Umami Peptides Research Articles

Umami-gcForest: Construction of a predictive model for umami peptides based on deep forest

Umami peptides have recently gained attention for their ability to enhance umami flavor, reduce salt content, and provide nutritional benefits. However, traditional wet laboratory methods to identify them are time-consuming, laborious, and costly. Therefore, we developed the Umami-gcForest model using the deep forest algorithm. It constructs amino acid feature matrices using ProtBERT, amino acid composition, composition-transition-distribution, and pseudo amino acid composition, applying mutual information for feature selection to optimize dimensions. Compared to other machine learning baseline, umami peptide prediction, and composite models, the validation results of Umami-gcForest on different test sets demonstrated outstanding predictive accuracy. Using SHapley Additive exPlanations to calculate feature contributions, we found that the key features of Umami-gcForest were hydrophobicity, charge, and polarity. Based on this, an online platform was developed to facilitate its user application. In conclusion, Umami-gcForest serves as a powerful tool, providing a solid foundation for the efficient and accurate screening of umami peptides.

Rapid screening of novel umami peptides in high-umami scored air-dried chicken under low-salt processing based on molecular sensory techniques

Rapid screening of novel umami peptides in high-umami scored air-dried chicken under low-salt processing based on molecular sensory techniques

Uncovering the taste features: Applying machine learning and molecular docking approaches to predict umami taste intensity of peptides

The umami taste, often described as the fifth basic taste, plays a pivotal role in the culinary world, significantly contributing to the overall flavor profile and consumer satisfaction of food products. Precise prediction and enhancement of umami taste intensity using the identification of umami peptides can lead to groundbreaking advancements in the food industry. This study presents a novel approach to combine machine learning and molecular docking techniques. The machine learning algorithm is based on a cascade algorithm combining CatBoost and BERT models to predict the umami taste intensity of peptides accurately. Our research reveals that combinations of specific amino acids, such as aspartic acid with alanine or glycine and lysine with glycine or histidine, have been identified to enhance the umami taste in foods. The model is available as a user-friendly web server at https://taste.infochemistry.ru. This study contributes to the scientific understanding of taste perception and provides a valuable tool for the food industry to innovate and improve product quality by optimizing umami taste profiles.

Identification of umami peptides and mechanism of the interaction with umami receptors T1R1/T1R3 in pigeon meat.

Pigeon meat offer an ideal source for extracting fresh flavor peptides. These peptides not only enhance the taste of food but also have potential health benefits, including providing low-sugar, low-sodium, and low-calorie options for individuals with conditions like diabetes, hypertension, and obesity. Therefore, further research into the pigeon industry holds promise for addressing both economic and nutritional needs. To explore umami peptides and their molecular binding mechanisms with umami receptor type 1 member 1 in pigeon meat, an enzymatic hydrolysate product is isolated, analyzed, and subjected to sensory evaluation. Fifteen peptides with high freshness characteristics are separated and identified by ultrafiltration, gel separation, reverse performance liquid chromatography, and nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS). The molecular docking results show that the amino acid residue Glu128 is a common ligand binding site for all of the fresh-flavored peptides to taste T1R1/T1R3 receptors and it exerts freshness-presenting effects with 15 fresh-flavored peptides through hydrogen bonding, electrostatic interactions, salt bridges, and hydrophobic interactions. This study provides a theoretical basis and technical support for the subsequent development of flavor peptide products in pigeon meat.

Screening of novel umami peptides with saltiness enhancement effect using molecular docking and structure-activity analysis

In this study, to explore a healthier and safer alternative method for reducing salt intake, new umami peptides were identified from the hydrolysate of Ruditapes philippinarum through structure–activity relationship and molecular docking. The impact of these peptides on enhancing salty taste was examined using a sensory test and an electronic tongue evaluation. The mechanism of salt reduction was investigated through molecular docking. In addition, eight new umami peptides: LEDKVE, DEELNKLK, DLKEKL, LEER, AEKEEEFENT, EQEEYKK, EFDKK, and GEDFDNKLV, extracted from the hydrolysate of Ruditapes philippinarum, were identified, and their taste thresholds ranged from 0.12 to 0.24 mmol/L. When new umami peptides were mixed with 3 g/L of NaCl, the saltiness of NaCl was enhanced. Among the umami peptides studied, LEDKVE had the most significant impact on improving saltiness, achieving a perception of salty taste equivalent to 5 g/L of NaCl in a solution of 3 g/L of NaCl. Molecular docking revealed that the umami peptide interacted with transmembrane channel-like protein 4 (TMC4) through hydrogen bonds, and the key active sites, including Lys568, Trp145, Tyr565, Arg151, and Gln155, potentially played a crucial role in taste perception. This study offers valuable insights into the development of flavorful and nutritious seasonings that can enhance nutritional quality.

Simulation screening of umami peptides in the microbiota of soy sauce based on molecular docking and molecular dynamics

Simulation screening and molecular simulation techniques are gradually being applied to the study of bioactive peptides. In this study, we explored and simulated the screening process using metagenomic sequencing data of microorganisms from soy sauce. An LSTM-based screening model was constructed to identify the top 10 scoring peptides from a sample dataset of 19,335 raw peptides, primarily derived from Bacillus and Weissella confusa. Through homology modeling, molecular docking, and molecular dynamics simulations, Ser, Asp, Asn, Glu, and Gln were identified as the main amino acids involved in ligand-receptor binding. Hydrogen bonding, van der Waals forces, and hydrophobic interactions were revealed as the primary forces mediating receptor and ligand binding. The umami thresholds of ISWCFTY and QISPYRRI were verified through wet experiments to be 0.13 mg/mL and 0.11 mg/mL, respectively, exhibiting high umami intensity. Overall, this study presents a high-throughput screening method for predicting umami peptides, offering cost reduction, simplified steps, and saved manpower.

ACE inhibitory effect and saltiness-enhancing properties of chicken-derived umami peptides: Digestive stability, inhibition kinetics, multiple ligand docking and central composite design

Angiotensin-I-converting enzyme (ACE) inhibitory activity and saltiness-enhancing properties of chicken-derived umami peptides were investigated. DGGRYY and NEFGYSNR were screened and the IC50 values were 28.71 μM and 283.24 μM, indicating their potential as novel ACE inhibitors. DGGRYY and NEFGYSNR have good pH and thermal stability. After gastrointestinal digestion, the ACE-inhibitory activity of DGGRYY retained about 53 %, whereas NEFGYSNR retained about 57 %. The inhibition pattern of both peptides was determined to be uncompetitive, consisting with the result of multiple ligand docking that the binding sites were outside the ACE active pocket. Trp59, Tyr62, Asp121, Arg124, and Ser516 were the key binding sites that contributed to the total binding energy. In addition, saltiness and palatability models were established according to sensory analysis and central composite design. In 0.1 % ~ 0.3 % NaCl solutions, the addition of DGGRYY and NEFGYSNR could enhance the salty intensity and compensate for the palatability loss caused by salt reduction.

Taste characterization and molecular docking study of novel umami flavor peptides in Yanjin black bone Chicken meat

Five polypeptides with a potential umami taste were isolated and purified from Yanjin black bone chicken. However, the flavor characteristics and umami mechanism have not been clarified. The umami properties of these five peptides were investigated in this work using a range of analytical techniques, computer simulation, and sensory evaluation. HE-10 and TP-7 exhibited the strongest umami flavors. Furthermore, dose-response experiments showed that the umami peptides enhanced umami by generating peptide mineral chelates. Environmental scanning electron microscopy (ESEM) microstructural analyses supported this finding. The molecular docking results indicated that the five polypeptides bind to four critical amino acid residues, namely Glu217, Glu148, Asp216, and His145, of the T1R1/T1R3 receptor. The binding occurred through van der Waals, electrostatic interactions, hydrogen bonding, and hydrophobic interactions. The main surface forces implicated include aromatic interactions, hydrogen bonding, hydrophilicity, and solvent accessibility.

Decoding of novel umami peptides from corn fermented powder and its mechanism via multisensory techniques, virtual screening, and molecular simulation approaches

This study aimed to identify umami peptides in corn fermented powder (CFP) and investigate their umami enhancing effect. Ultrafiltration and ethanol precipitation was used to separate the umami peptides in CFP. Dynamic sensory evaluations were used to identify the peptide fraction with the intense umami taste, and the peptides in the fraction were identified by nano-liquid chromatography-tandem mass spectrometry. Subsequently, ten umami-enhancing peptide candidates were screened using an integrated virtual screening strategy. Molecular docking revealed that Ser382, Ser104, Leu334, Glu338 and Glu148 of the T1R1 and T1R3 taste receptors are important amino acid residues for binding of the ten umami peptides. Three umami peptides (VDW, WGDDP, and WPAGE) exhibited the stronger binding affinity with the umami receptors. Moreover, molecular dynamics simulation revealed that the T1R1/T1R3 formed stable complexes with the three umami peptides during the simulation. Sensory evaluation indicated that the three peptides exhibited diverse taste characteristics (detection thresholds:0.0315–0.0625 mg/mL). The sigmoid curve analysis further confirmed peptides were identified as synergistically (VDW and WGDDP) or additively (WPAGE) enhancing the umami of 3 mg/mL MSG solution. This study uncovers the mechanism of umami-peptide-driven taste in fermented corn products.

Preparation of umami peptides from chicken breast by ultrasound-assisted gradient dilution feeding substrate and study of their formation mechanism

Gradient dilution feeding substrate (GDFS) is regarded as an effective method for the production of active peptides, as it compensates for substrate limitations and minimizes substrate contamination of the ultrafiltration membrane. This study further investigated the impact of ultrasound-assisted gradient dilution feeding substrate (UA-GDFS) on the peptide yield and the intensity of umami. The chicken breast was used as ingredients. The umami compounds in the hydrolysate were determined using an automatic amino acid analyzer and HPLC. The hydrolysate's umami intensity was determined by electronic tongue in all modes. These findings indicated that UA-GDFS enhanced the umami intensity and peptide yield. Hydrolysate from UA-GDFS with molecular weight below 1 kDa was analyzed by nano-scale liquid chromatography-tandem mass spectrometry (Nano-HPLC-MS/MS). Electronic tongue analysis showed that GPSGPVG and VDGPSGK had umami taste and could enhance the umami taste of monosodium glutamate (MSG) solution. Asn68 and His145 were important sites on T1R3 for umami peptide binding.

Novel umami-enhancing peptides of beef M. Semimembranosus hydrolysates and interactions with the T1R1/T1R3 taste receptor

The taste mechanisms of beef umami and umami-enhancing peptides are not well understood. Therefore, novel umami and umami-enhancing peptides from beef M. semimembranosus hydrolysates were explored. Beef hydrolysates treated with Flavourzyme® showed an overall strong umami intensity compared to those treated with Alcalase®, papain, or Protamex®. The peptides were isolated via consecutive separation processes, and 31 potential umami peptides were identified. Molecular docking results showed that WGSEPIRIQ and TERGYSF had considerably low docking energies with the T1R1/T1R3 taste receptor through potential key binding sites for hydrogen bonding, including Ser48, Gly49, and Gln278 in T1R1, and Ser67, Asn68, and Arg247 in the T1R3 subunit. The taste of the identified peptides dissolved in ultrapure water was dominated by sourness. Instead, they demonstrated an umami-enhancing effect in the presence of monosodium glutamate. These results broaden our understanding of the taste mechanisms of beef umami-enhancing peptides and their potential applications as flavoring agents.

Exploring Novel Umami Peptides from Bovine Bone Soups Using Nano-HPLC-MS/MS and Molecular Docking.

In this study, umami peptides were screened and characterized from bovine bone soups manufactured via atmospheric and high-pressure boiling. Peptide fractions with molecular weights less than 3 kDa were selected for peptide sequencing using LC-MS/MS, the toxicity prediction of the umami peptides was carried out by using an website, and the peptides were screened according to the binding energy, i.e., three peptides including YDAELS, TDVAHR, and ELELQ were selected. The three umami peptides were further synthesized, and their umami thresholds were determined through sensory evaluation and electronic tongue analysis, ranging from 0.375 to 0.75 mg/mL. All three peptides exhibited a significant synergistic taste enhancement effect when combined with MSG (monosodium glutamate) solution. The molecular docking of the umami peptides with the T1R1/T1R3 receptor revealed the mechanism of umami presentation, and the main interaction forces between the three umami peptides and the receptor were hydrogen bonding, electrostatic interactions, and hydrophobic interactions.

Revealing the formation mechanism of umami peptides in fermented broad bean paste based on peptidomics and macrogenomics

Revealing the formation mechanism of umami peptides in fermented broad bean paste based on peptidomics and macrogenomics

Screening and characterization of umami peptides from enzymatic and fermented products of wheat gluten using machine learning

To explore the umami peptides from wheat gluten hydrolysates (WGHs) with different degrees of hydrolysis (DH) after Corynebacterium glutamicum (C. glutamicum) fermentation, a high-throughput screening method for umami peptides was established based on machine learning. The results showed fermented WGHs with high DHs exhibited more favourable taste profile after 4 days of fermentation and produced more small molecule peptides. Fermentation increased the number of characteristic peptides in WGHs with DH10, DH15, and DH20. Among, fermented WGHs with DH15 were the important source of potential umami peptides. Six potential umami peptides were screened (QQLPQFEE, LSFE, EELR, YTCE, YTTD, EEDQ) and were demonstrated to form stable complex with the T1R1/T1R3 receptor through hydrogen bonding interactions, electrostatic interactions, and hydrophobic interactions forces. The affinity of peptides and receptor were in the range of −6.3 to −8.1 kcal/mol. GLU735, GLY755, SER734, SER757 and THR227 were the critical binding sites. Sensory evaluation and electronic tongue showed that six peptides exhibited umami profile and had the capacity to enhance umami and saltiness taste. EEDQ presented the strongest intensity of umami taste and QQLPQFEE had the lowest umami thresholds. This study provides reliable methods for effectively screening new microbial-derived umami peptides.

Identification of Novel Umami Peptides from Yeast Protein through Enzymatic, Sensory, and In Silico Approaches.

This study aimed to rapidly develop novel umami peptides using yeast protein as an alternative protein source. Yeast protein hydrolysates exhibiting pronounced umami intensity were produced using flavorzyme under optimum conditions determined via a sensory-guided response surface methodology. Six out of 2138 peptides predicted to possess umami taste by composite machine learning and assessed as nontoxic, nonallergenic, water-soluble, and stable using integrated bioinformatics were screened as potential umami peptides. Sensory evaluation results revealed these peptides exhibited multiple taste attributes (detection threshold: 0.37 ± 0.10-1.1 ± 0.30 mmol/L), including umami. In light of the molecular docking outcomes, it is inferred that hydrogen bond, hydrophobic, and electrostatic interactions enhanced the theoretically stable binding of peptides to T1R1/T1R3, with their contributions gradually diminishing. Hydrophilic amino acids within T1R1/T1R3, especially Ser, may play a particularly pivotal role in binding with umami peptides. Future research will involve establishing heterologous cell models expressing T1R1 and T1R3 to delve into the cellular physiology of umami peptides. Peptide sequences (FADL, LPDP, and LDIGGDF) also had synergistic saltiness-enhancing effects; to overcome the limitation of not investigating the saltiness enhancement mechanism, comprehensive experiments at the molecular and cellular levels will also be conducted. This study offers a rapid umami peptide development framework and lays the groundwork for exploring yeast protein taste compounds.

Isolation and characterization of novel umami peptides from bay scallop (Argopecten irradians) and molecular docking to the T1R1/T1R3 taste receptor

The study aimed to isolate, purify, screen, and identify novel umami peptides from bay scallops. Proteins were hydrolyzed using AP200-A alkaline protease and FF106 flavor protease to obtain umami peptides. Ultrafiltration membranes were employed to separate and purify the peptides from scallop by-products, revealing that fractions smaller than 3 kDa exhibited the highest umami levels. Further purification was achieved using the AKTA system superdexpeptide gel chromatography. The composition of umami peptides was identified using LC-MS/MS technology. Molecular docking identified four types of umami peptides: RPPVVR, APDFGNR, SWLDGK, and RGFGGAR. Hydrogen bonds, electrostatic interactions, and hydrophobic interactions were identified as the primary binding forces between T1R1/T1R3 and the umami peptides, with key binding sites including ASN150, ASP216, ASP219, GLN222, GLU148, GLU217, LEU51, and SER107. These findings supplement existing research on scallop umami peptides and provide insights into the interaction mechanisms between umami receptors and umami peptides.

Identification and screening of umami peptides from skipjack tuna (Katsuwonus pelamis) hydrolysates using EAD/CID based micro-UPLC-QTOF-MS and the molecular interaction with T1R1/T1R3 taste receptor

In this study, the enzymatic hydrolysates of skipjack tuna, Katsuwonus pelamis, were purified by ultrafiltration and further identified through micro-ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (micro-UPLC-QTOF-MS). The potential umami peptides were identified using both conventional collision-induced dissociation (CID) and novel electron-activated dissociation (EAD) fragmentation techniques. Nine novel umami peptides with iUmami-SCM > 588 were screened. Sensory evaluation and electronic tongue analysis were performed to confirm the taste characteristics of the umami peptides, indicating that these umami peptides all exhibited varying degrees of umami taste. Molecular docking and molecular dynamics simulation were utilized to investigate the interaction with T1R1/T1R3 taste receptors. The docking results revealed that Asp234, Ser23, Glu231, and Ile237 appeared most frequently in all docking sites and formed stable complexes through hydrogen bonding and electrostatic interactions. Furthermore, molecular dynamics simulation allowed for a more comprehensive analysis of their interactions within a dynamic environment, providing a deeper understanding of the umami perception mechanism involving umami peptides and receptors.

Extraction, characterization, and molecular docking study of umami peptides from Antarctic krill

The study investigated the extraction, characterization, and molecular docking of umami peptides from defatted Antarctic krill powder. Optimal conditions for enzymatic hydrolysis were hydrolysis temperature 41 °C, time 3 h, flavor protease to trypsin ratio 1:5 (2000 U/g), degree of hydrolysis 37.56%, and peptide yield 29.85%. After ultrafiltration (1 kDa), the Antarctic krill hydrolysate exhibited higher levels of bitter, umami, and sweet amino acids (27.55 g/100 g, 14.31 g/100 g, and 14.40 g/100 g, respectively) than before ultrafiltration (17.09 g/100 g, 8.05 g/100 g, and 9.03 g/100 g, respectively), indicating that ultrafiltration enhanced the flavor by concentrating umami and sweet amino acids. Overall, 179 peptides were identified via LC-MS/MS analysis, with VLALEELLAMPEN being the dominant (28.4% of the total peptides). Docking of Antarctic krill peptides with T1R1/T1R3 receptor showed hydrogen bonds and van der Waals forces as crucial interactions, deepening understanding of the taste mechanism of Antarctic krill umami peptides.

Investigating the interaction between umami peptides and umami receptor T1R1/T1R3-VFT: a computational approach

Investigating the interaction between umami peptides and umami receptor T1R1/T1R3-VFT: a computational approach

VmmScore: An umami peptide prediction and receptor matching program based on a deep learning approach

Peptides, with recognized physiological and medical implications, such as the ability to lower blood pressure and lipid levels, are central to our research on umami taste perception. This study introduces a computational strategy to tackle the challenge of identifying optimal umami receptors for these peptides. Our VmmScore algorithm includes two integral components: Mlp4Umami, a predictive module that evaluates the umami taste potential of peptides, and mm-Score, which enhances the receptor matching process through a machine learning-optimized molecular docking and scoring system. This system encompasses the optimization of docking structures, clustering of umami peptides, and a comparative analysis of docking energies across peptide clusters, streamlining the receptor identification process. Employing machine learning, our method offers a strategic approach to the intricate task of umami receptor determination. We undertook virtual screening of peptides derived from Lateolabrax japonicus, experimentally verifying the umami taste of three identified peptides and determining their corresponding receptors. This work not only advances our understanding of the mechanisms behind umami taste perception but also provides a rapid and cost-effective method for peptide screening. The source code is publicly accessible at https://github.com/heyigacu/mlp4umami/, encouraging further scientific exploration and collaborative efforts within the research community.