In silico design of a peptide receptor for triglyceride recognition using a structure-based approach

Background: Elevated triglyceride levels constitute a major risk factor for cardiovascular disease, accounting for millions of deaths annually. Current methods for triglyceride quantification rely on enzymatic assays or complex lipid panels, which present limitations in stability, cost, and turnaround time. Peptide-based biosensors offer a promising alternative due to their chemical stability, synthetic accessibility, and amenability to rational design. Methods: A structure-based computational approach was employed to design a selective peptide receptor for glycerol —the hydrolysis product of triglycerides— using molecular docking with AutoDock Vina. Twelve peptide receptors were initially derived from the active sites of glycerol-binding enzymes retrieved from the Protein Data Bank. The most promising receptor was subjected to structural modifications, including the addition of styrene units and methylene bridges to increase molecular volume, and the introduction of hydroxyl functional groups on aromatic rings. Selectivity was evaluated in silico against eight common fatty acids. A 4×4 Latin square experimental design was applied to optimize the combination of volume modifications and functionalization. Results: The receptor based on the TRR sequence (Thr-Arg-Arg), functionalized with styrene units and methylene bridges, and modified with hydroxyl groups, exhibited the highest affinity for glycerol (-4.9 kcal/mol) with interaction distances between 1.744 and 1.925 Å. Selectivity assays demonstrated that the TRR receptor discriminates glycerol preferentially over all tested fatty acids, with affinity differences of at least 1.3 kcal/mol. Statistical analysis confirmed that the receptor's affinity is significantly dependent on the specific arrangement of amino acids in the sequence. Conclusions: The TRR receptor represents a promising starting point for the experimental development of a peptide-based biosensor for indirect triglyceride quantification. The computational strategy employed demonstrates the feasibility of designing selective bioreceptors through a structure-based approach, although experimental validation through peptide synthesis and testing in biological matrices remains a necessary next step.