Evaluating the Efficacy of Aggregation Propensity as a Predictor of Oligopeptide Dimerization in Redox-Active Conditions

Overview

Peptide sequence determines its structure, function, ability to exhibit self-assembly behavior, and stimuli responsiveness. Different cysteine-containing amino acid sequences were synthesized and characterized to experimentally validate a computer model predicting peptide aggregation propensity. Oligopeptide aggregation occurs when the cysteine group in the peptide oxidizes to form a thiol-disulfide bond with another cysteine, forming a dimer. Those outputs yielded peptide sequences that are likely to aggregate based on the probability of the formation of thiol-disulfied bonds and dimerization. The oligopeptides’ structure and redox activity will be tested using a variety of light spectroscopy and microscopy techniques that show peptide structure, aggregation, and morphology. The goal of the study is to identify if oligopeptide sequences that exhibit high aggregation propensity will experimentally demonstrate redox-active aggregation. Experimental validation of aggregation propensity to predict oligopeptide dimerization will contribute to identifying the most precise metrics to predict oligopeptide dimerization in redox-active conditions. Oligopeptides were synthesized via solid state peptide synthesis and undertook cleavage, precipitation,and purification. MALDI mass spectrometry and UPLC determined oligopeptide purification accuracy by distinguishing mass and distribution which can be compared to the expected protein mass. Dissipative self-assembling peptides (DSAPs) require constant chemical fuel, also producing waste products which can interfere with chemical reactions. The synthesis of self-assembling oligopeptides that demonstrate self-assembly properties provide a more energy-efficient alternative compared to DSAPs. The testing of the aggregation propensity of oligopeptide dimerization in redox-active conditions will create more precise prediction methods, leading to the development of biomaterials and nanotechnologies for improved drug release precision.

Competition history

• CSEF 2026 Chemistry (Senior Division) · Entry S-05-12

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