Emerging Emulsifiers: Conceptual Basis for the Identification and Rational Design of Peptides with Surface Activity

Abstract

Emulsifiers are gradually evolving from synthetic molecules of petrochemical origin to biomolecules mainly due to health and environmental concerns. Peptides represent a type of biomolecules whose molecular structure is composed of a sequence of amino acids that can be easily tailored to have specific properties. However, the lack of knowledge about emulsifier behavior, structure–performance relationships, and the implementation of different design routes have limited the application of these peptides. Some computational and experimental approaches have tried to close this knowledge gap, but restrictions in understanding the fundamental phenomena and the limited property data availability have made the performance prediction for emulsifier peptides an area of intensive research. This study provides the concepts necessary to understand the emulsifying behavior of peptides. Additionally, a straightforward description is given of how the molecular structure and conditions of the system directly impact the peptides’ ability to stabilize emulsion droplets. Moreover, the routes to design and discover novel peptides with interfacial and emulsifying activity are also discussed, along with the strategies to address some of their major pitfalls and challenges. Finally, this contribution reviews methodologies to build and use data sets containing standard properties of emulsifying peptides by looking at successful applications in different fields.

Targeted hydrolysis of native potato protein: A novel route for obtaining hydrolysates with improved interfacial properties

“…patatin and protease inhibitors) all represent a large number of protein isoforms, mapping peptides to the isoforms is a challenging task. This is particularly the case as single AA substitutions and minor truncations/elongations may not have a detrimental effect of the peptide functionality (Enser et al, 1990; García-Moreno, Gregersen, et al, 2020; García-Moreno, Jacobsen, et al, 2020; Ricardo et al, 2021). To accommodate this, we established a workflow, where identified peptides were mapped onto representative target cluster sequences (Fig.…”

“…Peptides are complex polymer chains combining (at least) twenty different amino acid monomers with different physico-chemical properties and thus, the combinatorial space is tremendous and scales by peptide length, n , as (at least) 20 n . Although the specific mechanisms and prerequisites for potent peptide emulsifiers still remains only superficially characterized, our understanding of the underlying molecular properties continue to expand (Ricardo, Pradilla, Cruz, & Alvarez, 2021). Recent work has investigated the influence of factors such as interfacial peptide structure (Dexter, 2010; Du et al, 2020; García-Moreno et al, 2021; Lacou, Léonil, & Gagnaire, 2016), physico-chemical properties such as length and charge (García-Moreno, Gregersen, et al, 2020; Lacou et al, 2016; Liang et al, 2020; Yesiltas et al, 2021), amino acid composition (Enser, Bloomberg, Brock, & Clark, 1990; Saito, Ogasawara, Chikuni, & Shimizu, 1995; Siebert, 2001), and specific sequence patterns (Jafarpour, Gregersen, et al, 2020; Mondal et al, 2017; Nakai et al, 2004; Wychowaniec et al, 2020).…”

Oleic Acid-Based Self Micro-Emulsifying Delivery System for Enhancing Antifungal Activities of Clotrimazole

“…RPMI 1640 contains glucose, pH indicator, salts, amino acids, and vitamins, while the components of SIFsp are KH 2 PO 4 and NaOH. Ricardo F. et al reported that the covalent coupling of amino acid side chains can provide emulsifying characteristics [ 38 ]. Therefore, we speculate that the difference in particle size of the microemulsion might relate to amino acids present in the RPMI 1640 medium.…”

Thinking on the Construction of Antimicrobial Peptide Databases: Powerful Tools for the Molecular Design and Screening

“…The combination of biology and ML has greatly promoted the development of bioinformatics, in which many amino acid sequences of AMPs with higher-complexity structures are analyzed quickly, especially when processing high-throughput data from transcriptomics and proteomics [ 80 , 81 ]. At present, some mature ML algorithms are used in prediction software to categorize and analyze data, and the newly developed AMP databases also contain classical machine algorithms such as RF, SVM, DA, Artificial Neural Network (ANN), and Deep Neural Network (DNN) [ 82 , 83 , 84 ]. Previous studies have shown that MLs are an important feature of databases, especially in the CAMP database, which contains all of the above MLs algorithms for the prediction and design of AMPs [ 77 ], whereas only parameter spaces and thresholds or cut-off discriminator algorithms are embedded in the APD and DBAASP databases, respectively [ 66 , 67 ].…”