Human sweet taste receptor: Complete structure prediction and evaluation

Peptidomics Screening and Molecular Docking with Umami Receptors T1R1/T1R3 of Novel Umami Peptides from Oyster (Crassostrea gigas) Hydrolysates

In this study, novel umami peptides were prepared from oyster (Crassostrea gigas) hydrolysates, and their umami mechanisms were investigated. Umami fractions G2 and G3 were isolated by gel filtration chromatography (GFC) and sensory evaluation. The umami scores of the G2 and G3 fractions were 7.8 ± 0.12 and 7.5 ± 0.18, respectively. 36 potential umami peptides with molecular weights below 1500 Da, E and D accounting for >30% of the peptides and iUmami-SCM > 588 were screened by peptidomics. Peptide source analysis revealed that myosin, paramyosin, and sarcoplasmic were the major precursor proteins for these peptides. The electronic tongue results demonstrated that the synthetic peptides DPNDPDMKY and NARIEELEEE possessed an umami characteristic, whereas SIEDVEESRNK and ISIEDVEESRNK possessed a saltiness characteristic. Additionally, molecular docking results indicated that the umami peptide (DPNDPDMKY, NARIEELEEE, SIEDVEESRNK, and ISIEDVEESRNK) binds to H145, S276, H388, T305, Y218, D216, and Q389 residues in the T1R3 taste receptor via a conventional hydrogen bond and a carbon-hydrogen bond. This research provides a new strategy for the screening of umami peptides.

Identification and virtual screening of novel salty peptides from hydrolysate of tilapia by-product by batch molecular docking

Introduction

Tilapia produces a large number of by-products during processing, which contain potentially flavorful peptides.

Methods

The application of PyRx software enabled batch molecular docking andscreening of 16 potential salty peptides from 189 peptides identified in the enzymaticdigestion of tilapia by-products.

Results

According to sensory analysis, all 16 peptides werepredominantly salty with a threshold of 0.256 - 0.379 mmol/L with some sournessand astringency, among which HLDDALR had the highest salty intensity, followedby VIEPLDIGDDKVR, FPGIPDHL, and DFKSPDDPSRH. I addition, moleculardocking results showed these four core peptides with high salt intensity bound to thesalt receptor TRPV1 mainly via van der Waals interactions, hydrogen bonds, andhydrophobic forces; Arg491, Tyr487, VAL441, and Asp708 were the key sites for thebinding of salty peptides to TRPV1. Therefore, the application of batch moleculardocking using PyRx is effective and economical for the virtual screening of saltypeptides.

Revealing the Secret of Umami Taste of Peptides Derived from Fermented Broad Bean Paste

To understand the umami taste of fermented broad bean paste (FBBP) and explore the umami mechanism, eight peptides (PKALSAFK, NKHGSGK, SADETPR, EIKKAALDANEK, DALAHK, LDDGR, and GHENQR) were separated and identified via ultrafiltration, RP-HPLC, and UPLC-QTOF-MS/MS methods. Sensory experiments suggested that eight novel peptides showed umami/umami-enhancing and salt-enhancing functions. Significantly, the threshold of EIKKAALDANEK in aqueous solution exceeded that of most umami peptides reported in the past 5 years. The omission test further confirmed that umami peptides contributed to the umami taste of FBBP. Molecular docking results inferred that all peptides easily bind with Ser, Glu, His, and Asp residues in T1R3 through hydrogen bonds and electrostatic interactions. The aromatic interaction, hydrogen bond, hydrophilicity, and solvent-accessible surface (SAS) were the main interaction forces. This work may contribute to revealing the secret of the umami taste of FBBP and lay the groundwork for the efficient screening of umami peptides.

Introducing a New Model of Sweet Taste Receptor, a Class C G-protein Coupled Receptor (C GPCR)

The structure of sweet taste receptor (STR), a heterodimer of class C G-protein coupled receptors comprising T1R2 and T1R3 molecules, is still undetermined. In this study, a new enhanced model of the receptor is introduced based on the most recent templates. The improvement, stability, and reliability of the model are discussed in details. Each domain of the protein, i.e., VFTM, CR, and TMD, were separately constructed by hybrid-model construction methods and then assembled to build whole monomers. Overall, 680 ns molecular dynamics simulation was performed for the individual domains, the whole monomers and the heterodimer form of the VFTM orthosteric binding site. The latter's structure obtained from 200 ns simulation was docked with aspartame; among various binding sites suggested by FTMAP server, the experimentally suggested binding domain in T1R2 was retrieved. Local three-dimensional structures and helices spans were evaluated and showed acceptable accordance with the template structures and secondary structure predictions. Individual domains and whole monomer structures were found stable and reliable to be used. In conclusion, several validations have shown reliability of the new and enhanced models for further molecular modeling studies on structure and function of STR and C GPCRs.

e-Sweet: A Machine-Learning Based Platform for the Prediction of Sweetener and Its Relative Sweetness

Artificial sweeteners (AS) can elicit the strong sweet sensation with the low or zero calorie, and are widely used to replace the nutritive sugar in the food and beverage industry. However, the safety issue of current AS is still controversial. Thus, it is imperative to develop more safe and potent AS. Due to the costly and laborious experimental-screening of AS, in-silico sweetener/sweetness prediction could provide a good avenue to identify the potential sweetener candidates before experiment. In this work, we curate the largest dataset of 530 sweeteners and 850 non-sweeteners, and collect the second largest dataset of 352 sweeteners with the relative sweetness (RS) from the literature. In light of these experimental datasets, we adopt five machine-learning methods and conformational-independent molecular fingerprints to derive the classification and regression models for the prediction of sweetener and its RS, respectively via the consensus strategy. Our best classification model achieves the 95% confidence intervals for the accuracy (0.91 ± 0.01), precision (0.90 ± 0.01), specificity (0.94 ± 0.01), sensitivity (0.86 ± 0.01), F1-score (0.88 ± 0.01), and NER (Non-error Rate: 0.90 ± 0.01) on the test set, which outperforms the model (NER = 0.85) of Rojas et al. in terms of NER, and our best regression model gives the 95% confidence intervals for the R²(test set) and ΔR² [referring to |R²(test set)- R²(cross-validation)|] of 0.77 ± 0.01 and 0.03 ± 0.01, respectively, which is also better than the other works based on the conformation-independent 2D descriptors (e.g., 2D Dragon) according to R²(test set) and ΔR². Our models are obtained by averaging over nineteen data-splitting schemes, and fully comply with the guidelines of Organization for Economic Cooperation and Development (OECD), which are not completely followed by the previous relevant works that are all on the basis of only one random data-splitting scheme for the cross-validation set and test set. Finally, we develop a user-friendly platform “e-Sweet” for the automatic prediction of sweetener and its corresponding RS. To our best knowledge, it is a first and free platform that can enable the experimental food scientists to exploit the current machine-learning methods to boost the discovery of more AS with the low or zero calorie content.