Elsevier

Food Chemistry

Volume 321, 15 August 2020, 126712
Food Chemistry

Characterization of the protein and peptide of excipient zein by the multi-enzyme digestion coupled with nano-LC-MS/MS

https://doi.org/10.1016/j.foodchem.2020.126712Get rights and content

Highlights

  • Multi-enzyme digestion can improve protein sequence coverage as well as the number of detectable zein proteins.

  • 51 proteins were identified in excipient zein, including 37 zeins and 14 non-zein proteins.

  • 420 zein peptides were identified, with 116 predicted as potentially bioactive by using in silico approach.

Abstract

Zein, a class of prolamine proteins extracted from maize, is extensively used in the food and pharmaceutical industries. Characterization of its components is essential for quality control and safety evaluation. We performed in silico digestion of zein proteins using tandem combinations of different proteinases, to improve protein sequence coverage and subsequent identification by nano-LC-MS/MS analysis. Trypsin/chymotrypsin yielded the highest protein sequence coverage of up to 79.5% and increased the number of proteins from 11 to 35 compared to trypsin/Lys-C. Besides, SDS-PAGE analysis revealed 37 proteins in the zein extract, as well as the possibility of protein polymers. Also, 420 peptides originating from 71 proteins were identified, of which 116 were predicted as bioactive by in silico approach. In conclusion, in silico prediction coupled with multi-enzyme digestion can significantly improve the coverage of complex zein protein proteome, and the potential function of zein proteins and peptides need be further investigated.

Introduction

Zein, also known as prolamine, is an essential protein extract of maize kernels (Zea mays). It is widely used as an excipient for producing inks, adhesives, coatings, plastics, candy, nuts, fruit, encapsulated foods, as well as an essential source of protein in the human diet. Recent studies have demonstrated its usage in nano-emulsion for alga oil delivery (Wang et al., 2016) and drug delivery systems (Xiao, Davidson, & Zhong, 2011). Although certified as one of the safest biomaterial excipients by the US FDA in 1985, subsequent reports associated zein proteins with increased serum IgA reactivity (Cabrera-Chavez, Rouzaud-Sandez, Sotelo-Cruz, & Calderon de la Barca, 2008), and a gluten-like cellular immune response in some patients with celiac disease (CD) (Ortiz-Sanchez, Cabrera-Chavez, & de la Barca, 2013). Besides, the peptides of zein digestion are potentially immunogenic for some CD patients (Ortiz Sánchez, Mata Haro, Cabrera Chávez, & Calderón de la Barca, 2016). Therefore, it is crucial to characterize its constituent proteins and peptides and identify the bioactive components.

The known zein proteins are classified into the α-zein (~80%), β-zein (10–15%), γ-zein (10–15%) and δ-zein types based on their solubility and sequence, with each type containing different forms of zein proteins (Ştefănescu & Băncilă, 2017). Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) had been widely used in the past for characterizing zein proteins, which only provide the molecular weights (MW). The advent of mass spectrometry (MS) and similar techniques that involve peptide ionization has accelerated proteome research due to their high sensitivity and high mass accuracy. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) for example has helped identify and characterize zein proteins with better mass accuracy (Adams et al., 2004, Ştefănescu and Băncilă, 2017, Wang et al., 2003). But lack of pre-fractionation limits protein recovery from the complex mixture. Postu et al. combined 2-DE (two-dimensional gel electrophoresis) separation technology with MALDI-TOF-MS to comprehensively map protein composition in maize flour, but they only focused on α zein proteins (Postu, Ion, Drochioiu, Petre, & Glocker, 2019). Separation of zein proteins based on their electrophoretic mobility by capillary electrophoresis (CE) has enabled the identification of more fractions by MS (Erny, Marina, & Cifuentes, 2007). However, the study only focused on proteins with MW between 17 and 27 kDa. Liquid chromatography-mass spectrometry (LC-MS) is widely used in proteome analysis on account of its high-capacity chromatographic separation and high sensitivity. However, no study so far has reported using nano-LC-MS/MS to identify zein proteins.

The “bottom-up” approach is currently used for proteomics analysis, although the protein sequence coverage is often low. Modern proteomics involves structural analysis, identification of posttranslational modifications, and differentiating between similar proteins of the same group, which requires a more extensive sequence coverage. Numerous studies have described new digestion strategies to improve proteome identification, including multiple enzyme digestion (MED) (Wisniewski, 2016), which combines proteases with different cleavage sites and specificities. It is however impossible to test all possible combinations of enzymes in practice, a limitation that can be obviated via in silico digestion programs that screen for preferable cleavage conditions, and help determine the suitability of various proteases and reagents (Wang et al., 2018). To this end, we evaluated the combination of three proteases on zein protein using the in silico approach and validated the cleavage sites using LC-MS/MS analysis. The proteins were further fractionated using SDS-PAGE to determine the presence of oligomers and polymers. Studies show that zein peptides and protein hydrolysates are bioactive, and exhibit antioxidant (Li et al., 2010), anti-hypertensive (Huang, Sun, He, Dong, & Li, 2011), anti-hyperglycemic (Mochida, Hira, & Hara, 2010), and anti-tumor activities (Ortiz-Martinez et al., 2017). Therefore, we also predicted the function of the zein proteins and peptides. Our findings will provide new insights into the composition of zein and uncover its potential applications in the food and pharmaceutical industries.

Section snippets

Materials and reagents

Three lots of excipient zein were acquired from Beijing Tong Ren Tang Pharmaceutical Co. Ltd. Reagents (ACS grade, purity >99%), including urea, trifluoroacetic acid, iodoacetamide (IAA) dithiothreitol (DTT), thiourea snd Tris-HCl, were all purchased from Sigma-Aldrich (St Louis, MO, USA). Coomassie Blue stain G-250, SilverQuest Silver Staining Kit, pre-mixed electrophoresis buffer, sample loading buffer, protein precision standards, and NuPAGE Gels were supplied by Invitrogen (Carlsbad, CA,

In silico digestion of zein proteins in the UniProt database

The entire maize proteome with 16,724 proteins was screened in the UniProt database, which revealed 392 putative zein proteins. The frequencies of the 20 amino acids (AAs) in both the maize proteome and zein proteins were then calculated. Lysine (K) and arginine (R) were relatively abundant in the maize proteome (Fig. 1A, blue bar), which would generate tryptic peptides that are relatively suited for LC-MS/MS analysis. In contrast, the proportion of both K and R were extremely low in the zein

Cleavage conditions optimization assisted by in silico digestion

Due to the high percentage of leucine, proline, and alanine, and lower content of lysine and arginine residues in the zein proteins, trypsin digestion alone cannot sufficiently cover all peptides. Therefore, we first predicted suitable protease cleavage sites on zein using in silico digestion with different combinations of trypsin, LysC, chymotrypsin, and thermolysin. Trypsin cleaves at the C-terminal of arginine (R) and lysine (K), and LysC at the C-terminal of K, and is therefore often used

Conclusion

A combination of multiple proteases predicted by in silico digestion can maximize proteome coverage in large-scale mass spectrometric-based protein profiling. Trypsin and chymotrypsin digestion enabled the identification of multiple zein proteins, as well as possible zein protein polymers. Numbers of peptides were also identified, including potentially bioactive ones. Their possible function include ACE inhibitory, DPP Ⅳ inhibitory and antioxidant. Our findings provide further insights into the

CRediT authorship contribution statement

Menglin Li: Investigation, Methodology, Writing - original draft, Visualization. Hao Zheng: Investigation. Miao Lin: Investigation. Wenwen Zhu: Resources. Jinlan Zhang: Conceptualization, Writing - review & editing, Supervision, Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We thank Dr. Matthew Monroe for providing help with the Software Protein Digestion Simulator, and Ms. Haidan Sun for operating the mass spectrometer. The study was supported by grants from the CAMS Innovation Fund for Medical Sciences, China (2016-I2M-3–017, 2016-12M-3-010) and the National Key R&D Program of China (2018YFF0212504).

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