Enzymatic release of dipeptidyl peptidase‐4 inhibitors (gliptins) from pigeon pea (<i>Cajanus cajan</i>) nutrient reservoir proteins: In silico and in vitro assessments
Bibliographic record
Abstract
In silico and in vitro parameters were used to assess the potential of pigeon pea (Cajanus cajan) nutrient reservoir proteins as sources of dipeptidyl peptidase (DPP)-4 inhibitors. In silico, 40 pigeon pea proteins evaluated had 46% of amino acids associated with DPP-4 inhibition. After virtual hydrolysis, pepsin had the highest frequency of release and bioactivity of released DPP-4 inhibiting peptides, compared to papain and thermolysin. In vitro, thermolysin released the most active DPP-4 inhibitors. The protein hydrolysates contained similar amino acids but different particle sizes. Thus, the bioactivity patterns are attributable to the different nature and behavior of proteins/peptides under actual and virtual conditions. Using eight physicochemical variables, a random forest model with moderate prediction accuracy was developed for predicting DPP-4 inhibitory activity of papain hydrolysates. The findings demonstrate that proteins from pigeon pea are precursors of DPP-4 inhibitors, with potential use in formulating functional food for managing type 2 diabetes. PRACTICAL APPLICATIONS: The emerging use of in silico simulations to predict bioactivity of peptides can provide a framework to direct further wet lab assessments. This pattern can enhance focusing on factors relevant to the bioactive properties of interest. However, there is still limited evidence to confirm the reliability and accuracy of this tool. This study therefore provides insight into the practical use of in silico simulations to predict bioactivity of food peptides by assessing the factors relevant to the enzymatic release of dipeptidyl peptidase-4 inhibitors from pigeon pea seed storage proteins and validating the findings with wet lab assessment. This work also provides important information that can enhance the utilization of pigeon pea, which is an orphan crop, in developing functional food products for managing type 2 diabetes mellitus in developing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".