DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning

Domain: chemistry
Published: 2026-03-05
Editorial outcome: minor_revision

Abstract
Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insufficient protein digestion in current single protease-based proteomic experiments. Here, we present a software system, named DiNovo, for high-coverage and high-confidence de novo peptide sequencing by leveraging the complementarity of mirror proteases. DiNovo is empowered by several innovative algorithms, including a mirror-spectra recognition algorithm independent of pre-sequencing, two sequencing algorithms based on deep learning and graph theory, respectively, and target-decoy mapping, a method for sequencing result evaluation free of prior peptide identification. Compared with the trypsin protease used alone, DiNovo using two pairs of mirror proteases leads to two to three times high-confidence amino acids sequenced. Compared with previous single-protease de novo sequencing algorithms, DiNovo achieves much higher sequence coverage. DiNovo also shows great potential as a practical and powerful alternative to database search for peptide identification with quality control.

Introduction
De novo peptide sequencing from tandem mass spectra imposes no prior restrictions on the space of possible amino acid sequences, thus allowing for the identification of peptides or proteins outside existing knowledge databases, e.g., neoantigens, noncanonical antigens, synthetic peptides, antibodies, venom proteins, metaproteomes, and proteins from unknown species or species without sequenced genomes1–5. Traditionally, de novo sequencing algorithms have predominantly used the graph representation of tandem mass spectra and dynamic programming to search for the optimal sequence, such as PEAKS6, PepNovo7, pNovo8. Later, the Novor9 algorithm introduced a machine learning approach to predict amino acids. In recent years, with rapid advancements in deep learning, neural networks have been intensively applied to de novo sequencing, with DeepNovo10,11 as the pioneer, and dozens of followers such as pNovo312, PointNovo13, Casanovo14,15, PepNet16, GraphNovo17, Denovo-GCN18 and π-PrimeNovo19. These algorithms show great capabilities in learning the hidden features of spectra and improving the accuracy of de novo sequencing.

However, existing de novo sequencing methods are still facing many challenges. Firstly, incomplete peptide fragmentation results in poor coverage of fragment ions for many peptides, leading to inaccurate or incomplete sequencing results20. Secondly, the digestion efficiency of commonly used proteases, e.g., trypsin, is often insufficient, resulting in missed cleavages of proteins, so that undigestible peptides cannot be sequenced. Thirdly, although recent deep learning-based algorithms, like GraphNovo17, can fill in missing ions to some extent through a trained network, these predicted ions still lack experimental evidence, making it difficult to confirm the correctness of the predicted sequences. Furthermore, the evaluation of de novo sequencing algorithms heavily depends on constructing benchmark datasets from peptide-spectrum matches (PSMs) obtained by database search, which impedes the direct performance comparison between the two approaches to peptide identification.

Mirror protease technology is a powerful way to improve fragment ion coverage of tandem mass spectra21. It typically uses two proteases to cleave proteins at the C- and N-termini of the same specific amino acid(s), such as K/R for trypsin and LysargiNase (Ac-LysargiNase), producing mirror peptides that share identical intermediate sequences but have the specific amino acid at the C- and N-termini, respectively. Such mirror peptides exhibit complementarity in their mass spectra, known as mirror spectra. Fragment ions absent in one spectrum can be present in the other spectrum22. For example, in higher-energy collisional dissociation (HCD) mass spectra, trypsin-digested peptides usually have rich y-type fragment ions, while LysargiNase-digested peptides have rich b-type fragment ions.

Although de novo sequencing methods based on mirror proteases, such as pNovoM23 and Lys-Sequencer24, have been developed, they still suffer from several serious flaws. Firstly, there is still a lack of available software to support the entire workflow of mirror-spectra recognition and peptide sequencing. For instance, Lys-Sequencer has not been made public at all. Meanwhile, pNovoM lacks the program of mirror-spectra recognition, a crucial prerequisite for mirror peptide sequencing, in its publicly available package. Secondly, existing methods use only one pair of mirror proteases, which leaves the incomplete-digestion problem unsettled, leading to many sequences being undetectable. Thirdly, some key algorithmic problems have not been adequately addressed. For example, recognizing which spectral pairs are mirror spectra is the first step in using mirror spectra for de novo sequencing. In pNovoM, the recognition of mirror spectra relies on pre-sequencing of peptides from separate spectra obtained by single-protease digestion, which is both time-consuming and sensitive to spectrum quality, and the results of recognition lack an effective quality control standard. Moreover, deep learning techniques have not yet been explored for de novo sequencing of mirror peptides. All these issues hinder the practical application of the mirror-protease strategy in proteomics.

In this paper, we present DiNovo, a comprehensive software system that supports the full workflow of de novo peptide sequencing from tandem mass spectra generated by mirror proteases. The features of DiNovo include support for multiple pairs of mirror proteases, a novel mirror-spectra recognition algorithm independent of pre-sequencing and with quality control, two optional de novo sequencing algorithms based on deep learning and graph theory, respectively, and a target-decoy approach for confidence evaluation of de novo sequencing results in the presence of a sequence database but free of prior peptide identification. We evaluate the performance of DiNovo using two pairs of mirror proteases, including trypsin/LysargiNase and Lys-C/Lys-N, which were used to digest proteins from E. coli and yeast proteomes. By taking full advantage of the complementarity of mirror spectra, DiNovo achieves much higher sequence coverage and confidence than state-of-the-art single-protease de novo sequencing algorithms. Compared with the trypsin protease used alone, DiNovo using two pairs of mirror proteases, leads to two to three times high-confidence amino acids sequenced. Furthermore, DiNovo identifies a comparable number of proteins to database search at the same false discovery rate (FDR) level, showing great potential as a powerful complement or even an alternative to database search for protein identification. DiNovo is designed to dramatically increase the coverage and confidence of de novo peptide sequencing and to facilitate mirror-protease proteomics.

Results
DiNovo workflow
Figure 1 outlines the workflow and modules of DiNovo. In the first step, protein samples are digested by one or more pairs of mirror proteases, and the resulting peptides are analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). In this work, two pairs of mirror proteases are used, including trypsin/LysargiNase and Lys-C/Lys-N. To be specific, trypsin/LysargiNase cleaves C-/N-terminal to lysine and arginine, while Lys-C/Lys-N cleaves C-/N-terminal to lysine alone, producing longer peptides.

In the second step, mirror spectral pairs are recognized using a novel algorithm, called MirrorFinder. This algorithm does not rely on pre-sequencing of peptides from separate spectra, making it less sensitive to spectrum quality and more efficient. It directly utilizes the internal information of spectra to match spectral pairs, and employs a matching score to measure the possibility of a pair of spectra being mirror spectra (Fig. 1b). The internal information includes precursor mass, fragment ion mass, and fragment ion intensity, which possess specific patterns for mirror spectral pairs. Furthermore, a target-decoy strategy is used to estimate and control the FDR of mirror spectral pairs. More details about the MirrorFinder algorithm are given in “Methods”.

In the third step, peptides are sequenced from the recognized mirror spectra using two developed algorithms. The first algorithm, called MirrorNovo, is based on a deep neural network (DNN), as shown in Fig. 1c. Specifically, a T-Net is used to extract peak features from the feature matrix for matching peaks and theoretical fragment ions. Then, a Gated Recurrent Unit (GRU) layer is employed to capture the relationships between adjacent peak features. Finally, a fully connected (FC) layer outputs the probabilities of amino acids. In addition, a knapsack algorithm is applied at each step to constrain the search space. The second sequencing algorithm is pNovoM2 (Supplementary Fig. 1), an improved version of pNovoM23, which is based on the graph representation of spectra and dynamic programming. While MirrorNovo is more accurate than pNovoM2, it requires GPU(s) for acceptable running speed. On the other hand, pNovoM2 runs on a CPU and is much faster than MirrorNovo when GPU(s) are unavailable. We recommend using MirrorNovo if GPU(s) are available, and pNovoM2 otherwise. In addition to mirror spectra, all separate spectra are also subjected to de novo sequencing for as high sequence coverage as possible. Further details on the sequencing algorithms can be found in “Methods”.

Performance evaluation
To evaluate the performance of DiNovo, we constructed eight datasets using E. coli and yeast proteome samples, which were digested by two pairs of mirror proteases, i.e., trypsin/LysargiNase and Lys-C/Lys-N (see “Methods” for details on sample preparation and mass spectrometry experiments). In addition, a public antibody dataset23 was also used to test the performance of DiNovo. We compared our mirror-protease strategy with the conventional single-protease strategy for de novo sequencing. We also compared DiNovo with previous single-protease de novo sequencing algorithms as well as the commonly used database search approach for peptide identification. Additionally, we evaluated the accuracy and speed of the two built-in sequencing algorithms in DiNovo, i.e., MirrorNovo and pNovoM2.

The traditional approach to evaluating de novo sequencing results primarily relies on constructing a benchmark dataset from peptide identifications obtained by protein sequence database search. However, this method cannot guarantee data quality due to the inherent error rate in database search results. Moreover, it directly prevents the horizontal performance comparison between de novo sequencing and database search. Another approach is to map de novo peptides to protein sequences in the database, and evaluate the number of peptide sequences mapped16. However, this approach lacks error control, as incorrect sequences can randomly match database proteins. Furthermore, we focus on high-confidence sequencing results. A de novo peptide is considered high-confidence if it achieves 100% ion coverage, meaning that every amino acid residue of the peptide is supported by at least one spectral peak in the mass spectrum (considering only the top 200 spectral peaks). These high-confidence peptides are backed by more complete experimental evidence and are more convincing in practical applications.

Near-complete fragment ion coverage in mirror spectra
The absence of some fragment ions presents a major challenge for de novo peptide sequencing. Our strategy in this paper is to improve the ion coverage using mirror proteases. To demonstrate the effectiveness of this strategy, we calculated the ion coverage of separate spectra and mirror spectra recognized by MirrorFinder. Figure 3 and Supplementary Fig. 2 show that mirror spectra significantly complement the missing ions in separate spectra on both E. coli and yeast datasets, which is highly beneficial for subsequent de novo peptide sequencing. Notably, a large portion of mirror spectra achieved near-complete fragment ion coverage, with average coverages of 98.4% for the E. coli datasets and 98% for the yeast datasets. In contrast, the average ion coverages for the separate spectra were only 90.2% and 89.7% for the E. coli and yeast datasets, respectively.

Comparison with single-protease strategy
To compare the mirror-protease strategy with the commonly used single-protease strategy for de novo peptide sequencing, in addition to DiNovo we also used GraphNovo (the built-in algorithm in PEAKS-online12), to sequence the E. coli and yeast proteomes, which were digested by trypsin/LysargiNase and Lys-C/Lys-N. PEAKS is the most popularly used software for de novo sequencing. We evaluated the number of peptides sequenced at an estimated 1% FDR, as well as amino acid coverage (the proportion of sequenced amino acids in the database), and protein coverage (the proportion of identified proteins). Throughout this paper, MirrorNovo was used within DiNovo unless otherwise specified.

As shown by Fig. 4a, b, the mirror-protease strategy (DiNovo) significantly outperformed the single-protease strategy (GraphNovo) in terms of all three metrics. In most cases, using either pair of mirror proteases, Try+Lys or LysC+LysN, was superior to using any of the four proteases alone. Combined use of the two pairs of mirror proteases within DiNovo resulted in the highest sequencing coverage and confidence. On the combined results of four proteases (All) for the E. coli (yeast) datasets, DiNovo sequenced 133.8% (111.9%) more high-confidence peptides than GraphNovo, with 43.3% (35.9%) higher amino acid coverage and 15.2% (10.2%) higher protein coverage. Compared to the traditional single-protease strategy, such as Try(GraphNovo), the advantages of DiNovo using two pairs of mirror proteases were more pronounced, as evidenced by sequencing 458.9% (509.2%) more high-confidence peptides for the E. coli (yeast) datasets, corresponding to 153.8% (194.5%) higher amino acid coverage and 28.7% (34.3%) higher protein coverage. Notably, DiNovo with one pair of mirror proteases (Try+Lys) was superior to or at least comparable to the combined results of GraphNovo with four proteases (All), revealing the immense contribution of mirror spectra to peptide sequencing. Furthermore, DiNovo identified 68.4% (68.4%) of E. coli (yeast) high-confidence proteins in the database, demonstrating the capability of the multiple mirror-protease strategy in protein identification. The advantage of DiNovo over GraphNovo was greater in terms of the number of peptides than amino acid and protein coverages. This was because DiNovo sequenced many mirror peptides with large sequence redundancy.

Figure 4c further shows the intersection of high-confidence amino acids and proteins sequenced by DiNovo and GraphNovo. Across all datasets with four proteases, DiNovo covered 87.2% to 98.7% of the high-confidence amino acids and 96% to 99% of the high-confidence proteins sequenced by GraphNovo, while sequencing 49,622 to 125,526 additional high-confidence amino acids and 392 to 512 additional high-confidence proteins. More results are given in Supplementary Fig. 3, Supplementary Fig. 4. We first report the high-confidence results below.

Comparison with different de novo sequencing algorithms
To further demonstrate the advantages of the mirror-protease strategy in de novo peptide sequencing, we compared the performance of DiNovo with more single-protease de novo sequencing algorithms. The description and configuration of these various software tools are detailed in “Methods”. The comparison was conducted on the combined results of four proteases for the E. coli and yeast datasets. As in the previous section, sequenced peptides were mapped to the target-decoy database, and the estimated FDR for each algorithm was controlled at 1% using different mass thresholds (Supplementary Table 1).

The results in Fig. 5a, b show that DiNovo outperformed all competitors at the peptide, amino acid, and protein levels for both E. coli and yeast datasets. Notably, GCNovo (the updated version of Denovo-GCN) is the built-in single-protease sequencing model in DiNovo, and GCNovo* is its retrained model. Compared to GCNovo and GCNovo*, DiNovo on average sequenced 138.6% (116.7%) more peptides for the E. coli (yeast) datasets, achieving 46.3% (38.9%) higher amino acid coverage and 14.4% (9.5%) higher protein coverage. The performance advantage of DiNovo over GCNovo is due to the contribution of the mirror-protease sequencing model (MirrorNovo), as detailed in Supplementary Fig. 5b. Compared to other commonly used algorithms, DiNovo sequenced 124.7% to 184.8% more E. coli peptides and 110.2% to 195.3% more yeast peptides. In terms of amino acid coverage, DiNovo exceeded other algorithms by 40.7% to 68.6% (35.9% to 74.2%) for the E. coli (yeast) datasets. At the protein level, DiNovo possessed 15.2% to 19.9% (10.3% to 17.4%) higher protein coverage than other algorithms for the E. coli (yeast) datasets. This superior performance holds even when compared to CasanovoV2, which was trained on a large-scale dataset15. Figure 5c shows the significant overlap of high-confidence amino acids sequenced by DiNovo and other algorithms. Specifically, DiNovo covered most (96.4%–98.6%) of the amino acids sequenced by other algorithms except GCNovo, and uniquely sequenced an additional proportion (39.4%–76.1%) of amino acids than other algorithms.

All results (green hollow bar) from DiNovo also surpassed those of other algorithms across the three levels for both E. coli and yeast datasets. More results are given in Supplementary Figs. 6–8. All these results demonstrate that DiNovo is not only a high-coverage but also a high-confidence de novo peptide sequencing algorithm.

In addition to complex proteomes, DiNovo also demonstrated high proficiency in sequencing purified proteins, highlighting the generality of the software (Supplementary Note 2). To be specific, we used different algorithms to sequence two monoclonal antibodies (PXL1 and PXL2), which were expressed in Chinese hamster ovary (CHO) cells23. It has been shown that DiNovo achieved the highest (83.5%) average amino acid coverage among all compared sequencing algorithms.

Comparison with database search
As previously mentioned, the TD mapping method enables us to directly compare the results of de novo sequencing with those of database search at the same FDR level. To do this, we used three database search engines including pFind27–29, MSFragger30, and MS-GF+31 to search the mass spectra against the protein sequence databases. The FDR of the database search results was controlled at 1%, and other search parameters are detailed in “Methods”. The results reported below were not filtered by high-confidence peptides.

As shown in Fig. 6a, b, DiNovo sequenced more peptides than the three search engines for all four proteases on the E. coli and yeast datasets. This advantage of DiNovo stems primarily from its use of the complementarity of mirror spectra, which allows for the simultaneous accurate sequencing of mirror peptides that may be difficult to sequence alone. Of course, there is sequence redundancy in mirror peptides, and overall, DiNovo sequenced comparable numbers of amino acids and proteins to all search engines. It should be noted that MS-GF+ showed a deficiency in identifying N-terminal digested peptides; therefore, we retrained its scoring model on peptides digested by four different proteases (denoted as MS-GF+*), and much better results were obtained. Importantly, there are significant overlaps between the results of DiNovo and those of each search engine, demonstrating the reliability of the results (Fig. 6c, Supplementary Figs. 9–12). Specifically, DiNovo covered 85.4% (73.7%) of the amino acids and 92.4% (91%) of the proteins identified by pFind for the E. coli (yeast) datasets. For MSFragger, the proportions are 85.4% (71.3%) and 94.8% (93.1%). For MS-GF+*, the proportions are 86.2% (75.4%) and 92.9% (95.4%). Figure 6d further illustrates that the peptide sequences of spectra identified by both DiNovo and pFind are highly consistent. These results show that thanks to the mirror-protease strategy and the powerful DiNovo software, de novo sequencing is expected to become a practical and robust complement—or even an alternative—to traditional database search approach for peptide and protein identification. In this advancement, the TD mapping-based FDR control also plays a crucial role.

Interestingly, it can be observed that database search engines showed proficiency in the identification of C-terminal digested peptides but were not as good as DiNovo at sequencing N-terminal digested peptides. This phenomenon suggests that current search engines may have been optimized for C-terminal proteases, mainly trypsin, and can be improved for N-terminal proteases.

Comparison of mirror-spectra sequencing algorithms
There are two optional de novo peptide sequencing algorithms in DiNovo, i.e., MirrorNovo and pNovoM2. MirrorNovo is based on deep learning and runs on a GPU, while pNovoM2 is based on graph theory and runs on a CPU. pNovoM2 is an updated version of pNovoM, which we developed previously23. In this section, we compared the sequencing accuracies and running speeds of these three algorithms. Different mass thresholds were applied to control the FDR of each algorithm at 1%, as detailed in Supplementary Table 2. As illustrated in Fig. 7a, b, MirrorNovo sequenced more amino acids than pNovoM2 and pNovoM, but pNovoM2 had a much faster speed. Therefore, we recommend using MirrorNovo if one or more GPUs are available; otherwise, pNovoM2 is the better option for efficiency. Certainly, the two algorithms in DiNovo can be selected simultaneously, and their results can be combined by DiNovo to achieve optimal sequencing sensitivity.

Furthermore, we analyzed the intersection of the sequencing results of MirrorNovo, pNovoM2, and pNovoM. As shown in Fig. 7c, there were significant overlaps between the three algorithms, with each identifying a number of unique amino acids. Finally, we analyzed the consistency of spectral pairs whose peptides were sequenced by both MirrorNovo and pNovoM2 (Supplementary Fig. 13). Under the 1% FDR, over 99% of these spectral pairs exhibited identical sequences, with a maximum of only 0.54% exhibiting differences, regardless of species and proteases. This outcome further demonstrated the reliability of the sequencing results and our quality control methodology.

Methods
Proteins sample preparation and LC-MS/MS analysis
Proteins of E. coli or yeast strains were extracted using a lysis buffer containing 8M urea, 150mM NaCl, 50mM Tris-HCl (pH 7.5), 1mM phenylmethysulfonylfluoride (PMSF), 1mM 2-chloroacetamide, and 1× cocktail (Roche, 11697498001). The proteins were reduced with 5mM DTT at 45°C for 30 min, and then fully alkylated with 15mM IAA at room temperature in the dark for 30 min. Samples were processed by in-solution digestion or the FASP method as previously described, with slight modifications33,34. After reducing the urea concentration in the lysate to <1M by dilution or displacement, the proteins were aliquoted into four portions and digested with different proteases at an enzyme-to-protein ratio of 1:50. For trypsin/Lys-C or Lys-C alone digestion, the sample was solubilized in a buffer of 50mM ABC (pH 8.3), and trypsin and/or Lys-C were added for overnight digestion at 37°C. For LysargiNase/Lys-N digestion, the sample was solubilized in a buffer of 20mM HEPES, 10mM CaCl2, and 1mM Zn(Ac)2 (pH 8.3); after digestion with LysargiNase at 37°C for 4 h, Lys-N was added for overnight digestion. For Lys-N alone digestion, the sample was solubilized in a buffer of 50mM ABC and 1mM Zn(Ac)2 (pH 7.5–8.5), and Lys-N was added for overnight digestion at 37°C. All of trypsin, LysargiNase, Lys-C and Lys-N were provided by Enzyme & Spectrum (Beijing, China)23,35–37. After digestion, 1% formic acid (FA) was added to stop the reaction. The digested peptides were desalted using an optimized StageTip38.

LC-MS/MS analysis was conducted using an Orbitrap Q-Exactive HF mass spectrometer (Thermo Fisher Scientific, USA) coupled with an EASY-nLC 1000 system (Thermo Fisher Scientific, USA). The resulting peptides were resolved in buffer containing 1% acetonitrile and 1% FA, then loaded onto a 75 μm I.D. × 20 cm LC column packed with 1.9 μm C18 reverse phase packing particles (Dr. Maisch GmbH, Germany). LC separation was performed with a 120 min gradient from 6% to 45% buffer B (80% acetonitrile with 0.1% FA) at a flow rate of 300 nL/min. MS measurements were performed in data-dependent acquisition mode. For trypsin/Lys-C digested peptides, MS1 spectra were acquired with a survey scan (300–1400 m/z) at a resolution of 60,000 at 200 m/z, with an AGC target of 3e6 and a maximum injection time (MIT) of 30 ms. Precursors with an intensity > 3.3e4 were selected for MS2 analysis, collected at an MIT of 60 ms or an AGC target of 5e4, then fragmented at a normalized collision energy (NCE) of 27. The resulting fragment ions were analyzed in the Orbitrap at a resolution of 15,000 at 400 m/z. For LysargiNase/Lys-N digested peptides, MS1 spectra were acquired with the same survey scan (300–1400 m/z) at a resolution of 60,000 at 200 m/z, an AGC target of 3e6 and an MIT of 50 ms. Precursors with an intensity > 1.3e4 were selected for MS2 analysis, collected with an MIT of 150 ms or an AGC target of 1e5, then fragmented with an NCE of 27. The resulting fragment ions were analyzed in the Orbitrap at a resolution of 15,000 at 400 m/z.

Datasets and preprocessing
The LC-MS/MS analysis of the E. coli and yeast proteins digested by four proteases resulted in eight datasets of MS2 spectra. The number of spectra in each dataset is shown in Supplementary Table 3. In addition, we conducted a database search using pFind v3.2.0 and calculated the pairing rate of the dataset at both spectrum and peptide levels (Supplementary Table 3). For simplicity, we refer to trypsin/Lys-C mixed digestion as trypsin digestion, and LysargiNase/Lys-N mixed digestion as LysargiNase digestion.

Centroided spectra were extracted in Mascot generic format from RAW files using pParse39. In order to better recognize the mirror spectra, all mass spectra were preprocessed with the following steps. First, we retained the most abundant N peaks (N = 200 by default) for each spectrum. Second, we detected isotopic clusters and transformed each cluster into a singly charged monoisotopic peak with intensities combined. To enhance the signals of backbone fragment ions, we also accumulated the intensities from their isotopic and neutral-loss (−H2O and −NH3) peaks. Third, we normalized the peak intensities and then took their square root to reduce variance effects. Fourth, we performed local denoising by retaining the most abundant K peaks (K = 7 by default) per mass bin (with a default bin width of 100 Da). Additionally, we removed peaks corresponding to precursor ions and immonium ions. At last, to alleviate the problem of imbalanced ion types, we generated a complementary peak for each peak after denoising, while maintaining the same ion intensity.

It should be noted that these preprocessing steps were applied only to the mirror-spectra recognition process in DiNovo and not to the other algorithms in order to avoid any unexpected influence on their performance.

Mirror-spectra recognition algorithm
In DiNovo, we developed a novel mirror-spectra recognition algorithm, named MirrorFinder, which is free of prior peptide sequencing from separate spectra as done in pNovoM23. The algorithm directly utilizes the information of precursor and fragment ion to recognize spectra generated from mirror peptides. To be specific, it first selects candidate spectral pairs whose precursor masses comply with the rule of mirror peptides (as outlined in Supplementary Table 4) and whose retention time difference falls in a reasonable range. The rule states that each type of mirror peptides has a theoretical precursor mass difference and two theoretical fragment ion mass differences. Each candidate spectral pair is then assigned a matching score, reflecting the confidence level that they are indeed a pair of mirror spectra. Notably, the parameters for preprocessing and mirror-spectra recognition were optimized through extensive testing to ensure better performance, as detailed in Supplementary Note 3. Furthermore, the results in Supplementary Note 3 show that MirrorFinder was more accurate than pMerge, the undisclosed mirror-spectra recognition algorithm used in pNovoM.

To begin with, we select candidate spectral pairs with precursor mass differences matching the theoretical values (as listed in Supplementary Table 4) within a specified tolerance. We set the tolerance to 20 ppm, and calculate the maximum and minimum values of precursor mass difference, which are
D1 = [m1(1+20*10^-6) - m2(1-20*10^-6)]
D2 = [m1(1-20*10^-6) - m2(1+20*10^-6)]
where m1 and m2 are the precursor masses of the two spectra, respectively. If one of the theoretical precursor mass differences in Supplementary Table 4 falls within the interval [D2, D1], then the spectral pair is a candidate of a certain category (e.g., A-G).

After that, each candidate spectral pair is assigned a matching score, which is calculated through the following steps (as illustrated in Fig. 1b). First, a fixed mass range (−200 to +200 Da in this work) is divided into equal small intervals (e.g., 0.02 Da), with the total number of intervals denoted as N. Next, for the i-th interval, the number of fragment ion pairs whose mass differences fall in this interval is counted, and their intensities are summed, denoted as counti and suminteni (i=1, ..., N), respectively. Then, a statistics si is calculated for the i-th interval by multiplying the number of pairs by the summed intensity, which is
si = suminteni * counti, i=1, ..., N

Finally, two Bonferroni-corrected p-values are estimated for the intervals corresponding to two theoretical fragment ion mass differences for a certain category (as shown in Supplementary Table 4) from the distribution of the observed statistics40. The smaller of the two p-values is denoted as p*, and the matching score S is obtained by
S = -log p*

Obviously, a higher matching score indicates stronger evidence that the spectral pair is a pair of mirror spectra.

Subsequently, we use a target-decoy strategy25,26 to filter spectral pairs according to their matching scores. In MirrorFinder, the matching score is closely related to the theoretical fragment ion mass differences, allowing us to directly use the score derived from a random mass difference as the decoy score to compete with the corresponding original (target) score. For each spectral pair, if the original score is higher than the decoy score, the spectral pair is labeled as a target spectral pair, and its final score is set as the original score. Otherwise, it is labeled as a decoy spectral pair, and its final score is set as the decoy score. Then the final scores are sorted in descending order, and the FDR of target spectral pairs with final scores above a given threshold t can be estimated as
FDR(t) = (D(t)+1) / T(t)
where T(t) and D(t) are the numbers of target and decoy spectral pairs, respectively. Through this strategy, we can filter target spectral pairs and control the FDR under a specified level, e.g., 2%. This approach eliminates the need to construct additional decoy spectra and offers a reasonable quality control standard for the recognized mirror spectra. We also verified that our quality control method satisfies the assumption of the target-decoy strategy in Supplementary Note 4.

DNN-based de novo sequencing algorithm for mirror spectra
We proposed a DNN-based de novo sequencing algorithm called MirrorNovo, which fully leverages complementary fragment ions in mirror spectra without the need to explicitly merge the spectra. In MirrorNovo, we preprocess each spectrum in a mirror spectral pair individually, padding the peak list with zeros if there are fewer than the top N peaks. To improve computational efficiency and reduce intensity variance, we convert the m/z values of all peaks to neutral masses and normalize their square-root-transformed intensities to the base peak.

As illustrated in Fig. 1c, the input to the MirrorNovo model consists of two peak-ion matching matrices, each corresponding to one spectrum in a mirror spectral pair, along with the category of the spectral pair. We compute the two matrices by comparing the observed peak masses in each spectrum to 18 types of theoretical fragment ion masses (i.e., singly and doubly charged b, y, and a ions, in their singly charged neutral-loss ions, and 6 types of internal ions), and then concatenate them along the peak dimension. Theoretical fragment ions are generated by enumerating all possible subsequent amino acids based on the already predicted prefix sequence. To ensure meaningful matrix concatenation, we align the two matrices according to the corresponding fragmentation sites in the overlapping regions of the mirror peptides. For example, when iteratively predicting a trypsin-digested peptide of length L with either lysine or arginine (identified by MirrorFinder) at its C-terminal end, we align its bi(yL-i) fragments with the bi+1(yL-i-1) fragments derived from the LysargiNase-digested peptide, which has either lysine or arginine at its N-terminal end.

MirrorNovo employs a T-Net architecture13 with three one-dimensional convolutional layers to extract peak features from the concatenated matching matrices. A subsequent GRU layer captures both short- and long-term dependencies across peaks while maintaining the dimensionality of the input and output matrices. Next, three fully connected (FC) layers are utilized to learn higher-level feature representations, with batch normalization applied between them to enhance training stability and accelerate convergence. The Rectified Linear Unit (ReLU) activation function introduces non-linearity to prevent the collapse of adjacent linear layers. Finally, a softmax layer outputs probabilities for 20 amino acids, where isoleucine and leucine are not distinguished.

MirrorNovo predicts complete peptide sequences from mirror spectral pairs through iterative amino acid inference. At each iteration, it employs a beam search strategy to avoid local optima and utilizes a knapsack algorithm to reduce the number of candidate amino acids. A bidirectional prediction strategy is integrated to mitigate the loss of correct sequences. The iteration terminates when the difference between the prefix mass and precursor mass falls within 20 ppm. The top M (M=10 by default) candidate peptides are retained based on peptide probability scores, calculated as the average of individual amino acid probabilities. To enhance data utilization, DiNovo also includes a single-protease de novo sequencing algorithm, GCNovo (the updated version of Denovo-GCN18), which allows peptide sequencing from spectra that cannot form mirror spectral pairs. As an independent sequencing algorithm, the running speed of GCNovo is similar to that of PointNovo and Casanovo, while GraphNovo and pNovo3 have significantly faster speeds (Supplementary Fig. 5a). To process the sequencing results, we first resolved conflicts where a single spectrum was paired with different spectra to produce different sequences. After assigning a unique mirror sequence to each spectrum, we further filtered out results where the mirror sequence was inconsistent with the sequence derived from the single spectrum.

In this study, MirrorNovo was trained on three high-resolution tandem mass spectrometry datasets from protein samples of Vero, MC2155, and human testis. Annotated mirror spectral pairs were constructed based on pFind (v3.2.0) search results at 1% FDR. Since a pair of mirror peptides may correspond to multiple mirror spectral pairs, and to ensure high-quality PSMs and sequence diversity, we ranked the mirror spectral pairs by pFind’s final scores and selected up to the top K highest-scoring pairs. Only spectral pairs of types A, B, and C with charge states of 2+ or 3+ were retained for the final annotated dataset. The dataset was split into training and validation sets at a 9:1 ratio based on peptide sequences (as detailed in Supplementary Table 5), ensuring no sequence overlap between the sets. The single-protease sequencing model, GCNovo, was trained on all trypsin- and LysargiNase-digested PSMs from the same dataset.

Graph-based de novo sequencing algorithm for mirror spectra
In addition to the DNN-based sequencing algorithm MirrorNovo, we also developed a graph-based sequencing algorithm, pNovoM2, which generates candidate peptides through spectrum graphs. Building on pNovoM23, we have enhanced the performance of peptide sequencing and expanded the types of mirror peptides that can be sequenced, enabling pNovoM2 to deliver more results than pNovoM. Furthermore, we have optimized the speed and memory management of pNovoM2, facilitating rapid de novo peptide sequencing on large-scale datasets without the need for GPU support.

Supplementary Fig. 1 illustrates the schematic diagram of pNovoM2. Unlike pNovoM, pNovoM2 interprets not only the merged mirror spectra but also the separate spectra. This strategy allows pNovoM2 to sequence peptides even when MirrorFinder fails to find the mirror spectral pairs. In the workflow of pNovoM2, all spectra are first preprocessed, with each generating a spectrum graph similar to pNovo8. If MirrorFinder recognizes the corresponding mirror spectrum for the input spectrum, the spectrum graphs of the two spectra will be merged and used for subsequent analysis. Otherwise, the spectrum graph of the input spectrum will be used directly. For the merged spectrum graph, an edge is created between two nodes if their mass difference matches the total mass of any two or three amino acids. However, for the spectrum graph from a separate spectrum, the maximum number of amino acids is increased to five, due to the relatively lower abundance of signal peaks. Our scoring function aligns with that of pNovo, but we’ve made slight adjustments to better accommodate the merged spectrum graph. If the mass of a node in one spectrum graph differs from the mass of a node in another spectrum graph by a specific value, such as the mass of the amino acid K or R in the case of trypsin and LysargiNase, the peaks corresponding to these nodes are likely signal peaks of fragment ions. These nodes are then assigned a higher score. The pDag41 algorithm is subsequently used to identify the top N highest-scoring paths from the source node to the target node. For the merged spectrum graph, the value of N is set to 20; for the spectrum graph from a separate spectrum, it is set to 40. All peptides corresponding to these paths from the spectrum graphs are rescored together, and a final list of candidate peptides is generated.

De novo sequencing results evaluation
In this paper, we invented a simple yet effective method for evaluating de novo sequencing results, called TD mapping (as illustrated in Fig. 2a). This method requires the existence of a sequence database of proteins to be sequenced, but eliminates the need for peptide identification by traditional database search. Through the introduction of a decoy database, it addresses the lack of quality control in existing mapping-based methods16.

Building on the TD method25,26 we first generate a decoy protein database by simply reversing or shuffling the sequences in the original database. Then, we map all the de novo sequences separately to the protein sequences in the target and decoy databases. Those successfully mapped peptides are retained and subjected to subsequent analysis. Unmatched peptides are excluded, as well as those mapped to both target and decoy sequences. Next, mapped peptides are ordered according to their masses, and the FDR of target peptides with masses above a threshold m is estimated as
FDR(m) = (D(m)+1) / T(m)
where T(m) and D(m) are the numbers of target and decoy peptides, respectively. Finally, an appropriate mass threshold is selected so that the FDR is controlled under the desired level.

Configuration of de novo sequencing and database search
To fairly compare the performance of de novo sequencing software tools, we configured different tools with the same parameters. In addition, to perform a horizontal comparison between de novo sequencing and database search, all raw files were analyzed using pFind, MSFragger, and MS-GF+ for peptide identification. Both precursor and fragment ion mass tolerances were set to 20 ppm. Carbamidomethyl of cysteine (C) was specified as a fixed modification, and oxidation of methionine (M) was considered as a variable modification. The detailed configurations for each software tool are as follows:

• DiNovo: We offer two optional de novo sequencing algorithms in DiNovo, i.e., MirrorNovo and pNovoM2. By default, MirrorNovo is used for optimal performance, unless otherwise specified. The MirrorNovo model was trained on an NVIDIA GeForce RTX 3090 GPU with the following parameters: optimizer=Adam, loss function=Focal, maximum epochs=5, batch size=20, learning rate=1×10^-3, decay factor=0.5. For the mirror-spectra recognition experiment, we first retained the 200 most abundant peaks and then selected 7 peaks per 100 Da. For the sequencing experiment, we only retained spectra with a precursor mass in the range of [300, 3500] Da. When comparing these two algorithms (as discussed in “Results”), we set the number of processes of pNovoM2 as 15, and the batch size of MirrorNovo as 350. Additionally, MirrorNovo was run on an NVIDIA GeForce RTX 4090 GPU, while pNovoM2 was executed on an Intel(R) Xeon(R) Silver 4410Y CPU.

• pNovo3: pNovo312 uses the pDag41 algorithm to find the maximum scoring path within the spectrum graph, after which it rescores candidate peptides using an SVM model. The peptide with the highest score is then reported as the output. For our analysis in “Results”, we used the latest version of pNovo3, pNovo v3.1.5, which was released on December 6, 2023.

• PointNovo*: PointNovo13 is a neural network model that employs a T-Net structure, specifically designed for order-invariant data. This design allows it to be applicable to mass spectra of varying resolutions without increasing computational complexity. Since the authors of PointNovo did not provide a trained model file, and the original 9-species training dataset used in13 included the yeast species, we excluded the yeast species and trained a PointNovo model from scratch on the remaining 8-species dataset for our analysis.

• GraphNovo: We acquired the commercial software Peaks-Online12 and used its built-in de novo peptide sequencing algorithm to analyze E. coli and yeast datasets in “Results”. Peaks-Online 12 utilizes an integrated deep learning-based de novo peptide sequencing algorithm (GraphNovo17), which addresses the missing fragments problem in de novo sequencing by using graph deep learning to accurately reconstruct full peptide sequences from incomplete mass spectrometry data. However, the specifics of the training set used to train the model remain undisclosed to us.

• Casanovo* and CasanovoV2: Casanovo14,15 is a deep learning algorithm that employs a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the corresponding amino acid sequence of the generating peptide. Notably, Casanovo does not constrain the peptides it generates by precursor mass, so the precursor masses of its generated peptides may not be equal to those provided by the spectra. In contrast, all the other algorithms mentioned above use precursor mass as a constraint for de novo peptides. Therefore, it would be unfair to Casanovo to directly use the highest-scoring results without considering the precursor mass error. However, if we filter out peptides generated by Casanovo with precursor mass errors exceeding 20 ppm, some low-quality spectra will not yield any sequencing results. As a result, these spectra will be discarded in the results of Casanovo, and the TD mapping method will be applied only to the remaining spectra with higher quality. This could unfairly underestimate the FDR of Casanovo compared to other software tools. For a fairer comparison, we adopted a compromise strategy: we filtered out peptides generated by Casanovo with precursor mass errors exceeding 20 ppm and applied the same peptide mass thresholds to the Casanovo results as we did to DiNovo. For our analysis in “Results”, we used two models of Casanovo 4.2.0, which was released on May 14, 2024. The first model, referred to as Casanovo*, was trained by us from scratch on the 8-species dataset, similar to the approach used with PointNovo. The second model, referred to as CasanovoV2, is the latest version provided by its authors, trained on a large-scale dataset derived from the MassIVE Knowledge Base (MassIVE-KB), which consists of 30 million PSMs. It is important to note that CasanovoV2 is a trained model that does not allow for the specification of modifications. For a fair comparison, we selected the highest-ranking peptide without unexpected modifications as the result for each spectrum.

• pFind: In our analysis, we used pFind v3.2.027–29 for database search. The E. coli database includes 4789 protein entries along with contaminant protein sequences from UniProt. The yeast database contains 7021 protein entries from UniProt, also with contaminant protein sequences. For the closed search parameters, the maximum number of modification sites per peptide sequence was limited to 3. The length of identified peptides was set to 6 to 100. The digestion mode was set as full specificity with a maximum of 3 missed cleavage sites allowed. The search results were filtered to achieve a 1% FDR at both the PSM and protein levels.

• MSFragger: In our analysis, we used FragPipe 23.0, in which the version of MSFragger30 is v4.2. The searched E. coli and yeast databases were the same as those used in pFind, and the basic search parameters were consistent with those of pFind.

• MS-GF+ and MS-GF+*: In our analysis, we used two models of MS-GF+ v2024032631. The first model, referred to as MS-GF+, was the original model provided by its authors. The second model, referred to as MS-GF+*, was retrained on peptides digested by four different proteases. Specifically, we used the program provided in the original text to train scoring parameters for each protease. The searched E. coli and yeast databases were the same as those used in pFind, and the basic search parameters were consistent with those of pFind.

Design of DiNovo software
Previously, the only software available for mirror-protease de novo sequencing was pNovoM, which required users to manually carry out step-by-step operations for pre-sequencing, mirror-spectra recognition, and de novo sequencing (note that the programs for the first two steps were not publicly released). This makes its use of it cumbersome and not user-friendly. As a result, there remains a need for software that can recognize and sequence mirror spectra with a simple, one-click operation.

To meet this need, we integrate the various algorithms developed in this paper into a complete software solution named DiNovo, making it easily accessible to participants in the field. The DiNovo software includes the following modules: configuration settings, MS data I/O, spectra preprocessing, mirror-spectra recognition, de novo sequencing, and real-time log output during the process. As illustrated in Supplementary Fig. 14, parallelization is fully supported in each analytical component of DiNovo, significantly improving computational efficiency and reducing overall processing time.

In DiNovo, error-tolerant precursor mass indexes are built for all spectra to enable high-speed mirror-spectra recognition. As a result, the time complexity of DiNovo for selecting candidate spectral pairs involving n trypsin-digested spectra and m LysargiNase-digested spectra is O(m+n), in contrast to O(mn) by brute force. For example, DiNovo takes approximately 20 s to select candidate spectral pairs from a pool of 10 million spectral pairs, 1000 times faster than brute force. On the 16 million spectral pairs of E. coli dataset, DiNovo performed mirror-spectra recognition 2.1 and 46.5 times faster than pMerge (the mirror-spectra recognition module of pNovoM, unreleased) with 1 and 30 processes on a workstation, respectively. In combination of mirror-spectra recognition and sequencing, DiNovo completed the whole task four to five times faster than pMerge and pNovoM.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript introduces DiNovo, a novel software system for de novo peptide sequencing that leverages the\ncomplementarity of mirror proteases. The core contribution is the development of this system and the demonstration that the\nmirror-protease strategy significantly increases sequencing coverage and confidence compared to traditional single-\nprotease approaches. The paper is generally well-structured and presents convincing evidence of performance\nimprovements through comprehensive comparisons.\nMajor Comments:\n1. Details of the MirrorFinder Algorithm: While the MirrorFinder algorithm for recognizing mirror spectral pairs is presented as\na crucial prerequisite and an innovative component of DiNovo, the details of its matching score calculation and the rationale\nbehind certain parameters could be more elaborated. The method section mentions calculating statistics s_i by multiplying\nthe number of fragment ion pairs by their summed intensities within fixed mass intervals (-200 to +200 Da) . Given that the\ntheoretical fragment ion mass differences for mirror peptides are specifically ±128 Da (Lys) and ±156 Da in Table S4, it is\nunclear why a broader fixed range of ±200 Da is used for interval counting.\nAlso, a clearer explanation of how the matching score formulas is chosen is desired. For example, the impact of peak\nintensity normalization (e.g., square-root transformation used before input to MirrorNovo, or log transformation) on the\nmatching score calculation within MirrorFinder is not discussed. Would different normalization schemes significantly impact\nthe calculated score, and were other formulas for calculating the matching score considered and evaluated? More details on\nthe internal workings and parameter choices of MirrorFinder are needed given its direct impact on the number and quality of\nrecognized mirror pairs, which in turn affects the potential for downstream sequencing improvements\n2. MirrorFinder Accuracy Evaluation on Real Data: The authors state that \"Annotated mirror spectral pairs were constructed\nbased on pFind (v3.2.0) search results at a 1% FDR\" for training MirrorNovo . Since such a dataset exists, it would be\nvaluable to report the accuracy of the MirrorFinder algorithm itself in identifying true mirror spectral pairs (sensitivity and\nprecision) using this or a similar expertly annotated dataset.\n3. Potential Error Amplification from Sequential FDR Control: The workflow involves two distinct steps with FDR control: 1)\nrecognizing mirror spectral pairs using a target-decoy strategy for MirrorFinder scores, and 2) evaluating the de novo\nsequencing results using TD mapping of peptides to a target-decoy protein database. Performing FDR control sequentially\nat multiple steps could potentially lead to an accumulation or complex interaction of false positives, making the overall\nconfidence of the final peptide/protein list less straightforward than the reported 1% FDR in the final step. A discussion on\nthe statistical implications of these sequential filtering steps and whether the final reported FDR accurately reflects the error\nrate considering both stages would enhance confidence in the results.\n4. Contribution of the Single-Protease Module (Denovo-GCN): DiNovo incorporates a single-protease de novo sequencing\nalgorithm (Denovo-GCN) to process spectra that cannot form mirror spectral pairs. The extent to which the overall reported\nimprovements of DiNovo (e.g., in Figures 4, 5) are attributable solely to the mirror-spectra sequencing\n(MirrorNovo/pNovo/pNovoM2) versus the contribution from Denovo-GCN processing single spectra is not clearly measured. It\nwould be insightful to see a breakdown of such results or an evaluation of DiNovo without the Denovo-GCN module to\nunderstand the isolated benefit provided by the mirror protease strategy and mirror-spectra-specific algorithms. If the\ncontribution of Denovo-GCN is significant, this should be explicitly discussed.\n5. Comparison with Other Recent Deep Learning Algorithms: While DiNovo's mirror-protease strategy is unique, the paper\ncompares its sequencing performance primarily with PEAKS, pNovo3, PointNovo, and two versions of Casanovo . Given\nthat Denovo-GCN is integrated into DiNovo, a direct comparison against Denovo-GCN on the single-protease datasets\nwould be relevant. Furthermore, other recent deep learning de novo sequencing algorithms mentioned in the introduction,\nsuch as PepNet, GraphNovo, and π-PrimeNovo, are not included in the head-to-head comparisons.\n6. Case Study on Mirror Matching Errors: A small case study illustrating instances where MirrorFinder might incorrectly pair\nspectra, and how this impacts the downstream sequencing results (e.g., leading to incorrect peptide sequences or failure to\nsequence), would be informative. This could help users understand the potential failure modes and the robustness of the\nmethod.\nMinor Comments:\n1. Details on Combining Results for Single-Protease Tools: When comparing DiNovo with other single-protease algorithms\nor PEAKS results from multiple proteases (e.g., All(PEAKS) ), the method used to combine the results from different\nproteases and potentially different software outputs should be clearly described, e.g., how the 1% FDR is controlled for the\ncombined list. Was the FDR controlled independently for each protease/software and then lists merged, or was a pooled list\nsubjected to FDR control?\n2. Comparison with Other Popular Database Search Engines: The comparison with database search relies solely on pFind.\nWhile pFind is a capable search engine, including comparisons with other widely used and high-performing search engines\nsuch as MSGF+ would strengthen the conclusion about de novo sequencing's potential as an alternative or complement to\ntraditional database search .", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "The core technical contributions are sound and the work meets publication standards. The innovation in both algorithmic\napproaches and evaluation methodology will be of high interest to the proteomics community.\nMy main criticism on the technical aspects would be:\n- The relatively small training dataset compared to recent large-scale models (e.g., Casanovo's 30M spectra) may limit\ngeneralizability.\n- Short peptides are more likely to randomly match database sequences, so filtering by mass removes many false matches.\nThe TD mapping approach is clever and addresses a real need in the field, though score-based ranking might be more\neffective than mass-0based filtering. For instance, a high-confidence short peptide might be more reliable than a low-\nconfidence long peptide so score-based might retain more high-quality results. Also, since most other algorithms are\nevaluated using their confidence scores, it will be a fairer comparison.\nHere are some recommendations for improvement:\n- Please clarify limitations regarding PTM handling and large-scale applicability\n- Please provide more detailed computational benchmarking for runtime and memory usage. Adding statistical significance\ntesting for key performance comparisons would be great as well.\n- Please provide more implementation details for full reproducibility, could be in supplemental info.\n- Expanding validation to at least one additional species would be great.\nThe software appears production-ready with excellent documentation.", "reviewer_4": "In this manuscript, the authors present DiNovo, a novel de novo peptide sequencing framework that leverages mirror\nproteases to get complementary ion coverage. The authors propose two algorithms: MirrorFinder, which is designed to find\nthe mirror-spectra, and MirrorNovo, which performs de novo sequencing based on the paired spectra. In addition, the authors\nalso introduced a target-decoy strategy to evaluate and control the FDR of the proposed method. Several experiments were\ndone to show the performance of the proposed method. However, there are still several areas that require clarification and\nfurther justification.\n1.The authors compare DiNovo with other single protease de novo algorithms including pointNovo. However, as far as I\nknow, pointNovo is not the latest and best de novo algorithm in PEAKS. In the paper “Mitigating the missing-fragmentation\nproblem in de novo peptide sequencing with a two-stage graph-based deep learning model”, a more recent method,\nGraphNovo, was proposed. GraphNovo uses a two-stage de novo peptide-sequencing algorithm based on a graph neural\nnetwork and has demonstrated better performance than other algorithms. According to the PEAKS website\nhttps://www.bioinfor.com/peaks-deepnovo/, GraphNovo is already integrated in the PEAKS platform. Therefore, it is\nrecommended to include GraphNovo in the performance comparison to ensure a fair and up-to-date evaluation.\n2.In the manuscript, the authors proposed TD mapping approach to evaluate the FDR in de novo sequencing results, and\nthe method generated decoy peptides similar to database search approach. However,this approach assumes that de novo\npeptide candidates are limited to known protein sequences, which contradicts one of the key strengths of de novo\nsequencing—its ability to discover novel peptides not present in existing databases. A potentially more approach is\ndiscussed in the paper “NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of\nDe Novo Peptide Sequencing”, where decoy spectra, rather than decoy peptides, are used to estimate FDR. The authors\nare encouraged to review this method and either justify their current approach more rigorously or consider adopting the\nNovoBoard framework for a more robust FDR estimation.\n3.The authors compare DiNovo against pFind for database search-based identification. However, it is unclear why pFind\nwas selected as the representative method. The authors should provide justification for this choice, particularly with regard to\npFind's performance in comparison to other widely used search engines such as MSFragger, MaxQuant, or PEAKS.\n4.In the Methods section, the manuscript describes using a target-decoy strategy to filter mirror-spectral pairs by comparing\nthe original score with the decoy score. This approach requires further clarification and validation. The target-decoy strategy\nrelies on the assumption that decoy matches exhibit a similar score distribution as false positives among the targets. The\napplicability of this assumption to spectral pair filtering should be discussed in more depth. Ideally, the authors should\ninclude an analysis or experimental evidence demonstrating the reliability of this approach in the context of MirrorFinder.\n5.In the Online Methods section, when describing the pFind search settings, important parameters such as digestion mode\n(semi-tryptic or fully tryptic) and the number of allowed missed cleavages are not specified. These parameters can\nsignificantly impact identification results and should be clearly described to ensure the reproducibility of the experiments.\nThis work is accompanied by a user-friendly software tool featuring an intuitive GUI. A comprehensive user manual is also\nprovided to facilitate ease of use and reproducibility."}, "author_response": "Responses to reviewers’ comments\nReviewer #1\nThis manuscript introduces DiNovo, a novel software system for de novo peptide sequencing that leverages the\ncomplementarity of mirror proteases. The core contribution is the development of this system and the\ndemonstration that the mirror-protease strategy significantly increases sequencing coverage and confidence\ncompared to traditional single-protease approaches. The paper is generally well-structured and presents\nconvincing evidence of performance improvements through comprehensive comparisons.\nRe: We greatly appreciate the reviewer’s accurate summary and positive evaluation of our manuscript.\nMajor Comments:\n1. Details of the MirrorFinder Algorithm: While the MirrorFinder algorithm for recognizing mirror spectral\npairs is presented as a crucial prerequisite and an innovative component of DiNovo, the details of its matching\nscore calculation and the rationale behind certain parameters could be more elaborated. The method section\nmentions calculating statistics s_i by multiplying the number of fragment ion pairs by their summed intensities\nwithin fixed mass intervals (-200 to +200 Da). Given that the theoretical fragment ion mass differences for\nmirror peptides are specifically ±128 Da (Lys) and ±156 Da in Table S4, it is unclear why a broader fixed range\nof ±200 Da is used for interval counting.\nAlso, a clearer explanation of how the matching score formulas is chosen is desired. For example, the impact\nof peak intensity normalization (e.g., square-root transformation used before input to MirrorNovo, or log\ntransformation) on the matching score calculation within MirrorFinder is not discussed. Would different\nnormalization schemes significantly impact the calculated score, and were other formulas for calculating the\nmatching score considered and evaluated? More details on the internal workings and parameter choices of\nMirrorFinder are needed given its direct impact on the number and quality of recognized mirror pairs, which in\nturn affects the potential for downstream sequencing improvements.\nRe: Thank you for your suggestion. We have provided more details regarding the MirrorFinder algorithm and\nits preprocessing steps in the revised manuscript (Methods 4.2, 4.3, and Supplementary Note 3). Parameters in\nthe matching score calculation were either selected based on expert experience or tested through ablation\nexperiments. To be specific, we used four benchmark datasets constructed from pFind search results at 1% FDR\nto evaluate the recall and precision of the recognized spectral pairs. We compared the recall under 90% precision,\nand the AUC (Area Under the Curve) of different processing methods, including intensity processing methods,\ndenoising methods, and matching score methods. As shown in Figure S21, we found that the parameters used\nin the article have achieved the best results. For peak intensity processing method, the square-root operation\nperformed slightly better than taking logarithm or no processing. In fact, the square-root transformation for peak\nintensity is a commonly used preprocessing method based on expert experience for peptide identification. For\ndenoising method, we chose to preserve 7 peaks per 100Da to reduce computational complexity without\nsacrificing accuracy. As for the form of the matching score formula, we considered the number of fragment ion\npairs, the summed intensity, and the product of these two terms, and ultimately selected the last one.\nWhen calculating the matching scores, the mass difference interval is taken as [-200, 200] Da, which was\ndetermined to cover all theoretical fragment ion mass differences in Table S4. Larger intervals will lead to\nincreased computational complexity, while smaller intervals will result in insufficient samples for calculating\np-values. A reasonable interval length can alleviate errors caused by high-intensity noise peaks (with large mass\ndifferences). At the same time, for random spectral pairs, the scores of mass differences within [-200, 200] Da\ntend not to show a clear pattern. After comprehensive consideration, we have taken the interval currently in use.\nPeak intensity\nprocessing\nDenoising\nMatching score\nSupplementary Figure 21. Ablation experiments on different parameters in MirrorFinder. The comparison\nmethods include peak intensity processing methods, denoising methods, and matching score methods. Orange\nbars show the parameter used in our article.\n2. MirrorFinder Accuracy Evaluation on Real Data: The authors state that \"Annotated mirror spectral pairs were\nconstructed based on pFind (v3.2.0) search results at a 1% FDR\" for training MirrorNovo. Since such a dataset\nexists, it would be valuable to report the accuracy of the MirrorFinder algorithm itself in identifying true mirror\nspectral pairs (sensitivity and precision) using this or a similar expertly annotated dataset.\nRe: Thank you for your advice. In the initial stage of algorithm design, we have conducted very detailed\nevaluations on the performance of the MirrorFinder algorithm. Due to the length limitation of the article, we\ndid not include them in the article before. In Supplementary Note 3 of the revised version, we have provided\nmore evaluation results for the MirrorFinder algorithm. To be specific, we used four benchmark datasets\nconstructed from pFind search results at 1% FDR to evaluate the recall and precision of MirrorFinder, and drew\nROC (Receiver Operating Characteristic) curves and calculated the AUC. To further demonstrate the\neffectiveness of our algorithm in identifying true mirror spectral pairs, we also compared it with pMerge, which\nis the undisclosed mirror-spectra recognition algorithm used in pNovoM. As shown in Table S6 and Figure S22,\nour MirrorFinder algorithm outperformed pMerge across all datasets.\nSupplementary Table 6. Evaluation results of two mirror-spectra recognition algorithms. The precision and\naccuracy shown in the table were calculated under 80% recall.\nprecision (%) accuracy (%) AUC\nMirrorFinder 94.93 97.53 0.9909\nE. coli-TryLys\npMerge 85.01 89.86 0.9435\nMirrorFinder 97.11 93.09 0.9844\nE. coli-LysCLysN\npMerge 92.11 88.69 0.9484\nMirrorFinder 90.01 97.65 0.9846\nYeast-TryLys\npMerge 84.36 93.54 0.9576\nMirrorFinder 93.15 95.04 0.9825\nYeast-LysCLysN\npMerge 74.80 90.68 0.9398\nTry+Lys LysC+LysN\nE. coli\nYeast\nSupplementary Figure 22. ROC curves of two mirror-spectra recognition algorithms.\n3. Potential Error Amplification from Sequential FDR Control: The workflow involves two distinct steps with\nFDR control: 1) recognizing mirror spectral pairs using a target-decoy strategy for MirrorFinder scores, and 2)\nevaluating the de novo sequencing results using TD mapping of peptides to a target-decoy protein database.\nPerforming FDR control sequentially at multiple steps could potentially lead to an accumulation or complex\ninteraction of false positives, making the overall confidence of the final peptide/protein list less straightforward\nthan the reported 1% FDR in the final step. A discussion on the statistical implications of these sequential\nfiltering steps and whether the final reported FDR accurately reflects the error rate considering both stages\nwould enhance confidence in the results.\nRe: Thank you for your constructive suggestion. We will explain from both theoretical and experimental\nperspectives that the FDR of sequenced peptides is controllable. The related content has been added to Results\n2.2 and Supplementary Note 1 in the revised version.\nIn our workflow, we perform quality control in two steps: mirror-spectra recognition and peptide sequencing.\nSpecifically, quality control for mirror-spectra recognition is performed at the spectral-pair level, while quality\ncontrol for sequencing is performed at the peptide level. These two steps are totally orthogonal to each other\nwithout information sharing or leakage. Incorrect spectral pairs most likely lead to incorrectly sequenced\npeptides that cannot be mapped to the protein database and are thus discarded. Only those peptides successfully\nmapped to the protein database are retained and are subjected to FDR control. Importantly, the FDR estimation\nin the sequencing step fully satisfies the assumption of target-decoy strategy; that is, an incorrect peptide is\nequally probable to be mapped to the target or decoy database, ensuring the accuracy of estimated FDR at the\npeptide level. Admittedly, a part of incorrect spectral pairs may lead to correct peptides sequenced (in such cases\nthe peptide is sequenced from one of the two spectra), resulting in inflated FDR at the spectrum level. This is\nwhy we did not compare different sequencing methods at the spectrum level in our article. Furthermore, we\nremoved conflicting sequencing results between the spectral pairs and their corresponding single spectra to\nreduce peptide-spectrum mismatches.\nTo demonstrate that the FDR of final sequenced peptides can be effectively controlled, we further conducted a\nverification experiment based on pFind search results of the yeast dataset with Trypsin and LysargiNase\ndigestion. We constructed a benchmark dataset from spectra that were identified by pFind at 0.1% FDR. In the\nexperiment, the FDR thresholds of both recognized spectral pairs and sequenced peptides were set to 1%. The\nresult in Figure S16 shows that after peptide FDR control (TD mapping), the actual FDR of spectral pairs\ndecreased from 5.11% to 2.28%, and the actual FDR of sequenced peptides decreased from 81.85% to 0.11%.\nThis result demonstrates that the FDR of sequenced peptides after TD mapping was effectively controlled and\nthe errors in spectral pairs did not propagate to the sequencing results. In fact, the error rate of spectral pairs\nwas significantly reduced after TD mapping.\nSupplementary Figure 16. FDR analysis at the spectral pair and peptide levels.\n4. Contribution of the Single-Protease Module (Denovo-GCN): DiNovo incorporates a single-protease de novo\nsequencing algorithm (Denovo-GCN) to process spectra that cannot form mirror spectral pairs. The extent to\nwhich the overall reported improvements of DiNovo (e.g., in Figures 4, 5) are attributable solely to the mirror-\nspectra sequencing (MirrorNovo/pNovoM2) versus the contribution from Denovo-GCN processing single\nspectra is not clearly measured. It would be insightful to see a breakdown of such results or an evaluation of\nDiNovo without the Denovo-GCN module to understand the isolated benefit provided by the mirror protease\nstrategy and mirror-spectra-specific algorithms. If the contribution of Denovo-GCN is significant, this should\nbe explicitly discussed.\nRe: Thank you for pointing out this oversight. The updated content can be found in Results 2.5 of the revised\nmanuscript. It should be noted that the single-protease sequencing model of DiNovo is called GCNovo now,\nwhich is the updated version of Denovo-GCN.\nTo evaluate the contribution of the single-protease module (GCNovo), we split the sequencing results of DiNovo\ninto those from the single-protease sequencing algorithm GCNovo and those from the mirror-protease\nsequencing algorithm MirrorNovo. On the one hand, we compared the sequencing results of GCNovo and\nDiNovo (Figure 5). On the E. coli and yeast datasets, DiNovo significantly outperformed GCNovo at all three\nevaluation levels, demonstrating the contribution of mirror-spectra sequencing. This advantage of DiNovo stems\nprimarily from its use of complementarity of mirror spectra, which allows for the simultaneous accurate\nsequencing of mirror peptides that may be difficult to be sequenced alone. Furthermore, these results indicate\nthat GCNovo can be effectively used as a standalone tool for single-protease sequencing with considerable\nperformance. On the other hand, we also compared the sequencing results of GCNovo and MirrorNovo within\nthe DiNovo software (Figure S5b). While the contribution of mirror-protease sequencing is significant, single-\nprotease sequencing remains indispensable for spectra that cannot form mirror pairs.\nE. coli (Try+Lys) E. coli (LysC+LysN) Yeast (Try+Lys) Yeast (LysC+LysN)\nAmino\nacid\nPeptide\nProtein\nSupplementary Figure 5. (b) Venn diagrams of high-confidence amino acids, peptides, and proteins sequenced\nby GCNovo and MirrorNovo within the DiNovo software.\n5. Comparison with Other Recent Deep Learning Algorithms: While DiNovo's mirror-protease strategy is\nunique, the paper compares its sequencing performance primarily with PEAKS, pNovo3, PointNovo, and two\nversions of Casanovo. Given that Denovo-GCN is integrated into DiNovo, a direct comparison against Denovo-\nGCN on the single-protease datasets would be relevant. Furthermore, other recent deep learning de novo\nsequencing algorithms mentioned in the introduction, such as PepNet, GraphNovo, and π-PrimeNovo, are not\nincluded in the head-to-head comparisons.\nRe: Thank you for your critical suggestion. In Results 2.5 of the revised version, we have added a comparison\nagainst GCNovo (the built-in single-protease sequencing model in DiNovo), and we have replaced PEAKS\n(version 11 with PointNovo as its built-in algorithm) with GraphNovo (the built-in algorithm in PEAKS-online\n12) because it is the latest algorithm of PEAKS. The open-source version of GraphNovo requires too much\nmemory to run, so we didn't use it. PepNet and PrimeNovo are not suitable for comparison because PepNet\ncannot predict modified peptides (e.g., peptides with oxidation of methionine (M)), while PrimeNovo cannot be\nstably retrained on the 8-species dataset (a phenomenon also pointed out by other researchers). For all\nsequencing algorithms under consideration, the asterisks represent models trained on the 8-species dataset\n(excluding yeast), such as PointNovo*, Casanovo*, and GCNovo*. CasanovoV2 was trained on a large-scale\ndataset derived from the MassIVE Knowledge Base (MassIVE-KB).\nAs shown in Figure 5a and 5b, DiNovo outperformed all competitors across all datasets at all three evaluation\nlevels, demonstrating the contribution of mirror-spectra sequencing. Compared to GraphNovo, DiNovo also\nsequenced more high-confidence peptides with higher amino acid coverage and higher protein coverage. Figure\n5c shows the significant overlap of high-confidence amino acids sequenced by DiNovo and other algorithms.\nMore results are given in Results 2.5 and Supplementary Information (Figure S6-S8) in the revised version.\na.\nb.\nc. pNovo3 PointNovo* GraphNovo Casanovo* CasanovoV2 GCNovo* GCNovo\nE.\ncoli\nYeast\nFig. 5 Comparison with different de novo sequencing algorithms. (a) De novo sequencing results on the E.\ncoli datasets. (b) De novo sequencing results on the yeast datasets. Green hollow bars indicate all results, while\norange solid bars indicate high-confidence (full fragment ion coverage) results. (c) Venn diagrams of high-\nconfidence amino acids sequenced by DiNovo and other algorithms on the combined results of four proteases.\nMore detailed configurations of these algorithms are given in Methods.\n6. Case Study on Mirror Matching Errors: A small case study illustrating instances where MirrorFinder might\nincorrectly pair spectra, and how this impacts the downstream sequencing results (e.g., leading to incorrect\npeptide sequences or failure to sequence), would be informative. This could help users understand the potential\nfailure modes and the robustness of the method.\nRe: Thank you for your suggestion. In Supplementary Note 1 of the revised version, we have elaborated on this\nissue. Indeed, MirrorFinder might incorrectly pair some spectra, even if there is a quality control procedure.\nThese incorrect spectral pairs have two kinds of destinies. In most cases, the sequenced peptides from incorrect\nspectral pairs cannot be mapped to the protein database, or are filtered out by TD mapping due to their low\npeptide mass, and therefore they will not appear in the final sequencing results. Figure S17 serves as an example\nfor incorrect peptide sequence. In this example, the sequencing result of this spectral pair could not be mapped\nto the protein database and was discarded. In a few cases, incorrect spectral pairs may lead to correct peptides\nsequenced (this can happen when the sequence is totally derived from one single spectrum in the pair). Figure\nS18 serves as such an example. In this example, the spectral pair was falsely recognized by MirrorFinder,\nhowever, the sequencing result of this spectral pair was correctly mapped to the protein database. This is because\nthe sequence was derived from the LysargiNase-digested spectrum. Admittedly, such phenomenon will inflate\nthe FDR of sequencing results at the spectrum level. This is why we did not compare different sequencing\nmethods at the spectrum level in our article. Furthermore, to alleviate this issue, we have removed conflict\nsequencing results between the spectral pairs and their corresponding single spectra to reduce peptide-spectrum\nmismatches. The confidence of the final sequencing results of DiNovo is guaranteed, as the TD mapping\nalgorithm can effectively control the FDR of sequenced peptides (refer to Supplementary Note 1 and our\nresponse to the third comment).\nSupplementary Figure 17. Example of an incorrect spectral pair with incorrect peptide sequence. The\nsequencing result of DiNovo is ALLTTAEYLAYQTER.\nSupplementary Figure 18. Example of an incorrect spectral pair with correct peptide sequence. The sequencing\nresult of DiNovo is HLEDNTLLLSGPFK.\nMinor Comments:\n1. Details on Combining Results for Single-Protease Tools: When comparing DiNovo with other single-protease\nalgorithms or PEAKS results from multiple proteases (e.g., All(PEAKS) ), the method used to combine the\nresults from different proteases and potentially different software outputs should be clearly described, e.g., how\nthe 1% FDR is controlled for the combined list. Was the FDR controlled independently for each\nprotease/software and then lists merged, or was a pooled list subjected to FDR control?\nRe: Thank you for your advice. For other single-protease algorithms in Results 2.5, the 1% FDR is controlled\nindependently for each protease, and then the combined list is simply merged. The FDR of combined list may\nexceed 1%, however, this precisely gives other algorithms an advantage in the number of sequencing results.\nUnder this advantage, our algorithm can still stand out, which indicates the exceptional performance of our\nalgorithm.\n2. Comparison with Other Popular Database Search Engines: The comparison with database search relies solely\non pFind. While pFind is a capable search engine, including comparisons with other widely used and high-\nperforming search engines such as MSGF+ would strengthen the conclusion about de novo sequencing's\npotential as an alternative or complement to traditional database search.\nRe: Thank you for your constructive suggestion. Besides pFind, we have added two database search engines\nMS-GF+ and MSFragger into comparison in Results 2.6 of the revised version. In our analysis, we used\nMSFragger v4.2 (the built-in algorithm in FragPipe 23.0) and MS-GF+ v20240326. The E. coli and yeast\ndatabases are the same as used in pFind, and the basic search parameters were consistent with those of pFind.\nMore detailed configurations are given in Methods 4.7.\nAs shown in Figure 6a and 6b, DiNovo sequenced more peptides than the three database search engines across\nall datasets, with comparable numbers of amino acids and proteins sequenced. It should be noted that MS-GF+\nshowed deficiency in identifying N-terminal digested peptides; therefore, we retrained its scoring model on\npeptides digested by four different proteases (denoted as MS-GF+*) and much better results were obtained.\nImportantly, there are significant overlaps between the results of DiNovo and those of each search engine,\ndemonstrating the reliability of the results (Figure 6c). Figure 6d further illustrates that the peptide sequences\nof spectra identified by both DiNovo and pFind are highly consistent. More results are given in Results 2.6 and\nSupplementary Information (Figure S9-S12) in the revised version.\na.\nb.\nc. pFind MSFragger MS-GF+* d. Try+Lys LysC+LysN\nE. coli E. coli\nYeast Yeast\nFig. 6 Comparison of de novo sequencing and database search results. (a) De novo sequencing and database\nsearch results on the E. coli datasets. (b) De novo sequencing and database search results on the yeast datasets.\n(c) Venn diagrams of amino acids sequenced by DiNovo and database search engines on the combined results\nof four proteases. Database search engines for comparison include pFind, MSFragger, and MS-GF+.\n(d) The consistency of peptide sequences of spectra identified by both DiNovo and pFind. Gray indicates that the\nDiNovo sequence is consistent with the pFind sequence; otherwise, it is shown in yellow. To ensure a fair\ncomparison, only pFind was included here, as it used the same set of spectra (mgf format) as DiNovo (exported\nby the pParse algorithm with the co-eluted precursors detected) while other engines directly used the raw spectra.\nReviewer #3\nThe core technical contributions are sound and the work meets publication standards. The innovation in both\nalgorithmic approaches and evaluation methodology will be of high interest to the proteomics community.\n(Remarks on code availability) The software appears production-ready with excellent documentation.\nRe: Thank you so much for your high recognition of our work.\n- The relatively small training dataset compared to recent large-scale models (e.g., Casanovo's 30M spectra)\nmay limit generalizability.\nRe: Thank you for your comment. Indeed, due to the limited availability of mirror spectra data, MirrorNovo\nhas been trained on a relatively small dataset in this paper. As the species of model training dataset needs to be\ndifferent from the species of test dataset (E. coli and yeast), we selected three other species to train the model\n(the size of the training dataset is shown in the Table S5). The independence of test data was aimed to test the\ngeneralizability of our algorithm. The primary goal of our algorithm is to demonstrate the effectiveness of\nmirror-protease strategy in improving de novo sequencing, and extensive evaluation experiments demonstrated\nthat our goal has been achieved without the need for a large-scale training dataset. In fact, not all state-of-the-\nart de novo sequencing algorithms were trained on very large dataset, such as PointNovo, GraphNovo and\nPepNet. Furthermore, we can expect that as more training data becomes available in the future, there will be a\nlot of room for improvement in the performance of MirrorNovo.\n- Short peptides are more likely to randomly match database sequences, so filtering by mass removes many\nfalse matches. The TD mapping approach is clever and addresses a real need in the field, though score-based\nranking might be more effective than mass-0based filtering. For instance, a high-confidence short peptide might\nbe more reliable than a low-confidence long peptide so score-based might retain more high-quality results. Also,\nsince most other algorithms are evaluated using their confidence scores, it will be a fairer comparison.\nRe: Thank you for your positive comment and constructive suggestion. In this paper, we proposed the TD\nmapping approach to evaluate the de novo sequencing results of various algorithms. To be specific, we map the\nde novo sequences separately to the target and decoy databases, and filter the sequences according to their\nmasses to ensure the estimated FDR under control. As you suggested and we mentioned in the Discussion\nsection, an alternative and potentially better approach would be to rank the peptides by some scoring function,\nwhich could increase the number of peptides retained at the given FDR. Additionally, the pattern of mapping\ncan be extended, e.g., from precise mapping to error-tolerant mapping to discover sequence variations or protein\nmodifications. These are our ongoing work and will be reported elsewhere.\n- Please clarify limitations regarding PTM handling and large-scale applicability.\nRe: We thank the reviewer for this valuable suggestion regarding PTM handling and large-scale applicability.\nDe novo peptide sequencing still faces difficulties in handling PTM currently. To our knowledge, up to now\nthere are no published sequencing algorithms that can consider arbitrary PTM types (masses). It is the same\nwith DiNovo. However, DiNovo still has the capability to handle specific PTMs as long as corresponding\ntraining data is provided, such as the common carbamidomethyl of cysteine (C) and oxidation of methionine\n(M).\nAs for the application to large-scale data, indeed our algorithm will take a longer time. This is because the\nnumber of spectral pairs increases quadratically with the number of spectra. However, our results show that\nproteome-level sequencing using DiNovo can be successfully accomplished in feasible time (in days). Moreover,\nfor large-scale data, we recommend using pNovoM2, which is much faster than MirrorNovo for peptide\nsequencing. We have clarified this in Results 2.7 of the revision.\n- Please provide more detailed computational benchmarking for runtime and memory usage. Adding statistical\nsignificance testing for key performance comparisons would be great as well.\nRe: Thanks for your valuable advice. In the revised manuscript, we have conducted comprehensive experiments\nto compare the running speeds of different sequencing algorithms. To be specific, we constructed benchmark\ndatasets for single spectra and mirror spectra, and then tested the speed of single-protease sequencing and\nmirror-protease sequencing algorithms, respectively. As shown in Figure S5a and Figure S13b, the running\nspeed of GCNovo is similar to that of PointNovo and Casanovo, while GraphNovo and pNovo3 have\nsignificantly faster speeds. In the mirror-protease sequencing algorithms, the speed of pNovoM2 is significantly\nfaster than that of MirrorNovo and pNovoM. Due to inconsistent dataset sizes, the comparison of memory usage\nis not very meaningful. Moreover, most algorithms can dynamically adjust the memory usage according to the\navailable memory size of computers.\nAs for statistical significance testing, the results in our paper only contain two datasets (E. coli and yeast), which\nare not sufficient to support statistical testing. After careful consideration, we did not find a way to perform\neffective statistical testing based on the current results. Nevertheless, we believe that the results presented are\nsufficient to suggest the advantages of our approach. For example, the bar plot is an intuitive and commonly\nused manner to compare de novo sequencing algorithms.\nS5. (a) S13. (b)\nSupplementary Figure 5. (a) The running speeds of single-protease de novo sequencing algorithms. All\nsoftware tools were run on an NVIDIA GeForce RTX 4090 GPU.\nSupplementary Figure 13. (b) The running speeds of mirror-protease de novo sequencing algorithms.\nMirrorNovo was run on an NVIDIA GeForce RTX 4090 GPU, while pNovoM2 and pNovoM were executed\non an Intel(R) Xeon(R) Silver 4410Y CPU.\n- Please provide more implementation details for full reproducibility, could be in supplemental info.\nRe: Thank you for your kindly advice. We have provided the configuration file in the standalone software\npackage. In addition, we have added more implementation details in Methods 4.7 of the revised version for full\nreproducibility. To be specific, for the mirror-spectra recognition experiment, we first retained the 200 most\nabundant peaks and then selected 7 peaks per 100 Da. For the sequencing experiment, we only retained spectra\nwith a precursor mass in the range of [300, 3500] Da. Both precursor and fragment ion mass tolerances were\nset to 20 ppm. Carbamidomethyl of cysteine (C) was specified as a fixed modification and oxidation of\nmethionine (M) was considered as a variable modification. In addition, to process the sequencing results, we\nfirst resolved conflicts where a single spectrum was paired with different spectra to produce different sequences.\nAfter assigning a unique mirror sequence to each spectrum, we further filtered out results where the mirror\nsequence was inconsistent with the sequence derived from the single spectrum.\n- Expanding validation to at least one additional species would be great.\nRe: Thanks for your constructive suggestion. In Results 2.5 of the revised manuscript, we have added an\nexperiment on an additional dataset. To be specific, we conducted experiments on purified proteins to\ndemonstrate the generalizability of DiNovo. The data was from two monoclonal antibodies (PXL1 and PXL2),\nwhich were expressed in Chinese hamster ovary (CHO) cells. The light and heavy chains of purified antibodies\nwere digested by trypsin and LysargiNase. For the sake of fairness, we used various de novo peptide sequencing\nalgorithms with the same parameters to sequence PXL1 and PXL2, which include pNovo3, PointNovo,\nGraphNovo, Casanovo, and DiNovo. Moreover, we compared the single-protease strategy and mirror-protease\nstrategy for purified protein sequencing. As shown in Figure S19 and S20, DiNovo demonstrated high\nproficiency in sequencing PXL1 and PXL2 with the highest amino acid coverage.\nSupplementary Figure 19. The peptides sequenced by different sequencing algorithms on the heavy and light\nchains of monoclonal antibodies.\nSupplementary Figure 20. The amino acid coverage of de novo sequencing results on the heavy and light\nchains of monoclonal antibodies.\nReviewer #4\nIn this manuscript, the authors present DiNovo, a novel de novo peptide sequencing framework that leverages\nmirror proteases to get complementary ion coverage. The authors propose two algorithms: MirrorFinder, which\nis designed to find the mirror-spectra, and MirrorNovo, which performs de novo sequencing based on the paired\nspectra. In addition, the authors also introduced a target-decoy strategy to evaluate and control the FDR of the\nproposed method. Several experiments were done to show the performance of the proposed method. However,\nthere are still several areas that require clarification and further justification.\n(Remarks on code availability) This work is accompanied by a user-friendly software tool featuring an\nintuitive GUI. A comprehensive user manual is also provided to facilitate ease of use and reproducibility.\nRe: Thank you for your positive feedback and constructive comments.\n1.The authors compare DiNovo with other single protease de novo algorithms including pointNovo. However,\nas far as I know, pointNovo is not the latest and best de novo algorithm in PEAKS. In the paper “Mitigating\nthe missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning\nmodel”, a more recent method, GraphNovo, was proposed. GraphNovo uses a two-stage de novo peptide-\nsequencing algorithm based on a graph neural network and has demonstrated better performance than other\nalgorithms. According to the PEAKS website https://www.bioinfor.com/peaks-deepnovo/, GraphNovo is\nalready integrated in the PEAKS platform. Therefore, it is recommended to include GraphNovo in the\nperformance comparison to ensure a fair and up-to-date evaluation.\nRe: Thank you for your suggestion. In the revision, we have used PEAKS-online 12, the newest version with\nGraphNovo integrated, to re-analyze all data. As expected, the results of new version are much better than those\nof the previous version. But this has not changed the conclusion of our paper.\nAs shown by Figure 4a and 4b, the mirror-protease strategy (DiNovo) significantly outperformed the single-\nprotease strategy (GraphNovo) in terms of all three metrics. Compared to the traditional single-protease strategy,\nsuch as Try (GraphNovo), the advantages of DiNovo using two pairs of mirror proteases were more pronounced.\nFigure 4c further shows the intersection of high-confidence amino acids and proteins sequenced by DiNovo and\nGraphNovo. More results are given in Supplementary Information (Figure S3, S4) in the revised version.\na.\nb.\nc. E. coli (Try/Lys) E. coli (LysC/LysN) Yeast (Try/Lys) Yeast (LysC/LysN)\nAmino acid\nProtein\nFig. 4 Comparison of de novo sequencing results obtained by single-protease and mirror-protease\nstrategies. (a) De novo sequencing results on the E. coli datasets. (b) De novo sequencing results on the yeast\ndatasets. Single-protease spectra were analyzed by GraphNovo, and mirror spectra were analyzed by DiNovo.\nAll(GraphNovo) represents the union of LysN(GraphNovo), LysC(GraphNovo), Lys(GraphNovo) and\nTry(GraphNovo), and All(DiNovo) for the union of LysC+LysN(DiNovo) and Try+Lys(DiNovo). Green\nhollow bars indicate all results, while orange solid bars indicate high-confidence (full fragment ion coverage)\nresults. (c) Venn diagrams of high-confidence amino acids and proteins sequenced by DiNovo and GraphNovo.\n2.In the manuscript, the authors proposed TD mapping approach to evaluate the FDR in de novo sequencing\nresults, and the method generated decoy peptides similar to database search approach. However, this approach\nassumes that de novo peptide candidates are limited to known protein sequences, which contradicts one of the\nkey strengths of de novo sequencing—its ability to discover novel peptides not present in existing databases. A\npotentially more approach is discussed in the paper “NovoBoard: A Comprehensive Framework for Evaluating\nthe False Discovery Rate and Accuracy of De Novo Peptide Sequencing”, where decoy spectra, rather than\ndecoy peptides, are used to estimate FDR. The authors are encouraged to review this method and either justify\ntheir current approach more rigorously or consider adopting the NovoBoard framework for a more robust FDR\nestimation.\nRe: Thanks for your valuable suggestion. In Supplementary Note 1 of the revised version, we have proofed that\nTD mapping is reasonable through a verification experiment. To be specific, we constructed a benchmark dataset\nbased on pFind search results, and analyzed the FDR of recognized spectral pairs and sequenced peptides. The\nresult in Figure S16 shows that the FDR at the spectral pair level decreases from 5.11% to 2.28%, and the FDR\nat the peptide level decreases from 81.85% to 0.11%, which indicates that the FDR of sequenced peptides after\nTD mapping is controllable.\nTD mapping is not a general method for quality control of de novo sequencing results. Rather, it provides a\nframework to compare different sequencing algorithms. In the past, sequencing algorithms were mostly\nevaluated based on benchmark datasets which were constructed by peptide identification via database search.\nThe drawbacks of such a way lie in that the peptide identifications from database search may contain errors,\nand the parallel comparison between de novo and database search algorithms is prevented. TD mapping still\nrelies on the existence of protein database, but is free of database search. It enables de novo sequencing as an\nalternative peptide identification approach with FDR control when the database is available.\nNovoBoard is a database-free method to evaluate the de novo sequencing results, but it cannot be directly\napplied to mirror-protease based peptide sequencing. If decoy spectra are generated before mirror-spectra\nrecognition, the recognized spectral pairs are subject to quality control and thus there are few false spectra,\nwhich does not comply with the assumption of target-decoy strategy in the TD mapping stage. If decoy spectral\npairs are generated after recognition and then applied to NovoBoard, the previously quality controlled spectral\npairs have an advantage in competition (due to their complementarity), which is also unfair. Although\nNovoBoard is currently not suitable for this work, we will try to extend this methodology to mirror spectra in\nthe future.\n3.The authors compare DiNovo against pFind for database search-based identification. However, it is unclear\nwhy pFind was selected as the representative method. The authors should provide justification for this choice,\nparticularly with regard to pFind's performance in comparison to other widely used search engines such as\nMSFragger, MaxQuant, or PEAKS.\nRe: Thank you for your constructive suggestion. In Results 2.6 of the revised version, we have added two search\nengines MSFragger and MS-GF+ for comparison. Please refer to the last response to the first reviewer’s\ncomments for detailed analysis results.\n4.In the Methods section, the manuscript describes using a target-decoy strategy to filter mirror-spectral pairs\nby comparing the original score with the decoy score. This approach requires further clarification and validation.\nThe target-decoy strategy relies on the assumption that decoy matches exhibit a similar score distribution as\nfalse positives among the targets. The applicability of this assumption to spectral pair filtering should be\ndiscussed in more depth. Ideally, the authors should include an analysis or experimental evidence demonstrating\nthe reliability of this approach in the context of MirrorFinder.\nRe: We thank the reviewer for raising this critical point regarding the validity of target-decoy strategy in the\nmirror-spectra recognition algorithm. In MirrorFinder, we adopted the target-decoy strategy to filter mirror\nspectral pairs for quality control. The specific approach is to consider the score related to the theoretical mass\ndifference of paired fragment ions as the target score, and the score derived from a random mass difference as\nthe decoy score. This approach eliminates the need to construct additional decoy spectra and to calculate\nadditional matching score of decoy spectral pairs. Instead, we directly use existing matching score to leverage\ncompetitive decoy score. That is, our method bypasses decoy matching and directly obtains decoy score. For\nthe assumption that decoy matches exhibit a similar score distribution as false positives among the targets, we\nverify it from the perspective of the distribution of null target scores and decoy scores (refer to Supplementary\nNote 4 in the revised manuscript).\nFirstly, we recognized mirror spectral pairs by MirrorFinder and obtained the original target and decoy scores.\nNext, we classified them into null and nonnull based on the pFind search results. Then, we calculated the null\ntarget scores and decoy scores after competition and drew their distributions. Finally, we calculated the ratio\nbetween the number of null targets and the number of decoys. As we can see from Figure S23 and Table S7, the\nnull target/decoy ratios are basically close to 1 across the four datasets. More importantly, the distribution of\nnull target scores and decoy scores is very close, and there is a significant difference from the distribution of\nnonnull target scores, which satisfies the assumption of target-decoy strategy. Of course, the figures also show\nthat the assumption is not perfectly satisfied, but we consider it acceptable, since the target-decoy strategy is an\nempirical method after all.\nTry+Lys LysC+LysN\nE. coli\nYeast\nSupplementary Figure 23. Distribution of target and decoy scores in MirrorFinder.\nSupplementary Table 7. Validation of the assumption of target-decoy strategy in MirrorFinder.\nnull target decoy null target/decoy\nE. coli-TryLys 2,065,460 1,905,855 1.083745\nE. coli-LysCLysN 588,343 459,682 1.279891\nYeast-TryLys 1,009,319 853,284 1.182864\nYeast-LysCLysN 412,738 306,003 1.348804\n5.In the Online Methods section, when describing the pFind search settings, important parameters such as\ndigestion mode (semi-tryptic or fully tryptic) and the number of allowed missed cleavages are not specified.\nThese parameters can significantly impact identification results and should be clearly described to ensure the\nreproducibility of the experiments.\nRe: Thank you for your kindly reminding. In Methods 4.7 of the revised manuscript, we have added more\ndetails about pFind search settings. To be specific, the length of identified peptides was set to 6 to 100, the\ndigestion mode was set as fully tryptic, and the number of allowed missed cleavages was set to 3. In addition,\nwe have also provided the detailed configurations for MSFragger and MS-GF+, as they are newly added\ncomparative search engines in the revised version."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript has addressed my previous comments.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "Thank you for submitting the revised version of your manuscript. I appreciate the time and effort you have taken to address\nthe comments and improve the clarity and quality of the work.", "reviewer_4": "The authors have fully addressed all of my comments."}, "author_response": ""}]

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