Protein hydrolysates for agricultural formulations: LC-MS/MS identification of dipeptides and field evaluation in soybean

ABSTRACT

This study establishes LC-NL(46)-MS/MS as a selective and efficient method for profiling bioactive dipeptides in protein hydrolysates, addressing limitations of conventional full-scan analyses. Four formulations were evaluated: two commercial biostimulants (Biost1 and Biost2) and two prototypes (MVP1 and MVP2). Computational predictions revealed non-toxic dipeptides with diverse bioactivities, including ACE and DPP-IV inhibition, antioxidant effects, and similarities to CLE peptide fragments involved in plant signaling. MVP1 exhibited superior peptide diversity and CLE-related sequences, suggesting enhanced biostimulant potential. Field trials in soybean demonstrated the safety of these hydrolysates, with no negative impacts on growth or yield, though statistical significance was not achieved. Qualitative productivity gains (up to 8.7%) in MVP1 highlight their role as sustainable signaling mediators. These findings provide a foundation for quality control in biostimulant production and future research on combined formulations under stress conditions, emphasizing the dual function of protein hydrolysates as nutrient carriers and sources of bioactive peptides for agricultural applications.

Key words
plant biostimulants; dipeptides; mass spectrometry; liquid chromatography; soybean yield

INTRODUCTION

Protein hydrolysates exert beneficial effects on both animals and plants. In both contexts, they enhance growth, bolster resistance to environmental stressors, and promote overall health. Nutraceutical food consumption holds growing interest for humanity. The rising popularity of fast food, typically rich in fats, carbohydrates, and sodium, underscores the urgency of enhancing these foods’ nutritional quality. From a nutritional and metabolic perspective, incorporating antioxidants, antihypertensives, and glucose regulators yields substantial health benefits (Hamadou 2025).

In animal nutrition, hydrolysates derived from protein residues are widely used as palatabilizers (Monteiro et al. 2025) and to boost protein bioavailability (Shokri and Jarpa-Parra 2025). The market available nutraceutical products already add significant value to fish, dog, and cat food for sick animals.

Protein hydrolysates have also gained significant prominence in agriculture (Kergaravat et al. 2025). Derived from sources like fish and collagen, they reportedly boost productivity in crops such as soybeans, corn, beans, and others (Madende and Hayes 2020). Agricultural biostimulants containing these hydrolysates are applied via seed treatment or foliar spraying. Manufactured through acidic, basic, enzymatic hydrolysis, or their combinations, hydrolysates combined with macro and micronutrients offer extensive possibilities in field applications (Colla et al. 2017). Commercially, protein hydrolysates facilitate amino acid availability to plants. Research indicates that soil microorganisms utilize 30–40% of these amino acids for respiration, while the remainder supports cell biomass production and maintenance (Jones et al. 2005, Jones et al. 2009). Furthermore, 30–40% of the nitrogen from respired amino acids is excreted as ammonium into the soil, in which plants and microbes can uptake it or oxidize it into nitrate.

Peptides in protein hydrolysates represent an emerging underexplored aspect of plant immunity yet. Mounting evidence indicates that these small molecules act as signaling mediators, interacting with membrane receptors to initiate perception and response cascades. Consequently, peptides are increasingly viewed as phytocytokine like compounds that amplify defense mechanisms, bridging initial stress perception with intricate morphophysiological and biochemical adaptations.

This role positions them as potential key players in plant immune signaling against biotic stress (Pastor-Fernández et al. 2023). Recent enzymatic and molecular studies revealed that hydrolysates applied to maize plants modulate metabolic pathways, including glycolysis and the Krebs cycle (Ertani et al. 2009). Specifically, they stimulate enzymes in carbon metabolism (e.g., malate dehydrogenase, isocitrate dehydrogenase, citrate synthase) and nitrogen assimilation (e.g., nitrate reductase, glutamine synthetase, glutamate synthase, aspartate aminotransferase) (Schiavon et al., 2008). Likewise, from an enzymatic and molecular perspective, hydrolysates enhance nitrogen and carbon metabolism enzymes (Ertani et al. 2013).

Protein hydrolysates enhance plant metabolism by boosting photosynthesis and energy supply, potentially improving nitrogen assimilation through carbon skeleton production and amino acid biosynthesis (Colla et al. 2015). However, high concentrations of free amino acids can repress root nitrate uptake by overloading phloem, with effects varying by nitrate levels and amino acid types—e.g., exogenous glutamine reduces nitrate influx in barley roots (Fan et al. 2006, Miller et al. 2007).

In grapevine, whey derived hydrolysates improve heat and drought tolerance, accelerate photosynthetic recovery, up-regulate stress genes (HSFA2, HSP101), and inhibit pathogens like Plasmopara viticola, acting as both biostimulants and elicitors for sustainable viticulture (Alfonso et al. 2025).

Foliar applications of animal-derived hydrolysates benefit horticulture by enhancing product quality, resource efficiency, and stress tolerance, linked to their molecular composition (Colla et al. 2015). In kiwi fruit, low-molecular-weight fractions (1–3 kDa) stimulate shoot and root growth at low rates, while higher fractions (> 3 kDa) require higher doses (Quartieri et al. 2002). Amino acids and peptides also improve leaf mineral status, boosting cation uptake and crop growth (Garcia et al. 2011).

Hydrolysis cleaves proteins into peptides and amino acids, with bioactivity depending on reagent reactivity and reaction time. Peptides exhibit stronger effects than free amino acids in plants; for instance, dipeptides GG and GN outperform free glycine in regulating tobacco growth (Fedoreyeva et al. 2022). Protein hydrolysates surpass free amino acids and inorganic nitrogen in bioactivity (Santi et al., 2017), with activities including antioxidant effects, ACE/DPP-IV inhibition, and stimulation (Minkiewicz et al. 2019). In plants, they may contain CLE peptide fragments (CLV3/CLE), involved in signaling (Breiden and Simon 2016, Yamaguchi et al. 2016, Wang et al. 2025).

Peptide analysis typically uses proteomics for longer sequences (> 4 residues), missing many bioactive dipeptides (Li et al. 2007, Bechaux et al. 2019, Peng et al. 2019). Specialized methods include capillary electrophoresis and liquid chromatography-tandem mass spectrometry (LC-MS/MS) for dipeptide quantification (Ozawa et al. 2020), and fast screening for di-/tripeptides in hydrolysates (Poliseli et al. 2021).

This study aimed to identify dipeptides in commercial biostimulants (Biost1 and Biost2) and prototypes (MVP1 and MVP2) using LC-NL(46)-MS/MS, and evaluate MVP1 and Biost1 in soybean seed treatments for yield improvement.

MATERIAL AND METHODS

The commercial protein hydrolysates Biost1 and Biost2 were purchased from the local market in Jandaia do Sul, Paraná, Brazil. Biost1 is a liquid from wet blue cattle skin collagen hydrolysis and is composed of 45% of a mixture of soluble protein, peptides, and amino acids. Biost2 is a liquid from tannin cattle skin collagen hydrolysis and is composed of 20% of a mixture of soluble protein, peptides, and amino acids.

MVP1 (minimally viable product 1) is a soluble solid from chicken residues hydrolysis, provided by BRFoods Company in Toledo, Paraná, Brazil, and contains 70% of a mixture of proteins, peptides, and amino acids. MVP2 (minimally viable product 2) is a liquid from untreated cattle skin collagen hydrolysis under development by a company in Jandaia do Sul and contains 30% of a mixture of soluble proteins, peptides, and amino acids.

Commercial HPLC-grade acetonitrile and formic acid were purchased from Vetec-Sigma-Aldrich in Duque de Caxias, Rio de Janeiro, Brazil. A whey protein hydrolysate was used as a sensitivity standard and was purchased from the local market in Brazil. Deionized water was produced using a Milli-Q system from Millipore in Billerica, MA, United States of America.

Liquid chromatographic mass spectrometry parameters

The analysis was based on the protocol described by Poliseli et al. (2021). A mixture of acetonitrile: water: formic acid (70.0: 29.9: 0.1 v/v/v) was used as the mobile phase and was pumped isocratically at a flow rate of 300 μL.min^-1 using a LC pump (Waters 515). A XBridge C18 3.5 mm (4.6 × 50 mm) column (Waters) was used for the separation. The injection volume was 5 μL, and the run time was 5 min at room temperature. The PremierXE triple-quadrupole mass spectrometer (Waters Corporation, Milford, MA, United States of America) was used for the Neutral Loss of 46 Da (NL46, LC-NL(46)-MS/MS) and collision-induced dissociation (CID, LC-CID-MS/MS) MS/MS experiments. The electrospray ionization source (ESI) was set at 4 KV and operated in positive ion mode. The desolvation gas was set at 350°C, and the source block was set at 110°C. The cone voltage (20 V), collision energy (15 V), and collision gas pressure (argon) (3 × 10^-3 Torr) were previously optimized.

The analysis was conducted using the LC-NL(46)-MS/MS method to search for protonated dipeptide candidates (PDC), and CID spectra were obtained for the PDC. The ions with an intensity of at least 1,000 arbitrary units were selected for CID fragmentation. The CID mass spectra were manually interpreted according to the method described by Poliseli et al. (2021). The search for y1 ions (protonated amino acids MH+) with an intensity above 5% of the base peak was initiated. The second amino acid was determined by subtracting y1 from the protonated dipeptide candidate (DCP) (amino acid residue, amino acid molecular weight minus water, Y) resulting in the dipeptide YX. Confirmatory immonium ions and ions related to the observed amino acids were investigated after determining the YX peptides. Isobaric peptides can appear for the same precursor ion. Leucine and isoleucine are isobaric and cannot be unambiguously distinguished under the MS/MS fragmentation conditions used. Thus, whenever this ambiguity was present during manual interpretation, residues were annotated as (I/L).

Sample preparation

One hundred milligrams of protein hydrolysate was dissolved in 1 mL of a 50 mM ammonium bicarbonate solution in water, following the protocol developed by Poliseli et al. (2021). The solution was vortexed for 40 seconds and centrifuged at 4,000 revolutions per minute for 15 minutes at room temperature (25°C). One hundred microliters of the supernatant were collected and diluted in 900 microliters of the mobile phase, vortexed for 1 minute, centrifuged at 4,000 revolutions per minute for 10 minutes, and stored at -20°C for 1 hour. Finally, 100 microliters of the supernatant were diluted in 900 microliters of the mobile phase and directly injected into the LC-MS/MS system.

Dipeptide candidates from commercial biostimulant samples were identified through neutral loss of 46 Da, as described in the previous section. Protonation occurred due to sample dilution in the mobile phase. The extraction was performed using a modified proteomic protocol (Poliseli et al. (2021), dissolving peptides in an NH4HCO3 water solution. Whey protein hydrolysate samples were used as a positive control standard due to their high nitrogen content and sensitivity to the method.

Bioactivity and toxicity prediction of di- and tripeptides by computational analysis

Peptides functionality was evaluated loading the sequences into the BIOPEP-UWM database. The web server PeptideRanker was employed to predict the probability (between 0 and 1) of a peptide being bioactive (Mooney et al. 2012). To infer toxicity risks associated with the identified peptides, the program OSIRIS2 was used (Sander et al. 2009).

Soybean test

The experiments were carried out in the field on soybean crops (Glycine max L.) during the 2020/2021 harvest. The first experiment was conducted at the Technological Center (CTC) of Cooperativa Agropecuária e Industrial (COCARI) in Mandaguari, Paraná, Brazil, located at the geographic coordinates 23°30’S and 51°42’W, with an average altitude of 655 meters, and soil classified as Red Latosol. The second experiment was conducted in a commercial area at Sítio Santo Antônio in Borrazópolis, Paraná, Brazil, located at coordinates 23°52’ S and 51°32’ W, with an average altitude of 447 meters, and soil classified as Red Latosol. Both experiments consisted of three treatments: control (dose of 0), RE (200 grams per hectare), and a commercial product, Bless (1 liter per hectare).

The soybean productivity was evaluated in both experiments. The mean confidence interval was adopted as a statistical criterion for discriminating and comparing the effects of treatments on soybean crop characteristics. The confidence interval establishes the range of values within which the average of the data set values is located and is an efficient and reliable method for interpreting significant differences (Payton et al. 2000). Statistical analyses were performed using the Microsoft Office Excel software, with a 95% confidence level.

RESULTS

Protein hydrolysates from commercial biostimulants and MVPs

Figure 1 shows the results of the NL46 experiment for commercial biostimulant samples (Biost1 and Biost2) and both MVP samples. The identified PDC were ions of m/z 205, m/z 219, m/z 223, m/z 227, m/z 235, and m/z 247 (6 ions) from the Biost1 protein hydrolysate (Fig. 1a). The identified PDC were ions of m/z 205, m/z 219, m/z 223, m/z 227, m/z 235, m/z 243, m/z 247, m/z 263, and m/z 276 (9 ions) (Fig. 1b) for the Biost2 protein hydrolysate. The identified PDC were ions of m/z 203, m/z 205, m/z 219, m/z 221, m/z 223, m/z 227, m/z 229, m/z 231, m/z 247, m/z 253, m/z 263, m/z 267, and m/z 269 (13 ions) (Fig. 1c) for MVP1 protein hydrolysate. The identified PDC were ions of m/z 203, m/z 219, m/z 221, m/z 223, m/z 231, m/z 235, m/z 249, m/z 263, m/z 269, and m/z 275 (10 ions) (Fig. 1d) for MVP2 protein hydrolysate. It should be noted that the same m/z value obtained by NL46 may have more than one peptide (isobaric peptides) or none; they are just candidates.

Figure 1
LC-NL(46)-MS/MS for (a) Biost1, (b) Biost2, (c) MVP1, and (d) MVP2 protein hydrolysates. All sample analyses were performed in triplicate. Spectra with greater number of ions are presented.

Dipeptide sequencing

The Biost1, Biost2, MVP1, and MVP2 dipeptidogram analyses resulted in 14, 21, 33, and 19 dipeptides (not considering the isobaric amino acids), respectively. Table 1 shows the identified peptides and their biological activities for Biost1, Table 2 for Biost2, Table 3 for MVP1, and Table 4 for MVP2.

Table 1
Peptide amino acid sequences identified by liquid chromatography – tandem mass spectrometry in hydrolysates from Biost1, along with their known biological activities as found in the BIOPEP database.

Observed ions					Amino acid sequence	Biological activity	References
[M+H]+	y1	Residue	immonium	related
205	76(G)	129(E)	30, 102	-	EG	ACE inhibitor; DPP IV inhibitor	Cheung et al. (1980), Lan et al. (2015)
	90(A)	115(D)	44, 88	-	DA	ACE inhibitor; DPP III inhibitor	Cushman et al. (1981), Dhanda et al. (2008)
	118(V)	87(S)	72	-	SV	DPP IV inhibitor	Lan et al. (2015)
	148(E)	57(G)	30, 102	-	GE	ACE inhibitor; DPP III and IV inhibitor;	Lan et al. (2015), Moreno-Mariscal et al. (2025)
219	90(A)	129(E)	44, 102	-	EA	ACE inhibitor; α-glucosidase inhibitor	Cheung et al. (1980), Moreno-Mariscal et al. (2025)
	120(T)	99(V)	72	-	VT	DPP IV inhibitor	Lan et al. (2015)
	148(E)	71(A)	44, 102	-	AE	DPP IV inhibitor	Lan et al. (2015)
223	120(T)	103(C)	-	-	CT	-	-
	166(F)	57(G)	120	91	GF	ACE inhibitor; DPP III and IV Inhibitor	Cheung et al. (1980), Dhanda et al. (2008), Lan et al. (2015)
227	156(H)	71(A)	110	121, 166	AH	Antioxidant; ACE inhibitor; DPP IV inhibitor	Chen et al. (1995), Van Platerink et al. (2008), Lan et al. (2015)
235	106(S)	129(E)	60	-	ES	DPP IV inhibitor	Lan et al. (2015)
	132(I/L)	103(C)	-	-	C(I/L)	-	-
247	116(P)	131(M)	70	-	MP	DPP IV inhibitor	Hatanaka et al. (2012)
	150(M)	97(P)	70	-	PM	DPP IV inhibitor	Lan et al. (2015)

Table 2
Peptide amino acid sequences identified by liquid chromatography – tandem mass spectrometry in hydrolysates from Biost2, along with their known biological activities as found in the BIOPEP database.

Observed ions					Amino acid sequence	Biological activity	References
[M+H]+	y1	Residue	immonium	related
205	76(G)	129(E)	30, 102	-	EG	ACE inhibitor; DPP IV inhibitor	Cheung et al. (1980), Lan et al. (2015)
	106(S)	99(V)	72	-	VS	DPP IV inhibitor	Lan et al. (2015)
	148(E)	57(G)	30, 102	-	GE	ACE inhibitor; DPP III and IV inhibitor	Lan et al. (2015), Moreno-Mariscal et al. (2025)
219	120(T)	99(V)	-	-	VT	DPP IV inhibitor	Lan et al. (2015)
	122(C)	97(P)	-	-	PC	-	-
	132(I/L)	87(S)	86	-	S(I/L)	DPP IV inhibitor; kinase regulator	Nongonierma et al. (2013), Lan et al. (2015), Luzarowski et al. (2021)
	148(E)	71(A)	102	-	AE	DPP IV inhibitor	Lan et al. (2015), Moreno-Mariscal et al. (2025)
223	76(G)	147(F)	120	-	FG	ACE inhibitor	Cheung et al. (1980), Moreno-Mariscal et al. (2025)
	120(T)	103(C)	76	-	CT	-	-
	166(F)	57(G)	120	-	GF	ACE inhibitor; DPP III and IV Inhibitor	Cheung et al. (1980), Dhanda et al. (2008), Lan et al. (2015)
227	156(H)	71(A)	-	121, 166	AH	ACE inhibitor; DPP IV inhibitor; antioxidant	Chen et al. (1995), Van Platerink et al. (2008), Lan et al. (2015),
235	120(T)	115(D)	88	-	DT	-	-
	122(C)	113(I/L)	-	-	(I/L)C	-	-
243	156(H)	87(S)	110	-	SH	DPP IV inhibitor	Lan et al. (2015)
247	132(I/L)	115(D)	86	72	D(I/L)	-	-
263	116(F)	147(F)	70	-	FP	ACE inhibitor; DPP IV inhibitor	Abubakar et al. (1998), Hatanaka et al. (2012)
	134(D)	129(E)	-	-	ED	-	-
	166(F)	97(P)	70	-	PF	DPP III and IV Inhibitor	Dhanda et al. (2008), Lan et al. (2015)
276	147(Q/K)	129(E)	-	-	E(Q/K)	ACE inhibitor; DPP IV inhibitor	Van Platerink et al. (2008), Nongonierma et al. (2013)
	175(R)	101(T)	-	59, 100	TR	DPP IV inhibitor.	Lan et al. (2015)
	205(W)	71(A)	159	117	AW	ACE inhibitor; DPP IV inhibitor; antioxidant	Loponen et al. (2004), Nongonierma et al. (2014), Liu et al. (2015)

Table 3
Peptide amino acid sequences identified by liquid chromatography – tandem mass spectrometry in hydrolysates from MVP1, along with their known biological activities as found in the BIOPEP database.

Observed ions					Amino acid sequence	Biological activity	References
[M+H]+	y1	Residue	immonium	related
203	116(P)	87(S)	60, 70	-	SP	DPP IV inhibitor	Hatanaka et al. (2012)
	132(I/L)	71(A)	44, 86	-	A(I/L)	ACE inhibitor; DPP IV inhibitor	Nakahara et al. (2010), Nongonierma et al. (2013)
205	118(V)	87(S)	60, 72	-	SV	DPP IV inhibitor	Lan et al. (2015)
219	90(A)	129(E)	44, 102	-	EA	DPP IV inhibitor; α-glucosidase Inhibitor	Cheung et al. (1980), Moreno-Mariscal et al. (2025)
	118(V)	101(T)	72, 74	-	TV	DPP IV inhibitor	Lan et al. (2015)
	120(T)	99(V)	72, 74	-	VT	DPP IV inhibitor	Lan et al. (2015)
	132(I/L)	87(S)	60, 86	72	S(L/I)	DPP IV inhibitor; kinase regulator	Nongonierma et al. (2013), Lan et al. (2015), Luzarowski et al. (2021)
	148(E)	71(A)	44, 102	-	AE	DPP IV inhibitor	Lan et al. (2015), Moreno-Mariscal et al. (2025)
221	106(S)	115(D)	60, 88	-	DS	-	-
	118(V)	103(C)	72	-	CV	DPP IV inhibitor	-
	120(T)	101(T)	74	-	TT	DPP IV inhibitor	Lan et al. (2015)
	150(M)	71(A)	44, 104	-	AM	-	-
223	120(T)	103(C)	-	-	CT	-	-
	166(F)	57(G)	120	91	GF	ACE inhibitor; DPP III and IV inhibitor	Cheung et al. (1980), Dhanda et al. (2008), Lan et al. (2015)
227	156(H)	71(A)	44, 110	121, 166	AH	ACE and DPP IV inhibitor; antioxidant	Chen et al. (1995), Van Platerink et al. (2008), Lan et al. (2015)
229	116(P)	113(I/L)	70, 86	72	(I/L)P	ACE inhibitor; DPP IV inhibitor	Cheung et al. (1980), Hatanaka et al. (2012), Wei et al. (2021)
231	118(V)	113(I/L)	72, 86	72	(I/L)V	DPP IV inhibitor, stimulating	Morifuji et al. (2009), Lan et al. (2015)
	132(I/L)	99(V)	72, 86	72	V(I/L)	DPP IV inhibitor, stimulating	Morifuji et al. (2009), Lan et al. (2015)
247	116(P)	131(M)	70, 104	61	MP	DPP IV inhibitor	Hatanaka et al. (2012)
	118(V)	129(E)	72	-	EV	ACE inhibitor; DPP IV inhibitor	Van Platerink et al. (2008), Lan et al. (2015)
	132(I/L)	115(D)	86, 88	72	D(I/L)	-	-
	148(E)	99(V)	-	-	VE	ACE and DPP IV inhibitor; α-glucosidase inhibitor	Van Platerink et al. (2008), Lan et al. (2015), Mora et al. (2020), Moreno-Mariscal et al. (2025)
253	156(H)	97(P)	70, 110	166	PH	ACE inhibitor; DPP IV inhibitor	Lan et al. (2015), Wang et al. (2024), Moreno-Mariscal et al. (2025)
	166(F)	87(S)	60, 120	91	SF	ACE, Renin and DPP IV inhibitor	Suetsuna (1998), Udenigwe et al. (2012), Lan et al. (2015)
	182(Y)	71(A)	44, 136	91	AY	ACE and DPP IV inhibitor; antioxidant	Yano et al. (1996), Yokomizo et al. (2002), Lan et al. (2015)
263	116(P)	147(F)	70, 120	-	FP	ACE inhibitor; DPP IV inhibitor	Abubakar et al. (1998), Hatanaka et al. (2012)
	132(I/L)	131(M)	86, 104	-	M(I/L)	DPP IV inhibitor	Lan et al. (2015)
	150(M)	113(I/L)	86, 104	-	(I/L)M	DPP IV inhibitor	Lan et al. (2015)
267	120(T)	147(F)	74, 120	91	FT	Renin inhibitor	Udenigwe et al. (2012)
	166(F)	101(T)	74, 120	91	TF	ACE, Renin, DPP III and IV inhibitor	Lee and Snyder (1982), Yokomizo et al. (2002), Nogata et al. (2009), He et al. (2013), Lan et al. (2015), Wang et al. (2024)
269	156(H)	113(I/L)	110	166	(I/L)H	DPP III and IV inhibitor; antioxidant	Chen et al. (1995), Lan et al. (2015)
	166(F)	103(C)	120	-	CF	ACE inhibitor	Wu et al. (2008)
	182(Y)	87(S)	60	-	SY	ACE and DPP IV inhibitor	Meisel et al. (2006), Lan et al. (2015)

Table 4
Peptide amino acid sequences identified by liquid chromatography – tandem mass spectrometry in hydrolysates from MVP2, along with their known biological activities as found in the BIOPEP database.

Observed ions					Amino acid sequence	Biological activity	References
[M+H]+	y1	Residue	immonium	related
203	116(P)	87(S)	70	-	SP	DPP IV inhibitor	Hatanaka et al. (2012)
	132(I/L)	71(A)	44, 86	-	A(I/L)	ACE and DPP IV inhibitor	Nakahara et al. (2010), Nongonierma et al. (2013)
219	90(A)	129(E)	44, 102	-	EA	ACE and α-glucosidase inhibitor	Cheung et al. (1980)
	116(P)	103(C)	-	-	CP	-	-
	120(T)	99(V)	72	-	VT	DPP IV inhibitor	Lan et al. (2015)
	148(E)	71(A)	44, 102	-	AE	DPP IV inhibitor	Lan et al. (2015), Moreno-Mariscal et al. (2025)
221	150(M)	71(A)	44, 104	-	AM	-	-
223	120(T)	103(C)	-	-	CT	-	-
	166(F)	57(G)	120	91	GF	ACE, DPP III and IV inhibitor	Cheung et al. (1980), Dhanda et al. (2008), Lan et al. (2015)
231	116(P)	115(D)	70	-	DP	DPP IV inhibitor	Lan et al. (2015)
	132(I/L)	99(V)	72, 86	72	V(I/L)	DPP IV inhibitor, stimulating	Morifuji et al. (2009), Lan et al. (2015)
235	106(S)	129(E)	-	-	ES	DPP IV inhibitor	Lan et al. (2015)
	134(D)	101(T)	74, 88	-	TD	DPP IV inhibitor	Lan et al. (2015)
249	150(M)	99(V)	72	-	VM	ACE and DPP IV inhibitor	Yamada et al. (2013), Lan et al. (2015)
263	132(I/L)	131(M)	86, 104	-	M(I/L)	DPP IV inhibitor	Lan et al. (2015)
269	156(H)	113(I/L)	110	123, 138, 166	(I/L)H	DPP III and IV inhibitor; antioxidant	Chen et al. (1995), Lan et al. (2015)
	166(F)	103(C)	-	-	CF	ACE inhibitor	Wu et al. 2008
	182(Y)	87(S)	60, 136	-	SY	ACE and DPP IV inhibitor	Meisel et al. (2006), Lan et al. (2015)
275	147(Q/K)	128(Q/K)	101	129	(Q/K)(Q/K)	ACE and DPP IV inhibitor; binding	Sleigh et al. (1997), Meisel et al. (2006), Lan et al. (2015), Moreno-Mariscal et al. (2025)

The likelihood of a peptide being bioactive was predicted in the PeptideRanker server. The analysis performed in the OSIRIS program indicated that all evaluated peptides are nontoxic regarding mutagenic, tumorigenic, irritant, and reproductive aspects.

Soybean test

In the experiment conducted at the CTC station (Mandaguari), no statistically significant differences were observed among treatments for the soybean growth parameters evaluated. Nevertheless, grain yield was 6.2% higher in the RE and Bless treatments compared to the control (Fig. 2a). A similar trend was observed in the field trial carried out in a commercial area (Borrazópolis), where no significant differences were detected for the analyzed variables. However, in this case, the RE treatment resulted in an 8.7% higher soybean yield than the control and a 15% higher yield than the Bless treatment (Fig. 2b).

Figure 2
Productivity (kg·ha^-1) in (a) the Technological Center experiment (Mandaguari, Paraná, Brazil) with standard deviation of 686.49 (Control), 662.65 (RE) and 118.23 (Bless); and (b) a commercial field (Borrazópolis, Paraná, Brazil) with standard deviation of 682.56 (Control), 598.12 (RE) and 748.36 (Bless).

DISCUSSION

Protein hydrolysates from commercial biostimulants and MVPs

LC-NL(46)-MS/MS analyses revealed that Biost1 exhibited a higher total ion count but less intense PDC signals than Biost2, whose summed intensities suggest a higher dipeptide concentration. This suggests that the elevated protein content in Biost1 (45%) may stem largely from free amino acids. Hydrolysis duration and reagent concentration likely dictate both peptide yield and quality. Among the tested samples, MVP1 displayed the highest number and intensity of PDCs (13), followed by MVP2 (10), Biost2 (9), and Biost1 (6), indicating a superior peptide content in MVP1. Subsequent CID experiments confirmed these dipeptide sequences, as detailed in the following section.

Overall, LC-NL(46)-MS/MS proved effective for the rapid evaluation of hydrolysates, facilitating the comparison of peptide concentration (intensity) and diversity (PDC count) during production. While Brazilian industries commonly rely on full-scan analyses (up to m/z 2,000) to monitor hydrolysis, these may provide a distorted view by capturing all ions present. Conversely, neutral loss experiments offer greater selectivity by specifically detecting molecules containing carboxylic groups—an approach that facilitates dipeptide identification, which is critical for decision-making in hydrolysis processes.

Dipeptide sequencing and biological activity

Sequencing was performed using LC-CID-MS/MS for Biost1, Biost2, MVP1, and MVP2, followed by bioactivity and toxicity prediction. All dipeptides identified in the hydrolysates were predicted as non-toxic, indicating safety for agricultural applications.

Peptides are well recognized bioactive molecules whose effects depend on their amino acid sequence, influencing digestive, endocrine, cardiovascular, immune, and nervous systems (Saubenova et al. 2024). Owing to these physiological and biochemical functions, peptides also attract interest from pharmaceutical and food industries. Based on the BIOPEP platform (Tables 1–4), the identified dipeptides were screened for reported bioactivities, including ACE and DPP-IV inhibition, antioxidant and stimulant properties, and α-glucosidase inhibition (Purohit et al., 2024).

Figure 3 presents a Venn diagram comparing the four protein hydrolysates (Biost1, Biost2, MVP1, and MVP2), highlighting shared and unique dipeptides. Each biostimulant displayed specific dipeptide counts: Biost1 (3), Biost2 (11), MVP1 (15), and MVP2 (6). Biost1, Biost2, and MVP2 showed comparable numbers, consistent with their common origin from cattle skin collagen. In contrast, MVP1 exhibited higher peptide diversity, likely due to its composition from multiple chicken tissues.

Figure 3
Venn diagram showing the group map of dipeptides in (a) Biost1, (b) Biost2, (c) MVP1, and (d) MVP2 and their corresponding bioactivities.

Biost2 shared the fewest dipeptides with other samples (10), followed by Biost1 (11), MVP2 (12), and MVP1 (17). MVP1 and MVP2 had six identical dipeptides, likely resulting from their shared enzymatic hydrolysis process. The dipeptides AE, VT, GF, and CT identified in all biostimulants are associated with ACE, DPP-IV, and DPP-III inhibitory activities (though no data exist for DPP-III). According to the BIOPEP database (Tables 1–4), the detected dipeptides also exhibit various biological activities, including ACE and DPP-IV inhibition, stimulation, and antioxidant effects.

Although dipeptide sequences differed among biostimulants, most shared similar biological functions. These results suggest that peptides bioactive in animals may also influence plants, such as in glucose and stress regulation, antioxidant activity, and enhanced growth hormone release. Unfortunately, the BIOPEP database lacks plant bioactivity records, underscoring the need for studies on plant biostimulants and database enrichment.

However current databases like BIOPEP do not report plant-related bioactivities, recent studies demonstrated that dipeptides act as regulatory molecules in plants. Examples include YD, which enhances stress tolerance and delays senescence; γ-EC (γ-Glu–Cys), involved in redox balance; and cyclic DKPs such as cHP (cyclo[His–Pro]), cLP (cyclo[Leu–Pro]), and cPV (cyclo[Pro–Val]), which mimic hormone signaling and promote growth or defense. These findings, highlighted by Minen et al. (2023) and Agarwal et al. (2025), revealed that dipeptides play key roles in plant metabolism, development, and stress resilience, beyond their animal bioactivity.

CLE peptides exert diverse physiological effects on plants, including promoting lateral root emergence, shoot and root growth, pollen tube development, and maintenance of root phloem, embryo, endosperm, protoxylem vessels, vascular stem cells, bundles, floral meristem, shoot apical meristem (SAM), and root apical meristem (RAM) all potentially impacting productivity (Wang et al. 2025). With sequences up to 14 amino acids, these peptides suggest that dipeptides may represent CLE fragments with related biological activities. Manufacturers of Biost1 and Biost2 attribute the claimed productivity increase to root growth.

Figure 3 illustrates the group map for the analyzed samples and their common dipeptides. The right side of the Venn diagram includes a list of CLE dipeptide fragments corresponding to CLE peptides. Table 5 presents protein hydrolysate dipeptides that match fragments of cellular signaling peptides such as clavata3 (CLV3)/embryo surrounding region related (CLE) peptides (Breiden and Simon 2016, Yamaguchi et al. 2016, Wang et al. 2025).

Table 5
A list of dipeptides found in protein hydrolysates that match the CLE structures, showing the known CLE-receptor combinations and their functions in response to developmental and abiotic cues.

Dipeptide	CLE	Protein hydrolysate	Structure	Functions
SP	CLE1/2/3	MVP1, MVP2	RLSPGGPDPRHH	Lateral root emergence
	CLE5/6	MVP1, MVP2	RVSPGGPDPQHH	Shoot growth
	CLE7	MVP1, MVP2	RFSPGGPDPQHH	Lateral root emergence
	CLE43	MVP1, MVP2	RRIPSSPDRLHN	-
(I/L)H	CLE3	MVP1, MVP2	RTVHypSG[Ara3]HypDPLHHH	Maintenance of the SAM
	CLE8	MVP1, MVP2	RRVPTGPNPLHH	Embryo and endosperm development
	CLE9	MVP1, MVP2	RLVHypSGHypNPLHN	-
	CLE10	MVP1, MVP2	RLVPSGPNPLHN	Protoxylem vessel formation
	CLE11	MVP1, MVP2	RVVPSGPNPLHH
	CLE12	MVP1, MVP2	RRVPSGPNPLHH
	CLE13	MVP1, MVP2	RLVPSGPNPLHH
	CLE14	MVP1, MVP2	RLVPKGPNPLHN	Maintenance of RAM
	CLE16	MVP1, MVP2	RLVHTGPNPLHN
	CLE17	MVP1, MVP2	RVVHTGPNPLHN
	CLE18	MVP1, MVP2	RQIPTGPDPLHN	Root growth and development
	CLE19	MVP1, MVP2	RVIPTGPNPLHN	Maintenance of RAM
	CLE20	MVP1, MVP2	RKVKTGSNPLHN	Maintenance of RAM
	CLE21	MVP1, MVP2	RSIPTGPNPLHN
	CLE22	MVP1, MVP2	RRVFTGPNPLHN
	CLE25	MVP1, MVP2	RKVPNGPDPIHN
	CLE26	MVP1, MVP2	RKVPRGPDPIHN	Root growth
	CLE27	MVP1, MVP2	RIVPSCPDPLHN
	CLE40	MVP1, MVP2	RQVPTGSDPLHH	Maintenance of RAM, stem cell niche
	CLE43	MVP1, MVP2	RRIPSSPDRLHN
	CLE45	MVP1, MVP2	RRVRRGSDPIHN	Pollen tube growth, root phloem development
	SlCLV3	MVP1, MVP2	RGVPAGPDPLHH	Maintenance of the floral meristem and SAM
	FCP1	MVP1, MVP2	REVPTGPDPIHH	Maintenance of the SAM; Leaf cell fate determination
	FCP2	MVP1, MVP2	REVPSGPDPIHH	Metaxylem vessel formation; Maintenance of the SAM and RAM
	FOS1	MVP1, MVP2	RLVPTGPNPLHH
V(I/L)	CLE19	MVP1, MVP2	RVIPTGPNPLHN	Maintenance of RAM
EV	FCP1	MVP1	REVPTGPDPIHH	Maintenance of the SAM; Leaf cell fate determination
	FCP2	MVP1	REVPSGPDPIHH	Metaxylem vessel formation; Maintenance of the SAM and RAM
(I/L)V	CLE9	MVP1	RLVHypSGHypNPLHN
	CLE10	MVP1	RLVPSGPNPLHN	Protoxylem vessel formation
	CLE13	MVP1	RLVPSGPNPLHH
	CLE14	MVP1	RLVPKGPNPLHN	Maintenance of RAM
	CLE16	MVP1	RLVHTGPNPLHN
	CLE27	MVP1	RIVPSCPDPLHN
	FOS1	MVP1	RLVPTGPNPLHH
FT	CLE22	MVP1	RRVFTGPNPLHN
SV	FON2/FON4	MVP1, Biost1	RSVPAGPDPMHH	Maintenance of the floral meristems
CP	CLE27	MVP2	RIVPSCPDPLHN

• RAM: root apical meristema; SAM: shoot apical meristema.

The dipeptide SP (MVP1 and MVP2) corresponds to CLE1/3/4 fragments associated with lateral root emergence, shoot growth (CLE7, CLE5/6), and vascular bundle maintenance (CLE43) (Breiden and Simon 2016, Yamaguchi et al. 2016, Wang et al. 2025). The (I/L)H (MVP1 and MVP2) is part of multiple signaling peptides (CLV3, CLE8–CLE22, CLE25–CLE27, CLE40, CLE43, CLE45, SlCLV3, FCP1, FCP2) implicated in diverse plant developmental processes. The V(I/L) (MVP1 and MVP2) derives from CLE19. MVP1 also contains EV (FCP1, FCP2), (I/L)V (CLE9–CLE16, CLE27, FOS1), and FT (CLE22). SV (Biost1 and MVP1) originates from FON2 and FON4, while CP (MVP2) relates to CLE27.

Table 5 summarizes the dipeptides identified in the hydrolysates that align with CLE structures, along with their known receptor combinations and biological functions under developmental and abiotic stress cues. Seven dipeptides [SP, (I/L)H, V(I/L), EV, (I/L)V, FT, and SV] found in MVP1 are part of vegetal signaling peptides. MVP2 has four dipeptides [SP, (I/L)H, V(I/L), and CP] with high potential for signaling and effects on plants. Biost1 (SV) also shows potential activity, whereas Biost2 did not match any CLE signaling peptides.

Potentially, MVP1 contains the highest number of peptides that may result in plant biostimulation. Amino acids themselves can perform biostimulant activity in plants (Purohit et al. 2024). For example, glycine, phenylalanine, cysteine, and glutamate increase nitrate, amino acid, and total nitrogen contents in soybean leaves, suggesting a signaling role that improves crop productivity. The GG dipeptide regulates tobacco plant growth and is part of CLE1/3/4, CLE2, CLE5/6, and CLE7 signaling peptides; exogenous application of G, GG, and GN at low concentrations enhances tobacco root hair development by differentially regulating gene expression, indicating an epigenetic mechanism for short peptides (Fedoreyeva et al. 2022).

This hypothesis of dipeptides acting as biostimulants opens opportunities to measure their effects as signaling molecules in plants at different developmental stages. Furthermore, evaluating biostimulant effects through dipeptide identification and bioactivity can lead to more effective hydrolysate testing in plants, since reagent concentration and reaction time affect peptide composition.

Amino acid seed treatments induce changes in root morphology, reducing water and soil nutrient consumption while boosting productivity (Teixeira et al. 2018). Similarly, protein hydrolysates may enhance plant signaling and development due to their peptide content. Santi et al. (2017) compared biostimulatory effects of free amino acids and short peptides on maize seedlings, finding that hydrolysate peptides more effectively stimulated root growth and micronutrient accumulation than equivalent amino acid mixtures, suggesting a specific role for small peptides in root control (Colla et al. 2015, Fedoreyeva et al. 2022, Minen et al. 2023).

Despite these findings, the specific peptides responsible remain unidentified. Molecular mass fractionation and metabolomics explained biostimulating activity in vegetable hydrolysates on tomatoes, with optimal root growth in fractions below 0.5–1 kDa corresponding to very low mass peptides like those in our method (Lucini et al. 2020).

These peptide analysis results suggest that amino acid or peptide effects stem from bioactive signaling, as shown in experimental data (Purohit et al. 2024, Saubenova et al. 2024). The data indicate that very low mass peptides contribute to plant biostimulation. We hypothesize that they act as signaling peptides, like CLE peptides cleaved into dipeptides that influence plant development. Further studies on dipeptides may enhance understanding of protein hydrolysates as biostimulants, ultimately improving productivity.

The biostimulant protocol appears promising based on literature. To evaluate performance, the commercial Biost1 (14 dipeptides, 1 CLE fragment) and prototype MVP1 (33 dipeptides, 5 CLE fragments) were compared in soybean seed treatments to assess productivity enhancement. MVP1 was selected for field testing because LC–MS/MS profiling revealed a higher peptide count and a broader representation of short peptides and CLE-related fragments. MVP2 was retained as a comparator for evaluation in subsequent field trials.

Soybean productivity

In experiments conducted at both the CTC (Mandaguari) and a commercial site (Borrazópolis), no statistically significant differences were observed in soybean crop characteristics across treatments. Nevertheless, productivity was 6.2% higher in MVP1 and Biost1 compared to the control at the CTC site (Fig. 2a). Furthermore, MVP1 achieved a yield 8.7% higher than the control and 15% higher than Biost1 in the commercial area (Fig. 2b). Although literature evaluations of protein hydrolysates in soybean fields are scarce potentially due to non-significant results or high field variance, the mean comparison remains a standard commercial practice in Brazil. This provides a positive indication for MVP1 use relative to Biost1 and the negative control, likely stemming from the superior number of biostimulating peptides in MVP1.

Although field experiments revealed no significant increases in plant development parameters or soybean productivity, our results remain promising, especially for MVP1, which demonstrated qualitative productivity gains in both trials. This outcome is encouraging, as these were the initial field tests for the product in a single crop.

The absence of significant differences may stem from isolated seed application of the product. As noted by Colla et al. (2015), protein hydrolysate effects can vary by species, cultivar, developmental stage, dose, and application method. Numerous studies report enhanced outcomes when amino acids are combined with other formulations rather than applied alone. For instance, Castro et al. (2004) observed increases in height, pod number, grain count, and bean grain mass through Florogran application in greenhouse settings. Similarly, Schiavon et al. (2008) documented improved corn productivity with alfalfa protein hydrolysate.

Consentino et al. (2020) reported that protein hydrolysates primarily enhance root vigor, improving water and nutrient uptake and increasing productivity. In celery, they found significant improvements in yield and nutritional quality, highlighting the economic relevance of these biostimulants. These findings are consistent with Colla et al. (2017), who noted greater root dry mass, and Rouphael and Colla (2018), who observed better fruit quality in tomatoes.

This collective evidence supports using protein hydrolysates as a sustainable tool in agriculture. Building on these insights, our next step involves synthesizing selected peptides from MVP1, particularly CLE fragments and other sequences previously identified in hydrolysates. By prioritizing simpler structures, we aim to advance the targeted evaluation of their biostimulant potential.

CONCLUSION

The LC-NL(46)-MS/MS is a selective and efficient methodological advance for profiling bioactive dipeptides in protein hydrolysates, overcoming the limitations of conventional analyses. Our findings identify sequences with diverse biological activities, including CLE-related fragments essential for plant signaling. Field trials confirmed the safety of these formulations for soybean, with the MVP1 prototype exhibiting a positive productivity trend, likely due to its superior peptide diversity. These results highlight the dual role of hydrolysates as both nutrient carriers and signaling mediators. Future research should prioritize synthesizing these identified peptides to further elucidate their specific roles in enhancing crop resilience and sustainable productivity.

ACKNOWLEDGMENTS

The authors would like to acknowledge the support of Waters Technologies for providing the Premier XE equipment at the Fenn Lab and for their assistance in conducting the experiments at Universidade Federal do Paraná (UFPR), in Jandaia do Sul. The authors also express their gratitude to UFPR, and Universidade Estadual de Maringá for their support.

• How to cite:
  Rosa, F. A. D., Rappe, D. M., Tonin, A. P., Ribeiro, M. A. S., Sartori, F. F., Poliseli, C., Ribeiro, V. M. S., Philippsen, G. S., Meurer, E. C. and Guedes Filho, O. (2026). Protein hydrolysates for agricultural formulations: LC-MS/MS identification of dipeptides and field evaluation in soybean. Bragantia, 85, e20250225. https://doi.org/10.1590/1678-4499.20250225
• FUNDING
  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
  Finance Code 001
  Conselho Nacional de Desenvolvimento Científico e Tecnológico
  Grant no.: 301793/2025-5
• DECLARATION OF USE OF ARTIFICIAL INTELLIGENCE TOOLS
  The authors declare that no artificial intelligence tools were used in the preparation, writing, data analysis, or review of this manuscript.

DATA AVAILABILITY STATEMENT

The datasets are available from the corresponding author upon reasonable request.

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Edited by

• Section Editor:
  Gabriel Constantino Blain https://orcid.org/0000-0001-8832-7734

Publication Dates

• Publication in this collection
  17 Apr 2026
• Date of issue
  2026

History

• Received
  17 Oct 2025
• Accepted
  03 Feb 2026