iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides

Effects of Protein, Calcium, and pH on Gene Transcription, Cell-Envelope Peptidase Activity of Lactococcus lactis Strains, and the Formation of Bitter Peptides

Calcium- and protein-rich fermented milk products, such as concentrated yoghurts and fresh cheeses, may contain undesired bitter peptides, which are generated by the proteolytic cleavage of casein. Up to now, it is not clear whether this process is caused by endogenous milk enzymes, such as plasmin and cathepsin D, or whether proteolytic enzymes from applied starter cultures, such as the lactococcal cell-envelope peptidase PrtP, are involved. A sensory analysis of fresh cheese products made from milk concentrates fermented with prtP-negative and -positive Lactococcus lactis strains revealed bitterness in the products fermented with prtP-positive L. lactis strains. Two prtP-positive strains, LTH 7122 and LTH 7123, were selected to investigate the effect of increased calcium concentrations (additional 5 mM and 50 mM CaCl2) at neutral (pH 6.6) and acidic (pH 5.5) pH-values on the transcription of the prtP gene and its corresponding PrtP peptidase activity in milk citrate broth (MCB). For both strains, it was shown that prtP transcription was upregulated only under slightly elevated calcium conditions (5 mM CaCl2) after 5 h of growth. In concordance with these findings, PrtP peptidase activity also increased. When higher concentrations of calcium were used (50 mM), prtP expression of both strains decreased strongly by more than 50%. Moreover, PrtP peptidase activity of strain LTH 7123 decreased by 15%, but enzymatic activity of strain LTH 7122 increased slightly during growth under elevated calcium concentrations (50 mM CaCl2). Fermentations of reconstituted casein medium with 3.4% (w/v) and 8.5% (w/v) protein and different calcium concentrations using strain LTH 7122 revealed no clear relationship between prtP transcription and calcium or protein concentration. However, an increase in PrtP peptidase activity under elevated protein and calcium conditions was observed. The activity increase was accompanied by increased levels of bitter peptides derived from different casein fractions. These findings could be a possible explanation for the bitterness in fermented milk concentrates that was detected by a trained bitter panel.

BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides

Motivation The identification of bitter peptides through experimental approaches is an expensive and time-consuming endeavor. Due to the huge number of newly available peptide sequences in the post-genomic era, the development of automated computational models for the identification of novel bitter peptides is highly desirable. Results In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)-based model for predicting bitter peptides directly from their amino acid sequence without using any structural information. To the best of our knowledge, this is the first time a BERT-based model has been employed to identify bitter peptides. Compared to widely used machine learning models, BERT4Bitter achieved the best performance with an accuracy of 0.861 and 0.922 for cross-validation and independent tests, respectively. Furthermore, extensive empirical benchmarking experiments on the independent dataset demonstrated that BERT4Bitter clearly outperformed the existing method with improvements of 8.0% accuracy and 16.0% Matthews coefficient correlation, highlighting the effectiveness and robustness of BERT4Bitter. We believe that the BERT4Bitter method proposed herein will be a useful tool for rapidly screening and identifying novel bitter peptides for drug development and nutritional research. Availabilityand implementation The user-friendly web server of the proposed BERT4Bitter is freely accessible at http://pmlab.pythonanywhere.com/BERT4Bitter. Supplementary information Supplementary data are available at Bioinformatics online.

Structure–Activity Prediction of ACE Inhibitory/Bitter Dipeptides—A Chemometric Approach Based on Stepwise Regression

Forward and backward stepwise regression (FR and BR, respectively) was applied for the structure–bioactivity prediction of angiotensin converting enzyme (ACE)-inhibitory/bitter-tasting dipeptides. The datasets used in this study consisted of 28 sequences and numerical variables reflecting dipeptides’ physicochemical nature. The data were acquired from the BIOPEP-UWM, Biological Magnetic Resonance Databank, ProtScale, and AAindex databases. The calculations were computed using STATISTICA®13.1. FR/BR models differed in R2 (0.91/0.76, respectively). The impact of C-atC(−) and N-Molw(+) on the dual function of dipeptides was observed. Positive (+) and negative (−) correlations with log IC50 are presented in parens. Moreover, C-Bur(+), N-atH(+), and N-Pol(−) were also found to be important in the FR model. The additional statistical significance of N-bul(−), N-Bur(−), and N-Hdr(+) was reported in the BR model. These attributes reflected the composition of the dipeptides. We report that the “ideal” bitter ACE inhibitor should be composed of P, Y, F (C-end) and G, V, I, L (N-end). Functions: log Rcaf. = f (observed log IC50) and log Rcaf. = f (predicted log IC50) revealed no direct relationships between ACE inhibition and the bitterness of the dipeptides. It probably resulted from some structural discrepancies between the ACE inhibitory/bitter peptides and/or the measure of activity describing one of the two bioactivities. Our protocol can be applicable for the structure–bioactivity prediction of other bioactivities peptides.

A proteome-based design of bitter peptide digestion regime to attenuate bone soup bitterness: comparison with a rainbow trout extract-mediated bitter taste masking approach

ABSTRACTBACKGROUNDThe fresh bones (with some meat on them; frequently discarded as a large quantity of industry garbage) of marine fish such as cod and salmon are good materials for manufacture of food additives (taste adjusters). However, such fish-bone originated additives often have apparent bitter taste and need additional debittering regime.RESULTSIn this study, 46 known bitter peptides in the cod proteome were targeted for specific protease digestion to eliminate bitter taste from the cod bone soup. Though the debittering effect was apparent, the bitter taste was not completely removed. However, the bitter taste can be removed by addition of trout extract to a complete extent. The strong debittering power of rainbow trout extract was further confirmed by the debittering experiments on salmon bone soup and bitter melon, both with perfect results.CONCLUSIONThese results indicated that the cod bone soup bitterness comes not only from bitter peptide but also from other substances that can be masked by trout extract. Considering the fact that trout proteome has more potential bitter peptides than cod, trout extract shall have a strong bitter masking substance to be determined in the future.