Efficient screening and discovery of umami peptides in Douchi enhanced by molecular dynamics simulations
In this study, a partial least squares discriminant analysis (PLS-DA) discriminant model for umami peptides was constructed based on molecular dynamics simulation data, achieving a R2 value of 0.949 and a Q2 value of 0.558. Using this novel model and bioinformatics screening methods, five new umami...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Food Chemistry: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590157524008289 |
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| Summary: | In this study, a partial least squares discriminant analysis (PLS-DA) discriminant model for umami peptides was constructed based on molecular dynamics simulation data, achieving a R2 value of 0.949 and a Q2 value of 0.558. Using this novel model and bioinformatics screening methods, five new umami peptides (EALEATAQ, SPPTEE, SEEG, KEE, and FEE, with umami taste thresholds of 0.139, 0.085, 0.096, 0.060, and 0.079 mg/mL, respectively) were identified in Douchi. Molecular docking revealed that the residues ASN150 of T1R1, as well as SER170, GLU301 and GLN389 of T1R3, might be key amino acid residues for the binding of umami peptides to T1R1/T1R3. Molecular dynamics simulations revealed significant differences in the root-mean-square fluctuation (RMSF) values between the two complex systems of umami peptides-T1R1/T1R3 and non-umami peptides-T1R1/T1R3. The newly constructed umami peptide discriminant model can improve the accuracy of umami peptide screening and enhance the efficiency of discovering new umami peptides. |
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| ISSN: | 2590-1575 |