Computationally Assisted Peptide Screening to Identify Sequences from Reported Peptides to Achieve Enhance Abilities in Supporting Adhesion and Self-Renewal of Human Pluripotent Stem Cell Cultures

Abstract

Peptides that support the adhesion and pluripotency maintenance of human pluripotent stem cells (hPSCs) play a critical role in the culture and growth of hPSCs on peptide-displaying surfaces. We designed a number of optimized peptide sequences based on the interaction between RGD sequences and integrin receptors. In this study, AlphaFold-based structure prediction was used to generate a three-dimensional structural model of integrin αVβ5. We then successfully generated predicted peptide–integrin complex models of the peptide with αVβ3/αVβ5 integrin proteins through molecular docking. Following this, molecular dynamics simulations were used to evaluate the stability of multiple peptide–integrin complexes, investigating the pattern of amino acid sequences surrounding RGD that affect binding with integrin receptors and subsequent cell culture abilities. Finally, these findings lead to the identification of optimized peptides that can sustain long-term self-renewal of hPSCs in the absence of ROCK inhibitors under the tested conditions. Our study demonstrated how computational evaluation of RGD-flanking sequences can guide the discovery of a more efficient peptide for hPSCs culture, providing a practical strategy for large-scale clinical applications.