Ageing of human skin is driven in part by cumulative damage to extracellular matrix (ECM) proteins, resulting in wrinkles, laxity, and reduced capacity to heal. Bioactive peptide matrikines are promising therapeutic agents capable of stimulating ECM regeneration and remodelling. This review focuses on how the discovery strategies to identify these peptides have evolved over several decades of cosmeceutical use. Early peptide matrikines were identified primarily through repetitive in vitro testing, based on prior knowledge of a protein or functional region. More recently, there is a focus on in silico prediction of bioactive peptide sequences, implementing techniques such as protease cleavage prediction, protein sequence motif screening, molecular docking, and machine learning algorithms to rapidly derive peptide candidates in silico. We highlight our recently developed in silico to in vivo discovery pipeline, which integrates prediction of ageing-related cleavage susceptibility of ECM proteins with comprehensive in vitro and in vivo testing, culminating in the identification of two tetrapeptides (pal-GPKG and pal-LSVD) which act synergistically to enhance ECM regeneration in aged skin. Looking forward, advances in deep learning cleavage prediction models, molecular modelling, and in vitro testing with 3D skin models will accelerate the discovery and translation of novel matrikines for skin regeneration and broader biomedical applications, such as wound repair.
Birtles et al. (2026) studied this question.