Proline-Based Structural Rules for Predicting Prolyl Endopeptidase Inhibitory Peptides

243 food proteins were screened via in silico hydrolysis using a four-rule proline-based framework. Shang-Ming Huang developed this dataset, which includes in vitro validation results for 14 synthesized peptides (IC50 = 2.12–68.50 μM). The dataset was last updated on May 25, 2026.

Use Cases

• Predicting peptide inhibitory potency based on proline presence and motif rules.
• Prioritizing candidate peptides from food protein hydrolysates for in vitro testing.
• Analyzing the correlation between peptide structural features and binding energy.
• Benchmarking molecular dynamics and MM-PBSA simulation results against experimental IC50 values.
• Developing practical screening strategies for therapeutic peptide discovery.

Strengths

• Includes in vitro validation data for 14 synthesized peptides with specific IC50 values.
• Framework was applied to 243 food proteins across five enzyme treatments.
• Correlation analysis (r = 0.831, p < 0.001) between rule A1 and inhibitory potency is provided.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is 133.3 KB, indicating a limited scope of data.

Provenance

Source

figshare

Collection Method

Data likely generated from in silico hydrolysis of food proteins and subsequent in vitro validation.

Freshness

Last updated 2026-05-25 15:07:22; freshness should be verified.