# Identifying linear Ultra-Short Peptides in Agrifood: Challenges and Critical Insights.

Alexandre Weber, Barbara Deracinois, Benoit Cudennec, Maxime de Sousa

Lopes Moreira, Rozenn Ravallec, Thierry Grard, Christophe Flahaut

► To cite this version:

Alexandre Weber, Barbara Deracinois, Benoit Cudennec, Maxime de Sousa Lopes Moreira, Rozenn Ravallec, et al.. Identifying linear Ultra-Short Peptides in Agrifood: Challenges and Critical Insights.. Trends in Food Science and Technology, 2025, 163, pp.105129. 10.1016/j.tifs.2025.105129. hal-05114170

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Journal Pre-proof

Identifying linear Ultra-Short Peptides in Agrifood: Challenges and Critical Insights.

Alexandre Weber, Barbara Deracinois, Benoit Cudennec, Maxime de Sousa Lopes Moreira, Rozenn Ravallec, Thierry Grard, Christophe Flahaut

PII: S0924-2244(25)00265-1

DOI: https://doi.org/10.1016/j.tifs.2025.105129

Reference: TIFS 105129

To appear in: *Trends in Food Science & Technology*

Received Date: 12 February 2025

Revised Date: 23 May 2025

Accepted Date: 6 June 2025

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Please cite this article as: Weber, A., Deracinois, B., Cudennec, B., de Sousa Lopes Moreira, M., Ravallec, R., Grard, T., Flahaut, C., Identifying linear Ultra-Short Peptides in Agrifood: Challenges and Critical Insights., *Trends in Food Science & Technology*, https://doi.org/10.1016/j.tifs.2025.105129.

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© 2025 Published by Elsevier Ltd.
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# Identifying linear Ultra-Short Peptides in Agrifood: Challenges and Critical Insights.

Alexandre Weber, Barbara Deracinois, Benoit Cudennec, Maxime de Sousa Lopes Moreira, Rozenn Ravallec, Thierry Grard, Christophe Flahaut*

Univ. Artois, UMRt 1158 BioEcoAgro, INRAe, Univ. Littoral Côte d'Opale, Univ.Lille, Univ. Picardie Jules Verne, Univ. Liège, 62300 Lens, France

*Corresponding author: christophe.flahaut@univ-artois.fr

## Highlights

- Nearly 100% of peptides identified by peptidomics are medium/short peptides

- Peptidomics is not well-suited for identifying ultra-short peptides (USPs)

- Identifying USPs necessitates the development of dedicated peptidomics techniques

- Need of international guidelines for USP-identification and -reporting are mandatory

## Abstract

**Background:** Over the years, the agrifood industry has played a key role in valorizing protein-rich by-products generated during food processing. Fermentation and enzymatic hydrolysis of these by-products are efficient, industry-friendly, and cost-effective methods for generating protein hydrolysates which contain large-, medium/short, and ultra-short peptides (USPs). Despite their prevalence in such mixtures, the identification of USPs (2 to 5 amino acids) remains more challenging than other peptides.

**Scope and approach:** This review critically examines the technical challenges involved in USP identification, offering a detailed timeline of approximately forty key studies that have contributed to advancing the field. This research dedicated to USP peptidomics began only about twelve years ago.
Key findings and conclusion: The total theoretical number of di- to penta-peptides is 3,200,000. 30

We reviewed enrichment and clean-up protocols, the synthesis peptide sets used as standard 31

over the last decade to demonstrate efficiency of developed analytical methods (including pre- 32

analytical derivatization and post-column addition of chemical agents), mass spectrometry 33

techniques (including the mass analyzers used, the fragmentation-data acquisition modes, and 34

the instrumental technics used to fragment peptides), and the bioinformatics approaches used 35

for tandem mass spectrometry data processing in the context of USP identification. Finally, to 36

our point of view, publishing the identity of USPs should require an international guideline and 37

the deposition of tandem mass spectrometry raw data in online repositories. 38

Keywords: 39

Bioinformatics; liquid chromatography; mass spectrometry; peptidomics; ultra-short peptides. 40

Abbreviations 42

3-NBA, 3-nitrobenzyl alcohol 44

ACE, angiotensin-converting enzyme 45

ACN, acetonitrile 46

AMT, accurate mass tag 47

CCK, cholecystokinin 48

CE, capillary electrophoresis 49

CID, collision-induced dissociation 50

DDA, data-dependent acquisition 51

DIA, data-independent acquisition 52

DPP-IV, dipeptidyl-peptidase IV 53

GCB, graphitized carbon black 54

GIP, glucose-dependent insulinotropic peptide 55

GLP-1, glucagon-like peptide-1 56

HCD, higher energy collisional dissociation 57

HILIC, hydrophilic interaction liquid chromatography 58

HPLC, high-performance liquid chromatography 59

HR, high resolution 60
61 MRM, multiple reaction monitoring

62 MS, mass spectrometry

63 MS/MS, tandem mass spectrometry

64 PGC, porous graphitic carbon

65 RF, radio frequency

66 RP, reverse phase

67 SPE, solid-phase extraction

68 TFA, trifluoroacetic acid

69 UPLC, ultra-performance liquid chromatography

70 USPs, ultra-short peptides

71
## 1. Introduction

The sustainable consumption of land and sea resources has become a critical global imperative. While some of these resources took millennia to accumulate, others—such as those within the agri-food industries—naturally align with the cyclical processes of life. The agri-food sector, deeply attuned to the need for resource efficiency and optimization, has been undergoing a significant transformation, driven by European policies and an increasing awareness of environmental concerns. Over recent years, the industry has shifted focus, embracing the most thoughtful and innovative ways to valorize agri-resources. Beyond its primary role in food production for both humans and animals, the agri-food sector has long recognized that its by-products present a valuable opportunity for additional economic and environmental benefits. For instance, the extraction of bioactive peptides from these by-products—used in food, animal feed, cosmetics, and even pharmaceuticals—has emerged as a promising avenue for innovation and added value within the industry (Figure 1).

<figure><img src="image_3.png" /></figure>

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Figure 1: Schematic drawing illustrating i) the valorization of protein-rich products or protein-rich by-products through enzymatic hydrolysis or fermentation, ii) the analytical challenge of ultrashort peptide (USP, 2 to 5 amino acids) identification in a complex mixture of peptides of various lengths, and iii) the potential markets of bioactive peptides.

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## 2. Valorization of protein-rich by-products

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3. Bioactive peptides

Among the complex mixtures of food-derived peptides lie specific sequences with notable biological properties, offering potential applications across various fields, including human and animal nutrition, cosmetics, and healthcare (Figure 1). Bioactive peptides from food sources are typically inactive within the sequence of the parent protein but, after being released through enzymatic proteolysis, can exert a range of physiological functions (Karami & Akbari-adergani, 2019).

Research into bioactive peptides is well-established, with extensive experimental data now being collated in specialized bioactive peptide databases. However, not all bioactive peptides are yet fully documented in these resources. Currently, more than 100 bioactive peptide-related databases are freely accessible online (https://biochemia.uwm.edu.pl/bioactive-peptide-databases/), compiling biological activities in relation to the structure of bioactive peptides. In the realm of food-derived bioactive peptides, one of the most widely used databases, BioPep-UWM, lists 5,014 bioactive peptides (as of July 2024), representing 90 distinct bioactivities. Notably, anti-hypertensive activity, specifically the inhibition of angiotensin-converting enzyme (ACE), is the most common feature, referenced in 1,122 peptides (over 22.0%), while antioxidative activity is seen in 857 peptides (17%). Antibacterial activity follows with 526 peptides (10.5%), and anti-type-2 diabetes activity, associated with the inhibition of dipeptidyl-peptidase-IV (DPP-IV), is referenced in 489 peptides (less than 10%).

Among the 5,014 referenced bioactive peptides (redundancy of some of them included), nearly 45% are USPs—comprising 581 dipeptides, 754 tripeptides, 385 tetrapeptides, and 511 pentapeptides, totaling 2,231 USPs. In this review, we do not dispute the previously identified USPs reported in the literature, that is not the issue, but we are not the first authors to mention that identification of shorter peptides (2 to 4 amino acids) is challenging. Of these, 686 USPs (30.7%) are listed as antihypertensive, 519 (23.25%) as antioxidative, and 352 (15.75%) as anti-diabetic (inhibitors of DPP-IV). Dietary USPs are implicated in regulating energy homeostasis through intestinal peptide sensing, which drives the secretion of gut hormones such as cholecystokinin (CCK), glucose-dependent insulinotropic peptide (GIP), and glucagon like peptide-1 (GLP-1) (Duca et al., 2021), as well as modulating intestinal glucose absorption (Dugardin et al., 2022). USPs also influence key enzymes in carbohydrate metabolism, such as DPP-IV and α-glucosidase (Fleury et al., 2022). However, the relationship between peptide structure, their biological activity, and the molecular and cellular mechanisms behind these actions remains poorly understood.
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The identification and structural characterization of bioactive peptides have garnered significant interest over the past decade, with a growing body of research aimed at elucidating the molecular and cellular mechanisms underlying their bioactivities. Approximately 1,200 scientific papers on bioactive peptides are now published annually, compared to roughly 500 a decade ago (data extracted from Web of Science). The literature contains numerous scientifically validated examples of peptides with bioactive properties (Duarte Villas Mishima et al., 2024; Fan et al., 2024; Hu et al., 2024; Qiao et al., 2021). Nevertheless, the exhaustive and accurate identification of these peptides is crucial for assessing the bioactivity of peptide mixtures, necessitating further investigation into the individual effects of each peptide identified.

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## 4. Peptidomics technical challenges in USP identification

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To drive industrial applications supported by patents, the search for bioactive peptides is closely tied to the precise structural characterization of the peptides within complex mixtures, particularly the bioactive peptides themselves. In recent years, the identification and quantification of medium/short-chain peptides using peptidomic approaches have become more accessible, thanks to advances in i) (nano) high-pressure liquid chromatography (HPLC) coupled with single and tandem mass spectrometry (MS and MS/MS), ii) protein databases, and iii) bioinformatics tools. These tools provide essential connections between mass spectrometry data and protein databases, strengthening the robustness of peptide identification. However, significant limitations persist, particularly when it comes to the identification of USPs. Indeed, current technologies and methodologies are not fully optimized for the characterization and identification of USPs.

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In our view, three key technological hurdles hinder the accurate identification of USPs (Figure 2). While C18-based chromatographic separation methods (regardless of column diameter), mass spectrometry protocols (regardless of the type of MS analyzer), and bioinformatic tools for managing HPLC-run data are well-suited to the identification and quantification of medium/short-chain peptides, they are less effective for USP identification. Specifically, i) the increased hydrophilicity of most USPs, compared to medium/short-chain peptides, makes it difficult for conventional hydrophobic HPLC separation methods used in peptidomics to effectively separate these hydrophilic USPs; ii) the most common MS/MS fragmentation techniques generates a limited amount of MS/MS data for USPs; and iii) the existing bioinformatic tools are not well-suited to handle MS/MS data resulting from USP fragmentation, leading to challenges in processing and interpreting the data accurately.
<figure><img src="image_4.png" /><figcaption>Figure 2: Schematic drawing depicting the key points of peptidomics and the technical barriers that hinder the identification of ultra-short peptides (USPs - AAs, amino acids).</figcaption></figure>

## 5. Peptidomics for USP identification

In this section, we provide a bibliographic timeline (Figure 3) spanning the past decade to offer an overview of sample preparation and analytical methods reported in the literature for USP identification.

### 5.1 First reviews dedicated to USP identification

The study of USP peptidomics began approximately twenty years ago. In 2004, Halligan *et al.* introduced, from a bioinformatics perspective, the challenge of identifying peptides consisting of a small number of amino acids (Halligan *et al.*, 2004). This led to the development of innovative vector-based approaches for peptide identification by various bioinformatics research groups (Cittaro *et al.*, 2005).

From a practical standpoint, the analytical task of identifying USPs, especially di-, tri-, and tetra-peptides, and the challenges that arose at the interface between peptide and small molecule analysis (metabolomics), became more prominent just over a decade ago (Error! Reference source not found.). Panchaud *et al.* highlighted that “while USPs containing some hydrophobic amino acids are usually retained on classical C18 columns, USPs composed of only hydrophilic amino acids are not” (Panchaud *et al.*, 2012). Therefore, separating USPs
214 before injecting them into the mass spectrometer requires a combination of different
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216 characterization is far from straightforward. During this stage, two critical parameters emerge:
217 i) high mass accuracy, and ii) the collision energy used for fragmentation (Panchaud et al.,
218 2012).
<figure><img src="image_5.png" /><figcaption>Figure 3: Bibliographic timeline of the last 20 years, showing analytical methods reported for USP identification. (AA, amino acid; PC, computer; CE, capillary electrophoresis; RT, retention time; prep., preparation; GCB, graphitized carbon black; DDA, data-dependent acquisition; HCD, high collision dissociation; CEX, cation-exchange).</figcaption></figure>

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## 5.2 Strategy of USP identification based on predictive model of chromatographic retention time and combination of chromatographic separation methods

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Panchaud et al. discussed these limitations and proposed a pragmatic yet time-consuming solution based on HPLC-MS/MS data acquisition from a library of synthetic USPs (Panchaud et al., 2012). In their study, 117 peptides predominantly composed of branched-chain amino acids, were synthetized and used in a case study. A C18-based HPLC approach coupled with a data-depend analysis, specifically multiple reaction monitoring (MRM), was employed to separate and characterize the synthetic USPs (Lahrichi et al., 2013). This approach was also applied to characterize the USP heterogeneity in milk hydrolysate. The authors concluded that even for a highly complex peptide set, HPLC-MS/MS utilizing a MRM strategy is an effective method for discriminating peptides. This is achieved through a combination of retention time, diagnostic ions, and ratios of key diagnostic ions. However, while diagnostic ions allow for the unambiguous identification of dipeptides and at least the N-terminus of tripeptides, the analysis becomes more challenging for tetrapeptides when both leucine (Leu) and isoleucine (Ile) are present.

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In the same year (2012), Harscoat-Shiavo et al. introduced a computer-aided method designed for USP characterization. This method combined three separation techniques—reverse phase (RP) chromatography, hydrophilic interaction liquid chromatography (HILIC), and capillary electrophoresis (CE)—with predictive calculations of hydrophobicity, hydrophilicity, and electrophoretic mobility. The authors demonstrated that these values correlated with chromatographic retention times and migration times (Harscoat-Schiavo et al., 2012). They concluded that the selectivity characteristics of these three complementary separation techniques, when used in combination, significantly reduced the possible number of peptide combinations (by as much as 98%). A major advantage of the proposed method is the physicochemical information provided by such analyses. Since biological activities are often linked to specific physicochemical properties (Ahmed et al., 2022; Guha & Majumder, 2019; O’connor et al., 2022), these analyses could assist in the discovery of bioactive peptides. However, it is worth noting that the proof of concept for this methodology was based on a set of only 18 synthetic USPs, and no justification was provided for the selection of these specific USPs.

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Three to four years later, Fitzgerald’s research group developed a method to improve USP identification based on a predictive model of retention time for these peptides on HILIC coupled
to MS/MS (Le Maux, Nongonierma, & Fitzgerald, 2015). The reported predictive algorithmic model for chromatographic retention time was trained using the length of 153 USPs and the position (N-, C-terminal, or central) of amino acids in the peptide sequence. This model was validated using 14 synthetic peptides (excluding 10% of the training USP set) and ultimately linked the USP retention time to the apparent hydrophilicity coefficient and the peptide sequence size. The authors provided experimental validation of the model using a solid-phase extraction (SPE) fraction of whey protein hydrolysate analyzed with an MS method for USP detection (m/z range of 21–600), confirming the model's applicability for USPs. However, the experimental validation was performed on USPs purified using a hydrophobic SPE fraction, despite the unbound hydrophobic SPE fraction being reported by Nongonierma *et al.* 2013 (Nongonierma *et al.*, 2013). The method should have been evaluated using the entire whey protein hydrolysate sample, rather than just a subfraction. Moreover, the 14 synthetic peptides used as standards could have been spiked into the whey protein hydrolysate to assess the efficiency of the SPE enrichment and the influence of the matrix effect on both the SPE fractionation and the analytical method.

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Simultaneously, Fitzgerald's group applied this predictive algorithmic model of peptide retention time on HILIC in combination with a second predictive model of peptide retention time on RP chromatography, using the same set of 153 peptide sequences (Le Maux, Nongonierma, & Fitzgerald, 2015; Le Maux, Nongonierma, Murray, et al., 2015). This two-dimensional chromatographic separation approach was validated using eight free amino acids, twenty dipeptides (composed of A, V, I, G, L, W, and Y), and two tetrapeptides. The authors concluded that the elution order of peptides under RP chromatography and HILIC conditions was not symmetrical, highlighting the complementary nature of these two separation techniques. The differences observed between the two methods demonstrated that HILIC and RP-HPLC could work synergistically to enhance peptide separation. This approach was specifically optimized to differentiate between predominantly homologous dipeptides, which are often challenging to identify through MS/MS or *de novo* searches. However, from our perspective, the synthetic peptide dataset was tailored to address a specific research question—namely, identifying USPs with antidiabetic properties by inhibiting cellular and circulating dipeptidyl peptidase IV (DPP-IV).

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5.3 Optimization of MS characterization of USPs

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The Fitzgerald's group also published a short communication on optimizing MS/MS fragmentation conditions for USP characterization using micrOTOF QII-based mass spectrometry (O'keeffe & Fitzgerald, 2015). In this study, the authors employed a range of synthetic di-, tri-, and tetra-USPs separated on C18-UPLC and analyzed by MS/MS to fine-tune the fragmentation conditions, reducing over-fragmentation that typically occurs with standard MS/MS protocols. Specifically, they narrowed the mass range to allow the transfer of precursor ions with m/z values between 50 and 600, minimized the collision cell radio frequency (RF) to 140 Vpp, and adjusted the transfer time to 70 µs. These adjustments resulted in more efficient transfer and optimal fragmentation of USPs. However, the identification of USPs still requires extensive manual interpretation of the MS/MS spectra, and the authors expressed hope that ongoing advancements in software will alleviate the time-consuming nature of this process (Figure 3).

In 2017, Yesmine *et al.* reported the results of an accurate mass tag (AMT) study aimed at the characterizing a subset of short angiotensin-I-converting enzyme (ACE) inhibitory peptides. This study involved the creation of a C18-UPLC-ESI-MS/MS database based on retention time, the accurate mass of parent ions, and the most intense fragment ions from 11 di- and tri-peptides previously identified for their ACE-inhibitory effects (Yesmine *et al.*, 2017). The primary goal of the study was to detect these 11 USPs in a nanofiltration-based subfraction of Tilapia (*Oreochromis niloticus*) protein hydrolysate, but only two of the peptides were detected and unequivocally identified using the BioLynx module and Fragment Ion Calculator software. Additionally, the authors reported the *de novo* identification of 51 other USPs based on their collision-induced dissociation (CID) patterns. Both the peptide sequencing module of Waters MassLynx.V. 4.1 software and manual calculations were employed to process the MS/MS data and perform USP sequencing. A key limitation of the method was the inability to distinguish between isoleucine and leucine in the amino acid sequences of the USPs. Furthermore, the authors noted that the m/z range (50–1200) and scan time (0.5 sec) used in their analysis could have been further optimized.

## 5.4 Sample preparation step for cleaning and enrichment of USPs, orthogonal chromatographic separation and MS-based strategy for USP bioinformatics characterization.

Two years later, De Cicco *et al.* published the results of their study focused on characterizing the hidden digestome of whey proteins subjected to *in vitro* human gastrointestinal digestion
(Yesmine et al., 2017). The term "hidden digestome" or "hidden peptidome" resulting from gastrointestinal digestion" aptly refers to the array of short-chain peptides, USPs, and cross-linked peptides such as those containing disulphide bridges or desmosine/isodesmosine bridges. To enhance the identification of these "hidden" peptides, the authors utilized a multi-step approach: i) a protein precipitation protocol, ii) a nanochromatographic column, iii) a long elution gradient to improve chromatographic separation, iv) reduction of disulfide bridges followed by reanalysis of the reduced samples under the same conditions, and v) various peptide identification software. Overall, the peptides identified were larger than five amino acids, likely due to C18-SPE-based peptide purification prior to nanoHPLC-MS/MS analysis, with a 200–1200 m/z range used. This article also highlights that the definition of USPs may vary among scientists. From an analytical point of view, USPs should be defined as peptides containing between two and five amino acids.

The elimination of "contaminant" molecules from the analyte of interest is a crucial step in analytical sciences. This clean-up process minimizes undesirable molecular interferences that can hinder separation and MS detection. Piovesana et al. (2019) developed a solid-phase extraction method to clean and enrich USPs (di-, tri-, and tetra-peptides) from native urine, without the need for derivatization (Piovesana, Capriotti, et al., 2019). The proof of concept and method efficacy were evaluated using a set of 13 synthetic di- to tetra-peptides (6 di-, 4 tri-, and 3 tetra-peptides). The USPs were isolated using graphitized carbon black (GCB) Carbograph 4 sorbent, requiring fine-tuning of the short peptide elution process, which resulted in recovery rates ranging from 60 to 100%. However, in our opinion, the choice of amino acid sequences for the peptides in this study was not fully explained, the enrichment in USPs was not verified, as no quantitative comparison between spiked USPs and all urinary molecules (including larger peptides) was performed, and the 13 synthetic peptides used represent a small sample compared to the theoretical 168,400 combinations for di-, tri-, and tetra-peptides (400 dipeptides, 8,000 tripeptides, and 160,000 tetrapeptides). Nonetheless, this study marked the first time that pre-purified USPs were analyzed in triplicates using an orthogonal UHLPC-high resolution (HR) MS/MS approach, employing both RP C18 and zwitterionic HILIC columns. The USPs were identified based on HR-MS/MS data obtained with a hybrid quadrupole-Orbitrap mass spectrometer (Q Exactive, Thermo Fisher Scientific), with data-dependent acquisition (DDA) mode, suspect screening, inclusion lists, and higher-energy collisional dissociation (HCD). Today, high-resolution mass spectrometer measures the m/z ratio and the isotopic distribution with a measure accuracy of four digits (or more) after the coma, and this limits the number of potential raw formulae (Chavez et al., 2022). Therefore, a comparative analysis, through a metabolomic-based workflow of raw formulae (plus isotopic distribution if necessary) compared to a raw formulae database of di-, tri-, tetra- (and penta-)
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peptides should allow to distinguish small molecule analogues from USPs. In addition, the identification, via de novo peptidomic software, of y-, b-, c-...ions produce by MS fragmentation will bring additional information in favor to USPs.

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The USP identification strategy of the Italian team relies on amino acid combinations in di- tri- and tetrapeptides obtained using Matlab, duplicate masses were filtered out and the resulting list was employed for data acquisition (MS inclusion list) and data processing to overcome the limitations of matching data to metabolite databases, which are not complete with all the possible combinations of amino acids and/or modifications for USPs. Peptides were identified using a semi-automated workflow for feature extraction, candidate mass filtering, and MS/MS spectra comparison with *in silico* mass spectra. Based on the largely unknown number of USPs in urine, the authors reported identifying 152 peptides (101 from RP runs, 111 from HILIC runs, with 60 common identifications). The same team later expanded their USP database by incorporating 13 modified amino acids in addition to the 20 standard proteinogenic ones. They also demonstrated that using an MS inclusion list combined with suspect screening in DDA mode was much more effective (especially using mass spectrometer displaying low duty cycle) for identifying USPs than the data-independent acquisition (DIA) mode, leading to the tentative identification of over 200 natural and modified USPs.

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A supercharging agent, such as 3-nitrobenzyl alcohol (3-NBA), was employed in the post-column phase of the chromatographic method to address the issue of limited ionization of USPs and mitigate ionization suppression caused by the use of trifluoroacetic acid (TFA) in the mobile phases. TFA was preferred over formic acid due to its superior ion-pairing capability, which is essential for the separation of small ionizable compounds. To enhance electrospray ionization without compromising chromatographic separation, 3-NBA was added, resulting in a consistent increase in signal intensity for all analytes, up to 18-fold (Piovesana, Montone, et al., 2019).

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In a parallel study, Montone et al. (2019), part of the same research group as Piovesana et al., proposed a purification step for USP characterization in cow milk to avoid suppression phenomena caused by contaminants like carbohydrates or ions. After defatting the milk through centrifugation and precipitating proteins using organic solvents, the authors employed a cotton-HILIC microtip-based purification step to enrich the sample in USPs (Montone et al., 2019). As with Piovesana et al. (2019), the team utilized an orthogonal chromatographic
approach, leveraging the distinct selectivity mechanisms of two different chromatographic phases: a RP (C18) phase and a porous graphitic carbon (PGC) phase. USPs were identified using HR-MS/MS on a Q Exactive mass spectrometer, with suspect screening conducted in top-five DDA mode, in the 150–2000 m/z range. The mass data were processed as aforementioned using a database containing all possible di-, tri-, and tetrapeptides to identify precursor ions. Experimental HCD fragmentation spectra were matched with *in silico* fragmentation spectra, focusing on a, b and y ions. Special attention was given to iminium ions, as they are indicative of certain amino acids. Manual validation of amino acid sequences was performed based on the total number of matched ions, with a focus on the most intense ones. Validation of the orthogonal chromatographic method was conducted using a set of 14 synthetic di- to tetra-peptides (including the same 13 peptides from Piovesana *et al.*, 2019, plus one additional peptide, RKKH). The authors reported that all 14 synthetic peptides were successfully separated and eluted with well-resolved peaks. The results confirmed that the purification step effectively increased the number of peptide identifications while preventing ion suppression from the presence of carbohydrates. Through this method, a comprehensive peptidomic profile of cow milk USPs was obtained, identifying 72 USPs in total.

A comprehensive study on the extraction and clean-up of circulating endogenous USPs in plasma was conducted by Piovesana *et al.* in 2020, where four different analytical clean-up methods were tested and compared to maximize USP identification (Piovesana *et al.*, 2020). The most effective approach combined protein precipitation with phospholipid removal using the PhreeTM Phospholipid Removal Cartridge, followed by USP clean-up using GCB Carbograph 4. The same set of 13 synthetic peptides used in their previous studies was also referenced, though its purpose remained unclear. Chromatographic separation of both hydrophilic and hydrophobic USPs was performed using UPLC coupled to zwitterionic HILIC, followed by HR-MS/MS analysis. This untargeted investigation was based on suspect screening in top-five DDA mode with HCD fragmentation, spanning the range of 150–1000 m/z. The USP identification process mirrored the approach from previous work. The authors reported identifying 89 USPs, including 22 tripeptides and 57 dipeptides, using the optimized combination of purification steps. However, the identification of just one, six, and seven additional unique peptides using the other three extraction and clean-up strategies was also noted. Notably, the study did not involve synthetic peptide spiking in plasma to demonstrate the method's efficiency.
In the same year, the same research team published another study focusing on the identification of USPs from a digestome created by the *in vitro* gastrointestinal digestion of Yellowfin Tuna (*Thunnus albacares*) fillet proteins. The proteins were extracted using liquid nitrogen crushing, cold lysis buffer, centrifugation, and trichloroacetic acid-based precipitation (Cerrato, Capriotti, et al., 2020). The USP digestome was extracted and purified with GCB sorbent using the Carbograph 4 cartridge and chromatographed on a C18 phase, with TFA as the ion-pairing agent. The MS and MS/MS data were collected in positive ionization mode, ranging from 150–750 m/z, with HCD-based MS/MS spectra acquired using a suspect screening strategy in top-five DDA mode. An inclusion list of 4,980 unique singly charged precursor ions was used to target specific m/z values. The identification of USPs followed the same process as outlined in their previous work (Cerrato, Aita, et al., 2020). Despite the *in vitro* gastrointestinal digestion of the proteins, the authors reported a substantial number (572) of “tentative” USP identifications, as they described, indicating the high efficiency of the analytical workflow. However, the decision to use only a C18 column for HPLC separation of USPs remains unclear, particularly when considering the team’s prior successes with other chromatographic methods.

The same research team conducted an investigation into the endogenous peptides produced by peptidases after the death of sea bass (*Dicentrarchus labrax*) fillet proteins, using the same analytical workflow (Cerrato et al., 2021). The authors concluded that while *de novo* peptide sequencing software can aid in USP characterization, its current utility is somewhat limited unless such software is specifically adapted to the needs of USP sequences. Ultimately, 102 unmodified USPs (39 di-, 45 tri-, and 18 tetra-peptides) and 12 post-translationally modified peptides were identified, many of which are associated with multiple reported bioactivities. Note that presence of PTMs on the backbone of a protein generally hinders the enzyme activity. Therefore, PTMs is more often detected on medium/short peptides (e.g. between 6 to 20 amino acids or more) rather than USPs (Cerrato et al., 2021). Secondly, better characterization of PTMs is generally performed using enrichment and purification methods to focus analysis of a desired PTMs (lectins are used for N- or O-glycosylation, IMAC-bead or column for ion-chelator peptides, etc (Yan et al., 2025)). Thirdly, the MS fragmentation process plays a great role for the characterization of PTMs, and therefore this latter must be adapted accordingly to the PTMs that need to be characterized. Fourthly, some ion fragments (or neutral loss) are characteristic of some PTMs such as the oxonium ions for the glycopeptides, or the neutral loss of 80 for sulfation or phosphorylation.
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Using the same analytical methodology, the authors also studied the endogenous ultra short peptidome profile of Italian San Daniele ham, a dry-cured ham, at three different ripening stages (14, 22, and 34 months) (Cerrato et al., 2022). The aim of this study was to statistically compare the USP populations at different stage of ripening and identify the USPs that influence the sensory and functional properties of dry-cured ham. After homogenizing the dry-cured ham samples, buffer-based extraction with agitation and sonication, centrifugation, and ACN-based precipitation of proteins, lipid removal was achieved using hexane-based liquid-liquid extraction. The aqueous phase was then acidified with TFA to pH 2, and USPs were purified onto Carbograph 4 cartridges. The identification of the purified USPs was performed using the same methodology as in the earlier studies.

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A total of 236 USPs were identified, including 173 natural peptides (comprising 80 di-, 48 tri-, and 45 tetra-peptides), along with 63 peptides containing non-proteinogenic amino acids, which were tentatively identified in dry-cured ham. In a separate study, but with a different focus, the quantification of seven specific dipeptides (PA, GA, VG, EE, ES, DA, and DG) demonstrated the advantages of using HILIC for salt removal as a crucial clean-up step before dipeptide ionization in the ion source of triple quadrupole mass spectrometry (Heres et al., 2022). Peptides were extracted from biceps femoris muscles excised from the ham pieces using 0.01% HCl, deproteinized via overnight precipitation with ethanol following a method published in 2015, and fractionated through ultrafiltration using Amicon® Ultra (Merk Millipore Ltd., Cork, Ireland) with a 3 kDa cutoff. The resulting permeate underwent HPLC-MS/MS analysis on a SeQuant ZIC®-HILIC capillary column (5 µm, 150 mm x 0.3 mm; Merk KGaA, Darmstadt, Germany) coupled to an ESI-QQQ 6420 Triple Quad HPLC/MS (Agilent). Elution solvents included 10 mM ammonium acetate and ACN. The highest concentration was found for GA (44.88 µg.g⁻¹ dry-cured ham), while PA had the lowest concentration (0.18 µg.g⁻¹ dry-cured ham), with the remaining dipeptides ranging between 2 and 8 µg.g⁻¹ of dry-cured ham.

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The goal of Italian team's study was the comprehensive characterization of USPs and medium/short-chain peptides derived from the simulated *in vitro* gastrointestinal digestion of soy flour protein extraction (Cavaliere et al., 2021). To simplify the starting hydrolysate, the digestome was first fractionated by preparative RP-HPLC using an Xbridge BEH C18 OBD Prep (Waters) and a gradient of methanol/water/ammonium formate (pH 10). Six fractions were collected every five minutes, with fraction #2 (retention time between 5 to 10 minutes) and fraction #3 (retention time between 10 to 15 minutes) showing the most intense antioxidant activity. These fractions were then subjected to the same USP identification methodology described earlier, resulting in the identification of 132 USPs. However, we find it somewhat

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illogical to fractionate the digestome using preparative RP-HPLC in the search for USPs with
antioxidant bioactivity, especially since the fractions analyzed correspond to peptides retained
between five and 15 minutes on the RP(C18)-HPLC phase.

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In parallel, the same Italian research team conducted an in-depth investigation of endogenous
USPs in milk and its by-products (whole milk, whole whey, skimmed whey, and whey
permeate). This study utilized the previously described methodology, which included pre-
enrichment of USPs using a GCB Carbograph 4, followed by RP-HPLC-HR-MS/MS analysis
and MS/MS data reprocessing for USP identification. In total, 79 USPs (13 di-, 31 tri-, and 35
tetra-peptides) were tentatively identified after careful manual validation of the features.

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For these four studies (Cerrato et al., 2021; Cerrato, Capriotti, et al., 2020), the orthogonal
strategy developed a few years earlier by the same research team (Piovesana, Capriotti, et
al., 2019) might have been a more suitable approach.

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## 5.5 Chromatographic ion-pairing agent

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In a completely different field, Takano et al. (Takano et al., 2021) reported a seamless
analytical method for USP identification, applying cation-exchange and ion-pair
chromatography coupled with HR-MS/MS analysis in the geochemistry domain. The study
focused on 33 synthetic dipeptides, including six starting with glycine (GG, GD, GE, GA, GS,
and GV), six with alanine (AA, AG, AA, AD, AE, and AV), five with aspartate (DS, DG, DA, DD,
and DV), six with glutamate (ES, EG, EA, ED, EE, and EV), five with serine (SS, SA, SD, SE,
and SV), five with valine (VS, VA, VG, VD, and VE), along with three glycine-mers (GGG,
GGGG, and GGGGG) and the tripeptide AAA. These synthetic USPs were subjected to ion-
pair liquid chromatography coupled with an electrospray ionisation triple Quadrupole MS
system. The analysis used a reversed-phase Hypercarb stationary phase (4.6 x 150 mm,
particle size of 5 µm, porous graphitic carbon) and guard columns. Solvent A consisted of 20
mM nonafluoropentanoic acid in pure water with 0.1% formic acid, while solvent B was
acetonitrile with 0.1% formic acid. Synthetic USPs were eluted with a linear gradient of 1%
solvent B per minute over 60 minutes, followed by a column flush with 60% solvent B for 60
minutes, and a 30-minute re-equilibration. The authors reported that all the synthetic USPs
were perfectly eluted between 26.7 and 40.7 minutes, exhibiting a narrow Gaussian peak
shape. They also observed a linear retention time for the glycine series from dimer to
pentamer. Given the potential interference from organic and inorganic substances in

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532 calcareous, siliceous, microbial, and sedimentary samples, the authors developed a procedure
533 to overcome the sample matrix effect that causes ion suppression in HPLC-MS devices (Furey
534 et al., 2013). This was achieved through an isolation and desalting procedure for USPs based
535 on cation-exchange chromatography, using an analytical-grade AG-50 W-X8 (200-400 mesh)
536 cation-exchange resin (Takano et al., 2010). The recovery of the dipeptide quantification was
537 higher than 94.0 ± 4.9% at a concentration of 4 ppm for each dipeptide (n = 10), with the mixed
538 solution containing glycyl series including neutral (-Gly, -Leu and -Ser), acidic (-Asp and -Glu),
539 basic (-Arg), sulfur-containing (-Met), aromatic (-Phe), and heterocyclic (-Pro, -His, and -Trp)
540 amino acids.
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542 5.6 Targeted approaches for identification of specific USPs

543 In addition to untargeted approaches, a dipeptide-targeted strategy has been reported as a
544 sensitive, rapid, and reliable method for the detection and quantification of 36 dipeptides
545 (Heidenreich et al., 2021). In this study, amino acids and dipeptides were extracted from
546 pulverized frozen tissue using an ice-cold HCl solution (unreferenced). The samples were then
547 labeled with the AccQ-Tag™ reagent, separated on an ACQUITY HSS T3 column with
548 acetonitrile as the elution solvent and formic acid as a modifier. Detection and quantification
549 were performed using the MRM mode on a QTRAP 6500+ mass spectrometer (Sciex) with the
550 fragment mass 171.1, derived from the derivatization reagent AccQ-Tag™. Chromatographic
551 conditions were optimized; however, more than one-third of the standard dipeptides exhibited
552 low signal intensity, regardless of the identical concentration used for optimization (dipeptides
553 containing multiple amine groups generally showed lower intensities). Recovery rates were
554 evaluated between 80-100% by spiking lung tissue samples (which contain few dipeptides)
555 with a standard dipeptide mixture at the highest concentrations tested. This approach was
556 applied to 13 different organs, blood, and urine of wild-type mice on standard diets to reflect
557 physiological conditions. Over the 36 dipeptides, 30 were detected and quantified, while the
558 remaining six were consistently below the detection limit. This multi-dipeptide quantification
559 method revealed organ-specific dipeptide distribution.
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561 The increasing interest in USP identification is evident, as demonstrated by Huang *et al.*, who
562 published a case study on almond USPs in the same year. Their work outlines a
563 comprehensive workflow for discovering small bioactive peptides in foods using HPLC-MS/MS
564 (Huang *et al.*, 2022). The study begins with the extraction of protein-rich material from almond
565 flour, employing *i*) aqueous extraction with or without Neutral Protease (*Bacillus subtilis*, BIO-
566 CAT, Virginia, NY), *ii*) ethanol-based protein precipitation, and *iii*) centrifugation. Peptide
567 dimethyl labeling was performed on both peptide standards (VY and VYV) and protein-rich
568 extracts subjected to precipitation. The labeled and non-derivatized peptides were then purified
569 and desalted using a C18-SPE cartridge before being introduced into an enrichment column
570 (Agilent Polaris- HR-high-capacity Chip 3C18). Chromatographic separation was carried out
571 with ACN as the solvent and formic acid as the acidifier. MS and MS/MS measurements were
572 conducted using an Agilent 6520 Accurate-Mass Q-TOF-MS in the 130-1400 m/z range.
573 However, the study does not specify the data acquisition or fragmentation modes. USP
574 identification was performed using Peaks Studio® X+ *de novo* sequencing, with a threshold *de
575 novo* score of ≥ 20. A total of 843 USPs were manually inspected for accurate identification.
576 For dimethyl-labeled peptides, dimethylation was set as a fixed modification in PEAKS, except
577 for "dimethyl labeling on Lys at any position." Despite the methodological rigor, several
578 concerns arise. First, while the study proposes the use of apparent a1 ions for N-terminal
579 amino acid identification and full-length USP sequencing, the proof-of-concept is based solely
580 on two short-chain peptide standards—an insufficient validation in our opinion. Second, the
581 authors do not demonstrate whether the dimethylation efficiency approaches 100%, raising
582 questions about the suitability of C18-SPE for desalting and purification. Third, the lack of
583 information regarding the fragmentation mode and conditions introduces uncertainty into the
584 MS/MS data interpretation. Fourth, the *de novo* score threshold of ≥20 is notably low, casting
585 doubt on the reliability of peptide identification. Lastly, manually verifying 843 USPs is an
586 arduous, time-consuming, and error-prone process. It is also regrettable that the authors did
587 not upload their MS/MS data to ProteomExchange (https://www.proteomexchange.org/) and
588 instead provided only peptide tables as supplemental material, limiting the reproducibility and
589 transparency of their findings.
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591 5.7 Swapped-sequence chemical synthesis for semi-large library of synthetic peptide
592 standards

593 In early 2023, Hollebrands et al. introduced an improved C18-HPLC-(DDA)-MS/MS method for
594 identifying predominantly hydrophobic USPs, incorporating sequence-specific retention time
595 predictors (Hollebrands et al., 2023). Their approach began with swapped-sequence chemical
596 synthesis strategy to generate a mix of 75 unique tripeptides and four sets of tetrapeptide
597 mixture, totaling 216 distinct tetrapeptides. These five peptide mixtures were dissolved in an
598 ACN/H₂O/TFA (10/89/1, v/v/v) solution at a concentration of 10 mg per 54 peptides per mL
599 (approximately 370 µM total) and, following dilution, injected onto an Xselect peptide CSH C18
600 column coupled to an Ultimate 3000 RS chromatography system. Simultaneously, three food
601 proteins (chicken egg white albumin, bovine α-lactalbumin, and κ-casein) were hydrolyzed
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overnight at 37°C using papain (Carica papaya), and the resulting peptides were analyzed using the same instrumentation. Peptide elution was performed using a multi-step linear gradient of ACN/H₂O with 0.1% FA over a total run time of 50 minutes. Tri- and tetra-peptide ions were detected using a Q-Exactive Plus Hybrid Quadrupole-Orbitrap mass spectrometer in DDA mode; however, the study does not specify the fragmentation principle employed.

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To enhance retention time prediction, the authors developed a support vector regression model incorporating chromatographic retention times, MS and MS/MS-data (peptide sequences), and two categories of molecular descriptors: i) non-sequence-specific descriptors (molecular mass, peptide length, theoretical average hydrophobicity (Abraham-Leo scale), isoelectric point, and eight principal component vectors derived from hydrophobic, steric, and electronic properties (VHSE scales) of 713 experimentally identified peptides (269 tri-, 360 tetra-, and 84 penta-peptides); ii) sequence-specific descriptors: Four indices derived from the 514 amino acid indices available in the amino acid index database (https://www.genome.jp/aaindex/). These included (i) the amino acid at the N-terminus, (ii) the amino acid at the C-terminus, (iii) the average of derived indices for residues between the termini, and (iv) the overall average of all amino acids in the sequence. The study concluded that incorporating sequence-specific descriptors greatly improved retention time predictions, with 93% of hydrophobic USPs eluting in the predicted chromatographic order. However, retention times for USPs containing additional charged side groups proved more challenging to predict.

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## 5.8 Mesoporous magnetic absorbent for USP enrichment and ion transmission strategy for a better characterization of USPs

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A more recent study, published in May 2023, introduced a novel approach for USP purification using a mesoporous magnetic adsorbent, MOF@Fe3O4@GO, which enables the enrichment of USPs through both metal ion affinity and hydrophobic interactions (Du & Jia, 2023). The enriched USP extracts were separated using a Hypersil GOLD C18 column and analyzed by Q-Orbitrap (Thermo Fisher Scientific) with a mobile phase of water and ACN acidified with 0.1% TFA. To optimize USP detection, the authors compared three distinct ion transmission strategies within the scan range of m/z 150-750: i) full scan plus data-dependent MS2 (dd-MS2) fragmentation with an inclusion list, ii) full scan plus dd-MS2 with both inclusion and exclusion lists, and iii) full scan plus dd-MS2 with inclusion and exclusion lists, followed by data-independent acquisition (DIA), using a fixed isolation window of m/z 104.0 with a 2 m/z overlap at the intersection of adjacent windows. MS raw data were processed using Compound Discoverer, retaining only m/z values common to three replicates (excluding blank m/z) and
matching (± 5 ppm) a theoretical inclusion list of di-, tri-, and tetrapeptides. The MS/MS spectra of filtered candidate m/z values were scored based on ion fragment matching (± 5 ppm), followed by manual validation using mMass. Among the tested strategies, the third approach provided the highest USP screening coverage. Ultimately, 127 USPs were identified, comprising 47 di-, 59 tri-, and 21 tetrapeptides.

## 5.9 Recent algorithm development for USP identification

The question of what bioinformatic strategy used for identifying USPs is legítimo. Liu *et al.* recently developed a dedicated algorithm named Pepos-X-Exhaust (Liu et al., 2024). Although the authors have used glutathione to preliminary validate the bioinformatic method, they report, from prepared peptide mixtures but also commercial hydrolysates of proteins, that PeposX-Exhaust is efficient and more accurate for identification of USPs than the other commercial or not bioinformatic solutions. Notably, the accuracy rate was at least 70 % higher than the two database-search tools (MASCOT and SEQUEST), and 15 % higher than the other two de novo-sequencing tools (PepNovo + and pNovo3). Time will tell if this algorithm will be successful.

## 6. Conclusions and future perspectives

In modern proteomics or peptidomics, the identification of medium/short-chain peptides has become widely accessible and is routinely performed in laboratories worldwide. This is largely due to the global harmonization of experimental workflows, supported by standardized guidelines that invite to concomitantly analysis a standard to check the proper execution of the analytical protocol and define: i) pre-analytical protocols to ensure reproducibility, ii) optimized chromatographic methods for efficient peptide separation, iii) mass spectrometry operating conditions for high-resolution detection, and iv) bioinformatics settings integrated with protein databases for accurate peptide identification and quantification. Thanks to exponential advances in chromatographic separation techniques, mass spectrometry technologies, and bioinformatics, modern peptidomic analyses can now identify between 6,000 and 10,000 peptides in a single run. However, the vast majority of these peptides are longer than 5 amino acids. This raises an important question: while peptidomics offers remarkable peptide identification capacity, its ability to detect peptides shorter than five amino acids remains significantly limited. In fact, given the current state of the field, it is estimated that over 99% of peptides identified worldwide each year belong to the medium/short-chain categories. Given that amino acid identification techniques are well established, one pressing question remains— how can we improve the identification of USPs?
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## 6.1 Ideal library of synthetic USPs to validate an analytical workflow

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As reviewed here, the search for optimal analytical methods for identifying USPs is relatively recent, particularly in the agri-food sector, emerging just over a decade ago. A key distinction from protein databases is that USP databases can be theoretically constructed. In fact, the theoretical number of proteinogenic USPs increases exponentially with peptide length: dipeptides 400 (20 x 20), tripeptides 8,000, tetrapeptides 160,000, and pentapeptides 3,200,000. Despite the decreasing cost of peptide chemical synthesis, synthesizing all distinct USPs as large libraries of standards remains expensive and not really feasible. However, synthesizing a representative subset remains a viable approach, especially for quantitative analysis. This raises a crucial question: why have studies in the qualitative field—except perhaps (Lahrichi et al., 2013) —relied on such a limited number synthetic peptides, given the vast theoretical diversity of USPs? A small set of synthetic peptides cannot statistically represent all possible USP structures, yet selection criteria for these sets are rarely disclosed. Ideally, the selection of synthetic USPs should be based on their physicochemical properties to ensure broad representation. While defining such a representative set for dipeptides is feasible, the complexity increases significantly for tri-, tetra-, and pentapeptides due to their sheer number. However, swapped-sequence chemical synthesis should perhaps be economically suitable to synthetize semi-large libraries of synthetic peptide standards. Moreover, considering matrix effects in real samples, any chosen synthetic USPs should be spiked (as performed for quantitative analysis) into samples to rigorously evaluate the efficiency of the analytical workflow.

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## 6.2 Cleaning and enrichment in USPs are mandatory

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Regardless of the sample type, an enrichment and clean-up step is essential for USP analysis due to: i) the presence of 'contaminating' molecules (ions, saccharides, lipids, etc.) which interfere with liquid chromatography separation, regardless of the chromatographic phase used, and ii) the matrix effect, which suppresses ionization in mass spectrometer sources. Given the highly hydrophilic nature of some USPs, C18-SPE is not recommended. Instead, GBC Carbograph 4 sorbent has been evaluated for USP extraction from urine, achieving recovery rates of 60-100% for 13 synthetic USPs. Concomitantly, a cation-exchange chromatography approach using AG-50 W-X8 (200-400 mesh) cation-exchange resin has proven effective. In plasma, among four different clean-up methods tested for extracting endogenous circulating USPs, the most effective approach involved: i) simultaneous protein
precipitation and phospholipid removal using the Phree™ Phospholipid removal cartridge, ii)
subsequent clean-up using GCB Carbograph 4. Ultimately, best strategy for enriching and
purifying USPs from protein hydrolysates derived from food proteins is a parallel combination
of at least two orthogonal chromatographic phases.

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## 6.3 Ideal analytical workflow to exhaustively identify USPs

Initially, USP separation by liquid chromatography relied on a single chromatographic
stationary phase. However, the parallel use of two complementary phases was soon
established, with one phase dedicated to the separation of hydrophilic USPs, such as HILIC
and its derivatives, and another phase for hydrophobic USPs, such as C18 columns. In addition
to these conventional approaches, porous graphitic carbon (PGC) chromatography has proven
effective as a non-specific stationary phase. PGC retains nonpolar analytes through dispersive
forces, while polar compounds are retained through charge induction on its polarizable surface.
A notable limitation of using ion-pairing agents, especially TFA, is their potential for permanent
adsorption onto the PGC surface, leading to modifications of the stationary phase and
necessitating regular regeneration.

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## 6.4 MS conditions for “exhaustive” characterization of USPs

The post-column addition of supercharging agent (such as 3-nitrobenzyl alcohol) has been
shown to enhance ion-source ionization of peptides, leading to the generation of higher charge
states. However, no other supercharging agents have been tested for USP ionization, leaving
room for further exploration in this area.

The detection and fragmentation of USPs require specific mass spectrometer settings. Due to
their low molecular mass and distinct chemical structures, ion-source parameters must be
optimized for the ionization of ‘small’ molecules. USPs are predominantly detected as
monocharged ions, which necessitates limiting the MS and MS/MS range to 1000 m/z to
ensure optimal detection of both precursor and fragment ions. For MS/MS data acquisition,
DDA mode has proven to be more effective than DIA mode when combined with an m/z
inclusion list covering 4,980 unique USP m/z ratios. High-energy collision dissociation (HCD)
and ultraviolet photodissociation (UV-PD) have emerged as the preferred fragmentation
techniques, as they maximize the generation of peptide fragments. Moreover, any MS
fragmentation technique that efficiently produces immonium ions is highly recommended
because these latter are key diagnostic ions in USP identification. Obviously, the accuracy and
speed of the MS analyzer directly influence the reliability of USP identification, making
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advancements in high-resolution mass spectrometry particularly relevant. Despite these developments, recent innovations in mass spectrometry have yet to be fully explored in the context of USP analysis. Notably, no study has investigated the potential of ion mobility mass spectrometry (IM-MS) to enhance USP identification, highlighting an area for future research.

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## 6.5 Ideal bioinformatic tools for processing analytical data.

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The identification of medium/short-chain peptides is currently achieved through bioinformatic processing of MS/MS fragmentation data using specialized proteomics software in combination with protein databases. The peptide identification score generated by these tools ensures that the identification is statistically reliable, given the extensive MS/MS data available for longer peptides. In database-dependent peptide identification, the chromatographic retention time is not considered. In contrast, identification of USPs presents a significant challenge due to the limited number of MS/MS fragmentation data points available for these shorter peptides. This limitation makes it impossible to achieve a robust identification score that guarantees the result is not due to chance. As a result, the USP identification must rely on automatic *de novo* sequencing rather than database-dependent approaches. While manual *de novo* sequencing is impractical for large-scale studies, it remains valuable for validating specific identifications.

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A wide range of *de novo* sequencing algorithms has been developed, as recently reviewed by (Ng et al., 2023). These include graph-based, dynamic programming, and machine learning or neural network-based algorithms. However, none of these algorithms were specifically designed for USP identification, none of these algorithms consider chromatographic data as relevant for identification. Given the unique structural characteristics and analytical challenges associated with USPs, there is an urgent need to develop dedicated *de novo* sequencing algorithms optimized for their identification.

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## 6.6 Need of a worldwide guideline dedicated to USP characterization

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Publishing the identity of peptides and proteins in high-quality scientific journals now requires the deposition of MS/MS raw data or identification data in online repositories. This practice allows for thorough review, consultation, and integration into big data studies. The identification of USPs should adhere to the same standard for transparency and reproducibility. By depositing this data in accessible repositories, researchers enable others to validate, reuse, and build upon their work, promoting scientific collaboration and advancing the field.
<figure><img src="image_6.png" /><figcaption>Figure 4 : the ideal analytical steps for a better MS-based characterization of ultra-short peptides.</figcaption></figure>

In conclusion, the successive steps (Figure 4) below could constitute a convenient worldwide guideline for USP identification until authors propose changes:
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1. Use of a standard set of selected dipeptides, tripeptides, tetrapeptides or a mixture of them to check the proper execution of the analytical protocol.

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2. Isolation and cleaning of USPs using subsequently a solvent-based protein precipitation method followed by a clean-up of soluble material using a GCB Carbograph 4 cartridge. Note, that the efficacy of this step needs to be evaluated from the standard of USPs choose before.

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3. Orthogonal chromatographic separation of USPs using at least one C18-AQ chromatographic phase (if necessary, test distinct ion-pairing agents) and one HILIC phase. The use of a third GPC-type phase will be a plus.

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4. Use of specific mass spectrometry conditions: favor the use of high-resolution mass spectrometer, favor the m/z 40-1000 range, favor setting source that avoid in-source fragmentation, favor the data-dependent acquisition mode using the larger list of inclusion of m/z for mass spectrometer displaying low duty cycle, favor high-energy collision dissociation (HCD) and ultraviolet photodissociation (UV-PD) - anyway, favor MS fragmentation principles that efficiently produce iminium ions and promote as many MS/MS-fragments as possible, favor multi-charging of USP ions if necessary using post-column addition of supercharging agents.

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5. Bioinformatics software for USP identification have to parallelly consider raw data from C18-AQ and HILIC HPLC-MS/MS runs, and consider the chromatographic retention times for both runs. Data-retreatment process for USP identification have to be based on metabolomics pipeline and peptidomics one. Briefly, theoretical databases (ideally worldwide) of di-, tri-, tetra- and penta-peptides for USP identification have to reference at minima molecular formulae, the experimental or predicted chromatographic retention times, and the 3D-structure of USPs (via SMILE, InChl etc.). Bioinformatics pipeline have to i) limit possible precursor molecular formulae to typically a few candidates by automatically matching accurate mass and isotopic pattern fragment and precursor ion information to one or several potential USPs, ii) query of USP formula in local database returns possible structure candidates, iii) match theoretical MS/MS-fragment structures to measured MS/MS peaks and score most likely structure accordingly to statistical test(s), and iv) sort USP identification result accordingly to scores.

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Acknowledgements

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This review was written within the framework of: i) the industrial chair ProteinoPeps, funded by INRAe and the French state; ii) the Charles Viollette industrial chair, supported by the MEL, the I-SITE, and the IAR; iii) the University of Artois & the Université du Littoral Côte d'Opale;
812 and iv) the BiHauts Eco de France CPER/FEDER 2021-2027 program, financed by the
813 European Union, the French State, and the Hauts-de-France Region.

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815 Author contributions: CRediT

816 Alexandre Weber: conceptualization, visualization, writing-original draft. Barbara Deracinois:
817 conceptualization, visualization, writing-original draft. Benoit Cudennec: funding acquisition,
818 writing review & editing. Maxime de Sousa Lopes Moreira: visualization, writing-original draft.
819 Rozenn Ravallec: funding acquisition, writing review & editing. Thierry Grard:
820 conceptualization, project administration, supervision, writing review & editing. Rénato
821 Froidevaux and François Coutte: supervision. Christophe Flahaut: conceptualization, project
822 administration, supervision, writing review & editing.

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