructed database. Although database search methods are highly accurate, they are limited by their inability to identify novel, modified, or mutated peptides absent from the database. In contrast, \emph{de novo} sequencing is adept at discovering novel peptides but often struggles with missing peaks issue, further leading to lower precision. We introduce SearchNovo, a novel framework that synergistically integrates the strengths of database search and \emph{de novo} sequencing to enhance peptide sequencing. SearchNovo employs an efficient search mechanism to retrieve the most similar peptide spectrum match (PSM) from a database for each query spectrum, followed by a fusion module that utilizes the reference peptide sequence to guide the generation of the target sequence. Furthermore, we observed that dissimilar (noisy) reference peptides negatively affect model performance. To mitigate this, we constructed pseudo reference PSMs to minimize their impact. Comprehensive evaluations on multiple datasets reveal that SearchNovo significantly outperforms state-of-the-art models. Also, analysis indicates that many retrieved spectra contain missing peaks absent in the query spectra, and the retrieved reference peptides often share common fragments with the target peptides. These are key elements in the recipe for SearchNovo’s success. The code is available at: \textcolor{magenta}{\url{https://github.com/junxia97/SearchNovo}}.

Published as a conference paper at ICLR 2025
BRIDGING THE GAP DATABASE SEARCH AND De Novo PEPTIDE SEQUENCING WITH SEARCHNOVO
Jun ; Sizhe Liu"i, Jingbo Zhou Shaorong Chen 2.2, Hongxin Xiang,, Zicheng Liu Yue Liu', Stan Z. Li
1 Westlake University, ZZhejiang University, University of Southern California,
4 National University of Singapore, "Hunan University
(xiajun, zhoujingba Stan. ZQ. Li @ Lake edu cn; cn; cliu07270000 edu
ABSTRACT
Accurate protein identification from mass spectrometry (MS) data is fundamental unraveling the complex roles of proteins in biological systems, with peptide sequencing heing pivotal step in this process. The two main paradigms for peptide sequencing are database search, which matches experimental spectra with peptide sequences from databases, and de novo sequencing, which infers peptide sequences directly from MS without relying on pre-constructed database. Although database search methods are highly accurate, they are limited by their inability identify novel, modified, or mutated peptides absent from the database. In contrast, de novo sequencing is adept at discovering novel peptides but often struggles with miss- ing peaks issue, further leading to lower precision. We introduce SearchNovo, a novel framework that synergistically integrates the strengths of database search and de novo sequencing to enhance peptide sequencing. SearchNovo employs an effi- cient search mechanism to retrieve the most similar peptide spectrum match (PSM) from database for each query spectrum, followed by fusion module that utilizes the reference peptide sequence to guide the generation of the target sequence. Fur- thermore, we observed that dissimilar (noisy) reference peptides negatively affect model performance. To mitigate this, we constructed pseudo reference PSMs to minimize their impact. Comprehensive evaluations on multiple datasets reveal that SearchNovo significantly outperforms state-of-the-an models. Also, analysis indi- cates that many retrieved spectra contain missing peaks absent in the query spectra, and the retrieved reference peptides often share common fragments with the target peptides. These key elements in the recipe for SearchNovo's 's success. The code is available at: https:: /tmp:://githhn com/ http//juxxi77aaahhhaaa
INTRODUCTION
The identification of the proteins present in collected biological samples is fundamental task in biomedicine, steering a better understanding of disease etiology and pathology, which is essential for the identification of new therapeutic targets for developing new treatments or drugs (Uzozie & Aebersold, 2018 Lin et al.] (2200). Tandem mass spectrometry (MS/MS) stands out as the only high-throughput technique capable of analyzing the protein composition in biological samples due to its high sensitivity and specificity (Aebersold & Mann] 2003). In bottom-up proteomics (Zhang et al., 2013), proteins are digested into smaller peptide fragments, which are then analyzed using mass spectrometry to determine the amino acid sequences and, ultimately, to identify the original proteins.
The core of protein identification lies in the challenge of peptide sequencing, where the goal is to determine the peptide amino acid sequence for each observed mass spectrum. Two primary paradigms exist to tackle this problem: database search (Nesvizhskii 2010 Griss 2016) and de novo peptide sequencing (Tran al. 2017). Database search approaches involve matching the observed spectra to pre-constructed peptide-spectee match (PSM) databases, selecting the most similar match as the identification results. For example, SEQUEST (Eng et al. 1994) matches observed MS/MS against
Equal Contribution, "Corresponding Author

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Retriever
Fusion Module
e
Spectrum Encoder
Peplide Decoder
Search against
Spectrum Encoder
Peplide Docader
the database
m02
Observed spectrum
PSM Database
Observed spectrum
Peplide Sequence
Observed spectrum
Peptide Sequence
High Precision
Novel Protein
High Precision
Novel Protein Identification
High Predsson
Novel Protein
Identriiction
Identification
(a) Database Search
(b) De Novo Sequencing
(c) SearchNovo
Figure 1: The semantic diagrams of database search, de novo sequencing and our SearchNovo.
protein sequence database and scores the matches based on the correlation between experimental and peptides' theoretical spectra. MaxQuant (Cox & Mann 2008) uses the Andromeda search engine for database search, allowing for highly accurate protein identification, especially in large-scale proteomies experiments. Despite the high precision, these tools are inherently limited by the scope of the database and cannot identify novel proteins that are not included in the pre-constructed database.
On the other hand, de novo sequencing methods predict peptide sequences directly from the observed mass spectra without dependence on external databases. This makes it indispensable for applications where databases are incomplete or unavailable, such as antibody sequencing (Tran et al. 2016). human leukocyte antigen (HIAA neoantigen discovery (Tran et al. 2020). and the identification of novel proteins and peptides not yet cataloged in existing databases (Vitorino et al.) 2020). This family began with early rule-based methods that manually interpreted tandem mass spectra and infer peptide sequences (Eng et al., 1994; Li et al., 2005 Kong et al. 20. Modern deep learning methods, such as DeepNovo (Tran et al. 2017). PointNovo (Qiao et al. (021)). InstaNovo (Eloff et al. 2023) and Casanovo (Yilma et al. 2022), usually train encoder-decode
models to predict peptide sequence for observed spectrum, which have improved the models' ability to identify novel proteins. Despite the remarkable success, the performance of these methods remains unsatisfactory, partly due to missing signal peaks in mass spectra data and lack of additional cues guide peptide sequence inference.
To capitalize on the strengths and compensate for the weaknesses of the above two primary paradigms, we integrate database search and de novo sequencing into a unified framework, SearchNovo, which contains two core modules: retriever and fusion module. In the retriever, for each query spectrum, we design novel and efficient strategy to search for the top one most similar spectrum from the database with mass constraints. In fusion module, we employ novel fusion layer to fuse the prefix peptide sequence and retrieved peptide sequence, with the expectation that the latter can provide the clues for the generation of the target peptide sequence. Additionally, when numerous query spectra are matched with low-similarity reference peptide sequences (noisy reference peptides) from the database, the model's 's performance degrades significantly. To further address this issuc, we construct pseudo reference PSMs that could prevent the model from over-relying on noisy reference spectra when generating target peptide sequences. We highlight the core contributions of this work follows:
As illustrated in Figure integrate two primary paradigms-databeee search and de novo sequencing-inn into unified framework called SearchNovo, enjoying the strengths of both paradigms: high sequencing precision and the strong ability to identify novel proteins.
We design the retriever and fusion module to maximize the utilization of the retrieved reference PSMs to guide the generation of target peptide sequence. Additionally, we implemented a straightforward yet effective strategy to reduce the risk of the model over- relying noisy (dissimilar) reference spectra when inferring the target peptide sequence.
As revealed in section 5.4 the retrieved PSMs by SearchNovo contain the missing signal peaks in the query spectra and common peptide fragments in the target sequence, resulting in its superior performance compared to of-ee-of-the-rt methods across multiple datasets.
2 BACKGROUND
Protein identification is critical procedure in discovering drug targets and disease biomarkers, addressing a major bottleneck in the AI for Drug Discovery and Development (AIDD) pipeline. While the AI community has focused extensively drug design with known protein targets (Xia
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Emyyes
Biological Tissues
Mixture of P Peptides
Mass Spectrometer
Peptide Sequencing
Squuenco assemby,
Database searth
Internty
De novv sequercing
79/2
m/2
Protein Sequence
Peptide Sequence
The second scan M52
The first MS1
Figure 2: The semantic diagram of protein identification using mass spectrum.
et al. 2023b a) the challenge of identifying these key protein targets using Al remains underexplored. In this section, provide brief overview of the general pipeline for protein identification using mass spectrometry, aiming to foster greater understanding and attention to this important task from the AI community.
the process of mass spectrometry-based proteomics typically begins with the digestion of proteins into smaller peptides. These peptides are then ionized and introduced into the mass spectrometer for analysis. Identifying peptide sequences primarily involves two crucial steps: the first sean (MS1) and the second sean (MS2). During the MS1 phase, the mass spectrometer measures the mass-to-charge ratio (m/z) of intact peptide ions, generating spectrum that displays peaks corresponding to various peptides, referred to as precursors. Each peak signifies a specific peptide present in the biological sample, and its intensity indicates the peptide's relative abundance. In the subsequent MS2 phase, a chosen precursor ion undergoes fragmentation into smaller ions, typically at the peptide bond level, yielding detailed spectrum known as the MS2 spectrum. Each peak this spectrum comprises tuple that includes the m/z value and its associated intensity. The goal of peptide sequencing is to deduce the amino acid sequence of the peptide directly from the MS2 spectra and the precursor information (mass and charge of the intact peptide). Ultimately, we can reconstruct the entire protein sequence using protein sequence assembly techniques (Liu et 2015).
As depicted in Figure 2
3 RELATED WORK
There exist two lines of works for peptide sequencing. The first line database search, where we compare the observed mass spectra against the theoretical fragmentation mass spectra of peptide sequences in database and pick the peptide sequence with the highest matching score as
identification result. Typical methods and tools include SEQUEST (Fing et al. 1994), pFind (Li cct al. 2005). MaxQuant (Cox & Mann 2008). MSFragger (Kong et al. (017) and Open-pFind (Sun ct al. 2019). However, these methods cannot sequence the peptides out of the pre-constructed database. The second line of works de novo peptide sequencing, where we predict the peptide sequences for observed spectra without relying on pre-coontrected databases. Initially, researchers cast the de novo peptide sequencing task as finding the largest path in the spectrum graph (Dančík al. 1999 Taylor & Johnson 2001) or compute the best sequences whose fragment ions can best interpret the peaks in the observed MS2 spectrum using Hidden Markov Model (Fischer et al.) 2005) or dynamic programming algorithm (Ma et al. 2003). With the prosperity of deep learning. DeepNovo (Tran et al.) 2017) is the first method applying deep neural networks to the task of de novo peptide sequencing. regards the task as the image caption (Stefanini et al., 2022) in computer vision and incorporates the encoder-decoder architecture to predict the peptide sequence. To annotate the high-resolution MS data, PointNovo (Qiao et al., 2021 1) adopts an order invariant network structure for peptide sequencing. More recently, Casanovo Yilmaz et al. 2022) first employs transformer encoder decoder architecture (Vaswani et al., 2017) to predict the peptide sequence for the observed spectra. Following Casanovo, AdaNovo (Xi al. 2024 proposes conditional mutual information- based re- re-weighting methods to help identify Post- Translational Modifications (PTMs). Despite the remarkable advancements, the performance of de novo peptide sequencing methods remains inferior
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to database search, partially due to missing signal peaks in mass spectrum data and a lack of additional clues to guide peptide sequence generation. We recommend readers refer to a recent survey (Xia et al. 2025) for more detailed information.
4
METHOD
4.1 FORMULATION
We represent the peaks in an MS2 spectrum as x +)) where each peak defined by a pair (m,,ta) with m, representing the mass-to-charge ratio (m/2) and i; representing the intensity. The number of peaks, M, varies between spectra. The precursor ion is described as (Mpree; (pree), where Myree R represents the precursor mass (the total mass of the peptide sequence to be predicted), and Cprec E {1,2. .1....... 10) is the charge state. peptide sequence is denoted by y (y1, 1/2. ..... yN)) where yi is the i-th amino acid in the sequence, and N is the total number of amino acids in the peptide. The prefix subsequence of y up to position j is written as y<j
The task of de novo peptide sequencing models is to predict each amino acid y, conditioned on the MS2 spectrum x, the precursor ion z, and the previously generated sequence yzj. The probability distribution for peptide sequence is modeled as:
P(y x, x, = II plus Iy,j,x,;00,,
where j is the index for the current amino acid position, and @ represents the model parameters. Considering that both x and z can be derived from the spectrum, for simplicity, we will refer to them collectively as in the following discussion. Common approaches, such those in (Tran et al. 2017] Yilmaz et al. 2022 Xia et al. 2024). minimize the negative log-likelihood to optimize the model:
N
((0)
Σ
log p(U; x; 0).
(2)
During inference, the models typically use autoregressive decoding to predict each amino acid and apply heuristic search like beam search to generate candidate sequence y".
In contrast, database search approaches solve the peptide sequencing problem by comparing the observed spectrum to database of known peptides. For a given spectrum x, the goal is to identify the peptide y" in the database that best matches the spectrum by maximizing a similarity function:
x"yy arg max sim(x, (x,y'))
(x' Y')ED
where D is the PSM database. Various similarity scoring methods, including cross-correlation (Eng jet al. 1994 2013) or machine learning- baaeed models (Liu et al.) [0244 Degroeve Martens 2013 used to compute sim(x, (x', (x),))), and the top-scoring peptides are returned as the predictions.
4.2 MODEL ARCHITECTURES
As shown Figure 3. SearchNovo consists of a peak embedding layer (peak encoder), spectrum encoder, fusion layer and peptide decoder. In order to feed the mass spectrum peaks to the spectrum encoder, we regard each mass spectrum peak (m, t,) as a 'word' in natural language processing and obtain its embedding h; by individually encoding its m/z value (m,) and intensity value (t,) before combining them through summation. The detailed description of the peak embedding layer
can be found in Appendix A.1 And then, we feed the peak embeddings h = (h₂ J to the spectrum encoder that consists of multple transformer layers with Hued Self-Attentio (MHA). Similar to natural language processing (Kenton & Toutanova 2019), we select the top 150 peaks with the highest intensity values. If the number peaks exceeds 150,, only these top 150 peaks are used, as they are more likely to represent signal peaks. If the number of peaks is fewer than 1500, we pad the sequence to 150 embeddings using a special token [PAD].
As for the peptide sequence, the amino acid vocabulary encompasses the 20 canonical amino acids, a special [EOP] token indicating the end of peptide sequence and several Post- Translatioo
End Of Peptide
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Modifications (PTMs). Here, the PTMs can be regarded as variants of canonical amino acids. We summarize the types of PTMs in the experimental datasets in Appendix A..3 And then, we employ amino acid embedding layer (denated AA. Embedding in Figure B) learnable lookup table that maps each token in the vocabulary to a fixed-size vector. Also, we apply the positional embedding (denoted Pos. Emhedding in Figure (3) from Transformer (Vaswaní al., (017)) to capture the positional information of each amino acid within the peptide sequence. The final amino acid embedding obtained by summing AA. Embedding and Pos. Embedding. Similarly, we obtain the precursor embedding by individually encoding its mass (Mpree) and charge state (Cprea) before combining them through summation. The detailed embedding process is formulated in Appendix I And then, we prepend the precursor embedding to the amino acid embedding sequence and input this combined sequence into the fusion layer. Kindly note that, for simplicity, we have omitted the precursor embeddings before all peptide sequences in Figure g Finally, we feed the fused embedding sequence yzzj to the peptide encoder, which contains multiple identical transformer layers with causally masked multi-head self-attention and cross-attention to decode the target peptide sequence.
Prefix Peptide Sequence
Retrieved Reference Sequence
4.3 RETRIEVER
Y<j: PEPTID
y, :PETTE
For each query spectrum, we aim search for the most similar peptide- spectrum match (PSM) from the database. The most straightforward approach is to compute the similarity between the query spectrum and every spectrum in the database, selecting the peptide sequence corresponding to the spectrum with the highest similarity score as the reference. However, this method is computa- tionally expensive.
AA. Embedding
AA. Embedding
Pos. Embedding
Pos. Embedding
sim (x, yx,
MHA(Y v
MHA (y <tj e,)
Add & Norm
Fusion Layer
Spectrum Encoder
Feed Forward
MHA <ÿi x)
To mitigate this issue, we first narrow the database search range by leveraging the precursor mass of the query spectrum, which represents the total mass of the peptide sequence. Specifically, for each query spectrum x, we select subset of PSMs Dxx from the database where the difference between the precursor mass and peptide amino acids total mass is within + 20 Da (Dalton, unit of mass commonly used to express the mass of atoms, molecules, and subatomic particles). If no candidate PSMs are found (i.c.. Dx = f), we iteratively expand the mass tolerance window to +00 +-40, +550 Da, and so on, until at least one candidate PSM is identified (Dx # 0). We then calculate the similarity between x and each spectrum in Dx using the widely-usen spectral similarity score MatchMS (Florian et al.] 20200 de Jonge et al.) 2024), selecting the peptide sequence y, corresponding to the most similar spectrum x, as the reference peptide sequence:
MHA(x, x)
Feed Forward
Peptide Decoder
Peak Encoder
Linear + SoftMax
x : (mi,tz) (m2, tg), " (mm, txr)
y PEPTIDE
Spectrum
Target Peptide Sequence
Figure 3: The overview of SearchNovo. We provide the formulation of (-A(.,)) in Appendix A.2
X Y+ = arg
max sim(x, (x', s'). >
(4)
Now, the de novo peptide sequencing task is to predict each amino acid b) with the spectrum x, the precursor z, the prefix peptide sequence yej and the reference peptide sequence ys- And thus, the
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probability distribution of the peptide sequence y in Eq. [ can be re-defined
N
P(y x. Vs: = I plus I X,
(S)
where denotes the model parameters. In the experiments, we regard the training set as the database and search for the most similar PSM (excluding itself) for each query spectrum in the training set using the above retriever, and then we store these results for use subsequent model training.
4.4 FUSION LAYER
The fusion layer contains two multi-head attention mechanisms (MHA). The first, denoted as (H) follows the standard Transformer architecture and operates over the prefix sequence Yzj. The second, MHA(y<j.Ys)) is designed to extract information from the reference peptide sequence, where the query comes from y<j and the key and value are derived from the representation of the reference sequence ya. Due the limited space, we provide the detailed formula for MHA(:.) in Appendix A.2 Considering that if a reference PSM (X., ). is with high similarity to the query spectrum x, ys should be more helpful to infer the target peptide sequence. Therefore, we take the similarity into consideration when embed the peptide sequence. Formally,
C₂S = sim x, (x): E.;
where E, the peptide embedding from the AA. Embedding EAA(-) and Pos. Embedding Epos. layer,
E, = AA (vi) + (PPos.(1)), ... (un) + (uN)).
(7)
after applying these two parallel attention mechanisms, the outputs are combined using an Add Norm (layer normalization) operation:
Norm (MHAY=J..e
(8)
producing a new sequence y' < which is then used as the query input for the subsequent multi-head cross attention (i.e., MHA(y y <jt x)). The following sub-layer is the same as standard Transformer
model (Vaswani et al. 2017).
4.5
ROBUSTNESS TO THE NOISY REFERENCE PSMs
With the reference PSM (x,, ys), can redefine the training loss in Eq. for SearchNovo as,
N
((0) =
p(Uj Xej,X,X,
we observe that the model ("SearchNovo w/o pseudo reference PSM') exhibits significantly poorer performance spectra that lack similar reference in the database. Specifically, when the input spectrum is matched to a reference PSM with low similarity (c.g., similarity scores between and 0.3 Figure 5). the retrieved reference peptide sequence often deviates substantially from the target peptide sequence. This mismatch provides minimal useful information for target peptide sequence generation, leading notable decline the performance.
However, as shown in Figure 5
To further address this issue, we propose a simple-yet-effective method that can prevent the model from over-relying on dissimilar (noisy) reference spectra when generating target peptide sequence. Specifically, for each query PSM (x,y) in the training dataset, given that its retrieved reference PSM is (x,; ys), we construct a pseudo reference PSM as ([PAD], [EOP]) where the special token [PAD] J, [BOP] ] create empty reference PSM and it teaches the model to generate the target peptide sequence without relying on the true reference PSM (x). Formally, we minimize the following joint loss function for SearchNovo:
N
E(0) = Σ p(uj Yej X, Ya; + x logp(y) Yej. X, [PAD), [EOP]; (10) ==1
where À > is a trade-off coefficient. In experiments, we implement the similarity sim(x, ([PAD], [EOP])) in 6 as 1. This makes it possible that single unified model can handle

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both scenarios where reference PSM is similar or dissimilar the query speetrum. Kindly note that the second term of Eq. 10 i.c. log (y; Y<j: x, [PAD], [EOP]; f). cannot be replaced with log p (y; Yxj. x: 8), the SearchNovo model requires the reference PSMs as inputs. Even if the pseudo reference PSM is empty, it should still be included with placeholders like ([PAD), [ECP] )).
In the inference phase, regard each mass spectrum in the test set the query and search against the training set. The retrieved reference peptide sequence along with the prefix target sequence are fed into the fusion layer and the decoder at each decoding step. The decoding process concludes upon predicting the [EOP] token or reaching the predefined maximum peptide length 100 amino acids.
5 EXPERIMENTS
5.1 DATASETS AND METRICS
Previous studies have evaluated model performance on various datasets, with some using different versions of datasets under the same name (see Appendix [A.33 for details). Following NovoBench ZZou ct al. (2024). we conducted a comprehensive benchmark of the baseline models and SearchNovo using three datasets: Seven-species, Nine-species, and HC-PT. These datasets represent a range of spectrum resolutions and peptide sources, providing a diverse testing ground for performance comparison. The Seven-species dataset includes low- cow-resolution spectra from seven species, following the leave-one-out approach from DeepNovo (Tran et al. 2017), where the model is trained on six species and tested on yeast. The Nine-species dataset, used in studies like DeepNovo (Tran et al. 2017]. PointNovo (Qiao et al., [021], and Casanovo (Yilmaz et al., 2022), contains high-reso spectra from nine species and incorporates three post- tostttranslational modifications (PTMs), enabling comprehensive evaluation of model performance. Similarly, we also follow the lcave-one-out strategy where we train the model on 8 species and evaluate the model on the left yeast dataset. The HC-PT dataset features high-resolution spectra of synthetic tryptic peptides covering all canonical human proteins and isoforms, including peptides from alternative proteases and human leukocyte antigen (HLA) peptides, with labels derived from MaxQuant's high-confidence search results (Tyanova et al.) 2016). In each case, we split the training set 90/10 for training and validation. Kindly note that the target peptides in the test sets of the above 3 datasets are not present in the training sets. The database search methods cannot work on these datasets because they cannot identify unseen or novel peptide sequences. More information of these datasets can be found in Appendix A.3
We evaluate model predictions using precision both the amino acid and peptide levels, following previous works (Tran et al., 2017]] Qiao et al., [021]] Yilmaz et al. 2022). Amino acid-level precision is calculated as matthh/ Appoa pred where N Naa
match represents the number of correctly predicted amino acids with mass difference of 0.1 Da and correct prefix or suffix mass within 0.5 Da. Amino acid-level precision and recall is then defined as Nao
prod and Nae match /Nea- where Nas, and Nue represent the number of predicted amino acids in predicted peptide sequences and ground truth peptide sequences, respectively. Similarly, PTMs identification precision and recall can be formulated as N /Npttd and Ni /Nitth where Nr .NPErm and Nuth denote the number of matched match
match /Nae
prod
PTMs, predicted amino acids with PTMs and PTMs in ground truth peptide sequence, respectively. Peptide-level precision, the primary performance metric. is Nº N / where a predicted peptide is correct only if all amino acids match the ground truth. Given the peptide recall and precision, we also use the area under the precision-recall curve (AUC) as a summary of de novo sequencing accuracy.
5.2 BASELINES AND EXPERIMENTAL SETUPS
We use 5 representative models as baselines in our experiments: DeepNovo, PointNovo, InstaNovo, AdaNovo and Casanovo, which we have introduced in the related work section. Although SearchNovo enjoys the advantages of both database search and de novo sequencing, it is fundamentally a de novo sequencing method. Moreover, database search methods are not applicable to these datasets where the test peptides are not present in the training set. Therefore, we did not include comparison with database search methods. For training SearchNovo, we used batch size of 32 and trained the model for 30 epochs schedule. and gradient updates were performed using the Adam optimizer (Kingma & Bal 2014). Optimal hyperparameters were selected based on the validation set. For the baseline models, we used
Nvidia A100 GPU. The learning rate set to 0.0004 with linear warm-up
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the original hyperparameters from their respective papers. DeepNovo and PointNovo were validated every 3,000 steps, while the remaining models were validated every 50,000 steps.
5.3 MAIN RESULTS
SearchNovo outperforms state-of-the-art methods on 3 benchmarking datasets. As shown in [ SearchNovo shows notable superiority over previous peptide sequencing tools in terms of both amino acid-level and peptide-level metries. Also, we observe that SearchNovo also outperforms other competitors in PTMs identification in Table 2 probably because that the retrieved reference peptide sequence may contain some PTMs that are difficult to be identified, which provide valuable clues for the PTMs identification. Also, we can observe that the overall performance on high-resolution Nine-species and HC-PT datasets is significantly superior to the performance on low-resolution Seven-species dataset. This phenomenon indicates that higher-resolution data provides more detailed spectral information, which enhances the model's ability to accurately infer the peptide sequences.
Table I: An empirical comparison of models based on amino acid-level and peptide-level metrics. The top-perfone hop-performing model is highlighted in bold, while the runner-up is underlined. We trained five models with different random initializations and reported the standard deviation on the HC-PT dataset. For the other datasets, standard deviations were not reported, as training multiple models on all datasets would be computationally extensive.
Peplide level performance
Amino acid-level performance
Seven species
Nine species
HC PT
Seven-species
Nine species
HC-
Prec. AUC
Prec.
AUC
Prec.
AUC
Pres.
Recall
Prec.
Recall
Prec.
Recall
DeepNovo
0.204
0.136
0.428
0.376
0.313
0.014
0.255 + 0.100
0.492
01433
0.606
0.6688
0.531 + 0.018
00015
PoiniNova
0.922
0.007
0.480
0.436
0.419
0.008
0.373
+ 0.011
0.196
0.169
0.740
0.671
0.623+ 0.015
0622 00009
InstaNovo
0.031
0.009
0.164
0.123
0.057
0.008
0.034
+: 0.010
0.192
0.176
0.420
0.395
0.289 :: 0.006
0.285 = 0.009
AdaNovo
0.469
0.212
0.709
Casanovo
0.119
0.084
0.481
0.439
0.211
0.010
0.014
0.327
0.696
0.022
ScanchNovo
0.259
0.174
0.550
0.489
0.447 : 0.013
0.413
0.010
0.489
0.488
0.748
0.746
0.652 : 0.008
0.658 0.016
Table 2: An empirical comparison of models in terms of their ability to identify PTMs. The best results and the second best are highlighted with bold and underline, respectively.
PTM Recall
PTM Prec.
Method
Seven-species
Nine-species
HC-PT
Seven-species
Nine-species
HC-PT
DeepNovo
0.373
0.529
0.615 : 0.018
0.391
0,576
0.626 0 0.013
PointNovo
0.094
0.546
0,740 + 0.009
0.117
0.629
0.676 0.012
InstaNovo
0.115
0.294
0.261 0.010
0.126
0.443
0.350 + 0.012
AdaNovo
0,321
0.570
0,482 + 0.022
0.448
0.652
0.552 = 0.017
Casanovo
0,251
0.566
0.460 0.015
0,360
0.706
0.501 = 0.017
SearchNovo
0.447
0,599
0.772 0.011
0.472
0.764
0.715 0.016
5.4 WHY SEARCHNOVO CAN ACHIEVE SUPERIOR PERFORMANCE?
Table 3: Comparisons between the target peptide sequence of query spectra and the retrieved reference peptide sequence from the seven species dataset. '(+15.90 5,99 and '(+.98) denote the cdidation-modified (a kind of PTM) and isotopic labeling amino acids before itself, respectively. The overlapping fragments are underlined.
Target Peptide Sequence of Query Spectra
Retrieved Reference Peptide Sequence
LIDASHR
IIGPGINK AGWOGTITF
AGWOGTVTF
LTANDVIRK
LTANDIFRK
ATPIAEAMMALVIIDCIIS
ATPIAEAM(-15. .9) MAIVIIDOIR (RAAAO/O---9OO----99EEEEEEEEEEEE
NGAITAAVQQEGEEEIMISSIS
In this subsection, we aim to investigate why SearchNovo can achieve superior performance. Our findings follows:
(i) Many of the retrieved reference peptide sequences and the target sequences share overlapping fragments, providing important clues for generating the target sequence. We provide some
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examplar target sequence of query spectra and their corresponding reference peptide sequence from the seven-species dataset in Table 3 As can be observed, there exists notable overlaps between the pairs and thus the latter provides important clues to guide the generation of the former. Also, their shared fragments demonstrate various degrees of alignment. In some cases, there is a near-perfect match (e.g., LTANDVERK vs. LTANDIFRK), while in others, more differences are apparent. This phenomenon necessitates the robust methods to prevent the models from over-relying on the noisy (dissimilar) reference PSM when generating the target peptide sequence.
Pepcie: ROTEMATTTAAAAAAAAAA BETTE TAREEGGOO
Poptidic SAT.AP/ASSATTTTTTTSAAAAAAAAOOO
Pepcea: LS BRIMAN DQ
Ofher
Queryy Spectra
Poptado
Reference Spectrs
E 2 R 8. g.
125
100
740
470
405
1200
120
10
140
20
12:2
1265
(2)
(3)
Figure 4: Comparisons between the query spectra and the retrieved reference spectra. The spectra in the same column represent query-reference pairs.
(ii) The retrieved reference spectra by SearchNovo include some missing signal peaks in the query spectra, enabling SearchNovo to mitigate the issue of missing peaks. To verify this point, we annotate the b, y ions (signal peaks) using the pyteomics tool (Goloborodko et al. 2013). Specifically, we generate the theoretical b and ions for the peptide sequence, which represent the expected fragment ions formed from the peptide backbone cleavage during tandem mass spectrometry (MS/MS). Detailed explanations of b and y ions can be seen in Appendix A.5 And then, we compare each peak in the query spectrum to the theoretical ions, and the closest match is identified using defined tolerance (# 1.0 Da in our experiments). If observed peak matches a theoretical b or y ion within the tolerance, it is classified accordingly. As shown in Figure the retrieved spectra share some common b, y ions (e.g., y2, y4, y5, y6, y8 and b9 in Figure 1)) with the corresponding query spectra, indicating that the retriever can identify similar spectra from the database. More importantly, the retrieved reference spectra contain some missing peaks (e.g., y4, y6, y10, b12, and y14 in Figure 2)) that should theoretically be present in the query spectra. And thus, they offer valuable hints that aid in inferring the peptide sequences beyond what is available in the query spectra.
5.5 ABLATION STUDY
Search Nove
Ovena Precision SearchNovo
Precision Swart Novn wis preatin reference PSM
Ovena Precision of SwarchNnen we preuzi refereece PSM
0.16
40.00
0.25
23
0.25-
-0.6
hreuuny (Coum) (9t4))
sunwengy (Coutt/ Tatal)
Preweency prewuency (Count/ TRta))
0.12
-033
0.25
+0.5
0.20
0.08
0.30-
0.30
0.10
1.00
0.03
0.02
0.05
0.00
01 0/5 0.5.0. 2/9
0.00
0.00
04
0.8.0/
3.0
0.00
30
3/3 0.7 0.0
NatorMs Simibnity Score
HanchMS Smilnity Score
MotchMS Simlarity Score
(a) Seven-species
(b) Nine-species
(c) HC-PT
Figure 5: The MatchMS similarity score distribution and the peptide-level precision over different similarity intervals.
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0.50
0.45
0.60
Precision
precision pop precision
0.65
precision pep_ pro_srociioon
precision
0.35
pep. precision
0.50
0.30
0.60
0.25
0.35
0.20
0.40
0.5
1.0
1.5
2.0
0.5
1.0
1.5
2.0
0.5
1.0
1.5
2.0
(a) Seven-species
(b) Nine-species
(c) HC-PT
Figure 6: Amino acid-level (denoted as precision in the figure) and peptide-level (denoted as pep-precision in the figure) precision as function of the rrper-parameter X.
(i) The influence of pseudo reference PSM. As shown in Figure we plotted the distribution histogram MatchMS similarity scores between each query spectrum and its reference PSM the test set. The possible range of MatchMS similarity scores, [0, 1), was divided into ten -nbbintervals: [0, 0.1), [0.1, 00.10.2 0.2), ..., [0.9, 1.0). It can be seen that SearchNovo without the pseudo reference PSM performs significantly worse in the [0, 0.3) range compared to higher similarity intervals, indicating that noisy reference PSMs adversely affect the model's performance. In contrast, SearchNovo shows consistently strong performance across the entire similarity range of [O, 1), demonstrating that the pseudo reference PSMs effectively mitigate the impact of noisy PSMs.
(ii) The influence of syper-paramete"" A. As illustrated in Figure 6 we observe that key metrics in de novo peptide sequencing, such as Peptide-level Precision and Amino Acid-level Precision, generally improve with increasing However, when À becomes sufficiently large, the model's performance stabilizes, suggesting that the influence of the pseudo reference PSM has reached saturation point. Based on these observations, we set 0.5 for our experiments across the three datasets for convenience.
Table 4: Ablations on the similarity in SearchNovo.
Peptide-level Precision
Amino leids-level Precision
Method
Seven-species
Nine-species
HC-PT
Seven-species
Nine-species
HC-PT
Novo w/o the similarity
0.238
0.472
0.401
0.446
0.721
0.616
SearchNovo
0.259
0.550
0.447
0.489
0.748
0.652
(iii) The influence the similarity sim(x, (x., Yx)) In SearchNovo, we incorporate similarity into the peptide embedding in Eq. 6 To investigate the influence of the similarity, remove from ScarchNovo. As shown in Table the results confirm that the similarity score significantly enhances model performance by providing key information from highly similar reference PSMs, which aids in predicting the target peptide sequence. Removing the similarity leads to a marked decline in performance, underscoring its importance in the SearchNovo framework.
CONCLUSION
In this paper, we introduced SearchNovo, a unified framework that combines the advantages of database search and de novo peptide sequencing to improve peptide sequencing. By incorporating an efficient search strategy and fusion module, SearchNovo effectively mitigates missing signal peaks issue, leveraging reference PSMs for enhanced precision. Additionally, the pseudo reference PSMs prevent the models from over-relying on noisy reference, leading to superior performance across multiple benchmarking datasets. Moving forward, future work could focus on improving SearchNovo's robustness in handling low-quality spectra with multiple noisy peaks and exploring its potential applications in challenging domains like single-cell proteomics and metaproteomics.
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7 ACKNOWLEDGEMENTS
This work was supported by National Natural Science Foundation of China Project (No, 623B2086. No. U2IA20427), National Science and Technology Major Project (No. 20222D0115101, Project (No. WU2022A009) from the Center of Synthetic Biology and Integrated Bioengineering of Westlake University. Project (No. WU2023C019) from the Westlake University Industries of the Future Research Funding, CIE-Tencent Doctoral Research Incentive Project. Finally, Jun wants to thank, in particular, the invaluable love and support from Miss Chen.
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A APPENDIX
A.1 PEAK EMBEDDING METHODS
Following Casanovo (Yilmaz al. 2022 treat each peak (m;44) from the mass spectrum similarly to a "word" in natural language processing. We represent by encoding its m/z value and intensity separately, and then combine these encodings by summing them. For each peak, the m/z value treated as its position, and positional encoding is applied as inspired by (Vaswani et al.
2017), defined
my
d for j S
fúj = sin
(11)
Mlmas miiin
Minin
23/d 2n
2
m/1
fij = co8
for j > 2
(12)
2j/d
Manin 2
where fúy represents the j-th component of the embedding for the i-th peak, d is the embedding dimension, and mmmax and mmin are constants that we set to 10,000 and 0.001, respectively. These positional embeddings ensure that high-resolution m/2 values are captured. Following the positional encoding approach of the original transformer (Vaswani et al., 2017), these embeddings help the model focus on variations m/z between peaks, which is essential for accurately determining peptide sequence. The intensity values
encoded via a linear layer Wg, projecting them into d-dimensional space, i.e. 9: W where W, E Rd is the linear layer's weight matrix. The final embedding for each peak (m,,4; is obtained by adding the embeddings for intensity and m/z,
h; +i ft. Thus, the input to SearchNovo's spectrum encoder consists of embeddings h = {h+ where M the number of peaks the spectrum. Similarly, the precursor ion {mmrres Cpree) is embedded using the same sinusoidal positional encoding for mprec, while the precursor charge state Curee embedded through a Py PyTorch embedding layer.
A.2 MULTI-HEAD ATTENTION (MHA)
We present the specific formulation for the multi-head attention mechanism, MHA(:,)), designed with attention heads as follows:
MHA(g. u) = [Att (g, (u), v (
(13)
qu
Att u, v) softmax
In this formulation, y represents the query vector, while u is matrix composed of two dimensions.
The notation [15] indicates the concatenation of all individual vectors uj. The functions 6j and
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b refer to two distinct linear transformations that project one matrix into another. The term serves as scaling factor, where d denotes the dimensionality of the query vector q. We recommend consulting the original Transformer paper (Vaswani et al. (017) for more details.
A.3 DATASETS
Table 5: The datasets statistics of the tree datasets in SearchNovo.
Avg.
Dataset
precusor m/ precuser charge peaks num.
intensity
peptide len.
PTM class min m/ max m//
train/valid/esst num.
Seven species
719.07
2.42
466.05
956.17
15.79
3
70.17
3997.66
317,009 / 17,740 17,049
Nine-species IIC-PT 635,32
134.91
175082.65
15.01
35932.63
499,402 28.572 27.144
2.31
184.21
143363.17
12.53
1
99.99
1999.99
213,284 / 25,718 26.536
Since Peptide- Sprntt--ppeetumm Matches (PSMs) data utilized for training and testing are readily accessible through ProteomeXchange (Vizcaíno et al., 2014). researchers can easily download various sections to benchmark their models for de novo peptide sequencing. For instance, the performances of DeepNovo (Tran et al., (017) and PointNovo (Qiao et al. (021) have been assessed using the seven-species dataset, while InstaNovo (Bloff et al. 2023) conducts its evaluation on datasets curated by the respective authors. Additionally, there exist different versions of these datasets, leading to discrepancies even for models that claim to use the same dataset. As an example, PointNovo and Casanovo operate on different versions of the Nine-species dataset (MassIVE dataset identifiers: MSV000090982, MSV00008 1382). These inconsistencies dataset versions complicate the ability to gauge genuine advancements in the field. To address this issue and ensure a fair comparison of models, we re-evaluated the performance of different models comprehensively and consistently across three datasets. Detailed information about these datasets is provided in Table 5
A.4 RELATED WORK: RETRIEVAL AUGMENTED GENERATION
SearchNovo is inspired by recent advances in RAG that integrate retrieval mechanisms into various generative Natural Language Processing NLP and vision tasks, such as language modeling (Guu al., 2020 BBorgeaud et al. 2022), image generation T Teegg et al. (2020); Sheynin et al. (2022) and Video captioning Whitehead et al. (2018); Xu et al. (2024). In computational biochemistry. retrieval-basee strategies are also erucial, such as in multiple sequence alignment (MSA), where relevant protein sequences are retrieved and aligned, playing fundamental role in methods like
MSA Transformer (Rao et al. 202T) and AlphaFold (Jumper et al., 202T). RetMol (Wang et al.) 2023) employs retrieved molecules with desired properties to guide the model to realize controllable molecular generation. In contrast to these works, we propose new search (retrieval) strategy tailored for mass spectra data and design novel methods exploit the reference peptide sequence. SearchNovo integrates database search and de novo sequencing into a unified framework, enjoying the advantages of both worlds.
A.5 EXPLANATIONS OF B, Y TONS.
In mass spectrometry-based proteomics, peptides fragment into ions that provide information about their sequence. Two commonly observed ion types are b-ions and y-ions. b-ions are formed when the peptide bond breaks between the nitrogen and the alpha-carbon (N-Ca) of the peptide backbone, leaving the charge the N-terminal fragment. For a peptide sequence, b-ions correspond to fragments starting from the N-terminus. y-ions, on the other hand, form when the bond breaks between the carbonyl carbon and nitrogen (C-N), leaving the charge the C-terminal fragment. These ions represent fragments starting from the C-terminus. By analyzing the pattern of b- and y- ions, peptide sequences can be reconstructed, providing critical insights in de novo peptide sequeneing and database searches.
For example, consider a peptide sequence A-G E- Alaamine-Gyyciin-Guutaiee acid- crid-Tryytophann. During fragmentation, the following ions might be observed:
b-ions: b1
b2 A-G, b3
A-G-E
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y-ions: y1
W, y2 E-W, y3 G-E-W
In this example, b-ions correspond to fragments from the N-terminus, while y-ions represent frag- ments from the C-terminus. The complementary information from both ion types allows for the reconstruction of the full peptide sequence.
16


Reviews:
[{"review_id": "2zxdKqiue8", "reviewer": "Reviewer_xcET", "summary": "The paper proposes a novel approach to combine two dominant methods for predicting peptide sequences from tandem mass spectra—database search and machine learning-based *de novo* generation—through a retrieval-augmented generation paradigm.", "questions": "- Could retrieving more than 1 reference sequence be beneficial?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Originality**. The idea of linking database search with de novo generation is original in the context of peptide sequencing from mass spectrometry data.\n\n**Quality**. The experiments are well-designed, with ablations and qualitative examples that provide valuable insights into the method’s functionality.\n\n**Clarity**. The paper is generally well-written and clear.\n\n**Significance**. The task of peptide sequencing from tandem mass spectrometry data is highly important. This paper demonstrates that merging the two paradigms offers promising new opportunities in this area.", "weaknesses": "**Major Comments**\n\n- Quantitative analyses in section 5.4 are missing. The section lacks quantitative experiments to support the qualitative examples regarding why the method works.\n- Database search baselines are missing. Implementing at least a simple database search baseline would be beneficial. Even if test set contains distinct peptide sequences, meaning that per-sequence precision is ensured to be 0, per-amino-acid evaluation could provide interesting isights.\n- Potential data leakage. Please analyze the overlap between spectra and peptides in the training and test sets. This could be assessed using vector similarity between binned spectra and sequence identity upon alignment to evaluate spectral and peptide similarity, respectively.\n\n**Minor Comments**\n\n- Line 378: Please specify the validation set used.\n- Appendix A.1: Please add citations for related works that use similar approaches to encoding mass spectra, such as [1].\n- Line 248: Typo: “summarizing AA. Embedding and Pos. Embedding” should be corrected to “summing” and upper-case letter should be fixed.\n- Line 291: Same typo as above.\n- Equation 7: Comma instead of a dot at the end.\n- Line 304: Typo: “can redefined”.\n- Equation 10: Dot instead of a comma at the end.\n\n**References**\n\n[1] Yilmaz, Melih, et al. \"De novo mass spectrometry peptide sequencing with a transformer model.\" International Conference on Machine Learning. PMLR, 2022.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "iKnUw5RIJ1", "reviewer": "Reviewer_yzQz", "summary": "This paper introduces a new method for predicting peptide sequences from their MS/MS spectra, named SearchNovo. SearchNovo combines two main paradigms in computational proteomics: database search and de novo sequencing. Inspired by the success of retrieval-augmented generation, the authors propose a new transformer-based architecture that predicts a peptide sequence given its MS/MS spectrum and a reference peptide sequence corresponding to the most similar spectrum in a database. The effectiveness of this method is demonstrated empirically on three datasets across multiple metrics. Additionally, the authors discuss why SearchNovo outperforms purely de novo approaches, providing clear qualitative results. The text is well-written and easy to follow. Supplementary materials provide the corresponding source code.", "questions": "- Figure 5: Why does SearchNovo with pseudo reference PSMs outperform the model without pseudo reference PSMs, even when spectra with high similarity scores (e.g., > 0.8) are available?\n  \n- The lambda hyperparameter adds complexity to the model and requires two forward passes. Did the authors experiment with simpler approaches that replace this additional hyperparameter, perhaps by directly using matchms similarity? While the similarity is used in Equation 6, using it, for example, to weight the second term in Equation 8 might provide a more straightforward approach.\n\n- The computational efficiency results in Table 6 provide a valuable complement to the performance metrics presented in the main text. Since the model employs a database search, one could think that it would be significantly slower than the purely de novo methods. I suggest mentioning these results in the main text for added emphasis.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "Originality\n\nThe originality of the work lies primarily in the combination of the de novo and retrieval paradigms for annotating peptide MS/MS spectra within a unified machine learning framework.\n\nQuality\n\nThe paper is of very high quality. The authors carefully prepare evaluation datasets, ablate key model components, analyze results in depth, and provide sound justifications for why their model may outperform prior methods.\n\nClarity\n\nThe work is highly clear, with a logical structure that makes it easy to follow from beginning to end.\n\nSignificance\n\nThis work presents a significant finding by introducing a conceptually new combination of two independent approaches, opening up a new direction for the further research. The advantage of the new method is supported by strong empirical results.", "weaknesses": "Two concerns arise from the study:\n\n- Section 5.4 states, “(ii) The retrieved reference spectra by SearchNovo include some missing signal peaks in the query spectra, enabling SearchNovo to mitigate the issue of missing peaks.” and Figure 4 provides the corresponding examples. However, the architecture of SearchNovo does not appear to use the retrieved spectra as input but only the peptide sequence corresponding to the most similar spectrum. It is unclear how the model mitigates the issue of missing peaks without directly incorporating the retrieved spectra.\n\n- Although the authors explain their choice not to include database retrieval baselines, this would be very valuable. Specifically, the authors could show the performance of assigning a sequence to a spectrum based solely on matchms (e.g., using a k-NN classifier with matchms as a metric). As Table 3 suggests, such a model could still achieve high per-amino-acid metrics. Although it may not achieve non-zero per-peptide metrics, it would still provide context on the extent to which the de novo component contributes to the model's performance. Without such results, it is not clear how, for example, the change of the database for retrieval would affect the performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "5rLDhqbnkp", "reviewer": "Reviewer_UgvV", "summary": "The authors propose a new method, SearchNovo, for identifying the underlying amino acid sequences of peptides from tandem MS/MS data. This is an important problem, crucial for many applications, such as antigen and antibody discovery. This method attempts to overcome the limitations of de novo methods, which identify peptide sequences from the spectrum alone, and database search methods, which rely on a reference database of spectra to find similar spectra. SearchNovo uses a transformer architecture and concepts from the “retrieval augmented generation” field to seamlessly and organically “fuse” the de novo signal with the signal from similar spectra found in a database.", "questions": "Following the points outlined above, it would be helpful if the authors explained *exactly* what differs in their evaluations compared to previous reports on the 'nine-species' dataset. The discrepancies are significant and may impact reviewers' decisions.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- SearchNovo is an interesting method that combines recent transformer-based advancements in de novo MS/MS-based peptide discovery with new concepts from retrieval-augmented generation, and it has the potential to advance the state of the art in peptide discovery.\n- The authors have performed a large assessment study with other methods to demonstrate the contributions of their method.", "weaknesses": "**Major concerns**\n- My biggest concern is that the performance numbers reported for Casanovo (Yilmaz et al., 2022) deviate significantly from those reported in this paper. For example, focusing on the “Nine-species” dataset, both reported precision and AUPRC differ significantly.\n- Comparing de novo methods with hybrid (de novo + database search) methods is challenging, as all homologous sequences must be removed from the training/reference sets. It is not surprising that the reported ablation study (Section 5.3-iii) shows a significant performance drop when the “similarity” of the enumerated PSMs is ignored. A more balanced test set could involve immunopeptidome or metaproteomes.\n- I find the underlying mechanics of the proposed model insufficiently explained; a toy example would have helped (e.g., Fig. 1 of Yilmaz et al., 2022, is a good template).\n- Equation (2) represents the negative log-likelihood, not cross-entropy. \n- Although dataset versions differ as outlined in Section A.3, the drop in peptide-level precision—from 0.81 to 0.481 for Casanovo and from 0.74 to 0.480 for PointNovo (the former metrics reported by Yilmaz et al., 2022)—is concerning.\n- The peak encoder (Section A.1) seems heavily inspired by (if not identical to) Yilmaz et al., 2022, and maybe I am missing something, but I don’t see appropriate attribution.\n- The authors could improve reliability by using bootstrapping to compute confidence intervals or standard deviations.\n\n**Minor points**\n- “de novo” should always appear together without breaking across lines; perhaps \\emph{de~novo} would help.\n- References in the “Introduction” do not fully represent recent developments in MS/MS data analysis, especially for database search methods.\n- In line 160, “PTM” is used before it is defined in line 213.\n- In line 188, is the beam search citation correct? “(Science, 1977)”\n- In Fig. 3, both “PEPTID” and “PEITED” are misspelled at the top of the figure.\n- Some sections (e.g., 5.3 and 5.4) begin with table captions rather than text.\n- In Eq. (10), the first “y_j” should be bold.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "vaEYX0xSEF", "reviewer": "Reviewer_tavc", "summary": "The authors introduce an approach called searchnovo for the identification of peptide sequences of mass spectra. Without doubt, the identification of peptides remains a tough and important question and the authors contribute an approach that allows the incorporation of prior sequence knowledge into de novo sequencing approaches. They compare and benchmark their approach against commonly used de novo approaches on standard benchmarking data sets of the community and show superior performance", "questions": "- Parameters such as the +-20 Da threshold for similar searches appear arbitrarily set. Is there a deeper reason?\n\n- it is not clear to me what database information is provided to searchnovo in the various benchmark. In particular, what is the size of the database and to which extend are related sequences filtered out (are only absolutely identical ones filtered out or also those containing PTMs/chemical modifications/single amino acid substitions)?\n\n- can the authors compare to an error-tolerant/open database search approach (eg msfragger as current de facto gold standard in the field)? It could make sense to replicate a common scenario, e.g. in the 7/9 species setup that I can provide a complete database of the closed related organism. To give an example, human and chicken are evolutionary 300 million years apart, but it has been shown that error-tolerant approaches can overcome that distance, so unless going for very obscure organisms, there are related species available and that should be the starting point.\n\n- for practical purposes in proteomics, reliability is a key issue and the setup of the benchmark is of limited use for proteomics applications. Most mass spectra are of low quality and redundance of measurements is very high (almost all proteins are identified based on multiple spectra).  It is common standard (and even required for publications in practically all venues of the field) that peptide identifications are at least above a 1% false discovery rate threshold. Currently the authors only provide precision/recall metrics, which does not allow judging the number of peptide that the search algorithm itself considers reliable – it is by no means necessary that each spectrum is identified. Can the authors provide in addition a number of peptides above 1% FDR.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "-  The authors go beyond the thinking of current de novo approaches and allow the inclusion of prior knowledge\n\n-  The authors demonstrate superior performance to common de novo approaches\n\n-  The authors provide potential causal rationalization for the improved performance", "weaknesses": "- mixture approaches between denovo and database search have been commonly described (most noteworthy Spider 10.1109/CSB.2004.1332434, BICEPS 10.1074/mcp.M111.014167) also in a formal problem definition, this is by no means an innovation of this paper, but has been around for two decades and should be stated as related work.\n\n- error-tolerant and open database searches are commonly used in the field and also not mentioned here, although (see next point) they may be highly relevant\n\n- the authors argue that database comparison is not valid, at the same time feed prior information from databases into searchnovo that are not available to any other de novo approach. In this way, this is not a fair comparison.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)